Master_project_CAR_overlap.ipynb

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    "colab": {
      "name": "Master_project_CAR_overlap.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "jL2mu_uKKMsH",
        "8NncRQBDq1OJ",
        "5emY3c53eiXd"
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shayanAbbasi1995/Non-financial-misconduct-and-Market-reaction/blob/main/Master_project_CAR_overlap.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jL2mu_uKKMsH"
      },
      "source": [
        "#DATA and LIBs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PD_xnFuC7usI"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UgsUwpSveCm",
        "outputId": "9d660d40-e0cc-4452-b0ca-0add9654ba81"
      },
      "source": [
        "# Run this if running in Google Collab\n",
        "# Mount google drive if running from Google Collab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Set current directory if running from Google Collab\n",
        "\n",
        "import os\n",
        "os.chdir('/content/drive/My Drive/Colab Notebooks/SP500')# here use your path to current notebook"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUWiwwgYv0TT"
      },
      "source": [
        "import pandas as pd\n",
        "# SP500 data\n",
        "returns = pd.read_excel('./ALL returns.xlsx') # DAtaset was for 2016-2020\n",
        "lost_return = pd.read_excel('./lost returns.xlsx')\n",
        "#returns = pd.read_csv('./ALL Return2007-2020.csv')\n",
        "#returns = pd.read_excel('RET 2009-2020.xlsx')\n",
        "SPY = pd.read_csv('./INDEXES.csv')\n",
        "#SPY = pd.read_excel('index 2009-2020.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "d7my-LFZRSe_",
        "outputId": "af3b4d3c-6632-4ad7-abef-4a0defcf166c"
      },
      "source": [
        "SPY"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>vwretd</th>\n",
              "      <th>vwretx</th>\n",
              "      <th>ewretd</th>\n",
              "      <th>ewretx</th>\n",
              "      <th>sprtrn</th>\n",
              "      <th>spindx</th>\n",
              "      <th>totval</th>\n",
              "      <th>totcnt</th>\n",
              "      <th>usdval</th>\n",
              "      <th>usdcnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20160104</td>\n",
              "      <td>-0.014893</td>\n",
              "      <td>-0.015055</td>\n",
              "      <td>-0.012338</td>\n",
              "      <td>-0.012423</td>\n",
              "      <td>-0.015304</td>\n",
              "      <td>2012.66</td>\n",
              "      <td>27249876900</td>\n",
              "      <td>7200</td>\n",
              "      <td>27672981100</td>\n",
              "      <td>7199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20160105</td>\n",
              "      <td>0.001387</td>\n",
              "      <td>0.001384</td>\n",
              "      <td>0.000358</td>\n",
              "      <td>0.000334</td>\n",
              "      <td>0.002012</td>\n",
              "      <td>2016.71</td>\n",
              "      <td>27289653500</td>\n",
              "      <td>7199</td>\n",
              "      <td>27241303800</td>\n",
              "      <td>7197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20160106</td>\n",
              "      <td>-0.013823</td>\n",
              "      <td>-0.014091</td>\n",
              "      <td>-0.013466</td>\n",
              "      <td>-0.013516</td>\n",
              "      <td>-0.013115</td>\n",
              "      <td>1990.26</td>\n",
              "      <td>26905200800</td>\n",
              "      <td>7198</td>\n",
              "      <td>27289640600</td>\n",
              "      <td>7198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20160107</td>\n",
              "      <td>-0.023943</td>\n",
              "      <td>-0.023967</td>\n",
              "      <td>-0.024829</td>\n",
              "      <td>-0.024847</td>\n",
              "      <td>-0.023700</td>\n",
              "      <td>1943.09</td>\n",
              "      <td>26259997300</td>\n",
              "      <td>7196</td>\n",
              "      <td>26905110200</td>\n",
              "      <td>7195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20160108</td>\n",
              "      <td>-0.010955</td>\n",
              "      <td>-0.010960</td>\n",
              "      <td>-0.010283</td>\n",
              "      <td>-0.010305</td>\n",
              "      <td>-0.010838</td>\n",
              "      <td>1922.03</td>\n",
              "      <td>25969468700</td>\n",
              "      <td>7194</td>\n",
              "      <td>26257073100</td>\n",
              "      <td>7194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>20201224</td>\n",
              "      <td>0.002332</td>\n",
              "      <td>0.002197</td>\n",
              "      <td>-0.000277</td>\n",
              "      <td>-0.000458</td>\n",
              "      <td>0.003537</td>\n",
              "      <td>3703.06</td>\n",
              "      <td>49387699400</td>\n",
              "      <td>7515</td>\n",
              "      <td>49278049200</td>\n",
              "      <td>7515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>20201228</td>\n",
              "      <td>0.003870</td>\n",
              "      <td>0.003859</td>\n",
              "      <td>0.001350</td>\n",
              "      <td>0.001304</td>\n",
              "      <td>0.008723</td>\n",
              "      <td>3735.36</td>\n",
              "      <td>49579392700</td>\n",
              "      <td>7516</td>\n",
              "      <td>49387699400</td>\n",
              "      <td>7514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>20201229</td>\n",
              "      <td>-0.003649</td>\n",
              "      <td>-0.003657</td>\n",
              "      <td>-0.008483</td>\n",
              "      <td>-0.008656</td>\n",
              "      <td>-0.002227</td>\n",
              "      <td>3727.04</td>\n",
              "      <td>49396998600</td>\n",
              "      <td>7522</td>\n",
              "      <td>49576731700</td>\n",
              "      <td>7514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>20201230</td>\n",
              "      <td>0.003281</td>\n",
              "      <td>0.003114</td>\n",
              "      <td>0.010155</td>\n",
              "      <td>0.009754</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>3732.04</td>\n",
              "      <td>49551319700</td>\n",
              "      <td>7521</td>\n",
              "      <td>49396695300</td>\n",
              "      <td>7519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>20201231</td>\n",
              "      <td>0.003556</td>\n",
              "      <td>0.003497</td>\n",
              "      <td>-0.001058</td>\n",
              "      <td>-0.001129</td>\n",
              "      <td>0.006439</td>\n",
              "      <td>3756.07</td>\n",
              "      <td>49813525400</td>\n",
              "      <td>7525</td>\n",
              "      <td>49551000200</td>\n",
              "      <td>7519</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          DATE    vwretd    vwretx  ...  totcnt       usdval  usdcnt\n",
              "0     20160104 -0.014893 -0.015055  ...    7200  27672981100    7199\n",
              "1     20160105  0.001387  0.001384  ...    7199  27241303800    7197\n",
              "2     20160106 -0.013823 -0.014091  ...    7198  27289640600    7198\n",
              "3     20160107 -0.023943 -0.023967  ...    7196  26905110200    7195\n",
              "4     20160108 -0.010955 -0.010960  ...    7194  26257073100    7194\n",
              "...        ...       ...       ...  ...     ...          ...     ...\n",
              "1254  20201224  0.002332  0.002197  ...    7515  49278049200    7515\n",
              "1255  20201228  0.003870  0.003859  ...    7516  49387699400    7514\n",
              "1256  20201229 -0.003649 -0.003657  ...    7522  49576731700    7514\n",
              "1257  20201230  0.003281  0.003114  ...    7521  49396695300    7519\n",
              "1258  20201231  0.003556  0.003497  ...    7525  49551000200    7519\n",
              "\n",
              "[1259 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "FEAzD8m9RDIY",
        "outputId": "20d73aa6-abbd-4d72-bf51-aaa6771e7753"
      },
      "source": [
        "lost_return"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PERMNO</th>\n",
              "      <th>Names Date</th>\n",
              "      <th>Ticker Symbol</th>\n",
              "      <th>Price or Bid/Ask Average</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17778</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>BRK</td>\n",
              "      <td>196295.000000</td>\n",
              "      <td>-0.007609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17778</td>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>BRK</td>\n",
              "      <td>197000.000000</td>\n",
              "      <td>0.003592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17778</td>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>BRK</td>\n",
              "      <td>197415.000000</td>\n",
              "      <td>0.002107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17778</td>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>BRK</td>\n",
              "      <td>195580.000000</td>\n",
              "      <td>-0.009295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17778</td>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>BRK</td>\n",
              "      <td>193900.000000</td>\n",
              "      <td>-0.008590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14244</th>\n",
              "      <td>90199</td>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.930000</td>\n",
              "      <td>0.002869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14245</th>\n",
              "      <td>90199</td>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.970001</td>\n",
              "      <td>0.000636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14246</th>\n",
              "      <td>90199</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.110001</td>\n",
              "      <td>-0.013657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14247</th>\n",
              "      <td>90199</td>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.400002</td>\n",
              "      <td>0.004669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14248</th>\n",
              "      <td>90199</td>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.720001</td>\n",
              "      <td>0.005128</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14249 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       PERMNO Names Date Ticker Symbol  Price or Bid/Ask Average   Returns\n",
              "0       17778 2016-01-04           BRK             196295.000000 -0.007609\n",
              "1       17778 2016-01-05           BRK             197000.000000  0.003592\n",
              "2       17778 2016-01-06           BRK             197415.000000  0.002107\n",
              "3       17778 2016-01-07           BRK             195580.000000 -0.009295\n",
              "4       17778 2016-01-08           BRK             193900.000000 -0.008590\n",
              "...       ...        ...           ...                       ...       ...\n",
              "14244   90199 2020-12-24          CBRE                 62.930000  0.002869\n",
              "14245   90199 2020-12-28          CBRE                 62.970001  0.000636\n",
              "14246   90199 2020-12-29          CBRE                 62.110001 -0.013657\n",
              "14247   90199 2020-12-30          CBRE                 62.400002  0.004669\n",
              "14248   90199 2020-12-31          CBRE                 62.720001  0.005128\n",
              "\n",
              "[14249 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGWLyEbBQz2a"
      },
      "source": [
        "lost_return.columns = ['','date','TICKER','PRC', 'RET'] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SUtQONZTKLM",
        "outputId": "f634d561-09c4-40eb-9e58-b495df76e1e4"
      },
      "source": [
        "returns.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date       1\n",
              "TICKER     1\n",
              "PRC       10\n",
              "RET       10\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
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      },
      "source": [
        "returns "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>TICKER</th>\n",
              "      <th>PRC</th>\n",
              "      <th>RET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>54.80000</td>\n",
              "      <td>-0.012257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>55.05000</td>\n",
              "      <td>0.004562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>54.05000</td>\n",
              "      <td>-0.018165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>52.17000</td>\n",
              "      <td>-0.034783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423555</th>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>DG</td>\n",
              "      <td>211.28000</td>\n",
              "      <td>0.000521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423556</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>DG</td>\n",
              "      <td>210.39000</td>\n",
              "      <td>-0.004212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423557</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>DG</td>\n",
              "      <td>211.34000</td>\n",
              "      <td>0.004515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423558</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>DG</td>\n",
              "      <td>209.92999</td>\n",
              "      <td>-0.006672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423559</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>DG</td>\n",
              "      <td>210.30000</td>\n",
              "      <td>0.001763</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>423560 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             date TICKER        PRC       RET\n",
              "0             NaT    NaN        NaN       NaN\n",
              "1      2016-01-04   MSFT   54.80000 -0.012257\n",
              "2      2016-01-05   MSFT   55.05000  0.004562\n",
              "3      2016-01-06   MSFT   54.05000 -0.018165\n",
              "4      2016-01-07   MSFT   52.17000 -0.034783\n",
              "...           ...    ...        ...       ...\n",
              "423555 2020-12-24     DG  211.28000  0.000521\n",
              "423556 2020-12-28     DG  210.39000 -0.004212\n",
              "423557 2020-12-29     DG  211.34000  0.004515\n",
              "423558 2020-12-30     DG  209.92999 -0.006672\n",
              "423559 2020-12-31     DG  210.30000  0.001763\n",
              "\n",
              "[423560 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "6rutIwt4SMkn",
        "outputId": "7eb60ef2-682f-401d-e081-674d3b9785c2"
      },
      "source": [
        "lost_return = lost_return[['date','TICKER','PRC', 'RET']] \n",
        "returns = pd.concat([returns,lost_return], ignore_index=True)\n",
        "returns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>TICKER</th>\n",
              "      <th>PRC</th>\n",
              "      <th>RET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>54.800000</td>\n",
              "      <td>-0.012257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>55.050000</td>\n",
              "      <td>0.004562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>54.050000</td>\n",
              "      <td>-0.018165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>MSFT</td>\n",
              "      <td>52.170000</td>\n",
              "      <td>-0.034783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437804</th>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.930000</td>\n",
              "      <td>0.00286853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437805</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.970001</td>\n",
              "      <td>0.000635641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437806</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.110001</td>\n",
              "      <td>-0.0136573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437807</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.400002</td>\n",
              "      <td>0.00466915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437808</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>CBRE</td>\n",
              "      <td>62.720001</td>\n",
              "      <td>0.0051282</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>437809 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             date TICKER        PRC          RET\n",
              "0             NaT    NaN        NaN          NaN\n",
              "1      2016-01-04   MSFT  54.800000    -0.012257\n",
              "2      2016-01-05   MSFT  55.050000     0.004562\n",
              "3      2016-01-06   MSFT  54.050000    -0.018165\n",
              "4      2016-01-07   MSFT  52.170000    -0.034783\n",
              "...           ...    ...        ...          ...\n",
              "437804 2020-12-24   CBRE  62.930000   0.00286853\n",
              "437805 2020-12-28   CBRE  62.970001  0.000635641\n",
              "437806 2020-12-29   CBRE  62.110001   -0.0136573\n",
              "437807 2020-12-30   CBRE  62.400002   0.00466915\n",
              "437808 2020-12-31   CBRE  62.720001    0.0051282\n",
              "\n",
              "[437809 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "PG5I9wzGpz1A",
        "outputId": "28ee1fe5-3307-4f1c-c2f8-75d0d0543339"
      },
      "source": [
        "index_df = SPY[['DATE', 'vwretx']]\n",
        "index_df.columns = ['date', 'SPY']\n",
        "index_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>SPY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20160104</td>\n",
              "      <td>-0.015055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20160105</td>\n",
              "      <td>0.001384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20160106</td>\n",
              "      <td>-0.014091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20160107</td>\n",
              "      <td>-0.023967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20160108</td>\n",
              "      <td>-0.010960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>20201224</td>\n",
              "      <td>0.002197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>20201228</td>\n",
              "      <td>0.003859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>20201229</td>\n",
              "      <td>-0.003657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>20201230</td>\n",
              "      <td>0.003114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>20201231</td>\n",
              "      <td>0.003497</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          date       SPY\n",
              "0     20160104 -0.015055\n",
              "1     20160105  0.001384\n",
              "2     20160106 -0.014091\n",
              "3     20160107 -0.023967\n",
              "4     20160108 -0.010960\n",
              "...        ...       ...\n",
              "1254  20201224  0.002197\n",
              "1255  20201228  0.003859\n",
              "1256  20201229 -0.003657\n",
              "1257  20201230  0.003114\n",
              "1258  20201231  0.003497\n",
              "\n",
              "[1259 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "XZF9cqu370VT",
        "outputId": "d458c14a-e107-45d0-8fd8-31582fb29160"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "j = 0\n",
        "for i in returns.groupby('TICKER'):\n",
        "  j += 1\n",
        "  if(j==3):\n",
        "    df['date'] = pd.DataFrame(i[1])['date']\n",
        "    break\n",
        "df = pd.DataFrame(df.reset_index()['date'])\n",
        "\n",
        "for i in returns.groupby('TICKER'):\n",
        "  temp_df = pd.DataFrame(i[1])[['date', 'RET']]\n",
        "  temp_df.columns = ['date', i[0]]\n",
        "  temp_df = temp_df.reset_index()[['date', i[0]]]\n",
        "  df[i[0]] = temp_df[i[0]]\n",
        "df['SPY'] = np.nan\n",
        "i = len(index_df['SPY'])\n",
        "df['SPY'][-i::] = index_df['SPY'][-i::]\n",
        "\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>AA</th>\n",
              "      <th>AAL</th>\n",
              "      <th>AAP</th>\n",
              "      <th>ABBV</th>\n",
              "      <th>ABC</th>\n",
              "      <th>ABT</th>\n",
              "      <th>ADM</th>\n",
              "      <th>ADP</th>\n",
              "      <th>AEE</th>\n",
              "      <th>AEP</th>\n",
              "      <th>AFL</th>\n",
              "      <th>AIG</th>\n",
              "      <th>AIZ</th>\n",
              "      <th>AJG</th>\n",
              "      <th>ALB</th>\n",
              "      <th>ALK</th>\n",
              "      <th>ALL</th>\n",
              "      <th>ALXN</th>\n",
              "      <th>AME</th>\n",
              "      <th>AMGN</th>\n",
              "      <th>AMP</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>ANTM</th>\n",
              "      <th>AOS</th>\n",
              "      <th>APA</th>\n",
              "      <th>APD</th>\n",
              "      <th>ARNC</th>\n",
              "      <th>ATVI</th>\n",
              "      <th>AVB</th>\n",
              "      <th>AVGO</th>\n",
              "      <th>AVY</th>\n",
              "      <th>AWK</th>\n",
              "      <th>AXP</th>\n",
              "      <th>AYI</th>\n",
              "      <th>AZO</th>\n",
              "      <th>BA</th>\n",
              "      <th>BAC</th>\n",
              "      <th>BAX</th>\n",
              "      <th>BBT</th>\n",
              "      <th>...</th>\n",
              "      <th>UA</th>\n",
              "      <th>UAA</th>\n",
              "      <th>UAL</th>\n",
              "      <th>UHS</th>\n",
              "      <th>ULTA</th>\n",
              "      <th>UNH</th>\n",
              "      <th>UNM</th>\n",
              "      <th>UNP</th>\n",
              "      <th>UPS</th>\n",
              "      <th>URI</th>\n",
              "      <th>USB</th>\n",
              "      <th>UTX</th>\n",
              "      <th>V</th>\n",
              "      <th>VLO</th>\n",
              "      <th>VMC</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WAB</th>\n",
              "      <th>WABC</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WDC</th>\n",
              "      <th>WEC</th>\n",
              "      <th>WFC</th>\n",
              "      <th>WHR</th>\n",
              "      <th>WM</th>\n",
              "      <th>WMB</th>\n",
              "      <th>WMIH</th>\n",
              "      <th>WMT</th>\n",
              "      <th>WR</th>\n",
              "      <th>WRK</th>\n",
              "      <th>WU</th>\n",
              "      <th>WY</th>\n",
              "      <th>WYNN</th>\n",
              "      <th>XEC</th>\n",
              "      <th>XEL</th>\n",
              "      <th>XOM</th>\n",
              "      <th>XYL</th>\n",
              "      <th>YUM</th>\n",
              "      <th>ZBH</th>\n",
              "      <th>ZION</th>\n",
              "      <th>SPY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>-0.016211</td>\n",
              "      <td>-0.034002</td>\n",
              "      <td>0.011494</td>\n",
              "      <td>-0.027515</td>\n",
              "      <td>-0.017742</td>\n",
              "      <td>-0.044088</td>\n",
              "      <td>-0.022901</td>\n",
              "      <td>-0.03128</td>\n",
              "      <td>-0.004626</td>\n",
              "      <td>0.00103</td>\n",
              "      <td>-0.01202</td>\n",
              "      <td>-0.024851</td>\n",
              "      <td>-0.025081</td>\n",
              "      <td>-0.018075</td>\n",
              "      <td>-0.008213</td>\n",
              "      <td>-0.026208</td>\n",
              "      <td>-0.02287</td>\n",
              "      <td>-0.031822</td>\n",
              "      <td>-0.017541</td>\n",
              "      <td>-0.02458</td>\n",
              "      <td>-0.020391</td>\n",
              "      <td>-0.057554</td>\n",
              "      <td>-0.001649</td>\n",
              "      <td>-0.018666</td>\n",
              "      <td>-0.0009</td>\n",
              "      <td>-0.007071</td>\n",
              "      <td>-0.121634</td>\n",
              "      <td>-0.028158</td>\n",
              "      <td>-0.01852</td>\n",
              "      <td>-0.019773</td>\n",
              "      <td>-0.0233</td>\n",
              "      <td>0.00954</td>\n",
              "      <td>-0.028181</td>\n",
              "      <td>-0.002524</td>\n",
              "      <td>-0.008667</td>\n",
              "      <td>-0.028287</td>\n",
              "      <td>-0.023767</td>\n",
              "      <td>-0.025688</td>\n",
              "      <td>-0.03068</td>\n",
              "      <td>...</td>\n",
              "      <td>C</td>\n",
              "      <td>0.042986</td>\n",
              "      <td>-0.029494</td>\n",
              "      <td>-0.013139</td>\n",
              "      <td>-0.017243</td>\n",
              "      <td>-0.010031</td>\n",
              "      <td>-0.016221</td>\n",
              "      <td>0.009847</td>\n",
              "      <td>-0.014445</td>\n",
              "      <td>-0.024538</td>\n",
              "      <td>-0.027888</td>\n",
              "      <td>-0.005205</td>\n",
              "      <td>-0.023856</td>\n",
              "      <td>-0.01089</td>\n",
              "      <td>-0.026851</td>\n",
              "      <td>-0.007573</td>\n",
              "      <td>-0.013498</td>\n",
              "      <td>-0.032086</td>\n",
              "      <td>-0.02472</td>\n",
              "      <td>0.005829</td>\n",
              "      <td>0.000195</td>\n",
              "      <td>-0.026674</td>\n",
              "      <td>0.008307</td>\n",
              "      <td>-0.017238</td>\n",
              "      <td>0.021012</td>\n",
              "      <td>-0.003861</td>\n",
              "      <td>0.00261</td>\n",
              "      <td>-0.000943</td>\n",
              "      <td>-0.002192</td>\n",
              "      <td>-0.016192</td>\n",
              "      <td>-0.005003</td>\n",
              "      <td>-0.00607</td>\n",
              "      <td>0.006937</td>\n",
              "      <td>-0.005848</td>\n",
              "      <td>-0.006286</td>\n",
              "      <td>-0.011507</td>\n",
              "      <td>-0.011499</td>\n",
              "      <td>-0.007603</td>\n",
              "      <td>-0.021612</td>\n",
              "      <td>-0.015055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>-0.045314</td>\n",
              "      <td>-0.009533</td>\n",
              "      <td>-0.006831</td>\n",
              "      <td>-0.004166</td>\n",
              "      <td>0.014626</td>\n",
              "      <td>-0.000233</td>\n",
              "      <td>0.011161</td>\n",
              "      <td>0.002437</td>\n",
              "      <td>0.011852</td>\n",
              "      <td>0.008229</td>\n",
              "      <td>-0.001352</td>\n",
              "      <td>0.003475</td>\n",
              "      <td>0.016684</td>\n",
              "      <td>0.002736</td>\n",
              "      <td>-0.015302</td>\n",
              "      <td>-0.016709</td>\n",
              "      <td>-0.002308</td>\n",
              "      <td>0.001191</td>\n",
              "      <td>-0.005128</td>\n",
              "      <td>-0.001074</td>\n",
              "      <td>-0.003933</td>\n",
              "      <td>-0.005024</td>\n",
              "      <td>0.014582</td>\n",
              "      <td>-0.005587</td>\n",
              "      <td>-0.020257</td>\n",
              "      <td>-0.017881</td>\n",
              "      <td>-0.051797</td>\n",
              "      <td>-0.012759</td>\n",
              "      <td>0.023019</td>\n",
              "      <td>-0.033455</td>\n",
              "      <td>0.00768</td>\n",
              "      <td>-0.002818</td>\n",
              "      <td>-0.015387</td>\n",
              "      <td>0.012692</td>\n",
              "      <td>0.004596</td>\n",
              "      <td>0.004057</td>\n",
              "      <td>0</td>\n",
              "      <td>0.004036</td>\n",
              "      <td>0.000273</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.058155</td>\n",
              "      <td>0.020762</td>\n",
              "      <td>-0.00989</td>\n",
              "      <td>0.004325</td>\n",
              "      <td>0.00473</td>\n",
              "      <td>0.001889</td>\n",
              "      <td>-0.005191</td>\n",
              "      <td>-0.009624</td>\n",
              "      <td>0.009911</td>\n",
              "      <td>-0.010034</td>\n",
              "      <td>0.004822</td>\n",
              "      <td>0.00157</td>\n",
              "      <td>0.00753</td>\n",
              "      <td>0.017444</td>\n",
              "      <td>0.001407</td>\n",
              "      <td>0.013734</td>\n",
              "      <td>-0.017531</td>\n",
              "      <td>0.000663</td>\n",
              "      <td>-0.026249</td>\n",
              "      <td>-0.004305</td>\n",
              "      <td>0.010717</td>\n",
              "      <td>-0.000378</td>\n",
              "      <td>-0.021541</td>\n",
              "      <td>0.003813</td>\n",
              "      <td>0.00343</td>\n",
              "      <td>-0.034884</td>\n",
              "      <td>0.023755</td>\n",
              "      <td>0.006372</td>\n",
              "      <td>-0.000659</td>\n",
              "      <td>-0.003973</td>\n",
              "      <td>0.002347</td>\n",
              "      <td>0.018904</td>\n",
              "      <td>0.006444</td>\n",
              "      <td>0.010084</td>\n",
              "      <td>0.008521</td>\n",
              "      <td>-0.000277</td>\n",
              "      <td>-0.002493</td>\n",
              "      <td>0.020823</td>\n",
              "      <td>-0.010857</td>\n",
              "      <td>0.001384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>-0.071197</td>\n",
              "      <td>0.017522</td>\n",
              "      <td>-0.026455</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>-0.015286</td>\n",
              "      <td>-0.008388</td>\n",
              "      <td>-0.024283</td>\n",
              "      <td>-0.012277</td>\n",
              "      <td>0.006431</td>\n",
              "      <td>0.003741</td>\n",
              "      <td>-0.015228</td>\n",
              "      <td>-0.014677</td>\n",
              "      <td>-0.013153</td>\n",
              "      <td>-0.01935</td>\n",
              "      <td>-0.043693</td>\n",
              "      <td>0.014399</td>\n",
              "      <td>-0.011234</td>\n",
              "      <td>-0.004489</td>\n",
              "      <td>-0.022719</td>\n",
              "      <td>-0.009167</td>\n",
              "      <td>-0.033995</td>\n",
              "      <td>-0.001799</td>\n",
              "      <td>-0.024427</td>\n",
              "      <td>-0.019797</td>\n",
              "      <td>-0.114863</td>\n",
              "      <td>-0.024354</td>\n",
              "      <td>-0.025084</td>\n",
              "      <td>-0.009424</td>\n",
              "      <td>-0.007464</td>\n",
              "      <td>-0.030759</td>\n",
              "      <td>-0.011675</td>\n",
              "      <td>-0.004323</td>\n",
              "      <td>-0.027648</td>\n",
              "      <td>-0.026379</td>\n",
              "      <td>-0.013074</td>\n",
              "      <td>-0.015879</td>\n",
              "      <td>-0.021303</td>\n",
              "      <td>0.002412</td>\n",
              "      <td>-0.013912</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004583</td>\n",
              "      <td>0.001518</td>\n",
              "      <td>0.002543</td>\n",
              "      <td>-0.023052</td>\n",
              "      <td>0.002956</td>\n",
              "      <td>-0.010199</td>\n",
              "      <td>-0.027317</td>\n",
              "      <td>-0.043217</td>\n",
              "      <td>-0.014199</td>\n",
              "      <td>-0.047823</td>\n",
              "      <td>-0.015115</td>\n",
              "      <td>-0.027163</td>\n",
              "      <td>-0.013111</td>\n",
              "      <td>-0.007026</td>\n",
              "      <td>-0.023231</td>\n",
              "      <td>-0.008925</td>\n",
              "      <td>-0.034963</td>\n",
              "      <td>-0.009496</td>\n",
              "      <td>-0.015333</td>\n",
              "      <td>-0.022281</td>\n",
              "      <td>-0.002699</td>\n",
              "      <td>-0.019096</td>\n",
              "      <td>-0.025535</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.13027</td>\n",
              "      <td>-0.012048</td>\n",
              "      <td>0.010013</td>\n",
              "      <td>0.002345</td>\n",
              "      <td>-0.027698</td>\n",
              "      <td>-0.022792</td>\n",
              "      <td>-0.022074</td>\n",
              "      <td>-0.051805</td>\n",
              "      <td>-0.061161</td>\n",
              "      <td>0.010538</td>\n",
              "      <td>-0.008321</td>\n",
              "      <td>-0.012476</td>\n",
              "      <td>-0.00708</td>\n",
              "      <td>0.00433</td>\n",
              "      <td>-0.029145</td>\n",
              "      <td>-0.014091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>-0.039489</td>\n",
              "      <td>-0.018918</td>\n",
              "      <td>0.011073</td>\n",
              "      <td>-0.002963</td>\n",
              "      <td>-0.031932</td>\n",
              "      <td>-0.023966</td>\n",
              "      <td>-0.023756</td>\n",
              "      <td>-0.030519</td>\n",
              "      <td>0.000685</td>\n",
              "      <td>-0.01152</td>\n",
              "      <td>-0.01701</td>\n",
              "      <td>-0.023933</td>\n",
              "      <td>-0.022468</td>\n",
              "      <td>-0.020744</td>\n",
              "      <td>-0.045116</td>\n",
              "      <td>-0.053836</td>\n",
              "      <td>-0.023559</td>\n",
              "      <td>-0.052697</td>\n",
              "      <td>-0.023637</td>\n",
              "      <td>-0.023864</td>\n",
              "      <td>-0.031702</td>\n",
              "      <td>-0.039058</td>\n",
              "      <td>-0.017128</td>\n",
              "      <td>-0.048854</td>\n",
              "      <td>-0.051389</td>\n",
              "      <td>-0.033525</td>\n",
              "      <td>0.010863</td>\n",
              "      <td>-0.014134</td>\n",
              "      <td>-0.004578</td>\n",
              "      <td>-0.03181</td>\n",
              "      <td>-0.030025</td>\n",
              "      <td>-0.007514</td>\n",
              "      <td>-0.009003</td>\n",
              "      <td>-0.026529</td>\n",
              "      <td>0.00927</td>\n",
              "      <td>-0.041922</td>\n",
              "      <td>-0.03607</td>\n",
              "      <td>-0.016573</td>\n",
              "      <td>-0.028492</td>\n",
              "      <td>...</td>\n",
              "      <td>0.003601</td>\n",
              "      <td>-0.022128</td>\n",
              "      <td>-0.046558</td>\n",
              "      <td>-0.0242</td>\n",
              "      <td>-0.001255</td>\n",
              "      <td>-0.02944</td>\n",
              "      <td>-0.027769</td>\n",
              "      <td>-0.023386</td>\n",
              "      <td>-0.019276</td>\n",
              "      <td>-0.065067</td>\n",
              "      <td>-0.019001</td>\n",
              "      <td>-0.013101</td>\n",
              "      <td>-0.019662</td>\n",
              "      <td>0.020238</td>\n",
              "      <td>-0.02323</td>\n",
              "      <td>-0.005492</td>\n",
              "      <td>-0.033373</td>\n",
              "      <td>-0.00981</td>\n",
              "      <td>0.019339</td>\n",
              "      <td>-0.016497</td>\n",
              "      <td>-0.000193</td>\n",
              "      <td>-0.028527</td>\n",
              "      <td>-0.025142</td>\n",
              "      <td>-0.01182</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.00813</td>\n",
              "      <td>0.023289</td>\n",
              "      <td>-0.011699</td>\n",
              "      <td>-0.009948</td>\n",
              "      <td>-0.005831</td>\n",
              "      <td>-0.036936</td>\n",
              "      <td>-0.09407</td>\n",
              "      <td>-0.037394</td>\n",
              "      <td>0.003842</td>\n",
              "      <td>-0.016006</td>\n",
              "      <td>-0.025828</td>\n",
              "      <td>-0.034116</td>\n",
              "      <td>-0.022993</td>\n",
              "      <td>-0.03002</td>\n",
              "      <td>-0.023967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>-0.024184</td>\n",
              "      <td>-0.001978</td>\n",
              "      <td>-0.021971</td>\n",
              "      <td>-0.027268</td>\n",
              "      <td>-0.005785</td>\n",
              "      <td>-0.020944</td>\n",
              "      <td>-0.003766</td>\n",
              "      <td>-0.008251</td>\n",
              "      <td>-0.007754</td>\n",
              "      <td>-0.001542</td>\n",
              "      <td>-0.00874</td>\n",
              "      <td>-0.014918</td>\n",
              "      <td>-0.012206</td>\n",
              "      <td>-0.010592</td>\n",
              "      <td>-0.018819</td>\n",
              "      <td>-0.04879</td>\n",
              "      <td>-0.003593</td>\n",
              "      <td>-0.035786</td>\n",
              "      <td>-0.018207</td>\n",
              "      <td>-0.014871</td>\n",
              "      <td>-0.022547</td>\n",
              "      <td>-0.001464</td>\n",
              "      <td>-0.024367</td>\n",
              "      <td>-0.035581</td>\n",
              "      <td>0.002736</td>\n",
              "      <td>-0.005182</td>\n",
              "      <td>-0.019231</td>\n",
              "      <td>-0.01544</td>\n",
              "      <td>-0.017738</td>\n",
              "      <td>-0.006587</td>\n",
              "      <td>0.009641</td>\n",
              "      <td>0.003869</td>\n",
              "      <td>-0.003289</td>\n",
              "      <td>-0.037348</td>\n",
              "      <td>-0.032475</td>\n",
              "      <td>-0.022705</td>\n",
              "      <td>-0.019355</td>\n",
              "      <td>-0.016037</td>\n",
              "      <td>-0.02164</td>\n",
              "      <td>...</td>\n",
              "      <td>0.010287</td>\n",
              "      <td>0.00031</td>\n",
              "      <td>-0.01406</td>\n",
              "      <td>-0.004606</td>\n",
              "      <td>-0.004427</td>\n",
              "      <td>-0.017218</td>\n",
              "      <td>-0.012658</td>\n",
              "      <td>0.010673</td>\n",
              "      <td>-0.013067</td>\n",
              "      <td>-0.00882</td>\n",
              "      <td>-0.014154</td>\n",
              "      <td>-0.016322</td>\n",
              "      <td>-0.012332</td>\n",
              "      <td>-0.053128</td>\n",
              "      <td>-0.023216</td>\n",
              "      <td>-0.009719</td>\n",
              "      <td>-0.017729</td>\n",
              "      <td>-0.021392</td>\n",
              "      <td>-0.001478</td>\n",
              "      <td>-0.042193</td>\n",
              "      <td>-0.004834</td>\n",
              "      <td>-0.016667</td>\n",
              "      <td>-0.015183</td>\n",
              "      <td>-0.006076</td>\n",
              "      <td>-0.014556</td>\n",
              "      <td>-0.012295</td>\n",
              "      <td>-0.022912</td>\n",
              "      <td>-0.003551</td>\n",
              "      <td>-0.042475</td>\n",
              "      <td>-0.008211</td>\n",
              "      <td>-0.009233</td>\n",
              "      <td>-0.040704</td>\n",
              "      <td>0.005619</td>\n",
              "      <td>-0.010935</td>\n",
              "      <td>-0.020202</td>\n",
              "      <td>-0.00951</td>\n",
              "      <td>-0.013607</td>\n",
              "      <td>-0.004217</td>\n",
              "      <td>-0.011254</td>\n",
              "      <td>-0.010960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.014475</td>\n",
              "      <td>0.008726</td>\n",
              "      <td>-0.000194</td>\n",
              "      <td>-0.002777</td>\n",
              "      <td>0.008376</td>\n",
              "      <td>0.001824</td>\n",
              "      <td>0.00782</td>\n",
              "      <td>0.009905</td>\n",
              "      <td>0.008797</td>\n",
              "      <td>0.007122</td>\n",
              "      <td>-0.00187</td>\n",
              "      <td>0.001359</td>\n",
              "      <td>0.003088</td>\n",
              "      <td>-0.001801</td>\n",
              "      <td>0.007909</td>\n",
              "      <td>0.011322</td>\n",
              "      <td>-0.003981</td>\n",
              "      <td>0.003541</td>\n",
              "      <td>-4.5e-05</td>\n",
              "      <td>0.006153</td>\n",
              "      <td>-0.003949</td>\n",
              "      <td>0.001038</td>\n",
              "      <td>0.006714</td>\n",
              "      <td>-0.023681</td>\n",
              "      <td>0.007887</td>\n",
              "      <td>-0.0259383</td>\n",
              "      <td>0.008314</td>\n",
              "      <td>0.006508</td>\n",
              "      <td>0.014102</td>\n",
              "      <td>0.004518</td>\n",
              "      <td>0.011736</td>\n",
              "      <td>0.00342</td>\n",
              "      <td>0.004985</td>\n",
              "      <td>0.009413</td>\n",
              "      <td>-0.011562</td>\n",
              "      <td>-0.002995</td>\n",
              "      <td>0.010667</td>\n",
              "      <td>0.00287033</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.001533</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.01927</td>\n",
              "      <td>-0.000949</td>\n",
              "      <td>-0.014229</td>\n",
              "      <td>0.009479</td>\n",
              "      <td>0</td>\n",
              "      <td>0.011035</td>\n",
              "      <td>-0.002607</td>\n",
              "      <td>0.007318</td>\n",
              "      <td>-0.005136</td>\n",
              "      <td>-0.00321757</td>\n",
              "      <td>0.016561</td>\n",
              "      <td>-0.001972</td>\n",
              "      <td>0.003574</td>\n",
              "      <td>-0.002035</td>\n",
              "      <td>0.006176</td>\n",
              "      <td>0.002404</td>\n",
              "      <td>-0.00627</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.008457</td>\n",
              "      <td>-0.016804</td>\n",
              "      <td>0.019256</td>\n",
              "      <td>0.006452</td>\n",
              "      <td>-0.000482</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.001955</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000675</td>\n",
              "      <td>0.005983</td>\n",
              "      <td>0.007141</td>\n",
              "      <td>-0.007892</td>\n",
              "      <td>-0.007843</td>\n",
              "      <td>0.004976</td>\n",
              "      <td>-0.00407</td>\n",
              "      <td>0.005214</td>\n",
              "      <td>0.007023</td>\n",
              "      <td>0.003423</td>\n",
              "      <td>-0.003907</td>\n",
              "      <td>0.002197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.025543</td>\n",
              "      <td>-0.0117</td>\n",
              "      <td>0.00184</td>\n",
              "      <td>-0.001753</td>\n",
              "      <td>-0.005168</td>\n",
              "      <td>0.005259</td>\n",
              "      <td>0.000283</td>\n",
              "      <td>0.003662</td>\n",
              "      <td>-0.001719</td>\n",
              "      <td>0.001825</td>\n",
              "      <td>-0.008028</td>\n",
              "      <td>0.005731</td>\n",
              "      <td>-0.00316</td>\n",
              "      <td>0.000535</td>\n",
              "      <td>0.006316</td>\n",
              "      <td>0.002612</td>\n",
              "      <td>0.011335</td>\n",
              "      <td>0.006048</td>\n",
              "      <td>0.00323</td>\n",
              "      <td>0.001476</td>\n",
              "      <td>0.035071</td>\n",
              "      <td>0.013672</td>\n",
              "      <td>0.002523</td>\n",
              "      <td>-0.015246</td>\n",
              "      <td>0.000932</td>\n",
              "      <td>0.00566574</td>\n",
              "      <td>0.005167</td>\n",
              "      <td>0.016419</td>\n",
              "      <td>0.000973</td>\n",
              "      <td>0.00352</td>\n",
              "      <td>0.00342</td>\n",
              "      <td>0.008607</td>\n",
              "      <td>0.006641</td>\n",
              "      <td>-0.009633</td>\n",
              "      <td>-0.004881</td>\n",
              "      <td>0.005674</td>\n",
              "      <td>-0.004146</td>\n",
              "      <td>-0.000440261</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.015584</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.015257</td>\n",
              "      <td>-0.002921</td>\n",
              "      <td>0.051126</td>\n",
              "      <td>0.015141</td>\n",
              "      <td>0.007755</td>\n",
              "      <td>0.011112</td>\n",
              "      <td>-0.00482</td>\n",
              "      <td>-0.024203</td>\n",
              "      <td>0.003657</td>\n",
              "      <td>0.00117378</td>\n",
              "      <td>0.018831</td>\n",
              "      <td>-0.010596</td>\n",
              "      <td>0.020598</td>\n",
              "      <td>0.002379</td>\n",
              "      <td>-0.009821</td>\n",
              "      <td>0.004427</td>\n",
              "      <td>0.00101</td>\n",
              "      <td>0.002805</td>\n",
              "      <td>0.011741</td>\n",
              "      <td>0.003016</td>\n",
              "      <td>-0.04146</td>\n",
              "      <td>0.009316</td>\n",
              "      <td>-0.02893</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.011986</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.012815</td>\n",
              "      <td>-0.006862</td>\n",
              "      <td>-0.004136</td>\n",
              "      <td>0.009441</td>\n",
              "      <td>-0.023452</td>\n",
              "      <td>0.010676</td>\n",
              "      <td>0.003365</td>\n",
              "      <td>0.001197</td>\n",
              "      <td>0.020829</td>\n",
              "      <td>-0.002412</td>\n",
              "      <td>0.000461</td>\n",
              "      <td>0.003859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.012453</td>\n",
              "      <td>-0.013287</td>\n",
              "      <td>0.012083</td>\n",
              "      <td>0.003306</td>\n",
              "      <td>0.00501</td>\n",
              "      <td>-0.00503</td>\n",
              "      <td>-0.009852</td>\n",
              "      <td>-0.002345</td>\n",
              "      <td>-0.00123</td>\n",
              "      <td>-0.003871</td>\n",
              "      <td>0.002428</td>\n",
              "      <td>-0.006373</td>\n",
              "      <td>-0.012192</td>\n",
              "      <td>-0.023377</td>\n",
              "      <td>-0.01046</td>\n",
              "      <td>0.000465</td>\n",
              "      <td>0.006414</td>\n",
              "      <td>-0.009185</td>\n",
              "      <td>0.014398</td>\n",
              "      <td>-0.00479</td>\n",
              "      <td>0.011584</td>\n",
              "      <td>0.004602</td>\n",
              "      <td>-0.016361</td>\n",
              "      <td>-0.004222</td>\n",
              "      <td>0.000484</td>\n",
              "      <td>-0.000563393</td>\n",
              "      <td>-0.000656</td>\n",
              "      <td>-0.01946</td>\n",
              "      <td>-0.006576</td>\n",
              "      <td>-0.011173</td>\n",
              "      <td>0.007217</td>\n",
              "      <td>-0.001859</td>\n",
              "      <td>-0.012527</td>\n",
              "      <td>-0.012997</td>\n",
              "      <td>0.00074</td>\n",
              "      <td>-0.003983</td>\n",
              "      <td>0.009336</td>\n",
              "      <td>0.0123347</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001919</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.004098</td>\n",
              "      <td>-0.002564</td>\n",
              "      <td>-0.002984</td>\n",
              "      <td>0.004047</td>\n",
              "      <td>0.004527</td>\n",
              "      <td>-0.005981</td>\n",
              "      <td>-0.013597</td>\n",
              "      <td>-0.002467</td>\n",
              "      <td>-0.011359</td>\n",
              "      <td>0</td>\n",
              "      <td>0.008183</td>\n",
              "      <td>0.006535</td>\n",
              "      <td>-0.023261</td>\n",
              "      <td>-0.002882</td>\n",
              "      <td>-0.005786</td>\n",
              "      <td>0.000367</td>\n",
              "      <td>-0.006304</td>\n",
              "      <td>-0.035358</td>\n",
              "      <td>-0.001879</td>\n",
              "      <td>-0.005012</td>\n",
              "      <td>-0.010746</td>\n",
              "      <td>-0.010754</td>\n",
              "      <td>-0.002979</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.006335</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.01913</td>\n",
              "      <td>-0.005988</td>\n",
              "      <td>-0.011273</td>\n",
              "      <td>-0.010046</td>\n",
              "      <td>0.001889</td>\n",
              "      <td>0.001531</td>\n",
              "      <td>-0.01126</td>\n",
              "      <td>-0.008568</td>\n",
              "      <td>-0.003006</td>\n",
              "      <td>0.02391</td>\n",
              "      <td>-0.013607</td>\n",
              "      <td>-0.003657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.018285</td>\n",
              "      <td>0.006191</td>\n",
              "      <td>0.005444</td>\n",
              "      <td>-0.001647</td>\n",
              "      <td>0.001015</td>\n",
              "      <td>0.009302</td>\n",
              "      <td>-0.006062</td>\n",
              "      <td>0.005224</td>\n",
              "      <td>0.004065</td>\n",
              "      <td>0.005943</td>\n",
              "      <td>0.009688</td>\n",
              "      <td>0.011318</td>\n",
              "      <td>0.001399</td>\n",
              "      <td>0.020996</td>\n",
              "      <td>0.016721</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.005665</td>\n",
              "      <td>0.00986</td>\n",
              "      <td>0.001322</td>\n",
              "      <td>0.012059</td>\n",
              "      <td>-0.010882</td>\n",
              "      <td>-0.000891</td>\n",
              "      <td>0</td>\n",
              "      <td>0.029682</td>\n",
              "      <td>0.008819</td>\n",
              "      <td>-0.00112742</td>\n",
              "      <td>0.002298</td>\n",
              "      <td>0.015457</td>\n",
              "      <td>0.013495</td>\n",
              "      <td>0.012942</td>\n",
              "      <td>-0.000663</td>\n",
              "      <td>0.010919</td>\n",
              "      <td>0.019367</td>\n",
              "      <td>-0.001062</td>\n",
              "      <td>0.001942</td>\n",
              "      <td>-0.001</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.0221932</td>\n",
              "      <td>...</td>\n",
              "      <td>0.027532</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.020805</td>\n",
              "      <td>-0.000367</td>\n",
              "      <td>0.020336</td>\n",
              "      <td>-0.006794</td>\n",
              "      <td>0.011266</td>\n",
              "      <td>0.007631</td>\n",
              "      <td>-0.016565</td>\n",
              "      <td>0.016031</td>\n",
              "      <td>0.009538</td>\n",
              "      <td>-0.0126038</td>\n",
              "      <td>0.018613</td>\n",
              "      <td>0.022904</td>\n",
              "      <td>0.025916</td>\n",
              "      <td>-0.011393</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>0.005875</td>\n",
              "      <td>-0.001776</td>\n",
              "      <td>0.025678</td>\n",
              "      <td>0.002658</td>\n",
              "      <td>-0.001007</td>\n",
              "      <td>0.006496</td>\n",
              "      <td>0.000942</td>\n",
              "      <td>-0.006972</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.000832</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.008358</td>\n",
              "      <td>0.009268</td>\n",
              "      <td>0.009601</td>\n",
              "      <td>-0.007961</td>\n",
              "      <td>0.029895</td>\n",
              "      <td>0.003516</td>\n",
              "      <td>0.007996</td>\n",
              "      <td>0.012763</td>\n",
              "      <td>0.000548</td>\n",
              "      <td>-0.001509</td>\n",
              "      <td>0.010521</td>\n",
              "      <td>0.003114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.023529</td>\n",
              "      <td>-0.000951</td>\n",
              "      <td>0.017859</td>\n",
              "      <td>0.008251</td>\n",
              "      <td>0.009683</td>\n",
              "      <td>0.010018</td>\n",
              "      <td>0.013751</td>\n",
              "      <td>0.014161</td>\n",
              "      <td>0.021592</td>\n",
              "      <td>0.010452</td>\n",
              "      <td>0.009062</td>\n",
              "      <td>0.01634</td>\n",
              "      <td>0.016516</td>\n",
              "      <td>-0.011856</td>\n",
              "      <td>-0.017013</td>\n",
              "      <td>0.016459</td>\n",
              "      <td>6.4e-05</td>\n",
              "      <td>0.009263</td>\n",
              "      <td>0.012105</td>\n",
              "      <td>0.015574</td>\n",
              "      <td>-0.008801</td>\n",
              "      <td>0.022417</td>\n",
              "      <td>0.002011</td>\n",
              "      <td>-0.026081</td>\n",
              "      <td>0.012763</td>\n",
              "      <td>0.0118511</td>\n",
              "      <td>0.013868</td>\n",
              "      <td>0.015058</td>\n",
              "      <td>0.006945</td>\n",
              "      <td>0.005967</td>\n",
              "      <td>0.018854</td>\n",
              "      <td>0.012392</td>\n",
              "      <td>0.004646</td>\n",
              "      <td>0.008139</td>\n",
              "      <td>-0.012046</td>\n",
              "      <td>0.011007</td>\n",
              "      <td>0.013131</td>\n",
              "      <td>0.00510859</td>\n",
              "      <td>...</td>\n",
              "      <td>0.001515</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.031355</td>\n",
              "      <td>0.010064</td>\n",
              "      <td>0.014771</td>\n",
              "      <td>0.016493</td>\n",
              "      <td>0.022282</td>\n",
              "      <td>0.010875</td>\n",
              "      <td>0.013054</td>\n",
              "      <td>0.008041</td>\n",
              "      <td>0.009534</td>\n",
              "      <td>0.00534337</td>\n",
              "      <td>0.001694</td>\n",
              "      <td>-0.002645</td>\n",
              "      <td>0.012562</td>\n",
              "      <td>0.010492</td>\n",
              "      <td>0.01105</td>\n",
              "      <td>0.009126</td>\n",
              "      <td>0.013727</td>\n",
              "      <td>0.118312</td>\n",
              "      <td>0.016345</td>\n",
              "      <td>0.014454</td>\n",
              "      <td>-0.021151</td>\n",
              "      <td>0.008552</td>\n",
              "      <td>0.005516</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.000208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.002303</td>\n",
              "      <td>0.007346</td>\n",
              "      <td>-0.003566</td>\n",
              "      <td>-0.005026</td>\n",
              "      <td>-0.01909</td>\n",
              "      <td>0.015537</td>\n",
              "      <td>-0.009135</td>\n",
              "      <td>0.010022</td>\n",
              "      <td>-0.008675</td>\n",
              "      <td>0.012285</td>\n",
              "      <td>0.00509</td>\n",
              "      <td>0.003497</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 352 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           date        AA       AAL  ...       ZBH      ZION       SPY\n",
              "0    2016-01-04 -0.016211 -0.034002  ... -0.007603 -0.021612 -0.015055\n",
              "1    2016-01-05 -0.045314 -0.009533  ...  0.020823 -0.010857  0.001384\n",
              "2    2016-01-06 -0.071197  0.017522  ...   0.00433 -0.029145 -0.014091\n",
              "3    2016-01-07 -0.039489 -0.018918  ... -0.022993  -0.03002 -0.023967\n",
              "4    2016-01-08 -0.024184 -0.001978  ... -0.004217 -0.011254 -0.010960\n",
              "...         ...       ...       ...  ...       ...       ...       ...\n",
              "1254 2020-12-24       NaN -0.014475  ...  0.003423 -0.003907  0.002197\n",
              "1255 2020-12-28       NaN  0.025543  ... -0.002412  0.000461  0.003859\n",
              "1256 2020-12-29       NaN -0.012453  ...   0.02391 -0.013607 -0.003657\n",
              "1257 2020-12-30       NaN  0.018285  ... -0.001509  0.010521  0.003114\n",
              "1258 2020-12-31       NaN -0.023529  ...  0.012285   0.00509  0.003497\n",
              "\n",
              "[1259 rows x 352 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpPtq7JyUrRO",
        "outputId": "593039e9-9b19-401f-b52a-a690da82d664"
      },
      "source": [
        "df.LHX.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "497"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAZuSUb2QnUb",
        "outputId": "95b32af6-13b5-4da6-e28e-3eab4946c652"
      },
      "source": [
        "len(df.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "352"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plFSJGYia4fZ",
        "outputId": "a427f414-d408-42cd-e95b-35ba0ee02b8c"
      },
      "source": [
        "type(df.date[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas._libs.tslibs.timestamps.Timestamp"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9fPGYXLHYO9"
      },
      "source": [
        "#for i in range(len(df.date)):\n",
        "#  df.date.iloc[i] = pd.Timestamp(df.date.iloc[i])\n",
        "\n",
        "#type(df.date[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vL5ePWWuoFqg"
      },
      "source": [
        "#df.to_csv('returns.csv', index=False) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNstW9jTd7SM",
        "outputId": "94d37ebf-9d8d-4c9a-c637-a9ec247a8c76"
      },
      "source": [
        "df.AAL.isnull().value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    1259\n",
              "Name: AAL, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSQCfCQmhqsb"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsKJQgPP69UE"
      },
      "source": [
        "SAMPLE_TYPE = 'ALL' #OWEN\n",
        "if SAMPLE_TYPE == 'OWEN':\n",
        "  events = pd.read_csv('./event sample.csv')\n",
        "  events.columns = ['ticker', 'market', 'date']\n",
        "  events[\"date\"] = pd.to_datetime(events[\"date\"]).dt.date\n",
        "  mask = events.ticker == 'BRK.B'\n",
        "  events.loc[mask, 'ticker'] = 'BRK'\n",
        "  events\n",
        "if SAMPLE_TYPE == 'ALL':\n",
        "  events = pd.read_excel('./Sample_All.xlsx')\n",
        "  All_sample = events.copy()\n",
        "  events = events[['stock_ticker','date', 'industry',  'major_industry', 'govt_level', 'primary_offense', 'secondary_offense', 'offense_group', 'civil_criminal', 'penalty', 'agency_code1']]\n",
        "  events.columns = ['ticker','date', 'industry',  'major_industry', 'govt_level', 'primary_offense', 'secondary_offense', 'offense_group', 'civil_criminal', 'penalty', 'agency_code1']\n",
        "  mask = events.ticker == 'BRK.B'\n",
        "  events.loc[mask, 'ticker'] = 'BRK'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PqqyfXyOfMI",
        "outputId": "41b159f0-424c-49b2-e204-79650d2b07f3"
      },
      "source": [
        "events.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ticker', 'date', 'industry', 'major_industry', 'govt_level',\n",
              "       'primary_offense', 'secondary_offense', 'offense_group',\n",
              "       'civil_criminal', 'penalty', 'agency_code1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfamtSEBTKzj",
        "outputId": "4720a3bd-8b39-40da-a11c-78af715e0539"
      },
      "source": [
        "events.agency_code1.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OSHA             1440\n",
              "FMCSA            1072\n",
              "FRA              1037\n",
              "WHD               605\n",
              "EPA               271\n",
              "INS               227\n",
              "CMS               222\n",
              "AG                214\n",
              "MSHA              204\n",
              "MHC               123\n",
              "FAA               121\n",
              "NLRB              110\n",
              "EEOC               74\n",
              "OFCCP              57\n",
              "USAO               53\n",
              "FIN                35\n",
              "EBSA               30\n",
              "DBO                27\n",
              "FCC                23\n",
              "DFS                23\n",
              "BSEE               22\n",
              "MULTI              21\n",
              "DOJ_CIVIL          20\n",
              "SEC                14\n",
              "FTC                14\n",
              "PHMSA              11\n",
              "ACPD               11\n",
              "DLI                 9\n",
              "CFPB                9\n",
              "HHSOIG              9\n",
              "FERC                7\n",
              "BIS                 6\n",
              "CFTC                6\n",
              "DOJ_CRIMINAL        6\n",
              "HHSOCR              5\n",
              "DOJ_RIGHTS          4\n",
              "HUD                 4\n",
              "DEA                 4\n",
              "OFR                 3\n",
              "ED                  3\n",
              "DOJ_ANTITRUST       3\n",
              "NRC                 2\n",
              "DBI                 2\n",
              "BKG                 2\n",
              "AMS                 2\n",
              "DDTC                2\n",
              "LCO                 2\n",
              "CPSC                2\n",
              "OCDA                1\n",
              "CCC                 1\n",
              "NYCCO               1\n",
              "LACDCBA             1\n",
              "ONRR                1\n",
              "LACA                1\n",
              "DATC                1\n",
              "DBF                 1\n",
              "OHIC                1\n",
              "GIPSA               1\n",
              "Name: agency_code1, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0usFBSTzPCEh",
        "outputId": "df1fae8c-1320-4484-fdef-2cabfc5809f3"
      },
      "source": [
        "ticker_lst = events.ticker.value_counts().head(45).index\n",
        "ticker_lst"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['BRK', 'KMI', 'UNP', 'DLTR', 'BAC', 'CSX', 'WFC', 'JNJ', 'WMT', 'GE',\n",
              "       'JPM', 'VZ', 'MMM', 'NSC', 'PSX', 'CI', 'HD', 'DG', 'KR', 'XOM', 'UPS',\n",
              "       'AMZN', 'FCX', 'IP', 'AAL', 'ANTM', 'CVS', 'UNH', 'D', 'C', 'ABT',\n",
              "       'DAL', 'TSN', 'CMCSA', 'MLM', 'GM', 'FDX', 'PGR', 'NUE', 'MPC', 'LHX',\n",
              "       'WBA', 'TGT', 'COP', 'PFE'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "cd_SguGhYNxL",
        "outputId": "bc6cde91-65d8-4f74-84c8-5cfda878b30e"
      },
      "source": [
        "plt.figure(figsize=(15,12))\n",
        "events.date.groupby(events.date.dt.year).count().plot(kind=\"bar\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0d4f25890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        },
        "id": "_5NNjjwZbZKQ",
        "outputId": "1c1a6832-a1f9-4be2-dd6e-0a61260e8884"
      },
      "source": [
        "plt.figure(figsize=(20,12))\n",
        "events.date.groupby([events.date.dt.year, events.date.dt.month]).count().plot(kind=\"bar\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0d4f258d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "svIgzCeqccXY",
        "outputId": "7440b900-2613-437e-802b-ac633bd4ea20"
      },
      "source": [
        "plt.figure(figsize=(50,12))\n",
        "events.date.groupby([events.date.dt.year, events.date.dt.month, events.date.dt.week]).count().plot(kind=\"bar\")\n",
        "print(events.date.groupby([events.date.dt.year, events.date.dt.month, events.date.dt.week]).count().mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "29.566666666666666\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 3600x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "6VghqfcbYvh_",
        "outputId": "2419b697-37d4-4dde-f240-8f4b45e57beb"
      },
      "source": [
        "plt.figure(figsize=(50,12))\n",
        "events.date.groupby([events.date.dt.year, events.date.dt.month, events.date.dt.week, events.date.dt.day]).count().plot(kind=\"bar\")\n",
        "print(events.date.groupby([events.date.dt.year, events.date.dt.month, events.date.dt.week, events.date.dt.day]).count().mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "6.522058823529412\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 3600x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NncRQBDq1OJ"
      },
      "source": [
        "#CAR Overlap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40lDKIWuq8qE"
      },
      "source": [
        "#Main Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2M1UTs9fq5v1",
        "outputId": "96433e76-2b8f-493e-d9c0-49805ff11e88"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "import re\n",
        "\n",
        "def get_estwin_returns(df, ev_date, ticker, es_win_len, lower_ev_win, gap=0): \n",
        "  '''\n",
        "  ev_date is in '20170105' format.\n",
        "  ticker is string not obj.\n",
        "  lower_ev_win is already a negative value pointing to the negative lower bound of event window\n",
        "  '''\n",
        "  post_ev_df = df[['date', ticker]][df.date < pd.Timestamp(ev_date)]\n",
        "  if gap != 0:\n",
        "    es_win_df = post_ev_df[['date', ticker]].iloc[-es_win_len+lower_ev_win-gap:-gap+lower_ev_win:]\n",
        "  if gap ==0:\n",
        "    es_win_df = post_ev_df[['date', ticker]].iloc[-es_win_len+lower_ev_win:lower_ev_win:]\n",
        "  '''\n",
        "  the result will be a column of date and a column of ticker returns\n",
        "  '''\n",
        "  return es_win_df #returns df of date and return for the specific return\n",
        "\n",
        "def est_ols_params(X,Y):\n",
        "  #add column of 1 to X for constant coefficient\n",
        "  X = sm.add_constant(X)\n",
        "  model = sm.OLS(Y, X)\n",
        "  result = model.fit()\n",
        "  #OLS model\n",
        "  alpha = result.params[0]\n",
        "  beta = result.params[1]\n",
        "  return alpha, beta\n",
        "\n",
        "def find_bound_dates(df, ev_date, ticker, ev_win_upper, ev_win_lower):\n",
        "  up_bound = timestamp_convert(str(df.date[df.index[df.date == ev_date][0]+ev_win_upper+1])).split(' ')[0]\n",
        "  low_bound = timestamp_convert(str(df.date[df.index[df.date == ev_date][0]+ev_win_lower-1])).split(' ')[0] #ev_win_lower is already a negative value\n",
        "  post_upper_ev_df = df[['date', ticker]].query(f'date < {up_bound}')\n",
        "  ev_win_df = post_upper_ev_df.query(f'date > {low_bound}')\n",
        "  return ev_win_df\n",
        "\n",
        "def get_evwin_returns(df, ev_date, ticker, ev_win_upper, ev_win_lower):\n",
        "  '''\n",
        "  ev_win_upper, ev_win_lower are in '20170105' format.\n",
        "  ticker is string not obj like 'AA'.\n",
        "  '''\n",
        "  if pd.Timestamp(ev_date) in df['date'].values:\n",
        "    ev_win_df = find_bound_dates(df, ev_date, ticker, ev_win_upper, ev_win_lower)\n",
        "  else:\n",
        "    temp_ev_date = timestamp_convert(str(pd.Timestamp(ev_date)+pd.to_timedelta(1,'d')).split(' ')[0])\n",
        "    while pd.Timestamp(temp_ev_date) not in df['date'].values:\n",
        "      temp_ev_date = str(pd.Timestamp(temp_ev_date)+pd.to_timedelta(1,'d')).split(' ')[0]\n",
        "    ev_win_df = find_bound_dates(df, temp_ev_date, ticker, ev_win_upper, ev_win_lower)\n",
        "  return ev_win_df\n",
        "\n",
        "def timestamp_convert(date):\n",
        "  date = re.sub('-', '', date)\n",
        "  return date\n",
        "\n",
        "def check_win_NaN(series):\n",
        "  if series.isnull().sum()>0:\n",
        "    print(f'For ticker {series.name} we have {series.isnull().sum()} NaN values \\\n",
        "    in the window')\n",
        "    return 0 #False\n",
        "  else:\n",
        "    return 1 #True\n",
        "\n",
        "def cal_AR(R, Rm, alpha, beta, model='market_model'):\n",
        "  if model == 'market_model':\n",
        "    ER = alpha + beta*Rm\n",
        "  return np.array(R - ER)\n",
        "\n",
        "def cal_car(AR):\n",
        "  ev_car = AR.cumsum()\n",
        "  print(f'AR.shape[1] is {AR.shape[1]}')\n",
        "  ev_car[\"CAAR\"] = (1/AR.shape[1])*(ev_car.sum(axis=1)) #Adds CAAR to CAR matrix\n",
        "  return ev_car"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tkZMZ94QWrV"
      },
      "source": [
        "def AR_ev_es_vec(returns, events, es_win, ev_win, minNA=100, gap_=0):\n",
        "  '''\n",
        "  es_win is int like 252 and ev_win is list like [-5,10]\n",
        "  '''\n",
        "  AR = pd.DataFrame()\n",
        "  AR['date'] = df[df.date > '2016-01-01'].date\n",
        "  for ticker, ev_date in zip(events.ticker, events.date):\n",
        "    ev_date = str(ev_date)\n",
        "    X = np.array(get_estwin_returns(returns, timestamp_convert(ev_date), ticker, es_win,ev_win[0], gap=gap_)[ticker], dtype=float)\n",
        "    Y = np.array(get_estwin_returns(returns, timestamp_convert(ev_date), 'SPY', es_win,ev_win[0], gap=gap_)['SPY'], dtype=float)\n",
        "    try:\n",
        "      AR_temp = pd.DataFrame()\n",
        "      if check_win_NaN(pd.Series(X)) and check_win_NaN(pd.Series(Y)):\n",
        "        alpha, beta = est_ols_params(X,Y)\n",
        "        ev_X = get_evwin_returns(returns, ev_date, ticker, ev_win[1], ev_win[0])[ticker]\n",
        "        AR_temp['date'] = get_evwin_returns(returns, ev_date, ticker, ev_win[1], ev_win[0])['date']\n",
        "        ev_Y = get_evwin_returns(returns, ev_date, 'SPY', ev_win[1], ev_win[0])['SPY']\n",
        "        if ticker not in AR.columns:\n",
        "          AR_temp[ticker] = cal_AR(ev_X, ev_Y, alpha, beta)\n",
        "          AR = AR.merge(AR_temp, on='date', how='left')\n",
        "        else:\n",
        "          if f'{ticker}_{ev_date[0:10]}' not in AR.columns:\n",
        "            AR_temp[f'{ticker}_{ev_date[0:10]}'] = cal_AR(ev_X, ev_Y, alpha, beta)\n",
        "            AR = AR.merge(AR_temp, on='date', how='left')\n",
        "          else:\n",
        "            for m in range(2,100):\n",
        "              if f'{ticker}_{ev_date[0:10]}_{m}' not in AR.columns:\n",
        "                #print(f'{ticker}_{ev_date[0:10]}_{m}')\n",
        "                AR_temp[f'{ticker}_{ev_date[0:10]}_{m}'] = cal_AR(ev_X, ev_Y, alpha, beta)\n",
        "                AR = AR.merge(AR_temp, on='date', how='left')\n",
        "                break\n",
        "      else:\n",
        "        if pd.Series(X).isnull().sum() > minNA and pd.Series(Y).isnull().sum() > minNA:\n",
        "          X = np.array(pd.Series(X)[pd.Series(X).notna()])\n",
        "          Y = np.array(pd.Series(Y)[pd.Series(X).notna()])\n",
        "          alpha, beta = est_ols_params(X,Y)\n",
        "          ev_X = get_evwin_returns(returns, ev_date, ticker, ev_win[1], ev_win[0])[ticker]\n",
        "          AR_temp['date'] = get_evwin_returns(returns, ev_date, ticker, ev_win[1], ev_win[0])['date']\n",
        "          ev_Y = get_evwin_returns(returns, ev_date, 'SPY', ev_win[1], ev_win[0])['SPY']\n",
        "          if ticker not in AR.columns:\n",
        "            AR_temp[ticker] = cal_AR(ev_X, ev_Y, alpha, beta)\n",
        "            AR = AR.merge(AR_temp, on='date', how='left')\n",
        "          else:\n",
        "            if f'{ticker}_{ev_date[0:10]}' not in AR.columns:\n",
        "              AR_temp[f'{ticker}_{ev_date[0:10]}'] = cal_AR(ev_X, ev_Y, alpha, beta)\n",
        "              AR = AR.merge(AR_temp, on='date', how='left')\n",
        "            else:\n",
        "              for mm in range(2,100):\n",
        "                if f'{ticker}_{ev_date[0:10]}_{mm}' not in AR.columns:\n",
        "                  #print(f'{ticker}_{ev_date[0:10]}_{m}')\n",
        "                  AR_temp[f'{ticker}_{ev_date[0:10]}_{mm}'] = cal_AR(ev_X, ev_Y, alpha, beta)\n",
        "                  AR = AR.merge(AR_temp, on='date', how='left')\n",
        "                  break\n",
        "     \n",
        "\n",
        "    except:\n",
        "      print(f'Something is wrong with {ticker} in {ev_date}')\n",
        "      pass\n",
        "  return AR"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LU_RnCStk9yq"
      },
      "source": [
        "def cal_CAAR(AR_overlap, events, es_win_len, lower_ev_win, gap=0):\n",
        "  AR_len = []\n",
        "  for i in range(AR_overlap.shape[0]):\n",
        "    AR_len.append(len(AR_overlap.iloc[i,:][AR_overlap.iloc[i,:].notna()]))\n",
        "  AR_t = pd.DataFrame()\n",
        "  AR_t['AR_t'] = AR_overlap.iloc[:, 1::].sum(axis=1)\n",
        "  AR_t['date'] = AR_overlap['date']\n",
        "  CAR = []\n",
        "  '''\n",
        "  for ev_date in events.date:\n",
        "    post_ev_df = AR_t[['date', 'AR_t']][AR_t.date < pd.Timestamp(ev_date)]\n",
        "    es_win_df = post_ev_df[['date', 'AR_t']].iloc[-es_win_len+lower_ev_win-gap:-gap+lower_ev_win:]\n",
        "    CAR.append(es_win_df.AR_t.sum())\n",
        "  CAAR = (1/len(CAR))*sum(CAR)\n",
        "  '''\n",
        "  return AR_t.AR_t.sum()/AR_overlap.shape[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QE1cjmSRFjUp"
      },
      "source": [
        "def es_AR(returns, events, es_win, ev_win, minNA=100, gap_=0):\n",
        "  '''\n",
        "  es_win is int like 252 and ev_win is list like [-5,10]\n",
        "  '''\n",
        "  AR = pd.DataFrame()\n",
        "  AR['date'] = df[df.date > '2016-01-01'].date\n",
        "  for ticker, ev_date in zip(events.ticker, events.date):\n",
        "    ev_date = str(ev_date)\n",
        "    X = np.array(get_estwin_returns(returns, timestamp_convert(ev_date), ticker, es_win,ev_win[0], gap=gap_)[ticker], dtype=float)\n",
        "    Y = np.array(get_estwin_returns(returns, timestamp_convert(ev_date), 'SPY', es_win,ev_win[0], gap=gap_)['SPY'], dtype=float)\n",
        "    try:\n",
        "      AR_temp = pd.DataFrame()\n",
        "      if check_win_NaN(pd.Series(X)) and check_win_NaN(pd.Series(Y)):\n",
        "        alpha, beta = est_ols_params(X,Y)\n",
        "        AR_temp['date'] = get_estwin_returns(returns, timestamp_convert(ev_date), ticker, es_win,ev_win[0], gap=gap_)['date']\n",
        "        if ticker not in AR.columns:\n",
        "          AR_temp[ticker] = cal_AR(X, Y, alpha, beta)\n",
        "          AR = AR.merge(AR_temp, on='date', how='left')\n",
        "        else:\n",
        "          if f'{ticker}_{ev_date[0:10]}' not in AR.columns:\n",
        "            AR_temp[f'{ticker}_{ev_date[0:10]}'] = cal_AR(X, Y, alpha, beta)\n",
        "            AR = AR.merge(AR_temp, on='date', how='left')\n",
        "          else:\n",
        "            for m in range(2,100):\n",
        "              if f'{ticker}_{ev_date[0:10]}_{m}' not in AR.columns:\n",
        "                #print(f'{ticker}_{ev_date[0:10]}_{m}')\n",
        "                AR_temp[f'{ticker}_{ev_date[0:10]}_{m}'] = cal_AR(X, Y, alpha, beta)\n",
        "                AR = AR.merge(AR_temp, on='date', how='left')\n",
        "                break\n",
        "      else:\n",
        "        if pd.Series(X).isnull().sum() > minNA and pd.Series(Y).isnull().sum() > minNA:\n",
        "          X = np.array(pd.Series(X)[pd.Series(X).notna()])\n",
        "          Y = np.array(pd.Series(Y)[pd.Series(X).notna()])\n",
        "          alpha, beta = est_ols_params(X,Y)\n",
        "          AR_temp['date'] = get_estwin_returns(returns, timestamp_convert(ev_date), ticker, es_win,ev_win[0], gap=gap_)['date']\n",
        "          if ticker not in AR.columns:\n",
        "            AR_temp[ticker] = cal_AR(X, Y, alpha, beta)\n",
        "            AR = AR.merge(AR_temp, on='date', how='left')\n",
        "          else:\n",
        "            if f'{ticker}_{ev_date[0:10]}' not in AR.columns:\n",
        "              AR_temp[f'{ticker}_{ev_date[0:10]}'] = cal_AR(X, Y, alpha, beta)\n",
        "              AR = AR.merge(AR_temp, on='date', how='left')\n",
        "            else:\n",
        "              for mm in range(2,100):\n",
        "                if f'{ticker}_{ev_date[0:10]}_{mm}' not in AR.columns:\n",
        "                  #print(f'{ticker}_{ev_date[0:10]}_{m}')\n",
        "                  AR_temp[f'{ticker}_{ev_date[0:10]}_{mm}'] = cal_AR(X, Y, alpha, beta)\n",
        "                  AR = AR.merge(AR_temp, on='date', how='left')\n",
        "                  break\n",
        "     \n",
        "\n",
        "    except:\n",
        "      print(f'Something is wrong with {ticker} in {ev_date}')\n",
        "      pass\n",
        "  return AR"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KY8k9wU561K5"
      },
      "source": [
        "def cal_delta(AR, ev_win_len):\n",
        "  main_lst = []\n",
        "  for i in range(1, AR.shape[1]):\n",
        "    temp_df = AR[AR.iloc[:,i].notna()]\n",
        "    des_df = temp_df.drop(temp_df.columns[i],axis=1)\n",
        "    temp_lst = []\n",
        "    for i in range(1, des_df.shape[1]):\n",
        "      temp_lst.append(des_df.iloc[:,i].notna().sum())\n",
        "    main_lst.append(np.mean(temp_lst))\n",
        "  delta = np.mean(main_lst)/ev_win_len\n",
        "  return delta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhW01EwNmmB0"
      },
      "source": [
        "def cal_theta_varbar(es_AR):\n",
        "  M = (es_AR.iloc[:,1::].notna().sum(axis=1)*(es_AR.iloc[:,1::].notna().sum(axis=1)-1)).sum()\n",
        "  N = es_AR.iloc[:,1::].notna().sum(axis=1).sum()\n",
        "  eps_t = es_AR.iloc[:,1::].sum(axis=1)\n",
        "  eps_kt = es_AR.iloc[:,1::].pow(2).sum(axis=1)\n",
        "  sig2_cr = eps_t.pow(2).sum()*(1/N)\n",
        "  sig2_bar = eps_kt.sum()*(1/N)\n",
        "  theta = ((sig2_cr/sig2_bar)-1)*N/M\n",
        "  return theta, sig2_bar"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dh8OkKoamiaF"
      },
      "source": [
        "def sd_robust_CAAR(AR, es_AR, ev_win_len):\n",
        "  delta = cal_delta(AR, ev_win_len)\n",
        "  theta, sig2_bar = cal_theta_varbar(es_AR)\n",
        "  n = AR.shape[1]\n",
        "  var = ((1/n)*sig2_bar*(1+(delta*(n-1)*theta)))\n",
        "  return np.sqrt(var) , n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Abs3iM5H1u7s"
      },
      "source": [
        "Major_industry_group = ['retailing', 'financial services', 'railroads', 'oil and gas',\n",
        "      'healthcare services', 'pipelines', 'pharmaceuticals',\n",
        "      'telecommunications', 'diversified', 'utilities and power generation',\n",
        "      'miscellaneous manufacturing', 'airlines', 'food products',\n",
        "      'aerospace and military contracting', 'freight and logistics',\n",
        "      'paper and packaging', 'wholesalers', 'mining and minerals',\n",
        "      'chemicals', 'construction and engineering', 'building materials',\n",
        "      'waste management and environmental services', 'motor vehicles',\n",
        "      'industrial equipment', 'metals', 'information technology',\n",
        "      'housewares and home furnishings'] #Some categories have less than 30 events and we omitted them.\n",
        "off_group = ['safety-related offenses', 'employment-related offenses',\n",
        "        'consumer-protection-related offenses', 'environment-related offenses',\n",
        "        'government-contracting-related offenses',\n",
        "        'competition-related offenses', 'healthcare-related offenses'] #'miscellaneous offenses' has only 5 events so we omitted it, others have > 30\n",
        "\n",
        "gov_level_group = ['federal', 'state', 'local'] #local had 25 events but we put it anyway.\n",
        "\n",
        "primary_offence_group = ['workplace safety or health violation',\n",
        "      'motor vehicle safety violation', 'wage and hour violation',\n",
        "      'railroad safety violation', 'insurance violation',\n",
        "      'environmental violation', 'nursing home violation',\n",
        "      'employment discrimination', 'consumer protection violation',\n",
        "      'aviation safety violation', 'labor relations violation',\n",
        "      'False Claims Act and related', 'benefit plan administrator violation',\n",
        "      'Family and Medical Leave Act']\n",
        "\n",
        "secondary_offence_group = ['Fair Labor Standards Act', 'overtime violation', 'mining violation',\n",
        "      'misclassification', 'other pay violation', 'meal/rest break violation',\n",
        "      'off-the-clock work', 'Service Contract Act', 'racial discrimination',\n",
        "      'child labor or youth employment violation',\n",
        "      'Davis-Bacon Act and related', 'antiboycott violation']\n",
        "\n",
        "agency_group = ['OSHA', 'FMCSA', 'FRA', 'WHD', 'EPA', 'CMS', 'INS', 'AG', 'MSHA', 'MHC',\n",
        "      'FAA', 'NLRB', 'EEOC', 'OFCCP', 'USAO', 'FIN', 'EBSA']\n",
        "\n",
        "penalty_group = ['[1000,6000]', '[20000, 31000]', '[44000,60000]', '[71000, 87000]']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRXLK-tl2X-W"
      },
      "source": [
        "from scipy import stats\n",
        "from scipy.stats import t\n",
        "\n",
        "def output(returns, events, cat, cat_name, cat_members, es_win_, ev_win_):\n",
        "  for i in cat_members:\n",
        "    if cat_members[0] == '[1000,6000]':\n",
        "      j = eval(i)\n",
        "      mask1 = events[cat] > j[0] \n",
        "      mask2 = events[cat] < j[1]\n",
        "      AR = AR_ev_es_vec(returns, events[mask1][mask2], es_win_, ev_win_, gap_=0)\n",
        "      esAR = es_AR(returns, events[mask1][mask2], es_win_, ev_win_, minNA=100, gap_=0)\n",
        "      sd, n = sd_robust_CAAR(AR, esAR, ev_win_[1]-ev_win_[0]+1)\n",
        "      CAAR = cal_CAAR(AR, events[mask1][mask2], ev_win_[1]-ev_win_[0]+1, ev_win_[0]) #Last two arguments are useless now\n",
        "      print(f'Statistics for event window = {ev_win_}, category = {cat_name}, subcategory = {i} for {n} Obs. :')\n",
        "      print(f'sd ] {sd}, n={n}, CAAR = {CAAR}, t-stat = {CAAR/sd}, Pvalue = {stats.t.sf(np.abs(CAAR/sd), n-1)*2}')\n",
        "    else:\n",
        "      AR = AR_ev_es_vec(returns, events[events[cat] == i], es_win_, ev_win_, minNA=100, gap_=0)\n",
        "      esAR = es_AR(returns, events[events[cat] == i], es_win_, ev_win_, minNA=100, gap_=0)\n",
        "      sd, n = sd_robust_CAAR(AR, esAR, ev_win_[1]-ev_win_[0]+1)\n",
        "      CAAR = cal_CAAR(AR, events[events[cat] == i], ev_win_[1]-ev_win_[0]+1, ev_win_[0]) #Last two arguments are useless now\n",
        "      print(f'Statistics for event window = {ev_win_}, category = {cat_name}, subcategory = {i} for {n} Obs. :')\n",
        "      print(f'sd = {sd}, n = {n}, CAAR = {CAAR}, t-stat = {CAAR/sd}, Pvalue = {stats.t.sf(np.abs(CAAR/sd), n-1)*2}')\n",
        "    '''\n",
        "    result = pd.DataFrame()\n",
        "    result['CAAR'] = CAAR\n",
        "    result['obs'] = n\n",
        "    result['sd'] = sd\n",
        "    result['t_stat'] = CAAR/sd\n",
        "    result['P-value'] = stats.t.sf(np.abs(CAAR/sd), n-1)*2\n",
        "    print(result)\n",
        "    '''\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b15-qWV1ecsF"
      },
      "source": [
        "#Output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAwKdJJ8yWKc",
        "outputId": "7e160029-13c7-4164-f0e7-f71cc9a310f3"
      },
      "source": [
        "output(df, events, 'major_industry', 'Major Industry Group', Major_industry_group, 200, [-20,20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = retailing for 984 Obs. :\n",
            "sd = 0.0022495172921500597, n = 984, CAAR = 0.013829716431532491, t-stat = 6.147859578493938, Pvalue = 1.1403067670940885e-09\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = financial services for 849 Obs. :\n",
            "sd = 0.0030588484665078908, n = 849, CAAR = 0.01080202000995497, t-stat = 3.531400828850802, Pvalue = 0.00043565852932781314\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = railroads for 509 Obs. :\n",
            "sd = 0.008200753790586924, n = 509, CAAR = 0.007841018052338907, t-stat = 0.9561338204469775, Pvalue = 0.339459407476876\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = oil and gas for 347 Obs. :\n",
            "sd = 0.0028971741582021994, n = 347, CAAR = -0.008167703351317546, t-stat = -2.8191965361122437, Pvalue = 0.005091543112808599\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = healthcare services for 283 Obs. :\n",
            "sd = 0.003348203272548675, n = 283, CAAR = 0.022269757452006808, t-stat = 6.651256103413016, Pvalue = 1.5050976992995154e-10\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = pipelines for 250 Obs. :\n",
            "sd = 0.007551584546791487, n = 250, CAAR = 0.009280896857339368, t-stat = 1.2289999270792287, Pvalue = 0.22023201381804927\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = pharmaceuticals for 245 Obs. :\n",
            "sd = 0.003562458027096789, n = 245, CAAR = 0.015708879597296766, t-stat = 4.40956201527479, Pvalue = 1.5538562071919172e-05\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = telecommunications for 229 Obs. :\n",
            "sd = 0.0038694217438022217, n = 229, CAAR = -0.05364797080390307, t-stat = -13.864596406383658, Pvalue = 4.1449323204429206e-32\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = diversified for 509 Obs. :\n",
            "sd = 0.0027131728264272238, n = 509, CAAR = -0.03555153489150534, t-stat = -13.103306411306102, Pvalue = 5.32633962498331e-34\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = utilities and power generation for 181 Obs. :\n",
            "sd = 0.0026146797727120705, n = 181, CAAR = 0.01828877063859827, t-stat = 6.994650293113442, Pvalue = 5.040983302231417e-11\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = miscellaneous manufacturing for 161 Obs. :\n",
            "sd = 0.0031849804982129848, n = 161, CAAR = -0.0499465442253807, t-stat = -15.681899544880885, Pvalue = 3.6348841402555182e-34\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = airlines for 144 Obs. :\n",
            "sd = 0.0055955178639009, n = 144, CAAR = -0.03393758839662862, t-stat = -6.065138066232375, Pvalue = 1.118381334161895e-08\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = food products for 140 Obs. :\n",
            "sd = 0.0021314493264368247, n = 140, CAAR = -0.012411373700400054, t-stat = -5.8229738546720835, Pvalue = 3.829425443430766e-08\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 152 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 152 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = aerospace and military contracting for 91 Obs. :\n",
            "sd = 0.002490442024397574, n = 91, CAAR = 0.003760574322660754, t-stat = 1.5100027568681984, Pvalue = 0.13454571573655105\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = freight and logistics for 122 Obs. :\n",
            "sd = 0.002876741043330191, n = 122, CAAR = -0.008137933047704925, t-stat = -2.828872298593912, Pvalue = 0.005469644225678309\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = paper and packaging for 110 Obs. :\n",
            "sd = 0.003451889008930792, n = 110, CAAR = -0.011896118548676818, t-stat = -3.4462633409993653, Pvalue = 0.0008083321365229051\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = wholesalers for 85 Obs. :\n",
            "sd = 0.003769329487700611, n = 85, CAAR = -0.04432703060814354, t-stat = -11.759924610672384, Pvalue = 1.9371700315434217e-19\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = mining and minerals for 84 Obs. :\n",
            "sd = 0.007194004664909769, n = 84, CAAR = 0.006004447597739014, t-stat = 0.8346460528482748, Pvalue = 0.40631295898289643\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = chemicals for 64 Obs. :\n",
            "sd = 0.0035918753872917963, n = 64, CAAR = 0.010636799262686644, t-stat = 2.9613497451275954, Pvalue = 0.004314785638935246\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = construction and engineering for 56 Obs. :\n",
            "sd = 0.003539431550023509, n = 56, CAAR = -0.00633903037472873, t-stat = -1.7909741395300118, Pvalue = 0.07880083174007424\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = building materials for 55 Obs. :\n",
            "sd = 0.006120827483109042, n = 55, CAAR = 0.008069482782369967, t-stat = 1.3183646826574362, Pvalue = 0.19294633992065136\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = waste management and environmental services for 51 Obs. :\n",
            "sd = 0.0033991971133694155, n = 51, CAAR = -0.04441670882114412, t-stat = -13.066823529135254, Pvalue = 9.566966241012904e-18\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = motor vehicles for 49 Obs. :\n",
            "sd = 0.008416046001814743, n = 49, CAAR = -0.04402351632790914, t-stat = -5.2309025305251895, Pvalue = 3.6560607001035584e-06\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = industrial equipment for 44 Obs. :\n",
            "sd = 0.002547672423978541, n = 44, CAAR = -0.005743224716571815, t-stat = -2.2543026577973393, Pvalue = 0.02932686635725\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = metals for 35 Obs. :\n",
            "sd = 0.006070982248771605, n = 35, CAAR = -0.029736894482062137, t-stat = -4.898201520533035, Pvalue = 2.3267584799074765e-05\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = information technology for 35 Obs. :\n",
            "sd = 0.003138971992644571, n = 35, CAAR = -0.011073570609597202, t-stat = -3.5277698034724305, Pvalue = 0.0012236738996165765\n",
            "Statistics for event window = [-20, 20], category = Major Industry Group, subcategory = housewares and home furnishings for 31 Obs. :\n",
            "sd = 0.004063136370221421, n = 31, CAAR = 0.036112495731242124, t-stat = 8.887837483356279, Pvalue = 6.613740219823692e-10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9844vAryVEk",
        "outputId": "64cf2fea-db15-4f70-d538-e9c0a1241edc"
      },
      "source": [
        "output(df, events, 'major_industry', 'Major Industry Group', Major_industry_group, 200, [-10,10])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = retailing for 984 Obs. :\n",
            "sd = 0.0016599572031659189, n = 984, CAAR = 0.010037301647132525, t-stat = 6.04672314924089, Pvalue = 2.0973503592235776e-09\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = financial services for 849 Obs. :\n",
            "sd = 0.0022271042611997894, n = 849, CAAR = 0.004132532946842028, t-stat = 1.8555633065044483, Pvalue = 0.06386262579512471\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = railroads for 509 Obs. :\n",
            "sd = 0.007999495700657017, n = 509, CAAR = -0.0006212033489457184, t-stat = -0.0776553138086814, Pvalue = 0.9381328171891454\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = oil and gas for 347 Obs. :\n",
            "sd = 0.0022085879219639754, n = 347, CAAR = -0.001811117512736604, t-stat = -0.8200341470336744, Pvalue = 0.41276118655241556\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = healthcare services for 283 Obs. :\n",
            "sd = 0.002713602034052586, n = 283, CAAR = 0.007971613732218486, t-stat = 2.937650264181666, Pvalue = 0.0035801622313317086\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = pipelines for 250 Obs. :\n",
            "sd = 0.007100503153400633, n = 250, CAAR = -0.030729341383777184, t-stat = -4.3277695565926235, Pvalue = 2.1829638213672032e-05\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = pharmaceuticals for 245 Obs. :\n",
            "sd = 0.002928911136762571, n = 245, CAAR = 0.0008562654167280643, t-stat = 0.2923494011069673, Pvalue = 0.7702679254856335\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = telecommunications for 229 Obs. :\n",
            "sd = 0.003594645235131337, n = 229, CAAR = -0.025211335893727, t-stat = -7.013581103172719, Pvalue = 2.6149669976566873e-11\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = diversified for 509 Obs. :\n",
            "sd = 0.002477727928086985, n = 509, CAAR = -0.016295979108676684, t-stat = -6.576984875518013, Pvalue = 1.19523248933074e-10\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = utilities and power generation for 181 Obs. :\n",
            "sd = 0.002158199745993538, n = 181, CAAR = 0.011078003590862338, t-stat = 5.132983455969467, Pvalue = 7.348720802726097e-07\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = miscellaneous manufacturing for 161 Obs. :\n",
            "sd = 0.003083135953368553, n = 161, CAAR = -0.0005705164989196346, t-stat = -0.1850442236568593, Pvalue = 0.8534287026771237\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = airlines for 144 Obs. :\n",
            "sd = 0.004614421649949923, n = 144, CAAR = -0.03266346418949148, t-stat = -7.078560796420923, Pvalue = 6.03146236392137e-11\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = food products for 140 Obs. :\n",
            "sd = 0.001705630274435791, n = 140, CAAR = -0.004909992320347598, t-stat = -2.8786967456776553, Pvalue = 0.004625059888300265\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 162 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 162 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = aerospace and military contracting for 91 Obs. :\n",
            "sd = 0.00219040328346639, n = 91, CAAR = -0.0027642156531891143, t-stat = -1.261966540159055, Pvalue = 0.21022146896895186\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = freight and logistics for 122 Obs. :\n",
            "sd = 0.0022515799364825526, n = 122, CAAR = -0.012361395625376264, t-stat = -5.490098497096842, Pvalue = 2.2512853306600534e-07\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = paper and packaging for 110 Obs. :\n",
            "sd = 0.0026480180049411844, n = 110, CAAR = 0.0024481278369879013, t-stat = 0.924513289720731, Pvalue = 0.3572613478772052\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = wholesalers for 85 Obs. :\n",
            "sd = 0.0032561559945265264, n = 85, CAAR = -0.01730260724305363, t-stat = -5.313813979471085, Pvalue = 8.675997294602661e-07\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = mining and minerals for 84 Obs. :\n",
            "sd = 0.006148663310786777, n = 84, CAAR = -0.015242677798705532, t-stat = -2.479023005205841, Pvalue = 0.015198718085432435\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = chemicals for 63 Obs. :\n",
            "sd = 0.00319711198214535, n = 63, CAAR = -0.00969174619906869, t-stat = -3.031406548532986, Pvalue = 0.0035495413732816957\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = construction and engineering for 56 Obs. :\n",
            "sd = 0.003016137602179178, n = 56, CAAR = -0.0014412011379929743, t-stat = -0.47783003565609794, Pvalue = 0.6346648581836325\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = building materials for 55 Obs. :\n",
            "sd = 0.005153303719592648, n = 55, CAAR = 0.0023896004431150334, t-stat = 0.4637026212970664, Pvalue = 0.6447245886923834\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = waste management and environmental services for 51 Obs. :\n",
            "sd = 0.002787433895694156, n = 51, CAAR = -0.0166850204456285, t-stat = -5.985799509506726, Pvalue = 2.3029942194039407e-07\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = motor vehicles for 49 Obs. :\n",
            "sd = 0.008345941306970235, n = 49, CAAR = 0.018195043151261844, t-stat = 2.1801067707085346, Pvalue = 0.0341849229630717\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = industrial equipment for 44 Obs. :\n",
            "sd = 0.0021532102793238197, n = 44, CAAR = -0.010996542951505, t-stat = -5.10704553897926, Pvalue = 7.123883963060615e-06\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = metals for 35 Obs. :\n",
            "sd = 0.0048233521211539, n = 35, CAAR = -0.012140713239083917, t-stat = -2.5170696507597023, Pvalue = 0.016713969999655923\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = information technology for 35 Obs. :\n",
            "sd = 0.0028033363934290745, n = 35, CAAR = -0.03691704618338873, t-stat = -13.168967616558982, Pvalue = 6.5791154007599755e-15\n",
            "Statistics for event window = [-10, 10], category = Major Industry Group, subcategory = housewares and home furnishings for 31 Obs. :\n",
            "sd = 0.0036381619962551747, n = 31, CAAR = 0.021460712843555416, t-stat = 5.898778796998405, Pvalue = 1.8508274793903867e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zq8uHo6XyT_E",
        "outputId": "5e4292ea-32e8-49ba-d0b9-31c7c2fb1b95"
      },
      "source": [
        "output(df, events, 'major_industry', 'Major Industry Group', Major_industry_group, 200, [-5,5])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = retailing for 984 Obs. :\n",
            "sd = 0.0013037276363660815, n = 984, CAAR = 0.005077734449022079, t-stat = 3.8947816302915825, Pvalue = 0.00010493210136330882\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = financial services for 849 Obs. :\n",
            "sd = 0.0016537303002812863, n = 849, CAAR = 0.00419625583556948, t-stat = 2.5374487211462053, Pvalue = 0.011344446129442102\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = railroads for 509 Obs. :\n",
            "sd = 0.007530483681349604, n = 509, CAAR = -0.0010771062959486384, t-stat = -0.1430328172168087, Pvalue = 0.8863209780346296\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = oil and gas for 347 Obs. :\n",
            "sd = 0.0017620322085032923, n = 347, CAAR = 0.006462724452547497, t-stat = 3.6677674910591294, Pvalue = 0.00028316569290792986\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = healthcare services for 283 Obs. :\n",
            "sd = 0.002454733827426476, n = 283, CAAR = 0.004140171412878005, t-stat = 1.6866070637151438, Pvalue = 0.09278482154144069\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = pipelines for 250 Obs. :\n",
            "sd = 0.006604971173852272, n = 250, CAAR = -0.02259240029610375, t-stat = -3.420514594453105, Pvalue = 0.0007302959396439639\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = pharmaceuticals for 245 Obs. :\n",
            "sd = 0.0023871564158633634, n = 245, CAAR = 0.00464892800090864, t-stat = 1.9474752345573723, Pvalue = 0.052624874282972536\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = telecommunications for 229 Obs. :\n",
            "sd = 0.003214584718296664, n = 229, CAAR = -0.013481079749950413, t-stat = -4.193723585264144, Pvalue = 3.9295927595395015e-05\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = diversified for 509 Obs. :\n",
            "sd = 0.0023098187877072084, n = 509, CAAR = -0.010868455443396872, t-stat = -4.705328184721022, Pvalue = 3.272134394666437e-06\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = utilities and power generation for 181 Obs. :\n",
            "sd = 0.0017245800540478548, n = 181, CAAR = -0.0012859983127203514, t-stat = -0.745687803649309, Pvalue = 0.4568286906167478\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = miscellaneous manufacturing for 161 Obs. :\n",
            "sd = 0.0028598109481689834, n = 161, CAAR = -0.003689421614381426, t-stat = -1.290092835242696, Pvalue = 0.19887985426520527\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = airlines for 144 Obs. :\n",
            "sd = 0.004067651909744238, n = 144, CAAR = -0.02001589382515177, t-stat = -4.92074894048894, Pvalue = 2.3415495162966635e-06\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = food products for 140 Obs. :\n",
            "sd = 0.001476505467340221, n = 140, CAAR = -0.003943199400637305, t-stat = -2.6706297320663435, Pvalue = 0.008474089191937411\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 167 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 167 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = aerospace and military contracting for 90 Obs. :\n",
            "sd = 0.001939923413014759, n = 90, CAAR = 0.0020235063578454473, t-stat = 1.0430856931103252, Pvalue = 0.29973366740436386\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = freight and logistics for 122 Obs. :\n",
            "sd = 0.0018474412187121313, n = 122, CAAR = -0.007161375857625132, t-stat = -3.8763754890222675, Pvalue = 0.0001727794256192152\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = paper and packaging for 110 Obs. :\n",
            "sd = 0.002123941977944443, n = 110, CAAR = 0.0023768398337726698, t-stat = 1.1190700397912856, Pvalue = 0.2655703067571684\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = wholesalers for 85 Obs. :\n",
            "sd = 0.002997314947506457, n = 85, CAAR = 0.004977148148381846, t-stat = 1.6605355912038748, Pvalue = 0.10053531981317816\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = mining and minerals for 84 Obs. :\n",
            "sd = 0.005353483660419298, n = 84, CAAR = -0.009413601343291993, t-stat = -1.75840666384974, Pvalue = 0.0823644710936022\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = chemicals for 63 Obs. :\n",
            "sd = 0.0029271763229268915, n = 63, CAAR = -0.0038733700969988323, t-stat = -1.3232445434396787, Pvalue = 0.19061333355432883\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = construction and engineering for 56 Obs. :\n",
            "sd = 0.002695847606191214, n = 56, CAAR = -0.0014051936341291128, t-stat = -0.5212437197495814, Pvalue = 0.6042895622855653\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = building materials for 55 Obs. :\n",
            "sd = 0.004386807925133672, n = 55, CAAR = 0.004126493511374742, t-stat = 0.9406597192761755, Pvalue = 0.3510683210984421\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = waste management and environmental services for 51 Obs. :\n",
            "sd = 0.002439222700752723, n = 51, CAAR = -0.014081974304427104, t-stat = -5.773140066334053, Pvalue = 4.91852548967535e-07\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = motor vehicles for 49 Obs. :\n",
            "sd = 0.008275142569531505, n = 49, CAAR = 0.014927603237150878, t-stat = 1.8039088887861905, Pvalue = 0.0775228946097076\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = industrial equipment for 44 Obs. :\n",
            "sd = 0.00195657261519753, n = 44, CAAR = -0.006557868308776221, t-stat = -3.351712202163352, Pvalue = 0.0016822476259091784\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = metals for 35 Obs. :\n",
            "sd = 0.003864310378659722, n = 35, CAAR = -0.0010838707183492883, t-stat = -0.2804823143438112, Pvalue = 0.7808071411752971\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = information technology for 35 Obs. :\n",
            "sd = 0.0026418307516569268, n = 35, CAAR = -0.01691953868716388, t-stat = -6.404474880365494, Pvalue = 2.582223263179939e-07\n",
            "Statistics for event window = [-5, 5], category = Major Industry Group, subcategory = housewares and home furnishings for 31 Obs. :\n",
            "sd = 0.00328490148724804, n = 31, CAAR = 0.01473592790395616, t-stat = 4.485957329667544, Pvalue = 9.901332508632084e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OamkyarFxBnV",
        "outputId": "5e766977-ea2e-45d4-9ca6-610e9aba19b4"
      },
      "source": [
        "output(df, events, 'major_industry', 'Major Industry Group', Major_industry_group, 200, [-1,1])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = retailing for 984 Obs. :\n",
            "sd = 0.0008559921691570612, n = 984, CAAR = -0.000375199509156005, t-stat = -0.4383211934350789, Pvalue = 0.661249771496013\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = financial services for 849 Obs. :\n",
            "sd = 0.000965991942022004, n = 849, CAAR = 0.0018000162667332917, t-stat = 1.863386420144993, Pvalue = 0.06275340277705907\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = railroads for 509 Obs. :\n",
            "sd = 0.006702036937727384, n = 509, CAAR = 0.0037439720157146383, t-stat = 0.5586319577916552, Pvalue = 0.5766590716353448\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = oil and gas for 347 Obs. :\n",
            "sd = 0.001248734887724924, n = 347, CAAR = -0.0025792748890873483, t-stat = -2.06551039331218, Pvalue = 0.03961897739212894\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = healthcare services for 283 Obs. :\n",
            "sd = 0.002057335392997817, n = 283, CAAR = -0.0005830899061755252, t-stat = -0.28341995581278756, Pvalue = 0.7770628872353676\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = pipelines for 250 Obs. :\n",
            "sd = 0.004815470864632669, n = 250, CAAR = -0.00947204682829578, t-stat = -1.967003247359139, Pvalue = 0.050293172541303\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = pharmaceuticals for 245 Obs. :\n",
            "sd = 0.0016506414872601502, n = 245, CAAR = 0.0057761462592015745, t-stat = 3.499334230832417, Pvalue = 0.0005542670850901682\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = telecommunications for 229 Obs. :\n",
            "sd = 0.0022420704315249907, n = 229, CAAR = -0.003075493400281336, t-stat = -1.3717202443945864, Pvalue = 0.17149883698850127\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = diversified for 509 Obs. :\n",
            "sd = 0.0021923244666948167, n = 509, CAAR = -0.003989751971379716, t-stat = -1.8198729394261286, Pvalue = 0.06936673241019843\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = utilities and power generation for 181 Obs. :\n",
            "sd = 0.0011419780750714134, n = 181, CAAR = -0.0008419231336072574, t-stat = -0.7372498229045316, Pvalue = 0.4619303588756054\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = miscellaneous manufacturing for 161 Obs. :\n",
            "sd = 0.0022521050639274605, n = 161, CAAR = -0.0007249871834608623, t-stat = -0.3219153471448408, Pvalue = 0.7479370220505819\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = airlines for 144 Obs. :\n",
            "sd = 0.003003135539163848, n = 144, CAAR = -0.0018407420840844476, t-stat = -0.6129400621714725, Pvalue = 0.5408895607594497\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = food products for 140 Obs. :\n",
            "sd = 0.001262197370764741, n = 140, CAAR = -0.0039231127594216795, t-stat = -3.1081610929396417, Pvalue = 0.002283558787427585\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 171 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 171 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = aerospace and military contracting for 90 Obs. :\n",
            "sd = 0.0016379480792244887, n = 90, CAAR = -0.0010908754997306452, t-stat = -0.6660012692509378, Pvalue = 0.5071328847650207\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = freight and logistics for 122 Obs. :\n",
            "sd = 0.0014123546250807697, n = 122, CAAR = -0.0009161757561679701, t-stat = -0.6486867674013358, Pvalue = 0.5177701052972107\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = paper and packaging for 110 Obs. :\n",
            "sd = 0.0015228302120232652, n = 110, CAAR = 0.0008013387951691806, t-stat = 0.5262167698291883, Pvalue = 0.5998066857073818\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = wholesalers for 85 Obs. :\n",
            "sd = 0.0026264576411045904, n = 85, CAAR = -0.0032843330363656596, t-stat = -1.250480108631941, Pvalue = 0.21459641111512526\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = mining and minerals for 84 Obs. :\n",
            "sd = 0.004379509736142518, n = 84, CAAR = -0.007131998632085201, t-stat = -1.628492471024184, Pvalue = 0.10721061717585108\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = chemicals for 63 Obs. :\n",
            "sd = 0.0026791065573214144, n = 63, CAAR = -0.002588965336723615, t-stat = -0.9663539994885747, Pvalue = 0.3376215175252866\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = construction and engineering for 56 Obs. :\n",
            "sd = 0.0024337561739326196, n = 56, CAAR = 0.002410991253246847, t-stat = 0.9906461785574076, Pvalue = 0.3261967598610632\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = building materials for 55 Obs. :\n",
            "sd = 0.0031992982245928267, n = 55, CAAR = 0.0036883705405895612, t-stat = 1.1528686235741523, Pvalue = 0.2540421762042645\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = waste management and environmental services for 51 Obs. :\n",
            "sd = 0.0020762387358468845, n = 51, CAAR = -0.006679964852262926, t-stat = -3.217339478804306, Pvalue = 0.002271834721756513\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = motor vehicles for 49 Obs. :\n",
            "sd = 0.0070301257442239844, n = 49, CAAR = -0.00600141824056715, t-stat = -0.8536715357471337, Pvalue = 0.3975272649734356\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = industrial equipment for 44 Obs. :\n",
            "sd = 0.0018483746583076199, n = 44, CAAR = -0.0050546183384657896, t-stat = -2.734628672681447, Pvalue = 0.009034225750660001\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = metals for 35 Obs. :\n",
            "sd = 0.0030619717999953236, n = 35, CAAR = 0.0032394081089145376, t-stat = 1.0579483811442956, Pvalue = 0.2975378964620961\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = information technology for 35 Obs. :\n",
            "sd = 0.00252465158335076, n = 35, CAAR = -0.011356235441551168, t-stat = -4.498139670615056, Pvalue = 7.608844624062509e-05\n",
            "Statistics for event window = [-1, 1], category = Major Industry Group, subcategory = housewares and home furnishings for 31 Obs. :\n",
            "sd = 0.003099045689697181, n = 31, CAAR = 0.012557406065811625, t-stat = 4.052023533424786, Pvalue = 0.00033091021974185976\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gfsTyjXA7-8R",
        "outputId": "5da084f2-069b-4bfa-db49-1b3da363067b"
      },
      "source": [
        "output(df, events, 'secondary_offense', 'Offence Group', secondary_offence_group, 200, [-20,20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = Fair Labor Standards Act for 620 Obs. :\n",
            "sd = 0.0023090052270240873, n = 620, CAAR = -0.00431948328836106, t-stat = -1.8707117843679093, Pvalue = 0.06185665831909376\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = overtime violation for 392 Obs. :\n",
            "sd = 0.00199795823318197, n = 392, CAAR = -0.02196272157766235, t-stat = -10.992582934371095, Pvalue = 1.1242626825666809e-24\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = mining violation for 314 Obs. :\n",
            "sd = 0.002803828077410449, n = 314, CAAR = 0.002566057906826076, t-stat = 0.915198020698911, Pvalue = 0.3607922037561787\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = misclassification for 150 Obs. :\n",
            "sd = 0.004989700321881518, n = 150, CAAR = 0.026929494527026088, t-stat = 5.397016411773504, Pvalue = 2.6124551132751366e-07\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = other pay violation for 147 Obs. :\n",
            "sd = 0.003388294822372835, n = 147, CAAR = 0.006784459128820615, t-stat = 2.0023225499808883, Pvalue = 0.04710197461786575\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = meal/rest break violation for 98 Obs. :\n",
            "sd = 0.004491941530385308, n = 98, CAAR = -0.007973964334085532, t-stat = -1.77517099903158, Pvalue = 0.07900627768544102\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = off-the-clock work for 92 Obs. :\n",
            "sd = 0.004217958095598006, n = 92, CAAR = 0.02352475226486218, t-stat = 5.577284489718699, Pvalue = 2.484630935769995e-07\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = Service Contract Act for 60 Obs. :\n",
            "sd = 0.002467116303249802, n = 60, CAAR = -0.012701166433630423, t-stat = -5.148183090071533, Pvalue = 3.169059021918229e-06\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = racial discrimination for 58 Obs. :\n",
            "sd = 0.0043063373998677495, n = 58, CAAR = -0.016182121857174134, t-stat = -3.757745934554756, Pvalue = 0.00040532642698681723\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = child labor or youth employment violation for 54 Obs. :\n",
            "sd = 0.0023983258759972285, n = 54, CAAR = 0.002302229121897473, t-stat = 0.9599317361074635, Pvalue = 0.34144850937489046\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = Davis-Bacon Act and related for 43 Obs. :\n",
            "sd = 0.002763320202401533, n = 43, CAAR = 0.022988565837286243, t-stat = 8.319182777771266, Pvalue = 2.0147765070898388e-10\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = antiboycott violation for 32 Obs. :\n",
            "sd = 0.006270066164256935, n = 32, CAAR = 0.0263106324162013, t-stat = 4.196228831872203, Pvalue = 0.00021112511090449668\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYO9J0IB7-n2",
        "outputId": "3f19d467-0db8-4c20-ee8e-324e3f5af03f"
      },
      "source": [
        "output(df, events, 'secondary_offense', 'Offence Group', secondary_offence_group, 200, [-10,10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = Fair Labor Standards Act for 620 Obs. :\n",
            "sd = 0.0018471713025533776, n = 620, CAAR = 0.00168991261065153, t-stat = 0.9148651282723664, Pvalue = 0.3606186754138436\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = overtime violation for 392 Obs. :\n",
            "sd = 0.001599190928605916, n = 392, CAAR = -0.01026104069943287, t-stat = -6.416395013181987, Pvalue = 4.0386442820479755e-10\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = mining violation for 314 Obs. :\n",
            "sd = 0.0022500185914240867, n = 314, CAAR = -0.006915955363475235, t-stat = -3.073732541515567, Pvalue = 0.0023000263269868314\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = misclassification for 150 Obs. :\n",
            "sd = 0.0036650876795804536, n = 150, CAAR = 0.016568773594977035, t-stat = 4.5207031982584605, Pvalue = 1.2482650860034613e-05\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = other pay violation for 147 Obs. :\n",
            "sd = 0.002507369832966917, n = 147, CAAR = 0.007023860111587253, t-stat = 2.8012860405503366, Pvalue = 0.005780249124528094\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = meal/rest break violation for 98 Obs. :\n",
            "sd = 0.003392728739754678, n = 98, CAAR = 0.0009298942446051426, t-stat = 0.27408446590763447, Pvalue = 0.7846025367222387\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = off-the-clock work for 92 Obs. :\n",
            "sd = 0.0031840911758933867, n = 92, CAAR = 0.007761359134908641, t-stat = 2.4375429930114905, Pvalue = 0.016731476445267235\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = Service Contract Act for 60 Obs. :\n",
            "sd = 0.002262244785351908, n = 60, CAAR = -0.009686833597936064, t-stat = -4.281956426934236, Pvalue = 6.913493168749224e-05\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = racial discrimination for 58 Obs. :\n",
            "sd = 0.0033302726281852885, n = 58, CAAR = -0.006698088926776701, t-stat = -2.011273452536101, Pvalue = 0.049033859849346204\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = child labor or youth employment violation for 54 Obs. :\n",
            "sd = 0.0022104179097328113, n = 54, CAAR = 0.015940267432613467, t-stat = 7.211427016776334, Pvalue = 2.0601585602570077e-09\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = Davis-Bacon Act and related for 43 Obs. :\n",
            "sd = 0.0025199000472354545, n = 43, CAAR = 0.0075551395839446485, t-stat = 2.998190183072254, Pvalue = 0.004548381420923874\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = antiboycott violation for 32 Obs. :\n",
            "sd = 0.0048560062442155405, n = 32, CAAR = 0.020088697382472053, t-stat = 4.136876349037164, Pvalue = 0.0002493488412586574\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SILfcpms7_xl",
        "outputId": "11a2ed9d-21c7-4b0f-f07e-fe12e6d4c0ee"
      },
      "source": [
        "output(df, events, 'secondary_offense', 'Offence Group', secondary_offence_group, 200, [-5,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = Fair Labor Standards Act for 620 Obs. :\n",
            "sd = 0.0015144943693946, n = 620, CAAR = 0.006166046742300001, t-stat = 4.071356663257058, Pvalue = 5.280333407660868e-05\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = overtime violation for 392 Obs. :\n",
            "sd = 0.0012857870942030209, n = 392, CAAR = -0.007215536295201788, t-stat = -5.611765997444739, Pvalue = 3.7989283238384126e-08\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = mining violation for 314 Obs. :\n",
            "sd = 0.001835703878037165, n = 314, CAAR = -0.003964738522288582, t-stat = -2.1597919848205023, Pvalue = 0.031547822889462424\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = misclassification for 150 Obs. :\n",
            "sd = 0.0027440296417003515, n = 150, CAAR = 0.009715644609571751, t-stat = 3.5406485636763763, Pvalue = 0.0005329763083323066\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = other pay violation for 147 Obs. :\n",
            "sd = 0.0019460543042956815, n = 147, CAAR = 0.004225535282017274, t-stat = 2.1713347221040604, Pvalue = 0.031521229290241946\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = meal/rest break violation for 98 Obs. :\n",
            "sd = 0.002682712106735821, n = 98, CAAR = -0.0019438245569375326, t-stat = -0.7245744155911956, Pvalue = 0.47045730671618\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = off-the-clock work for 92 Obs. :\n",
            "sd = 0.002542248656869013, n = 92, CAAR = 0.007503182510613827, t-stat = 2.9513959975318107, Pvalue = 0.0040229440454211726\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = Service Contract Act for 60 Obs. :\n",
            "sd = 0.0021226928889440536, n = 60, CAAR = -0.003663359871729561, t-stat = -1.7258077656028337, Pvalue = 0.08961615965005512\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = racial discrimination for 58 Obs. :\n",
            "sd = 0.002694097996485691, n = 58, CAAR = 0.002957446387495535, t-stat = 1.0977501157542777, Pvalue = 0.27692932629990175\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = child labor or youth employment violation for 54 Obs. :\n",
            "sd = 0.0021189792222688813, n = 54, CAAR = 0.010074909947821618, t-stat = 4.754605350511169, Pvalue = 1.5650617627172415e-05\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = Davis-Bacon Act and related for 43 Obs. :\n",
            "sd = 0.0024060601834189404, n = 43, CAAR = 0.004869490604685884, t-stat = 2.023844057702032, Pvalue = 0.04937851725509797\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = antiboycott violation for 32 Obs. :\n",
            "sd = 0.003973593942857931, n = 32, CAAR = 0.013536461066369083, t-stat = 3.4066040116402143, Pvalue = 0.0018386766549801441\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8fbE8oI7_u-",
        "outputId": "fecd8fb2-2655-4d8f-9dba-5d336b99b3bc"
      },
      "source": [
        "output(df, events, 'secondary_offense', 'Offence Group', secondary_offence_group, 200, [-1,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = Fair Labor Standards Act for 620 Obs. :\n",
            "sd = 0.0010648163596187802, n = 620, CAAR = -0.0014919165075148446, t-stat = -1.401102165681388, Pvalue = 0.16168466041333157\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = overtime violation for 392 Obs. :\n",
            "sd = 0.0008678925762882478, n = 392, CAAR = -0.002418169593631503, t-stat = -2.786254497040854, Pvalue = 0.005591843584730923\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = mining violation for 314 Obs. :\n",
            "sd = 0.0013713206865924351, n = 314, CAAR = -0.0011381783131791368, t-stat = -0.8299869784706386, Pvalue = 0.4071787269663344\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = misclassification for 150 Obs. :\n",
            "sd = 0.0016527846323992298, n = 150, CAAR = 0.0023783939105878906, t-stat = 1.439022280316913, Pvalue = 0.15224160858450023\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = other pay violation for 147 Obs. :\n",
            "sd = 0.0012849693735966488, n = 147, CAAR = 0.0005134786712192619, t-stat = 0.3996038207370089, Pvalue = 0.6900323175010162\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = meal/rest break violation for 98 Obs. :\n",
            "sd = 0.00191813234487548, n = 98, CAAR = 0.0006889702453663049, t-stat = 0.35918806499821104, Pvalue = 0.7202351103573565\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = off-the-clock work for 92 Obs. :\n",
            "sd = 0.0018492324908285617, n = 92, CAAR = 0.0016581426256233045, t-stat = 0.8966653105258614, Pvalue = 0.3722635329651318\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = Service Contract Act for 60 Obs. :\n",
            "sd = 0.0019469533427808677, n = 60, CAAR = -0.0031385273508911108, t-stat = -1.6120198064984428, Pvalue = 0.11229471707559198\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = racial discrimination for 58 Obs. :\n",
            "sd = 0.0020618966550149864, n = 58, CAAR = 0.004010857701246801, t-stat = 1.9452273184941216, Pvalue = 0.05668595306033541\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = child labor or youth employment violation for 54 Obs. :\n",
            "sd = 0.002018934132299556, n = 54, CAAR = -0.0008495969998011483, t-stat = -0.4208146200557131, Pvalue = 0.6755919904586535\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = Davis-Bacon Act and related for 43 Obs. :\n",
            "sd = 0.0022293604568282325, n = 43, CAAR = -0.00047085034120529083, t-stat = -0.21120422216297025, Pvalue = 0.8337493873084405\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = antiboycott violation for 32 Obs. :\n",
            "sd = 0.003067120737124423, n = 32, CAAR = 0.009903383902915298, t-stat = 3.2288862264353533, Pvalue = 0.0029355928915636253\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ofn92n157_sV",
        "outputId": "b5c7f58a-160f-43ad-947d-333159b3a39e"
      },
      "source": [
        "output(df, events, 'agency_code1', 'Agency Group', agency_group, 200, [-20,20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = OSHA for 1436 Obs. :\n",
            "sd = 0.0015847345778609247, n = 1436, CAAR = 0.005288669275933172, t-stat = 3.3372587118480244, Pvalue = 0.0008677406610279162\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = FMCSA for 1073 Obs. :\n",
            "sd = 0.0022903014526904205, n = 1073, CAAR = -0.003715796422326645, t-stat = -1.6224049537067242, Pvalue = 0.10501070022405268\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = FRA for 870 Obs. :\n",
            "sd = 0.005117653582507778, n = 870, CAAR = -0.0009209083034639353, t-stat = -0.17994737014080334, Pvalue = 0.857235837923397\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = WHD for 604 Obs. :\n",
            "sd = 0.002890388005142082, n = 604, CAAR = -0.007898851277142654, t-stat = -2.732799632122184, Pvalue = 0.0064639924670682886\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = EPA for 270 Obs. :\n",
            "sd = 0.0016492030219368713, n = 270, CAAR = -0.009581349446271554, t-stat = -5.809684628772352, Pvalue = 1.763975379350476e-08\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = CMS for 223 Obs. :\n",
            "sd = 0.0024867728021468684, n = 223, CAAR = -0.004946479703386792, t-stat = -1.989116053994326, Pvalue = 0.047916037948033285\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = INS for 226 Obs. :\n",
            "sd = 0.0016351938075532537, n = 226, CAAR = 0.003943880636936968, t-stat = 2.411873515371374, Pvalue = 0.016672866553088556\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = AG for 215 Obs. :\n",
            "sd = 0.001845825386167383, n = 215, CAAR = 0.003172046790927213, t-stat = 1.7184977597006383, Pvalue = 0.08715210984616745\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = MSHA for 205 Obs. :\n",
            "sd = 0.0035817105629599635, n = 205, CAAR = -0.004637571846822906, t-stat = -1.2947924644671365, Pvalue = 0.19685538723776708\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = MHC for 124 Obs. :\n",
            "sd = 0.002848517986365817, n = 124, CAAR = 0.0024686104167047514, t-stat = 0.866629745194006, Pvalue = 0.3878323311409019\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = FAA for 122 Obs. :\n",
            "sd = 0.0039805387951044185, n = 122, CAAR = -0.037899964354750675, t-stat = -9.52131515496421, Pvalue = 2.2238643572059624e-16\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = NLRB for 82 Obs. :\n",
            "sd = 0.002804975838179733, n = 82, CAAR = 0.017694173022310137, t-stat = 6.308137411191617, Pvalue = 1.407366170599209e-08\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = EEOC for 73 Obs. :\n",
            "sd = 0.0028482787749201915, n = 73, CAAR = -0.01992628157317843, t-stat = -6.995902840913724, Pvalue = 1.1247793375472094e-09\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = OFCCP for 57 Obs. :\n",
            "sd = 0.0026923725441224114, n = 57, CAAR = -0.021149246302433346, t-stat = -7.855245125197568, Pvalue = 1.3384675668294042e-10\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = USAO for 53 Obs. :\n",
            "sd = 0.0024607879311891443, n = 53, CAAR = -0.009058588373417754, t-stat = -3.681173927507157, Pvalue = 0.0005525373128280634\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = FIN for 35 Obs. :\n",
            "sd = 0.0025817808880557475, n = 35, CAAR = 0.003526608365461169, t-stat = 1.3659595908299325, Pvalue = 0.18091962353471916\n",
            "Statistics for event window = [-20, 20], category = Agency Group, subcategory = EBSA for 31 Obs. :\n",
            "sd = 0.0060900787685620305, n = 31, CAAR = 0.029441119408273705, t-stat = 4.83427563535853, Pvalue = 3.7186517816742284e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwGJmKn87_p1",
        "outputId": "f7eb7c07-bdb9-417d-8ce9-1a63b161303e"
      },
      "source": [
        "output(df, events, 'agency_code1', 'Agency Group', agency_group, 200, [-10,10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = OSHA for 1436 Obs. :\n",
            "sd = 0.0012225786604867035, n = 1436, CAAR = -0.001828993602007507, t-stat = -1.496013026498755, Pvalue = 0.13487014754003931\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = FMCSA for 1073 Obs. :\n",
            "sd = 0.0016885432067626663, n = 1073, CAAR = 0.0005748233792918718, t-stat = 0.3404256266524226, Pvalue = 0.7336027199017198\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = FRA for 870 Obs. :\n",
            "sd = 0.004979589817464168, n = 870, CAAR = -0.0035092662761742168, t-stat = -0.7047299887767249, Pvalue = 0.48116711088176334\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = WHD for 604 Obs. :\n",
            "sd = 0.0024107808207227987, n = 604, CAAR = 0.004073020166672412, t-stat = 1.6895024764015012, Pvalue = 0.09164008827387352\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = EPA for 269 Obs. :\n",
            "sd = 0.001333564684855242, n = 269, CAAR = -0.00398060424889521, t-stat = -2.984935259685063, Pvalue = 0.0030983269689394727\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = CMS for 223 Obs. :\n",
            "sd = 0.0020596295150684227, n = 223, CAAR = -0.0014399625178063127, t-stat = -0.6991366686442517, Pvalue = 0.48519845742052814\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = INS for 226 Obs. :\n",
            "sd = 0.0013597170065732492, n = 226, CAAR = 0.003850176090091605, t-stat = 2.83160104012731, Pvalue = 0.005051324293245053\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = AG for 215 Obs. :\n",
            "sd = 0.0015205287185137142, n = 215, CAAR = -0.0021504199150884644, t-stat = -1.4142580070374837, Pvalue = 0.158739384833351\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = MSHA for 205 Obs. :\n",
            "sd = 0.0028601788295841606, n = 205, CAAR = -0.013520978110837065, t-stat = -4.727319135077603, Pvalue = 4.236670161592249e-06\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = MHC for 124 Obs. :\n",
            "sd = 0.0023761971806795946, n = 124, CAAR = 0.005683411743811396, t-stat = 2.3918098169723168, Pvalue = 0.018280093835239198\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = FAA for 122 Obs. :\n",
            "sd = 0.003046159434615147, n = 122, CAAR = -0.02783880024852682, t-stat = -9.138983315245936, Pvalue = 1.811404261139642e-15\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = NLRB for 82 Obs. :\n",
            "sd = 0.0024619003017644003, n = 82, CAAR = 0.0005117271183890981, t-stat = 0.20785858713383007, Pvalue = 0.8358611192685425\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = EEOC for 73 Obs. :\n",
            "sd = 0.002468837911044792, n = 73, CAAR = -0.03602426622446204, t-stat = -14.591588238053614, Pvalue = 3.3674900695703317e-23\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = OFCCP for 57 Obs. :\n",
            "sd = 0.0025149903877293577, n = 57, CAAR = -0.011406163255104566, t-stat = -4.535271113064788, Pvalue = 3.080970360971074e-05\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = USAO for 53 Obs. :\n",
            "sd = 0.0023411136668519813, n = 53, CAAR = -0.010092262369522782, t-stat = -4.310880976186652, Pvalue = 7.264295538817768e-05\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = FIN for 35 Obs. :\n",
            "sd = 0.0024461326686133268, n = 35, CAAR = -0.0031982093811413767, t-stat = -1.3074554059058416, Pvalue = 0.19983082364938035\n",
            "Statistics for event window = [-10, 10], category = Agency Group, subcategory = EBSA for 31 Obs. :\n",
            "sd = 0.005959903749686531, n = 31, CAAR = 0.027763903477729026, t-stat = 4.6584482977916055, Pvalue = 6.101345019548445e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9qHNqCo7_m2",
        "outputId": "3e44d728-5661-4446-e6a2-6b3b8acdc081"
      },
      "source": [
        "output(df, events, 'agency_code1', 'Agency Group', agency_group, 200, [-5,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = OSHA for 1436 Obs. :\n",
            "sd = 0.0009936918918674166, n = 1436, CAAR = -0.0010013234464250585, t-stat = -1.007680000833357, Pvalue = 0.31377798995126943\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = FMCSA for 1073 Obs. :\n",
            "sd = 0.001295243504409174, n = 1073, CAAR = 0.0008207949860532208, t-stat = 0.6336993648369053, Pvalue = 0.5264122195204988\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = FRA for 870 Obs. :\n",
            "sd = 0.004656359569810606, n = 870, CAAR = -0.004006351766215541, t-stat = -0.8604042935581316, Pvalue = 0.3898035326816678\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = WHD for 604 Obs. :\n",
            "sd = 0.002021738980484531, n = 604, CAAR = 0.008182959905299608, t-stat = 4.0474858447545365, Pvalue = 5.8512643144041473e-05\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = EPA for 268 Obs. :\n",
            "sd = 0.0011327541064164405, n = 268, CAAR = -0.0016303379082213208, t-stat = -1.439269033752636, Pvalue = 0.1512456209646519\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = CMS for 223 Obs. :\n",
            "sd = 0.0017246096131424333, n = 223, CAAR = -0.000680235137308612, t-stat = -0.3944284736237477, Pvalue = 0.6936433697634169\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = INS for 226 Obs. :\n",
            "sd = 0.0011840297081856007, n = 226, CAAR = 0.003255200219266868, t-stat = 2.74925552692011, Pvalue = 0.0064582869843132565\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = AG for 215 Obs. :\n",
            "sd = 0.001329127664054492, n = 215, CAAR = -0.0048387395008844735, t-stat = -3.64053779914861, Pvalue = 0.00034120774139666476\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = MSHA for 205 Obs. :\n",
            "sd = 0.002375793959878178, n = 205, CAAR = -0.008422906819587376, t-stat = -3.5453018914229717, Pvalue = 0.00048630867241923197\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = MHC for 124 Obs. :\n",
            "sd = 0.0020603752631381768, n = 124, CAAR = 0.002411297995363482, t-stat = 1.1703198143093658, Pvalue = 0.2441348408129538\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = FAA for 122 Obs. :\n",
            "sd = 0.002499204116998204, n = 122, CAAR = -0.01256654364135367, t-stat = -5.028218205901227, Pvalue = 1.7361950307692876e-06\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = NLRB for 82 Obs. :\n",
            "sd = 0.002103952550461049, n = 82, CAAR = -0.00011998657142362905, t-stat = -0.05702912425346087, Pvalue = 0.9546624423278846\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = EEOC for 73 Obs. :\n",
            "sd = 0.002277223713316854, n = 73, CAAR = -0.023235825383032878, t-stat = -10.203576068154106, Pvalue = 1.2514705929783435e-15\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = OFCCP for 57 Obs. :\n",
            "sd = 0.0024183694576850345, n = 57, CAAR = -0.010073306969570065, t-stat = -4.165330048127824, Pvalue = 0.00010850936406611577\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = USAO for 53 Obs. :\n",
            "sd = 0.0022502490343393896, n = 53, CAAR = -0.0033440240209107086, t-stat = -1.4860684172640566, Pvalue = 0.1432991989846284\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = FIN for 35 Obs. :\n",
            "sd = 0.002382247183596518, n = 35, CAAR = 0.002306741683583727, t-stat = 0.9683049263180159, Pvalue = 0.33973009517872454\n",
            "Statistics for event window = [-5, 5], category = Agency Group, subcategory = EBSA for 31 Obs. :\n",
            "sd = 0.005640413986918716, n = 31, CAAR = -0.000136573994606233, t-stat = -0.024213469955038102, Pvalue = 0.9808426783271247\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLMujDSX7_kP",
        "outputId": "e5482207-8293-4d4e-ac1b-02b0361d71e6"
      },
      "source": [
        "output(df, events, 'agency_code1', 'Agency Group', agency_group, 200, [-1,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = OSHA for 1436 Obs. :\n",
            "sd = 0.0006774063102211171, n = 1436, CAAR = -0.0009196578578886939, t-stat = -1.3576163138907014, Pvalue = 0.1747989133194575\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = FMCSA for 1073 Obs. :\n",
            "sd = 0.0007880064796598557, n = 1073, CAAR = 0.001120230891471451, t-stat = 1.4216011167255889, Pvalue = 0.15543292984804935\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = FRA for 870 Obs. :\n",
            "sd = 0.004160821906710125, n = 870, CAAR = 0.0006696983321883242, t-stat = 0.16095337584824454, Pvalue = 0.8721675316757665\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = WHD for 604 Obs. :\n",
            "sd = 0.0013941979528293035, n = 604, CAAR = -0.002554392155168549, t-stat = -1.8321588767110262, Pvalue = 0.06742078420080061\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = EPA for 268 Obs. :\n",
            "sd = 0.0009317661398732634, n = 268, CAAR = -0.002344373002152177, t-stat = -2.5160530114037556, Pvalue = 0.012454734675087106\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = CMS for 223 Obs. :\n",
            "sd = 0.0012357465538025306, n = 223, CAAR = 0.001931239696938875, t-stat = 1.5628121243763409, Pvalue = 0.1195211524197351\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = INS for 226 Obs. :\n",
            "sd = 0.0010215822036853401, n = 226, CAAR = -0.0008576213393710435, t-stat = -0.8395030143214999, Pvalue = 0.40207819234341247\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = AG for 215 Obs. :\n",
            "sd = 0.001141161681870564, n = 215, CAAR = -0.0033544593778650814, t-stat = -2.93951280625418, Pvalue = 0.0036481356730271616\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = MSHA for 205 Obs. :\n",
            "sd = 0.0019001747003367115, n = 205, CAAR = -0.0028591197868860028, t-stat = -1.5046615378993131, Pvalue = 0.13395778297564892\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = MHC for 124 Obs. :\n",
            "sd = 0.0017820864879853299, n = 124, CAAR = -0.0017200454486458207, t-stat = -0.9651862915982, Pvalue = 0.33634536088570155\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = FAA for 122 Obs. :\n",
            "sd = 0.001826227648574872, n = 122, CAAR = -0.0031120614974989554, t-stat = -1.7040928604533536, Pvalue = 0.09093044007674099\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = NLRB for 82 Obs. :\n",
            "sd = 0.0016212029258638031, n = 82, CAAR = 0.0008225077812742839, t-stat = 0.5073441258663153, Pvalue = 0.6132919661665666\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = EEOC for 73 Obs. :\n",
            "sd = 0.0020448100617015278, n = 73, CAAR = -0.005914136719420139, t-stat = -2.8922670277252385, Pvalue = 0.005055060651385038\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = OFCCP for 57 Obs. :\n",
            "sd = 0.002243617146645168, n = 57, CAAR = 0.003284025392642415, t-stat = 1.463719154381106, Pvalue = 0.14886166012766824\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = USAO for 53 Obs. :\n",
            "sd = 0.0021334752182831183, n = 53, CAAR = -0.0058159162880609, t-stat = -2.7260294557070925, Pvalue = 0.00871536887961555\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = FIN for 35 Obs. :\n",
            "sd = 0.0023085059040559752, n = 35, CAAR = 0.0033235760758356984, t-stat = 1.4397087180917647, Pvalue = 0.15909433392825978\n",
            "Statistics for event window = [-1, 1], category = Agency Group, subcategory = EBSA for 31 Obs. :\n",
            "sd = 0.004663209913478303, n = 31, CAAR = 0.010743586059858092, t-stat = 2.30390358984385, Pvalue = 0.028333158365726446\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Y8-bp3_7_hH",
        "outputId": "c041d2fa-eb86-46be-fa24-94dd667c913f"
      },
      "source": [
        "output(df, events, 'primary_offense', 'Primary Offence Group', primary_offence_group, 200, [-20,20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = workplace safety or health violation for 1620 Obs. :\n",
            "sd = 0.0016158848841993255, n = 1620, CAAR = 0.0046562850572634835, t-stat = 2.8815697843294594, Pvalue = 0.004009147049639573\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = motor vehicle safety violation for 1052 Obs. :\n",
            "sd = 0.0023343774067830065, n = 1052, CAAR = -0.0034647841956536845, t-stat = -1.484243372809406, Pvalue = 0.1380442006043392\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = wage and hour violation for 752 Obs. :\n",
            "sd = 0.0023806091646169825, n = 752, CAAR = -0.008276460157970634, t-stat = -3.4766144233096905, Pvalue = 0.0005369833100620024\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = railroad safety violation for 831 Obs. :\n",
            "sd = 0.005435718546539271, n = 831, CAAR = -0.0018815515962777613, t-stat = -0.3461458830453387, Pvalue = 0.7293208065537078\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = insurance violation for 410 Obs. :\n",
            "sd = 0.001675476448327963, n = 410, CAAR = 0.004437424653499313, t-stat = 2.648455403791339, Pvalue = 0.00839898407631969\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = environmental violation for 333 Obs. :\n",
            "sd = 0.001580512501317735, n = 333, CAAR = -0.007860442712723393, t-stat = -4.973350546844669, Pvalue = 1.0574908100729134e-06\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = nursing home violation for 188 Obs. :\n",
            "sd = 0.0029320801464249794, n = 188, CAAR = -0.012035287784367977, t-stat = -4.104692635718822, Pvalue = 6.042013847184126e-05\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = employment discrimination for 145 Obs. :\n",
            "sd = 0.002101917154481829, n = 145, CAAR = -0.01207086244395188, t-stat = -5.7427869686555875, Pvalue = 5.32505974808906e-08\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = consumer protection violation for 123 Obs. :\n",
            "sd = 0.0018881951276734992, n = 123, CAAR = 0.016135150768049097, t-stat = 8.545277197028726, Pvalue = 4.349086075627758e-14\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = aviation safety violation for 114 Obs. :\n",
            "sd = 0.004117737174611514, n = 114, CAAR = -0.039899360894379655, t-stat = -9.689632728476397, Pvalue = 1.589784662398499e-16\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = labor relations violation for 82 Obs. :\n",
            "sd = 0.002804975838179733, n = 82, CAAR = 0.017694173022310137, t-stat = 6.308137411191617, Pvalue = 1.407366170599209e-08\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = False Claims Act and related for 68 Obs. :\n",
            "sd = 0.0023345168005592656, n = 68, CAAR = -0.012811580595775513, t-stat = -5.4878939370692565, Pvalue = 6.723485780730443e-07\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = benefit plan administrator violation for 44 Obs. :\n",
            "sd = 0.004491247169383169, n = 44, CAAR = 0.021178121186788, t-stat = 4.715420992894634, Pvalue = 2.555982597498613e-05\n",
            "Statistics for event window = [-20, 20], category = Primary Offence Group, subcategory = Family and Medical Leave Act for 32 Obs. :\n",
            "sd = 0.0032060453405332342, n = 32, CAAR = 0.005624437216071303, t-stat = 1.7543224186392317, Pvalue = 0.08925706769003007\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UvszYEe7_eO",
        "outputId": "d4e861c9-aec3-45a4-defd-4c47de4eb091"
      },
      "source": [
        "output(df, events, 'primary_offense', 'Primary Offence Group', primary_offence_group, 200, [-10,10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = workplace safety or health violation for 1620 Obs. :\n",
            "sd = 0.0012368639941702229, n = 1620, CAAR = -0.002776570006404163, t-stat = -2.244846660175346, Pvalue = 0.02491252075575758\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = motor vehicle safety violation for 1052 Obs. :\n",
            "sd = 0.0017212472754106875, n = 1052, CAAR = 0.00046068114827572205, t-stat = 0.2676437922992814, Pvalue = 0.7890260324165463\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = wage and hour violation for 752 Obs. :\n",
            "sd = 0.0019606688174801266, n = 752, CAAR = 0.0013938068673947238, t-stat = 0.7108833755953032, Pvalue = 0.4773772226972841\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = railroad safety violation for 831 Obs. :\n",
            "sd = 0.0053071258837440296, n = 831, CAAR = -0.004037302239630314, t-stat = -0.7607323300916522, Pvalue = 0.4470330157050145\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = insurance violation for 410 Obs. :\n",
            "sd = 0.0013369879207387827, n = 410, CAAR = 0.0034715585327241226, t-stat = 2.5965519051255415, Pvalue = 0.00975576962487543\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = environmental violation for 332 Obs. :\n",
            "sd = 0.0012602266504253255, n = 332, CAAR = -0.0037992816417164775, t-stat = -3.01476059122716, Pvalue = 0.0027705465080987133\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = nursing home violation for 188 Obs. :\n",
            "sd = 0.002429901665056574, n = 188, CAAR = -0.0055073372418108545, t-stat = -2.2664856446701642, Pvalue = 0.024566815556289586\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = employment discrimination for 145 Obs. :\n",
            "sd = 0.0018029834129887376, n = 145, CAAR = -0.02185009131690988, t-stat = -12.11885320713506, Pvalue = 9.630791027026413e-24\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = consumer protection violation for 123 Obs. :\n",
            "sd = 0.0016465132738547714, n = 123, CAAR = 0.0050625818617483, t-stat = 3.0747288480073554, Pvalue = 0.002600452152245006\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = aviation safety violation for 114 Obs. :\n",
            "sd = 0.003168821731364059, n = 114, CAAR = -0.029467711500228394, t-stat = -9.299264521120172, Pvalue = 1.2770372672950258e-15\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = labor relations violation for 82 Obs. :\n",
            "sd = 0.0024619003017644003, n = 82, CAAR = 0.0005117271183890981, t-stat = 0.20785858713383007, Pvalue = 0.8358611192685425\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = False Claims Act and related for 68 Obs. :\n",
            "sd = 0.0021893950907591704, n = 68, CAAR = -0.011459122422879905, t-stat = -5.233921676012558, Pvalue = 1.7970176293155596e-06\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = benefit plan administrator violation for 44 Obs. :\n",
            "sd = 0.004291731880200971, n = 44, CAAR = 0.03319473752736475, t-stat = 7.734578592968935, Pvalue = 1.1302296384632953e-09\n",
            "Statistics for event window = [-10, 10], category = Primary Offence Group, subcategory = Family and Medical Leave Act for 32 Obs. :\n",
            "sd = 0.003074691988244814, n = 32, CAAR = -0.0044960651492079885, t-stat = -1.4622814793798464, Pvalue = 0.15372887241227123\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgXRptdh7_X6",
        "outputId": "8da983db-095b-4973-99ac-4e4737acb75f"
      },
      "source": [
        "output(df, events, 'primary_offense', 'Primary Offence Group', primary_offence_group, 200, [-5,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = workplace safety or health violation for 1620 Obs. :\n",
            "sd = 0.0009944507521951107, n = 1620, CAAR = -0.0015973403158269803, t-stat = -1.6062538162911288, Pvalue = 0.10841316075208886\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = motor vehicle safety violation for 1052 Obs. :\n",
            "sd = 0.001320520534686771, n = 1052, CAAR = 0.0007462260312761256, t-stat = 0.5650999069492936, Pvalue = 0.5721264117712657\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = wage and hour violation for 752 Obs. :\n",
            "sd = 0.0016275883175836947, n = 752, CAAR = 0.004210015983129924, t-stat = 2.5866590080838634, Pvalue = 0.009878114825648173\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = railroad safety violation for 831 Obs. :\n",
            "sd = 0.0049714492047919, n = 831, CAAR = -0.004520262014771733, t-stat = -0.9092443327017654, Pvalue = 0.36348511217636525\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = insurance violation for 410 Obs. :\n",
            "sd = 0.0011066975039240655, n = 410, CAAR = 0.0031735450811753683, t-stat = 2.8675813128002834, Pvalue = 0.004350223509943339\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = environmental violation for 331 Obs. :\n",
            "sd = 0.0010575439577667003, n = 331, CAAR = -0.0010314907334442141, t-stat = -0.9753644052985704, Pvalue = 0.330093966188303\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = nursing home violation for 188 Obs. :\n",
            "sd = 0.002031263320843852, n = 188, CAAR = -0.0018322417876842274, t-stat = -0.9020208108336517, Pvalue = 0.36820615809493984\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = employment discrimination for 145 Obs. :\n",
            "sd = 0.001636273354357284, n = 145, CAAR = -0.010467305644116425, t-stat = -6.397039722148324, Pvalue = 2.082156231019169e-09\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = consumer protection violation for 123 Obs. :\n",
            "sd = 0.001507759859032912, n = 123, CAAR = 0.0037003632738305914, t-stat = 2.4542126199088696, Pvalue = 0.015530669543401383\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = aviation safety violation for 114 Obs. :\n",
            "sd = 0.002614753213217058, n = 114, CAAR = -0.01340074767604635, t-stat = -5.125052570280144, Pvalue = 1.2397888580388822e-06\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = labor relations violation for 82 Obs. :\n",
            "sd = 0.002103952550461049, n = 82, CAAR = -0.00011998657142362905, t-stat = -0.05702912425346087, Pvalue = 0.9546624423278846\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = False Claims Act and related for 68 Obs. :\n",
            "sd = 0.0021432279070901024, n = 68, CAAR = -0.005199674037971328, t-stat = -2.4260947801071775, Pvalue = 0.0179609053262046\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = benefit plan administrator violation for 44 Obs. :\n",
            "sd = 0.004008549930059037, n = 44, CAAR = 0.0066635266081066236, t-stat = 1.6623284540223964, Pvalue = 0.10372086942450387\n",
            "Statistics for event window = [-5, 5], category = Primary Offence Group, subcategory = Family and Medical Leave Act for 32 Obs. :\n",
            "sd = 0.0029683802450655296, n = 32, CAAR = -0.00034639862047236396, t-stat = -0.1166961749756278, Pvalue = 0.9078535728185715\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qiOprVOk8wPd",
        "outputId": "0d7de5ea-003b-403f-adf7-4eb0ebd73974"
      },
      "source": [
        "output(df, events, 'primary_offense', 'Primary Offence Group', primary_offence_group, 200, [-1,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = workplace safety or health violation for 1620 Obs. :\n",
            "sd = 0.0006717894254858928, n = 1620, CAAR = -0.0010439595670302228, t-stat = -1.5539982134656947, Pvalue = 0.12038028896358245\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = motor vehicle safety violation for 1052 Obs. :\n",
            "sd = 0.0008019799341303229, n = 1052, CAAR = 0.0012312315336983307, t-stat = 1.5352398249633186, Pvalue = 0.12502589584211202\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = wage and hour violation for 752 Obs. :\n",
            "sd = 0.001110621086279917, n = 752, CAAR = -0.002405102183098125, t-stat = -2.165547019419683, Pvalue = 0.03065994225978074\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = railroad safety violation for 831 Obs. :\n",
            "sd = 0.004450479280093963, n = 831, CAAR = 0.0006075144253393442, t-stat = 0.13650539348798357, Pvalue = 0.8914548633034457\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = insurance violation for 410 Obs. :\n",
            "sd = 0.0008684902562914935, n = 410, CAAR = -0.001307396077902275, t-stat = -1.505366431495658, Pvalue = 0.13300175716923396\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = environmental violation for 331 Obs. :\n",
            "sd = 0.0008484523727755984, n = 331, CAAR = -0.0021355447217025108, t-stat = -2.5169883310201158, Pvalue = 0.012309744851226595\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = nursing home violation for 188 Obs. :\n",
            "sd = 0.0014235032164562828, n = 188, CAAR = 0.0017763401793527986, t-stat = 1.2478652375474641, Pvalue = 0.21364071270411114\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = employment discrimination for 145 Obs. :\n",
            "sd = 0.001458603191962536, n = 145, CAAR = -0.0006051827798343334, t-stat = -0.41490570099470714, Pvalue = 0.6788284266490301\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = consumer protection violation for 123 Obs. :\n",
            "sd = 0.001367651082878774, n = 123, CAAR = 0.0009028457575116314, t-stat = 0.660143342709332, Pvalue = 0.5104062711553217\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = aviation safety violation for 114 Obs. :\n",
            "sd = 0.0019125514995982193, n = 114, CAAR = -0.002981411630581521, t-stat = -1.5588660651531963, Pvalue = 0.12182445339092679\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = labor relations violation for 82 Obs. :\n",
            "sd = 0.0016212029258638031, n = 82, CAAR = 0.0008225077812742839, t-stat = 0.5073441258663153, Pvalue = 0.6132919661665666\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = False Claims Act and related for 68 Obs. :\n",
            "sd = 0.002037035836763397, n = 68, CAAR = -0.002471205088313766, t-stat = -1.2131377581654192, Pvalue = 0.2293369209198505\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = benefit plan administrator violation for 44 Obs. :\n",
            "sd = 0.003371899138417702, n = 44, CAAR = 0.010652110590800857, t-stat = 3.159083398858564, Pvalue = 0.00289477227322923\n",
            "Statistics for event window = [-1, 1], category = Primary Offence Group, subcategory = Family and Medical Leave Act for 32 Obs. :\n",
            "sd = 0.0028708089132573828, n = 32, CAAR = -0.011063613766900985, t-stat = -3.8538314813672434, Pvalue = 0.0005474581102752672\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoYWTIE78wM-",
        "outputId": "639d6d80-b7a0-49e3-d229-c19622702316"
      },
      "source": [
        "output(df, events, 'offense_group', 'Offence Group', off_group, 200, [-20,20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = safety-related offenses for 3814 Obs. :\n",
            "sd = 0.0017463632460519576, n = 3814, CAAR = -0.0009316646726340584, t-stat = -0.5334884794101649, Pvalue = 0.5937265787412013\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = employment-related offenses for 1074 Obs. :\n",
            "sd = 0.0019199512586872954, n = 1074, CAAR = -0.005621303287994376, t-stat = -2.927836455513855, Pvalue = 0.0034853628052824363\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = consumer-protection-related offenses for 592 Obs. :\n",
            "sd = 0.0014667144918361077, n = 592, CAAR = 0.004804569807343796, t-stat = 3.2757362350249855, Pvalue = 0.0011155579278881377\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = environment-related offenses for 333 Obs. :\n",
            "sd = 0.001580512501317735, n = 333, CAAR = -0.007860442712723393, t-stat = -4.973350546844669, Pvalue = 1.0574908100729134e-06\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = government-contracting-related offenses for 69 Obs. :\n",
            "sd = 0.0023048924850885477, n = 69, CAAR = -0.011547468624057521, t-stat = -5.00998146280732, Pvalue = 4.114256972487267e-06\n",
            "For ticker None we have 152 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 152 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = competition-related offenses for 58 Obs. :\n",
            "sd = 0.0025980100050688277, n = 58, CAAR = 0.00025554678666076734, t-stat = 0.09836251059933745, Pvalue = 0.9219895346886302\n",
            "Statistics for event window = [-20, 20], category = Offence Group, subcategory = healthcare-related offenses for 50 Obs. :\n",
            "sd = 0.002758627403915711, n = 50, CAAR = 0.009313060850010664, t-stat = 3.375976341274402, Pvalue = 0.0014470797170312137\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTZO2Bjx8wJ9",
        "outputId": "73090a8c-1f22-4d15-b374-a676bd035d0b"
      },
      "source": [
        "output(df, events, 'offense_group', 'Offence Group', off_group, 200, [-10,10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = safety-related offenses for 3814 Obs. :\n",
            "sd = 0.0013795573982928674, n = 3814, CAAR = -0.0029557436697604522, t-stat = -2.1425304038947823, Pvalue = 0.03221396183703709\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = employment-related offenses for 1074 Obs. :\n",
            "sd = 0.001552589946466615, n = 1074, CAAR = -0.0008782217649741527, t-stat = -0.5656495245076205, Pvalue = 0.5717503158399238\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = consumer-protection-related offenses for 592 Obs. :\n",
            "sd = 0.0011498793714983593, n = 592, CAAR = 0.0016876629109598448, t-stat = 1.4676869181162215, Pvalue = 0.14272125455447474\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = environment-related offenses for 332 Obs. :\n",
            "sd = 0.0012602266504253255, n = 332, CAAR = -0.0037992816417164775, t-stat = -3.01476059122716, Pvalue = 0.0027705465080987133\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = government-contracting-related offenses for 69 Obs. :\n",
            "sd = 0.00215726381188102, n = 69, CAAR = -0.010771061558513815, t-stat = -4.992927382915685, Pvalue = 4.3889165686001385e-06\n",
            "For ticker None we have 162 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 162 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = competition-related offenses for 58 Obs. :\n",
            "sd = 0.0024228378941522325, n = 58, CAAR = -0.003596774304939695, t-stat = -1.4845294906526263, Pvalue = 0.14317794958485094\n",
            "Statistics for event window = [-10, 10], category = Offence Group, subcategory = healthcare-related offenses for 50 Obs. :\n",
            "sd = 0.0025374818451290707, n = 50, CAAR = 0.008369704968939155, t-stat = 3.298429498128458, Pvalue = 0.0018155825189953372\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dsxyBGRF8wHP",
        "outputId": "c2201565-2db4-4d23-9bd0-e9601352c0f5"
      },
      "source": [
        "output(df, events, 'offense_group', 'Offence Group', off_group, 200, [-5,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = safety-related offenses for 3814 Obs. :\n",
            "sd = 0.0011336684071438896, n = 3814, CAAR = -0.0019086404870774943, t-stat = -1.6835967863707455, Pvalue = 0.09234147614798836\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = employment-related offenses for 1074 Obs. :\n",
            "sd = 0.0012717731846416647, n = 1074, CAAR = 0.001552608569956046, t-stat = 1.220821911254175, Pvalue = 0.22242165432823394\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = consumer-protection-related offenses for 592 Obs. :\n",
            "sd = 0.0009407939942841185, n = 592, CAAR = 0.0025921276328074653, t-stat = 2.7552552934608197, Pvalue = 0.006045410016886482\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = environment-related offenses for 331 Obs. :\n",
            "sd = 0.0010575439577667003, n = 331, CAAR = -0.0010314907334442141, t-stat = -0.9753644052985704, Pvalue = 0.330093966188303\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = government-contracting-related offenses for 69 Obs. :\n",
            "sd = 0.0021094397715936894, n = 69, CAAR = -0.005465087523957922, t-stat = -2.590776753881448, Pvalue = 0.011707507621108424\n",
            "For ticker None we have 167 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 167 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = competition-related offenses for 58 Obs. :\n",
            "sd = 0.002336293408684189, n = 58, CAAR = -0.005754835853889649, t-stat = -2.4632333560923745, Pvalue = 0.016812084734947174\n",
            "Statistics for event window = [-5, 5], category = Offence Group, subcategory = healthcare-related offenses for 50 Obs. :\n",
            "sd = 0.0023573593612292395, n = 50, CAAR = 0.001340655938028753, t-stat = 0.5687108890049208, Pvalue = 0.5721498635825927\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yAVTOnY8wEe",
        "outputId": "c379f06e-305f-491d-d333-dd59d5405de4"
      },
      "source": [
        "output(df, events, 'offense_group', 'Offence Group', off_group, 200, [-1,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = safety-related offenses for 3814 Obs. :\n",
            "sd = 0.0008448561249400715, n = 3814, CAAR = 3.028336094861377e-05, t-stat = 0.035844400075529866, Pvalue = 0.9714083059419298\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = employment-related offenses for 1074 Obs. :\n",
            "sd = 0.0008588203087696968, n = 1074, CAAR = -0.0018186454207204954, t-stat = -2.1176087735114186, Pvalue = 0.03443773333854999\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = consumer-protection-related offenses for 592 Obs. :\n",
            "sd = 0.0007195232240948561, n = 592, CAAR = -0.0008943722627587225, t-stat = -1.2430068034062731, Pvalue = 0.21435822923907202\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = environment-related offenses for 331 Obs. :\n",
            "sd = 0.0008484523727755984, n = 331, CAAR = -0.0021355447217025108, t-stat = -2.5169883310201158, Pvalue = 0.012309744851226595\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = government-contracting-related offenses for 69 Obs. :\n",
            "sd = 0.0020077024794494763, n = 69, CAAR = -0.002566580352014652, t-stat = -1.278366878701282, Pvalue = 0.20546631007568242\n",
            "For ticker None we have 171 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 171 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = competition-related offenses for 58 Obs. :\n",
            "sd = 0.002240642872576034, n = 58, CAAR = -0.0027916959260140227, t-stat = -1.2459352448275038, Pvalue = 0.2178850254938613\n",
            "Statistics for event window = [-1, 1], category = Offence Group, subcategory = healthcare-related offenses for 50 Obs. :\n",
            "sd = 0.0021947174589331286, n = 50, CAAR = -0.000475762992152867, t-stat = -0.21677641931373687, Pvalue = 0.8292826262822357\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJhucUML8wB2",
        "outputId": "84734cbd-865c-4679-aa34-5b0d8813019c"
      },
      "source": [
        "output(df, events, 'govt_level', 'Government levels Group', gov_level_group, 200, [-20,20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 152 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 119 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 152 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 98 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 199 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 194 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 193 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 188 NaN values     in the window\n",
            "For ticker None we have 187 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 68 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 89 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Government levels Group, subcategory = federal for 5287 Obs. :\n",
            "sd = 0.0015269457923982586, n = 5287, CAAR = -0.0022980085281808582, t-stat = -1.5049706018519162, Pvalue = 0.13239138188972427\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 154 NaN values     in the window\n",
            "For ticker None we have 45 NaN values     in the window\n",
            "For ticker None we have 51 NaN values     in the window\n",
            "For ticker None we have 33 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = Government levels Group, subcategory = state for 678 Obs. :\n",
            "sd = 0.001432120273869289, n = 678, CAAR = 0.003249576200538551, t-stat = 2.2690665440821367, Pvalue = 0.023578146443979514\n",
            "Statistics for event window = [-20, 20], category = Government levels Group, subcategory = local for 26 Obs. :\n",
            "sd = 0.0030906039311598497, n = 26, CAAR = 0.015456961411654464, t-stat = 5.001275399871033, Pvalue = 3.7148292779267726e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJ-VWevE8v2y",
        "outputId": "ffb990a5-0f54-4ac0-daf2-35211878185f"
      },
      "source": [
        "output(df, events, 'govt_level', 'Government levels Group', gov_level_group, 200, [-10,10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 162 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 129 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 162 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 198 NaN values     in the window\n",
            "For ticker None we have 197 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 78 NaN values     in the window\n",
            "For ticker None we have 15 NaN values     in the window\n",
            "For ticker None we have 99 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Government levels Group, subcategory = federal for 5286 Obs. :\n",
            "sd = 0.0011709019579505965, n = 5286, CAAR = -0.0024237716933289658, t-stat = -2.070003963074107, Pvalue = 0.03850043969138483\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 164 NaN values     in the window\n",
            "For ticker None we have 55 NaN values     in the window\n",
            "For ticker None we have 61 NaN values     in the window\n",
            "For ticker None we have 43 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = Government levels Group, subcategory = state for 678 Obs. :\n",
            "sd = 0.0011135459534977543, n = 678, CAAR = -0.0003567425122705829, t-stat = -0.3203662239084256, Pvalue = 0.7487895811184215\n",
            "Statistics for event window = [-10, 10], category = Government levels Group, subcategory = local for 26 Obs. :\n",
            "sd = 0.003025815935559747, n = 26, CAAR = 0.0014507771876048838, t-stat = 0.4794664376491572, Pvalue = 0.6357746597432296\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jueGB3ej86Sq",
        "outputId": "c75b5f7a-cf9a-47e2-9fe4-c36f477b7608"
      },
      "source": [
        "output(df, events, 'govt_level', 'Government levels Group', gov_level_group, 200, [-5,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 167 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 167 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Government levels Group, subcategory = federal for 5285 Obs. :\n",
            "sd = 0.0009315642776185139, n = 5285, CAAR = -0.0010028335680938379, t-stat = -1.076504962875476, Pvalue = 0.2817506290935462\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = Government levels Group, subcategory = state for 678 Obs. :\n",
            "sd = 0.0009035263687219938, n = 678, CAAR = 0.0003207577720098844, t-stat = 0.3550065422701332, Pvalue = 0.7226952560838427\n",
            "Statistics for event window = [-5, 5], category = Government levels Group, subcategory = local for 26 Obs. :\n",
            "sd = 0.0029889691446415077, n = 26, CAAR = 0.000940374045482848, t-stat = 0.31461483875426055, Pvalue = 0.7556648612939254\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSfHOEMqfUBJ",
        "outputId": "a3587001-8321-4348-a8a0-bd47eb23ca1c"
      },
      "source": [
        "output(df, events, 'govt_level', 'Government levels Group', gov_level_group, 200, [-1,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 171 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 138 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 171 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 117 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 87 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n",
            "For ticker None we have 24 NaN values     in the window\n",
            "For ticker None we have 108 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Government levels Group, subcategory = federal for 5285 Obs. :\n",
            "sd = 0.0006516428433514798, n = 5285, CAAR = -0.000504407668730721, t-stat = -0.7740554106855373, Pvalue = 0.43893265474785426\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 173 NaN values     in the window\n",
            "For ticker None we have 64 NaN values     in the window\n",
            "For ticker None we have 70 NaN values     in the window\n",
            "For ticker None we have 52 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = Government levels Group, subcategory = state for 678 Obs. :\n",
            "sd = 0.0006811610851236193, n = 678, CAAR = -0.0012899574971029682, t-stat = -1.893762760784936, Pvalue = 0.05868257403052123\n",
            "Statistics for event window = [-1, 1], category = Government levels Group, subcategory = local for 26 Obs. :\n",
            "sd = 0.0029689275190007505, n = 26, CAAR = 0.0002029893191000115, t-stat = 0.06837126127226287, Pvalue = 0.946034015721301\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMGtP8q3Hbrb",
        "outputId": "d6de8cac-947d-42b8-949c-d3c01dbd8d08"
      },
      "source": [
        "output(df, events, 'penalty', 'penalty Group', penalty_group, 200, [-20,20])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 186 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n",
            "Statistics for event window = [-20, 20], category = penalty Group, subcategory = [1000,6000] for 970 Obs. :\n",
            "sd ] 0.0024501597847203776, n=970, CAAR = -0.007929980372574722, t-stat = -3.236515602789442, Pvalue = 0.0012510082456499772\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 190 NaN values     in the window\n",
            "For ticker None we have 186 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-20, 20], category = penalty Group, subcategory = [20000, 31000] for 583 Obs. :\n",
            "sd ] 0.0019416582951730802, n=583, CAAR = 0.0029022359030700605, t-stat = 1.4947202143059648, Pvalue = 0.13552945698791707\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-20, 20], category = penalty Group, subcategory = [44000,60000] for 222 Obs. :\n",
            "sd ] 0.0015047263950074014, n=222, CAAR = -0.006402358787779303, t-stat = -4.254832512423504, Pvalue = 3.0907144963469364e-05\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 82 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 185 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-20, 20], category = penalty Group, subcategory = [71000, 87000] for 126 Obs. :\n",
            "sd ] 0.001968005196539894, n=126, CAAR = 0.006819501836287088, t-stat = 3.4651848726197447, Pvalue = 0.0007266439509239753\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKfg9JW1HcEO",
        "outputId": "68eb8417-b2d4-49be-c5a9-5d1bb2d8aa88"
      },
      "source": [
        "output(df, events, 'penalty', 'penalty Group', penalty_group, 200, [-10,10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 196 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n",
            "Statistics for event window = [-10, 10], category = penalty Group, subcategory = [1000,6000] for 970 Obs. :\n",
            "sd ] 0.002243125204892483, n=970, CAAR = -0.007011906868958644, t-stat = -3.125954295223898, Pvalue = 0.0018250789235278258\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 196 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-10, 10], category = penalty Group, subcategory = [20000, 31000] for 583 Obs. :\n",
            "sd ] 0.0014488211519863752, n=583, CAAR = -6.463392848582448e-05, t-stat = -0.04461139209433098, Pvalue = 0.9644323638539075\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-10, 10], category = penalty Group, subcategory = [44000,60000] for 222 Obs. :\n",
            "sd ] 0.0012746236068798095, n=222, CAAR = -0.0032124459491793132, t-stat = -2.5203094716275958, Pvalue = 0.012431469059823893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 92 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 195 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-10, 10], category = penalty Group, subcategory = [71000, 87000] for 126 Obs. :\n",
            "sd ] 0.0016832912292322885, n=126, CAAR = 0.00028431778279346717, t-stat = 0.16890587787541564, Pvalue = 0.8661436922094133\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFP_ecKbHcST",
        "outputId": "23738e26-b878-4a47-83c9-0ec065f661f1"
      },
      "source": [
        "output(df, events, 'penalty', 'penalty Group', penalty_group, 200, [-5,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-5, 5], category = penalty Group, subcategory = [1000,6000] for 970 Obs. :\n",
            "sd ] 0.0020518355060329843, n=970, CAAR = -0.004554678236285282, t-stat = -2.219806715934695, Pvalue = 0.026662698646979646\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 1 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-5, 5], category = penalty Group, subcategory = [20000, 31000] for 582 Obs. :\n",
            "sd ] 0.0011372163159149152, n=582, CAAR = 0.00046706400511302307, t-stat = 0.4107081463540733, Pvalue = 0.6814379776343772\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-5, 5], category = penalty Group, subcategory = [44000,60000] for 222 Obs. :\n",
            "sd ] 0.0011344992298250004, n=222, CAAR = -0.00570352399443887, t-stat = -5.027349375387989, Pvalue = 1.0269377115404625e-06\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 97 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-5, 5], category = penalty Group, subcategory = [71000, 87000] for 126 Obs. :\n",
            "sd ] 0.0015141712958732202, n=126, CAAR = 0.0018212210594488615, t-stat = 1.2027840340207785, Pvalue = 0.2313331972846461\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wN3aLDQHcvF",
        "outputId": "ac98fe26-90e5-4014-acfc-2920b711d945"
      },
      "source": [
        "output(df, events, 'penalty', 'penalty Group', penalty_group, 200, [-1,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "Statistics for event window = [-1, 1], category = penalty Group, subcategory = [1000,6000] for 970 Obs. :\n",
            "sd ] 0.0018326479130563408, n=970, CAAR = -0.0009366597960835831, t-stat = -0.5110964246926722, Pvalue = 0.6093999671220712\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 5 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-1, 1], category = penalty Group, subcategory = [20000, 31000] for 582 Obs. :\n",
            "sd ] 0.0007881344594768588, n=582, CAAR = -0.0004540668976012037, t-stat = -0.57612872034881, Pvalue = 0.564751281285151\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 2 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-1, 1], category = penalty Group, subcategory = [44000,60000] for 222 Obs. :\n",
            "sd ] 0.0009939120567533887, n=222, CAAR = -0.0022871614021998856, t-stat = -2.3011707994275596, Pvalue = 0.022313935241945155\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "For ticker None we have 101 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Statistics for event window = [-1, 1], category = penalty Group, subcategory = [71000, 87000] for 126 Obs. :\n",
            "sd ] 0.0013732247707519607, n=126, CAAR = -0.0010036520460509762, t-stat = -0.7308723724094993, Pvalue = 0.4662248675816656\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Mw9pdaTZyTc"
      },
      "source": [
        "#Micro-structure event samples (DON'R RUN)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rr6UVzITZ9oD"
      },
      "source": [
        "8 samples from each industry with highest negative or positive CAAR in event window (-20,20)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvPMmK-PZ5AI"
      },
      "source": [
        "ind_positive_CAAR_group = [ 'healthcare services', 'pharmaceuticals', 'utilities and power generation', 'housewares and home furnishings']\n",
        "ind_negative_CAAR_group = [ 'telecommunications', 'miscellaneous manufacturing', 'wholesalers', 'waste management and environmental services']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dj1wMvxhZ4mS"
      },
      "source": [
        "mic_event_pos = pd.DataFrame()\n",
        "mic_event_neg = pd.DataFrame()\n",
        "\n",
        "for i in ind_positive_CAAR_group:\n",
        "  if mic_event_pos.shape[0] == 0:\n",
        "    mic_event_pos = events[events['major_industry'] == i].sample(8)\n",
        "  else:\n",
        "    mic_event_pos = mic_event_pos.append(events[events['major_industry'] == i].sample(8))\n",
        "\n",
        "for j in ind_negative_CAAR_group:\n",
        "  if mic_event_neg.shape[0] == 0:\n",
        "    mic_event_neg = events[events['major_industry'] == j].sample(8)\n",
        "  else:\n",
        "    mic_event_neg = mic_event_neg.append(events[events['major_industry'] == j].sample(8))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYQ8p8o3oz42"
      },
      "source": [
        "# create excel writer object\n",
        "writer = pd.ExcelWriter('mic_sample_pos.xlsx')\n",
        "# write dataframe to excel\n",
        "mic_event_pos[['ticker', 'date']].to_excel(writer)\n",
        "# save the excel\n",
        "writer.save()\n",
        "\n",
        "# create excel writer object\n",
        "writer = pd.ExcelWriter('mic_sample_neg.xlsx')\n",
        "# write dataframe to excel\n",
        "mic_event_neg[['ticker', 'date']].to_excel(writer)\n",
        "# save the excel\n",
        "writer.save()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5emY3c53eiXd"
      },
      "source": [
        "#Testing the functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKWAoFEVFoLT"
      },
      "source": [
        "AR = AR_ev_es_vec(df, events[events.major_industry == 'retailing'], 200, [-50,50], minNA=100, gap_=0)\n",
        "esAR = es_AR(df, events[events.major_industry == 'retailing'], 200, [-50,50], minNA=100, gap_=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaEoGMF6ri2I",
        "outputId": "3789a7ed-5190-42ce-b27a-f1a500f2845c"
      },
      "source": [
        "sd_robust_CAAR(AR, esAR, 101)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.1710100923059584e-05, 984)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHN4Zy3SqpCR",
        "outputId": "773092f6-71e8-4ab0-ac82-bca18eb615c6"
      },
      "source": [
        "cal_CAAR(AR_ev_es_vec(df, events[events.major_industry == 'telecommunications'], 200, [-20,20], minNA=100, gap_=0), events[events.major_industry == 'telecommunications'], 41, 20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.05364797080390307"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uNdpV5-rOG5"
      },
      "source": [
        "#get_estwin_returns(df, '20170105', 'BRK', 20, -20)\n",
        "SS = get_evwin_returns(df, '20170105', 'BRK', 20, -20)\n",
        "MM = pd.DataFrame()\n",
        "MM['date'] = df[df.date > '2016-01-01'].date\n",
        "#MM.concat(SS, on='date')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PrSPN43jbIAJ",
        "outputId": "2adc9aa0-cfc5-4f88-d14a-0d5cb96920a0"
      },
      "source": [
        "es_win = 200\n",
        "ev_win = [-5,5]\n",
        "end_time = 20\n",
        "#AR_ev_es_vec(df, events, es_win, ev_win, minNA=100, gap_=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FCX_2020-01-09_2\n",
            "FCX_2020-01-09_3\n",
            "FCX_2020-01-12_2\n",
            "FCX_2020-01-08_2\n",
            "FCX_2020-01-09_4\n",
            "FCX_2020-01-09_5\n",
            "DLTR_2020-01-07_2\n",
            "LUV_2020-02-28_2\n",
            "VZ_2020-01-30_2\n",
            "VZ_2020-01-30_3\n",
            "VZ_2020-01-30_4\n",
            "VZ_2020-01-30_5\n",
            "VZ_2020-01-30_6\n",
            "VZ_2020-01-30_7\n",
            "VZ_2020-01-29_2\n",
            "VZ_2020-01-28_2\n",
            "VZ_2020-01-28_3\n",
            "VZ_2020-01-28_4\n",
            "VZ_2020-01-27_2\n",
            "VZ_2020-01-27_3\n",
            "VZ_2020-01-27_4\n",
            "VZ_2020-01-26_2\n",
            "VZ_2020-01-24_2\n",
            "VZ_2020-01-24_3\n",
            "VZ_2020-01-24_4\n",
            "VZ_2020-01-24_5\n",
            "VZ_2020-01-24_6\n",
            "VZ_2020-01-24_7\n",
            "VZ_2020-01-23_2\n",
            "VZ_2020-01-23_3\n",
            "VZ_2020-01-23_4\n",
            "VZ_2020-01-22_2\n",
            "VZ_2020-01-22_3\n",
            "VZ_2020-01-22_4\n",
            "VZ_2020-01-22_5\n",
            "VZ_2020-01-22_6\n",
            "VZ_2020-01-22_7\n",
            "VZ_2020-01-22_8\n",
            "VZ_2020-01-21_2\n",
            "VZ_2020-01-21_3\n",
            "VZ_2020-01-21_4\n",
            "VZ_2020-01-21_5\n",
            "CMG_2020-01-27_2\n",
            "D_2020-02-05_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "D_2020-01-30_2\n",
            "D_2020-01-27_2\n",
            "D_2020-01-22_2\n",
            "D_2020-01-17_2\n",
            "D_2020-01-17_3\n",
            "D_2020-01-16_2\n",
            "D_2020-01-16_3\n",
            "D_2020-01-15_2\n",
            "D_2020-01-10_2\n",
            "D_2020-01-06_2\n",
            "D_2020-01-06_3\n",
            "D_2020-01-09_2\n",
            "D_2020-01-16_4\n",
            "D_2020-01-24_2\n",
            "D_2020-01-24_3\n",
            "D_2020-01-24_4\n",
            "D_2020-02-05_3\n",
            "D_2020-02-11_2\n",
            "JNJ_2020-02-21_2\n",
            "JNJ_2020-02-21_3\n",
            "JNJ_2020-03-10_2\n",
            "JNJ_2020-04-17_2\n",
            "JNJ_2020-04-17_3\n",
            "JNJ_2020-04-20_2\n",
            "UNH_2020-04-21_2\n",
            "UNH_2020-04-21_3\n",
            "MMM_2019-12-03_2\n",
            "MMM_2019-09-11_2\n",
            "MMM_2019-08-21_2\n",
            "MMM_2019-09-11_3\n",
            "FCX_2019-12-03_2\n",
            "FCX_2019-02-13_2\n",
            "MLM_2019-12-30_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "DLTR_2019-11-25_2\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "DLTR_2019-11-07_2\n",
            "DLTR_2019-10-08_2\n",
            "DLTR_2019-09-20_2\n",
            "DHI_2019-09-17_2\n",
            "DLTR_2019-09-05_2\n",
            "WMT_2019-08-20_2\n",
            "UPS_2019-08-15_2\n",
            "UPS_2019-08-15_3\n",
            "TGT_2019-08-12_2\n",
            "DLTR_2019-04-30_2\n",
            "DLTR_2019-03-28_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 134 NaN values     in the window\n",
            "AAL_2019-01-15_2\n",
            "AAL_2019-01-15_3\n",
            "AAL_2019-01-15_4\n",
            "AAL_2019-01-07_2\n",
            "AAL_2019-01-07_3\n",
            "AAL_2019-05-16_2\n",
            "AAL_2019-05-16_3\n",
            "AAL_2019-05-16_4\n",
            "WMT_2019-04-30_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "AMZN_2019-02-28_2\n",
            "AMZN_2019-04-29_2\n",
            "AMZN_2019-06-10_2\n",
            "AMZN_2019-05-22_2\n",
            "AEP_2019-06-27_2\n",
            "AEP_2019-08-21_2\n",
            "AEP_2019-04-11_2\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "EFX_2019-07-22_2\n",
            "DG_2019-01-14_2\n",
            "DLTR_2019-07-25_2\n",
            "DLTR_2019-04-09_2\n",
            "IBM_2019-06-14_2\n",
            "DLTR_2019-01-12_2\n",
            "DLTR_2019-05-17_2\n",
            "DLTR_2019-05-17_3\n",
            "DLTR_2019-06-26_2\n",
            "DLTR_2019-08-26_2\n",
            "DLTR_2019-07-12_2\n",
            "For ticker None we have 97 NaN values     in the window\n",
            "JNJ_2019-07-22_2\n",
            "JNJ_2019-06-24_2\n",
            "JNJ_2019-06-24_3\n",
            "JNJ_2019-04-30_2\n",
            "JNJ_2019-07-01_2\n",
            "JNJ_2019-03-27_2\n",
            "KR_2019-03-27_2\n",
            "KR_2019-01-19_2\n",
            "KR_2019-06-03_2\n",
            "KR_2019-04-19_2\n",
            "KR_2019-01-10_2\n",
            "KR_2019-06-28_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "KR_2019-07-11_2\n",
            "KR_2019-07-11_3\n",
            "KR_2019-02-21_2\n",
            "KR_2019-05-28_2\n",
            "WBA_2019-01-22_2\n",
            "WBA_2019-01-22_3\n",
            "WMT_2019-06-20_2\n",
            "PFE_2019-03-28_2\n",
            "For ticker None we have 167 NaN values     in the window\n",
            "For ticker None we have 2 NaN values     in the window\n",
            "UPS_2019-09-18_2\n",
            "WBA_2019-09-10_2\n",
            "WBA_2019-09-10_3\n",
            "WBA_2019-09-10_4\n",
            "WBA_2019-09-10_5\n",
            "WBA_2019-09-10_6\n",
            "WBA_2019-09-09_2\n",
            "WBA_2019-09-09_3\n",
            "WBA_2019-09-09_4\n",
            "WBA_2019-09-09_5\n",
            "WBA_2019-09-09_6\n",
            "WMT_2019-09-09_2\n",
            "WMT_2019-09-09_3\n",
            "WMT_2019-09-07_2\n",
            "WMT_2019-09-06_2\n",
            "WMT_2019-09-06_3\n",
            "WMT_2019-09-06_4\n",
            "WMT_2019-09-05_2\n",
            "WMT_2019-09-05_3\n",
            "WMT_2019-09-05_4\n",
            "WMT_2019-09-05_5\n",
            "WMT_2019-09-05_6\n",
            "WMT_2019-09-05_7\n",
            "WMT_2019-09-05_8\n",
            "WMT_2019-09-05_9\n",
            "WMT_2019-09-04_2\n",
            "WMT_2019-09-04_3\n",
            "WMT_2019-09-03_2\n",
            "WMT_2019-09-03_3\n",
            "WMT_2019-09-03_4\n",
            "WMT_2019-09-03_5\n",
            "WMT_2019-09-01_2\n",
            "WMT_2019-08-31_2\n",
            "WMT_2019-08-31_3\n",
            "WMT_2019-08-30_2\n",
            "WMT_2019-08-30_3\n",
            "WMT_2019-08-30_4\n",
            "WMT_2019-08-29_2\n",
            "WMT_2019-08-29_3\n",
            "WMT_2019-08-29_4\n",
            "WMT_2019-08-29_5\n",
            "WMT_2019-08-29_6\n",
            "WMT_2019-08-29_7\n",
            "WMT_2019-08-29_8\n",
            "WMT_2019-08-29_9\n",
            "WMT_2019-08-29_10\n",
            "WMT_2019-08-29_11\n",
            "WMT_2019-08-29_12\n",
            "WMT_2019-08-28_2\n",
            "WMT_2019-08-28_3\n",
            "DG_2019-10-09_2\n",
            "DG_2019-10-09_3\n",
            "DG_2019-10-09_4\n",
            "DG_2019-10-09_5\n",
            "DG_2019-10-09_6\n",
            "DG_2019-10-09_7\n",
            "DG_2019-10-09_8\n",
            "DG_2019-10-09_9\n",
            "DG_2019-10-08_2\n",
            "DG_2019-10-08_3\n",
            "DG_2019-10-08_4\n",
            "DG_2019-10-08_5\n",
            "DG_2019-10-08_6\n",
            "DG_2019-10-07_2\n",
            "WMT_2019-10-03_2\n",
            "WMT_2019-10-03_3\n",
            "WMT_2019-10-03_4\n",
            "WMT_2019-10-03_5\n",
            "WMT_2019-10-03_6\n",
            "WMT_2019-10-02_2\n",
            "WMT_2019-10-02_3\n",
            "WMT_2019-10-02_4\n",
            "WMT_2019-10-01_2\n",
            "WMT_2019-10-01_3\n",
            "WMT_2019-10-01_4\n",
            "WMT_2019-10-01_5\n",
            "WMT_2019-10-01_6\n",
            "WMT_2019-10-01_7\n",
            "WMT_2019-10-02_5\n",
            "WMT_2019-09-12_2\n",
            "WMT_2019-10-23_2\n",
            "WMT_2019-10-02_6\n",
            "WMT_2019-09-09_4\n",
            "WMT_2019-10-11_2\n",
            "WMT_2019-10-02_7\n",
            "WMT_2019-10-01_8\n",
            "WMT_2019-11-15_2\n",
            "WMT_2019-11-14_2\n",
            "WMT_2019-10-28_2\n",
            "WMT_2019-10-28_3\n",
            "WMT_2019-10-28_4\n",
            "PFE_2019-10-28_2\n",
            "WMT_2019-10-28_5\n",
            "For ticker None we have 113 NaN values     in the window\n",
            "SYY_2019-09-27_2\n",
            "SYY_2019-09-26_2\n",
            "DLTR_2019-09-24_2\n",
            "DLTR_2019-11-05_2\n",
            "ABT_2019-03-30_2\n",
            "ABT_2019-03-30_3\n",
            "ABT_2019-03-29_2\n",
            "ABT_2019-03-29_3\n",
            "ABT_2019-03-29_4\n",
            "ABT_2019-03-29_5\n",
            "ABT_2019-03-29_6\n",
            "ABT_2019-03-28_2\n",
            "ABT_2019-03-28_3\n",
            "ABT_2019-03-28_4\n",
            "JNJ_2019-03-26_2\n",
            "JNJ_2019-03-26_3\n",
            "JNJ_2019-03-24_2\n",
            "JNJ_2019-03-24_3\n",
            "JNJ_2019-03-24_4\n",
            "JNJ_2019-03-24_5\n",
            "JNJ_2019-03-24_6\n",
            "JNJ_2019-03-23_2\n",
            "JNJ_2019-03-23_3\n",
            "JNJ_2019-03-22_2\n",
            "JNJ_2019-03-22_3\n",
            "JNJ_2019-03-22_4\n",
            "JNJ_2019-03-22_5\n",
            "JNJ_2019-03-21_2\n",
            "JNJ_2019-03-20_2\n",
            "JNJ_2019-03-20_3\n",
            "JNJ_2019-03-20_4\n",
            "JNJ_2019-03-20_5\n",
            "JNJ_2019-03-20_6\n",
            "JNJ_2019-03-10_2\n",
            "JNJ_2019-03-10_3\n",
            "JNJ_2019-03-10_4\n",
            "JNJ_2019-03-10_5\n",
            "JNJ_2019-03-10_6\n",
            "JNJ_2019-03-10_7\n",
            "JNJ_2019-03-09_2\n",
            "JNJ_2019-03-07_2\n",
            "JNJ_2019-03-07_3\n",
            "JNJ_2019-03-06_2\n",
            "JNJ_2019-03-06_3\n",
            "JNJ_2019-03-05_2\n",
            "JNJ_2019-03-05_3\n",
            "JNJ_2019-03-05_4\n",
            "JNJ_2019-03-05_5\n",
            "JNJ_2019-03-05_6\n",
            "JNJ_2019-03-03_2\n",
            "AAL_2019-03-01_2\n",
            "AAL_2019-03-01_3\n",
            "AAL_2019-02-28_2\n",
            "AAL_2019-02-28_3\n",
            "AAL_2019-02-28_4\n",
            "AAL_2019-02-28_5\n",
            "AAL_2019-02-27_2\n",
            "AAL_2019-02-27_3\n",
            "AAL_2019-02-26_2\n",
            "AAL_2019-02-26_3\n",
            "AAL_2019-02-26_4\n",
            "AAL_2019-02-26_5\n",
            "AAL_2019-02-25_2\n",
            "AAL_2019-02-25_3\n",
            "AAL_2019-02-25_4\n",
            "GS_2019-02-22_2\n",
            "GS_2019-02-21_2\n",
            "GS_2019-02-21_3\n",
            "BAC_2019-02-17_2\n",
            "BAC_2019-02-16_2\n",
            "BAC_2019-02-15_2\n",
            "BAC_2019-02-15_3\n",
            "BAC_2019-02-15_4\n",
            "BAC_2019-02-13_2\n",
            "BAC_2019-02-12_2\n",
            "BAC_2019-02-11_2\n",
            "BAC_2019-02-11_3\n",
            "BAC_2019-02-10_2\n",
            "BAC_2019-02-10_3\n",
            "BAC_2019-02-10_4\n",
            "BAC_2019-02-10_5\n",
            "BAC_2019-02-10_6\n",
            "BAC_2019-02-10_7\n",
            "BAC_2019-02-10_8\n",
            "BAC_2019-02-10_9\n",
            "CAH_2019-12-16_2\n",
            "BA_2019-12-06_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "MMM_2019-06-24_2\n",
            "CMI_2019-06-14_2\n",
            "CMI_2019-06-13_2\n",
            "DG_2019-04-26_2\n",
            "BRK_2019-04-15_2\n",
            "MMC_2019-08-08_2\n",
            "ANTM_2019-01-08_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "CNC_2019-03-25_2\n",
            "CVS_2019-04-02_2\n",
            "ANTM_2019-04-23_2\n",
            "UNH_2019-05-02_2\n",
            "ANTM_2019-12-18_2\n",
            "CNC_2019-12-18_2\n",
            "HIG_2019-09-04_2\n",
            "HIG_2019-09-04_3\n",
            "HIG_2019-09-04_4\n",
            "HIG_2019-09-04_5\n",
            "CI_2019-11-25_2\n",
            "LNC_2019-12-11_2\n",
            "L_2019-08-12_2\n",
            "CVS_2019-04-15_2\n",
            "For ticker None we have 169 NaN values     in the window\n",
            "For ticker None we have 60 NaN values     in the window\n",
            "TRV_2019-09-03_2\n",
            "For ticker None we have 66 NaN values     in the window\n",
            "CVS_2019-06-21_2\n",
            "For ticker None we have 48 NaN values     in the window\n",
            "MPC_2018-06-26_2\n",
            "BRK_2018-10-21_2\n",
            "CVX_2018-06-14_2\n",
            "FCX_2018-04-24_2\n",
            "BRK_2018-04-10_2\n",
            "FCX_2018-01-05_2\n",
            "FCX_2018-01-07_2\n",
            "FCX_2018-09-26_2\n",
            "FCX_2018-01-05_3\n",
            "FCX_2018-01-05_4\n",
            "FCX_2018-10-16_2\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "KMI_2018-12-19_2\n",
            "KMI_2018-12-19_3\n",
            "KMI_2018-12-14_2\n",
            "KMI_2018-12-19_4\n",
            "KMI_2018-12-19_5\n",
            "KMI_2018-12-19_6\n",
            "KMI_2018-12-17_2\n",
            "KMI_2018-12-19_7\n",
            "KMI_2018-12-19_8\n",
            "KMI_2018-12-19_9\n",
            "KMI_2018-12-19_10\n",
            "KMI_2018-12-12_2\n",
            "KMI_2018-12-19_11\n",
            "KMI_2018-12-14_3\n",
            "KMI_2018-12-17_3\n",
            "KMI_2018-12-19_12\n",
            "KMI_2018-12-20_2\n",
            "KMI_2018-12-19_13\n",
            "KMI_2018-12-19_14\n",
            "KMI_2018-12-17_4\n",
            "KMI_2018-12-18_2\n",
            "KMI_2018-12-19_15\n",
            "KMI_2018-12-12_3\n",
            "KMI_2018-12-19_16\n",
            "KMI_2018-12-19_17\n",
            "KMI_2018-12-19_18\n",
            "KMI_2018-12-20_3\n",
            "KMI_2018-12-18_3\n",
            "KMI_2018-12-17_5\n",
            "KMI_2018-12-17_6\n",
            "KMI_2018-12-12_4\n",
            "KMI_2018-12-14_4\n",
            "KMI_2018-12-18_4\n",
            "KMI_2018-12-18_5\n",
            "KMI_2018-12-19_19\n",
            "KMI_2018-12-19_20\n",
            "KMI_2018-12-19_21\n",
            "KMI_2018-12-17_7\n",
            "KMI_2018-12-19_22\n",
            "KMI_2018-12-17_8\n",
            "KMI_2018-12-19_23\n",
            "KMI_2018-12-18_6\n",
            "KMI_2018-12-14_5\n",
            "KMI_2018-12-13_2\n",
            "KMI_2018-12-19_24\n",
            "KMI_2018-12-18_7\n",
            "KMI_2018-12-19_25\n",
            "KMI_2018-12-18_8\n",
            "KMI_2018-12-20_4\n",
            "KMI_2018-12-18_9\n",
            "KMI_2018-12-17_9\n",
            "KMI_2018-12-18_10\n",
            "KMI_2018-12-14_6\n",
            "KMI_2018-12-18_11\n",
            "KMI_2018-12-18_12\n",
            "KMI_2018-12-18_13\n",
            "KMI_2018-12-18_14\n",
            "KMI_2018-12-14_7\n",
            "KMI_2018-12-14_8\n",
            "KMI_2018-12-18_15\n",
            "KMI_2018-12-18_16\n",
            "KMI_2018-12-14_9\n",
            "KMI_2018-12-18_17\n",
            "KMI_2018-12-17_10\n",
            "KMI_2018-12-18_18\n",
            "KMI_2018-12-17_11\n",
            "KMI_2018-12-12_5\n",
            "KMI_2018-12-18_19\n",
            "KMI_2018-12-18_20\n",
            "KMI_2018-12-14_10\n",
            "KMI_2018-12-18_21\n",
            "KMI_2018-12-17_12\n",
            "KMI_2018-12-17_13\n",
            "KMI_2018-12-14_11\n",
            "KMI_2018-12-17_14\n",
            "KMI_2018-12-18_22\n",
            "KMI_2018-12-11_2\n",
            "KMI_2018-12-11_3\n",
            "KMI_2018-12-14_12\n",
            "KMI_2018-12-18_23\n",
            "KMI_2018-12-17_15\n",
            "KMI_2018-12-17_16\n",
            "KMI_2018-12-18_24\n",
            "KMI_2018-12-18_25\n",
            "KMI_2018-12-18_26\n",
            "KMI_2018-12-17_17\n",
            "KMI_2018-12-18_27\n",
            "KMI_2018-12-17_18\n",
            "KMI_2018-12-17_19\n",
            "KMI_2018-12-17_20\n",
            "KMI_2018-12-17_21\n",
            "KMI_2018-12-17_22\n",
            "KMI_2018-12-17_23\n",
            "KMI_2018-12-17_24\n",
            "KMI_2018-12-17_25\n",
            "KMI_2018-12-17_26\n",
            "KMI_2018-12-06_2\n",
            "KMI_2018-12-17_27\n",
            "KMI_2018-12-17_28\n",
            "KMI_2018-12-14_13\n",
            "KMI_2018-12-14_14\n",
            "KMI_2018-12-14_15\n",
            "KMI_2018-12-17_29\n",
            "KMI_2018-12-13_3\n",
            "KMI_2018-12-17_30\n",
            "KMI_2018-12-13_4\n",
            "KMI_2018-12-17_31\n",
            "KMI_2018-12-17_32\n",
            "KMI_2018-12-11_4\n",
            "KMI_2018-12-12_6\n",
            "KMI_2018-12-17_33\n",
            "KMI_2018-12-17_34\n",
            "KMI_2018-12-14_16\n",
            "KMI_2018-12-17_35\n",
            "KMI_2018-12-13_5\n",
            "KMI_2018-12-17_36\n",
            "KMI_2018-12-13_6\n",
            "KMI_2018-12-06_3\n",
            "KMI_2018-12-14_17\n",
            "KMI_2018-12-13_7\n",
            "KMI_2018-12-17_37\n",
            "KMI_2018-12-13_8\n",
            "KMI_2018-12-17_38\n",
            "KMI_2018-12-17_39\n",
            "KMI_2018-12-17_40\n",
            "KMI_2018-12-07_2\n",
            "KMI_2018-12-17_41\n",
            "KMI_2018-12-14_18\n",
            "KMI_2018-12-14_19\n",
            "KMI_2018-12-14_20\n",
            "KMI_2018-12-11_5\n",
            "KMI_2018-12-14_21\n",
            "KMI_2018-12-07_3\n",
            "KMI_2018-12-12_7\n",
            "KMI_2018-12-13_9\n",
            "DLTR_2018-11-02_2\n",
            "BRK_2018-10-29_2\n",
            "BRK_2018-10-25_2\n",
            "LEN_2018-08-21_2\n",
            "SYY_2018-08-07_2\n",
            "BRK_2018-08-07_2\n",
            "BRK_2018-08-08_2\n",
            "CHTR_2018-07-23_2\n",
            "ITW_2018-06-27_2\n",
            "WMT_2018-05-22_2\n",
            "DLTR_2018-04-16_2\n",
            "DLTR_2018-03-20_2\n",
            "IP_2018-07-10_2\n",
            "KHC_2018-09-06_2\n",
            "LMT_2018-01-31_2\n",
            "LMT_2018-01-31_3\n",
            "LMT_2018-01-31_4\n",
            "LMT_2018-01-19_2\n",
            "LMT_2018-01-16_2\n",
            "LMT_2018-05-04_2\n",
            "LMT_2018-04-30_2\n",
            "VZ_2018-03-08_2\n",
            "VZ_2018-03-07_2\n",
            "VZ_2018-03-07_3\n",
            "VZ_2018-03-06_2\n",
            "VZ_2018-03-06_3\n",
            "VZ_2018-03-05_2\n",
            "VZ_2018-03-05_3\n",
            "VZ_2018-03-04_2\n",
            "VZ_2018-03-04_3\n",
            "VZ_2018-03-02_2\n",
            "VZ_2018-03-02_3\n",
            "VZ_2018-03-02_4\n",
            "VZ_2018-03-02_5\n",
            "VZ_2018-03-02_6\n",
            "VZ_2018-02-28_2\n",
            "VZ_2018-02-28_3\n",
            "VZ_2018-02-28_4\n",
            "VZ_2018-02-28_5\n",
            "VZ_2018-02-27_2\n",
            "VZ_2018-02-27_3\n",
            "VZ_2018-02-24_2\n",
            "VZ_2018-02-24_3\n",
            "VZ_2018-02-24_4\n",
            "VZ_2018-02-23_2\n",
            "VZ_2018-02-20_2\n",
            "VZ_2018-02-20_3\n",
            "VZ_2018-02-18_2\n",
            "VZ_2018-02-18_3\n",
            "VZ_2018-02-18_4\n",
            "M_2018-06-01_2\n",
            "M_2018-05-29_2\n",
            "M_2018-05-23_2\n",
            "JPM_2018-03-27_2\n",
            "JPM_2018-03-27_3\n",
            "JPM_2018-03-19_2\n",
            "JPM_2018-03-09_2\n",
            "JPM_2018-06-07_2\n",
            "JPM_2018-12-06_2\n",
            "JPM_2018-12-04_2\n",
            "WFC_2018-08-01_2\n",
            "WFC_2018-08-01_3\n",
            "WFC_2018-08-10_2\n",
            "WFC_2018-08-10_3\n",
            "WFC_2018-08-15_2\n",
            "WFC_2018-08-15_3\n",
            "WFC_2018-08-17_2\n",
            "WFC_2018-08-29_2\n",
            "WFC_2018-08-29_3\n",
            "WFC_2018-09-12_2\n",
            "WFC_2018-09-18_2\n",
            "WFC_2018-09-18_3\n",
            "WFC_2018-09-20_2\n",
            "WFC_2018-09-20_3\n",
            "WFC_2018-10-02_2\n",
            "WFC_2018-10-02_3\n",
            "For ticker None we have 200 NaN values     in the window\n",
            "JPM_2018-05-07_2\n",
            "JPM_2018-09-28_2\n",
            "GD_2018-07-17_2\n",
            "CVX_2018-09-27_2\n",
            "HD_2018-06-18_2\n",
            "HD_2018-06-21_2\n",
            "HD_2018-06-21_3\n",
            "HD_2018-06-21_4\n",
            "HD_2018-06-26_2\n",
            "HD_2018-06-27_2\n",
            "HD_2018-06-29_2\n",
            "HD_2018-06-29_3\n",
            "HD_2018-07-05_2\n",
            "HD_2018-07-13_2\n",
            "For ticker None we have 83 NaN values     in the window\n",
            "MMM_2018-10-05_2\n",
            "MMM_2018-10-05_3\n",
            "MMM_2018-10-05_4\n",
            "MMM_2018-10-05_5\n",
            "MMM_2018-10-04_2\n",
            "MMM_2018-10-04_3\n",
            "MMM_2018-10-04_4\n",
            "MMM_2018-10-04_5\n",
            "MMM_2018-10-04_6\n",
            "MMM_2018-10-04_7\n",
            "MMM_2018-10-04_8\n",
            "MMM_2018-10-03_2\n",
            "MMM_2018-10-03_3\n",
            "MMM_2018-10-02_2\n",
            "MMM_2018-10-02_3\n",
            "MMM_2018-10-02_4\n",
            "MMM_2018-10-02_5\n",
            "MMM_2018-10-01_2\n",
            "MMM_2018-09-28_2\n",
            "MMM_2018-09-27_2\n",
            "MMM_2018-09-27_3\n",
            "MMM_2018-09-27_4\n",
            "MMM_2018-09-26_2\n",
            "MMM_2018-09-26_3\n",
            "MMM_2018-09-26_4\n",
            "GE_2018-01-30_2\n",
            "GE_2018-01-30_3\n",
            "GE_2018-09-01_2\n",
            "GE_2018-12-14_2\n",
            "GE_2018-10-26_2\n",
            "GE_2018-12-26_2\n",
            "GE_2018-03-27_2\n",
            "GE_2018-03-16_2\n",
            "GE_2018-05-08_2\n",
            "GE_2018-04-12_2\n",
            "NUE_2018-03-29_2\n",
            "JPM_2018-03-09_3\n",
            "JPM_2018-09-28_3\n",
            "JPM_2018-02-01_2\n",
            "BRK_2018-09-28_2\n",
            "BRK_2018-09-28_3\n",
            "BRK_2018-09-28_4\n",
            "BRK_2018-09-28_5\n",
            "BRK_2018-09-28_6\n",
            "BRK_2018-09-28_7\n",
            "BRK_2018-09-28_8\n",
            "BRK_2018-09-28_9\n",
            "BRK_2018-09-28_10\n",
            "BRK_2018-09-28_11\n",
            "BRK_2018-09-28_12\n",
            "BRK_2018-09-28_13\n",
            "BRK_2018-09-28_14\n",
            "BRK_2018-09-28_15\n",
            "BRK_2018-09-28_16\n",
            "BRK_2018-09-28_17\n",
            "BRK_2018-09-28_18\n",
            "BRK_2018-09-28_19\n",
            "BRK_2018-09-28_20\n",
            "BRK_2018-09-28_21\n",
            "BRK_2018-09-28_22\n",
            "BRK_2018-09-28_23\n",
            "BRK_2018-09-28_24\n",
            "BRK_2018-09-28_25\n",
            "BRK_2018-09-28_26\n",
            "BRK_2018-09-28_27\n",
            "BRK_2018-09-28_28\n",
            "BRK_2018-09-28_29\n",
            "BRK_2018-09-28_30\n",
            "BRK_2018-09-28_31\n",
            "BRK_2018-09-28_32\n",
            "BRK_2018-09-28_33\n",
            "BRK_2018-09-28_34\n",
            "BRK_2018-09-28_35\n",
            "BRK_2018-09-28_36\n",
            "BRK_2018-09-28_37\n",
            "BRK_2018-09-28_38\n",
            "BRK_2018-09-28_39\n",
            "BRK_2018-09-28_40\n",
            "BRK_2018-09-28_41\n",
            "BRK_2018-09-28_42\n",
            "BRK_2018-09-28_43\n",
            "BRK_2018-09-28_44\n",
            "BRK_2018-09-28_45\n",
            "BRK_2018-09-28_46\n",
            "BRK_2018-09-28_47\n",
            "BRK_2018-09-28_48\n",
            "BRK_2018-09-28_49\n",
            "BRK_2018-09-28_50\n",
            "BRK_2018-09-28_51\n",
            "BRK_2018-09-28_52\n",
            "BRK_2018-09-28_53\n",
            "BRK_2018-09-28_54\n",
            "BRK_2018-09-28_55\n",
            "BRK_2018-09-28_56\n",
            "BRK_2018-09-28_57\n",
            "BRK_2018-09-28_58\n",
            "BRK_2018-09-28_59\n",
            "BRK_2018-09-28_60\n",
            "BRK_2018-09-28_61\n",
            "BRK_2018-09-28_62\n",
            "BRK_2018-09-28_63\n",
            "BRK_2018-09-28_64\n",
            "BRK_2018-09-28_65\n",
            "BRK_2018-09-28_66\n",
            "BRK_2018-09-28_67\n",
            "BRK_2018-09-28_68\n",
            "BRK_2018-09-28_69\n",
            "BRK_2018-09-28_70\n",
            "BRK_2018-09-28_71\n",
            "BRK_2018-09-28_72\n",
            "BRK_2018-09-28_73\n",
            "BRK_2018-09-28_74\n",
            "BRK_2018-09-28_75\n",
            "BRK_2018-09-28_76\n",
            "BRK_2018-09-28_77\n",
            "BRK_2018-09-28_78\n",
            "BRK_2018-09-28_79\n",
            "BRK_2018-09-28_80\n",
            "BRK_2018-09-28_81\n",
            "BRK_2018-09-28_82\n",
            "BRK_2018-09-28_83\n",
            "BRK_2018-09-28_84\n",
            "BRK_2018-09-28_85\n",
            "BRK_2018-09-28_86\n",
            "BRK_2018-09-28_87\n",
            "BRK_2018-09-28_88\n",
            "BRK_2018-09-28_89\n",
            "BRK_2018-09-28_90\n",
            "BRK_2018-09-28_91\n",
            "BRK_2018-09-28_92\n",
            "BRK_2018-09-28_93\n",
            "BRK_2018-09-28_94\n",
            "BRK_2018-09-28_95\n",
            "BRK_2018-09-28_96\n",
            "BRK_2018-09-28_97\n",
            "BRK_2018-09-28_98\n",
            "BRK_2018-09-28_99\n",
            "NSC_2018-08-03_2\n",
            "NSC_2018-08-03_3\n",
            "NSC_2018-08-03_4\n",
            "CSX_2018-09-26_2\n",
            "CSX_2018-09-26_3\n",
            "CSX_2018-09-26_4\n",
            "CSX_2018-09-26_5\n",
            "CSX_2018-09-26_6\n",
            "CSX_2018-09-26_7\n",
            "CSX_2018-09-26_8\n",
            "CSX_2018-09-26_9\n",
            "CSX_2018-09-26_10\n",
            "CSX_2018-09-26_11\n",
            "CSX_2018-09-26_12\n",
            "CSX_2018-09-26_13\n",
            "CSX_2018-09-26_14\n",
            "CSX_2018-09-26_15\n",
            "CSX_2018-09-26_16\n",
            "CSX_2018-09-26_17\n",
            "CSX_2018-09-26_18\n",
            "CSX_2018-09-26_19\n",
            "CSX_2018-09-26_20\n",
            "CSX_2018-09-26_21\n",
            "CSX_2018-09-26_22\n",
            "CSX_2018-09-26_23\n",
            "CSX_2018-09-26_24\n",
            "CSX_2018-09-26_25\n",
            "CSX_2018-09-26_26\n",
            "CSX_2018-09-26_27\n",
            "CSX_2018-09-26_28\n",
            "CSX_2018-09-26_29\n",
            "CSX_2018-09-26_30\n",
            "CSX_2018-09-26_31\n",
            "CSX_2018-09-26_32\n",
            "CSX_2018-09-26_33\n",
            "CSX_2018-09-26_34\n",
            "CSX_2018-09-26_35\n",
            "CSX_2018-09-26_36\n",
            "CSX_2018-09-26_37\n",
            "CSX_2018-09-26_38\n",
            "CSX_2018-09-26_39\n",
            "CSX_2018-09-26_40\n",
            "CSX_2018-09-26_41\n",
            "CSX_2018-09-26_42\n",
            "CSX_2018-09-26_43\n",
            "CSX_2018-09-26_44\n",
            "CSX_2018-09-26_45\n",
            "CSX_2018-09-26_46\n",
            "CSX_2018-09-26_47\n",
            "CSX_2018-09-26_48\n",
            "CSX_2018-09-26_49\n",
            "CSX_2018-09-26_50\n",
            "CSX_2018-09-26_51\n",
            "CSX_2018-09-26_52\n",
            "CSX_2018-09-26_53\n",
            "CSX_2018-09-26_54\n",
            "CSX_2018-09-26_55\n",
            "CSX_2018-09-26_56\n",
            "CSX_2018-09-26_57\n",
            "CSX_2018-09-26_58\n",
            "CSX_2018-09-26_59\n",
            "CSX_2018-09-26_60\n",
            "CSX_2018-09-26_61\n",
            "CSX_2018-09-26_62\n",
            "CSX_2018-09-26_63\n",
            "CSX_2018-09-26_64\n",
            "CSX_2018-09-26_65\n",
            "CSX_2018-09-26_66\n",
            "CSX_2018-09-26_67\n",
            "CSX_2018-09-26_68\n",
            "CSX_2018-09-26_69\n",
            "CSX_2018-09-26_70\n",
            "CSX_2018-09-26_71\n",
            "CSX_2018-09-26_72\n",
            "CSX_2018-09-26_73\n",
            "CSX_2018-09-26_74\n",
            "CSX_2018-09-26_75\n",
            "CSX_2018-09-26_76\n",
            "CSX_2018-09-26_77\n",
            "CSX_2018-09-26_78\n",
            "CSX_2018-09-26_79\n",
            "CSX_2018-09-26_80\n",
            "CSX_2018-09-26_81\n",
            "CSX_2018-09-26_82\n",
            "CSX_2018-09-26_83\n",
            "CSX_2018-09-26_84\n",
            "CSX_2018-09-26_85\n",
            "CSX_2018-09-26_86\n",
            "CSX_2018-09-26_87\n",
            "CSX_2018-09-26_88\n",
            "CSX_2018-09-26_89\n",
            "CSX_2018-09-26_90\n",
            "KSU_2018-08-01_2\n",
            "KSU_2018-08-01_3\n",
            "KSU_2018-08-01_4\n",
            "KSU_2018-08-01_5\n",
            "KSU_2018-08-01_6\n",
            "KSU_2018-08-01_7\n",
            "NSC_2018-09-28_2\n",
            "NSC_2018-09-28_3\n",
            "NSC_2018-09-28_4\n",
            "NSC_2018-09-28_5\n",
            "NSC_2018-09-28_6\n",
            "NSC_2018-09-28_7\n",
            "NSC_2018-09-28_8\n",
            "NSC_2018-09-28_9\n",
            "NSC_2018-09-28_10\n",
            "NSC_2018-09-28_11\n",
            "NSC_2018-09-28_12\n",
            "NSC_2018-09-28_13\n",
            "NSC_2018-09-28_14\n",
            "NSC_2018-09-28_15\n",
            "NSC_2018-09-28_16\n",
            "NSC_2018-09-28_17\n",
            "NSC_2018-09-28_18\n",
            "NSC_2018-09-28_19\n",
            "NSC_2018-09-28_20\n",
            "NSC_2018-09-28_21\n",
            "NSC_2018-09-28_22\n",
            "NSC_2018-09-28_23\n",
            "NSC_2018-09-28_24\n",
            "NSC_2018-09-28_25\n",
            "NSC_2018-09-28_26\n",
            "NSC_2018-09-28_27\n",
            "NSC_2018-09-28_28\n",
            "NSC_2018-09-28_29\n",
            "NSC_2018-09-28_30\n",
            "NSC_2018-09-28_31\n",
            "NSC_2018-09-28_32\n",
            "NSC_2018-09-28_33\n",
            "NSC_2018-09-28_34\n",
            "NSC_2018-09-28_35\n",
            "NSC_2018-09-28_36\n",
            "NSC_2018-09-28_37\n",
            "NSC_2018-09-28_38\n",
            "NSC_2018-09-28_39\n",
            "NSC_2018-09-28_40\n",
            "NSC_2018-09-28_41\n",
            "NSC_2018-09-28_42\n",
            "UNP_2018-09-28_2\n",
            "UNP_2018-09-28_3\n",
            "UNP_2018-09-28_4\n",
            "UNP_2018-09-28_5\n",
            "UNP_2018-09-28_6\n",
            "UNP_2018-09-28_7\n",
            "UNP_2018-09-28_8\n",
            "UNP_2018-09-28_9\n",
            "UNP_2018-09-28_10\n",
            "UNP_2018-09-28_11\n",
            "UNP_2018-09-28_12\n",
            "UNP_2018-09-28_13\n",
            "UNP_2018-09-28_14\n",
            "UNP_2018-09-28_15\n",
            "UNP_2018-09-28_16\n",
            "UNP_2018-09-28_17\n",
            "UNP_2018-09-28_18\n",
            "UNP_2018-09-28_19\n",
            "UNP_2018-09-28_20\n",
            "UNP_2018-09-28_21\n",
            "UNP_2018-09-28_22\n",
            "UNP_2018-09-28_23\n",
            "UNP_2018-09-28_24\n",
            "UNP_2018-09-28_25\n",
            "UNP_2018-09-28_26\n",
            "UNP_2018-09-28_27\n",
            "UNP_2018-09-28_28\n",
            "UNP_2018-09-28_29\n",
            "UNP_2018-09-28_30\n",
            "UNP_2018-09-28_31\n",
            "UNP_2018-09-28_32\n",
            "UNP_2018-09-28_33\n",
            "UNP_2018-09-28_34\n",
            "UNP_2018-09-28_35\n",
            "UNP_2018-09-28_36\n",
            "UNP_2018-09-28_37\n",
            "UNP_2018-09-28_38\n",
            "UNP_2018-09-28_39\n",
            "UNP_2018-09-28_40\n",
            "UNP_2018-09-28_41\n",
            "UNP_2018-09-28_42\n",
            "UNP_2018-09-28_43\n",
            "UNP_2018-09-28_44\n",
            "UNP_2018-09-28_45\n",
            "UNP_2018-09-28_46\n",
            "UNP_2018-09-28_47\n",
            "UNP_2018-09-28_48\n",
            "UNP_2018-09-28_49\n",
            "UNP_2018-09-28_50\n",
            "UNP_2018-09-28_51\n",
            "UNP_2018-09-28_52\n",
            "UNP_2018-09-28_53\n",
            "UNP_2018-09-28_54\n",
            "UNP_2018-09-28_55\n",
            "UNP_2018-09-28_56\n",
            "UNP_2018-09-28_57\n",
            "UNP_2018-09-28_58\n",
            "UNP_2018-09-28_59\n",
            "UNP_2018-09-28_60\n",
            "UNP_2018-09-28_61\n",
            "UNP_2018-09-28_62\n",
            "UNP_2018-09-28_63\n",
            "UNP_2018-09-28_64\n",
            "UNP_2018-09-28_65\n",
            "UNP_2018-09-28_66\n",
            "UNP_2018-09-28_67\n",
            "UNP_2018-09-28_68\n",
            "UNP_2018-09-28_69\n",
            "UNP_2018-09-28_70\n",
            "UNP_2018-09-28_71\n",
            "UNP_2018-09-28_72\n",
            "UNP_2018-09-28_73\n",
            "UNP_2018-09-28_74\n",
            "UNP_2018-09-28_75\n",
            "UNP_2018-09-28_76\n",
            "UNP_2018-09-28_77\n",
            "UNP_2018-09-28_78\n",
            "UNP_2018-09-28_79\n",
            "UNP_2018-09-28_80\n",
            "UNP_2018-09-28_81\n",
            "UNP_2018-09-28_82\n",
            "UNP_2018-09-28_83\n",
            "UNP_2018-09-28_84\n",
            "UNP_2018-09-28_85\n",
            "UNP_2018-09-28_86\n",
            "UNP_2018-09-28_87\n",
            "UNP_2018-09-28_88\n",
            "UNP_2018-09-28_89\n",
            "UNP_2018-09-28_90\n",
            "UNP_2018-09-28_91\n",
            "UNP_2018-09-28_92\n",
            "UNP_2018-09-28_93\n",
            "UNP_2018-09-28_94\n",
            "UNP_2018-09-28_95\n",
            "UNP_2018-09-28_96\n",
            "UNP_2018-09-28_97\n",
            "UNP_2018-09-28_98\n",
            "UNP_2018-09-28_99\n",
            "CSX_2018-09-26_91\n",
            "BRK_2018-02-22_2\n",
            "OXY_2018-05-08_2\n",
            "OXY_2018-05-08_3\n",
            "MPC_2018-04-11_2\n",
            "VLO_2018-05-31_2\n",
            "VLO_2018-05-31_3\n",
            "MPC_2018-05-07_2\n",
            "WMB_2018-09-24_2\n",
            "ABBV_2018-10-26_2\n",
            "CVS_2018-10-10_2\n",
            "ABT_2018-03-23_2\n",
            "ABC_2018-10-01_2\n",
            "TGT_2018-12-11_2\n",
            "AMZN_2018-09-07_2\n",
            "AMZN_2018-09-06_2\n",
            "AMZN_2018-09-06_3\n",
            "AMZN_2018-09-06_4\n",
            "AMZN_2018-09-06_5\n",
            "AMZN_2018-09-06_6\n",
            "AMZN_2018-09-06_7\n",
            "AMZN_2018-09-05_2\n",
            "AMZN_2018-09-05_3\n",
            "AMZN_2018-09-05_4\n",
            "AMZN_2018-09-05_5\n",
            "AMZN_2018-09-05_6\n",
            "AMZN_2018-09-05_7\n",
            "AMZN_2018-09-05_8\n",
            "AMZN_2018-09-05_9\n",
            "ABT_2018-08-21_2\n",
            "ABT_2018-08-21_3\n",
            "ABT_2018-08-21_4\n",
            "ABT_2018-08-21_5\n",
            "ABT_2018-08-21_6\n",
            "ABT_2018-08-21_7\n",
            "ABT_2018-08-21_8\n",
            "ABT_2018-08-20_2\n",
            "ABT_2018-08-20_3\n",
            "ABT_2018-08-20_4\n",
            "ABT_2018-08-20_5\n",
            "CMCSA_2018-08-05_2\n",
            "CMCSA_2018-08-05_3\n",
            "CMCSA_2018-08-05_4\n",
            "CMCSA_2018-08-04_2\n",
            "CMCSA_2018-08-04_3\n",
            "CMCSA_2018-08-04_4\n",
            "CMCSA_2018-08-04_5\n",
            "CMCSA_2018-08-04_6\n",
            "CMCSA_2018-08-04_7\n",
            "CMCSA_2018-08-04_8\n",
            "CMCSA_2018-08-04_9\n",
            "CMCSA_2018-08-04_10\n",
            "CMCSA_2018-08-04_11\n",
            "CMCSA_2018-08-04_12\n",
            "CMCSA_2018-08-04_13\n",
            "CMCSA_2018-08-04_14\n",
            "CMCSA_2018-08-03_2\n",
            "CMCSA_2018-08-03_3\n",
            "CMCSA_2018-08-03_4\n",
            "CMCSA_2018-08-03_5\n",
            "CMCSA_2018-08-03_6\n",
            "CMCSA_2018-08-03_7\n",
            "CMCSA_2018-08-03_8\n",
            "CMCSA_2018-08-02_2\n",
            "CMCSA_2018-08-02_3\n",
            "CMCSA_2018-08-02_4\n",
            "MMM_2018-02-28_2\n",
            "MMM_2018-02-28_3\n",
            "MMM_2018-02-28_4\n",
            "MMM_2018-02-28_5\n",
            "MMM_2018-02-28_6\n",
            "MMM_2018-02-28_7\n",
            "MMM_2018-02-28_8\n",
            "MMM_2018-02-28_9\n",
            "MMM_2018-02-28_10\n",
            "MMM_2018-02-28_11\n",
            "MMM_2018-02-28_12\n",
            "MMM_2018-02-28_13\n",
            "MMM_2018-02-28_14\n",
            "MMM_2018-02-27_2\n",
            "MMM_2018-02-27_3\n",
            "MMM_2018-02-27_4\n",
            "MMM_2018-02-27_5\n",
            "MMM_2018-02-27_6\n",
            "MMM_2018-02-27_7\n",
            "MMM_2018-02-26_2\n",
            "MMM_2018-02-26_3\n",
            "MMM_2018-02-26_4\n",
            "MMM_2018-02-26_5\n",
            "MMM_2018-02-26_6\n",
            "MMM_2018-02-26_7\n",
            "MMM_2018-02-25_2\n",
            "MMM_2018-02-25_3\n",
            "MMM_2018-02-25_4\n",
            "MMM_2018-02-25_5\n",
            "CMCSA_2018-04-30_2\n",
            "ANTM_2018-01-29_2\n",
            "CNC_2018-02-16_2\n",
            "UNH_2018-02-16_2\n",
            "UNH_2018-02-16_3\n",
            "CNC_2018-02-22_2\n",
            "ANTM_2018-03-23_2\n",
            "ANTM_2018-07-26_2\n",
            "ANTM_2018-07-26_3\n",
            "UNH_2018-11-30_2\n",
            "UNH_2018-02-06_2\n",
            "AIZ_2018-08-02_2\n",
            "CVS_2018-06-28_2\n",
            "CVS_2018-12-12_2\n",
            "UNH_2018-09-18_2\n",
            "TRV_2018-12-27_2\n",
            "AIG_2018-01-30_2\n",
            "PGR_2018-05-31_2\n",
            "BRK_2018-09-12_2\n",
            "NBL_2017-01-23_2\n",
            "NBL_2017-01-23_3\n",
            "NBL_2017-01-23_4\n",
            "NBL_2017-01-23_5\n",
            "TMO_2017-12-26_2\n",
            "NBL_2017-05-02_2\n",
            "BLL_2017-11-17_2\n",
            "For ticker None we have 1 NaN values     in the window\n",
            "FCX_2017-12-27_2\n",
            "FCX_2017-12-27_3\n",
            "AAL_2017-11-07_2\n",
            "BAC_2017-04-17_2\n",
            "VMC_2017-08-03_2\n",
            "BAC_2017-06-28_2\n",
            "FCX_2017-07-27_2\n",
            "VMC_2017-08-03_3\n",
            "FCX_2017-01-31_2\n",
            "DG_2017-04-11_2\n",
            "FCX_2017-01-31_3\n",
            "EFX_2017-10-11_2\n",
            "FCX_2017-12-27_4\n",
            "NEM_2017-08-29_2\n",
            "CVX_2017-02-01_2\n",
            "PCG_2017-07-25_2\n",
            "JPM_2017-03-08_2\n",
            "OXY_2017-12-05_2\n",
            "LEN_2017-06-15_2\n",
            "LEN_2017-06-15_3\n",
            "LEN_2017-06-15_4\n",
            "MLM_2017-12-28_2\n",
            "MLM_2017-12-21_2\n",
            "MLM_2017-12-22_2\n",
            "MLM_2017-12-27_2\n",
            "MLM_2017-12-27_3\n",
            "MLM_2017-12-11_2\n",
            "PXD_2017-11-28_2\n",
            "PXD_2017-11-28_3\n",
            "PXD_2017-11-28_4\n",
            "PXD_2017-11-28_5\n",
            "GE_2017-11-15_2\n",
            "MPC_2017-02-09_2\n",
            "For ticker None we have 20 NaN values     in the window\n",
            "WMT_2017-06-22_2\n",
            "NOC_2017-04-11_2\n",
            "AME_2017-03-20_2\n",
            "GT_2017-06-14_2\n",
            "ZBH_2017-01-12_2\n",
            "HD_2017-04-13_2\n",
            "HD_2017-05-05_2\n",
            "HD_2017-02-08_2\n",
            "HD_2017-04-27_2\n",
            "HD_2017-06-02_2\n",
            "HD_2017-02-08_3\n",
            "WFC_2017-05-04_2\n",
            "XOM_2017-04-27_2\n",
            "XOM_2017-02-27_2\n",
            "XOM_2017-05-23_2\n",
            "XOM_2017-06-09_2\n",
            "XOM_2017-12-21_2\n",
            "XOM_2017-12-11_2\n",
            "XOM_2017-12-11_3\n",
            "XOM_2017-11-27_2\n",
            "XOM_2017-11-14_2\n",
            "XOM_2017-10-31_2\n",
            "XOM_2017-10-12_2\n",
            "XOM_2017-10-18_2\n",
            "XOM_2017-11-16_2\n",
            "XOM_2017-11-27_3\n",
            "XOM_2017-11-30_2\n",
            "XOM_2017-12-05_2\n",
            "XOM_2017-12-11_4\n",
            "XOM_2017-01-04_2\n",
            "XOM_2017-01-04_3\n",
            "XOM_2017-01-10_2\n",
            "XOM_2017-01-11_2\n",
            "XOM_2017-01-11_3\n",
            "XOM_2017-01-12_2\n",
            "XOM_2017-01-17_2\n",
            "XOM_2017-01-18_2\n",
            "XOM_2017-01-18_3\n",
            "XOM_2017-01-19_2\n",
            "IP_2017-01-23_2\n",
            "VZ_2017-01-25_2\n",
            "VZ_2017-01-25_3\n",
            "VZ_2017-01-25_4\n",
            "VZ_2017-01-25_5\n",
            "VZ_2017-01-25_6\n",
            "VZ_2017-01-27_2\n",
            "VZ_2017-01-27_3\n",
            "VZ_2017-01-27_4\n",
            "VZ_2017-01-30_2\n",
            "VZ_2017-01-30_3\n",
            "VZ_2017-01-30_4\n",
            "VZ_2017-01-31_2\n",
            "VZ_2017-01-31_3\n",
            "VZ_2017-02-03_2\n",
            "VZ_2017-02-06_2\n",
            "VZ_2017-02-06_3\n",
            "VZ_2017-02-06_4\n",
            "VZ_2017-02-07_2\n",
            "VZ_2017-02-07_3\n",
            "VZ_2017-02-07_4\n",
            "VZ_2017-02-07_5\n",
            "VZ_2017-02-07_6\n",
            "VZ_2017-02-08_2\n",
            "VZ_2017-02-09_2\n",
            "VZ_2017-02-10_2\n",
            "VZ_2017-02-10_3\n",
            "MPC_2017-02-27_2\n",
            "MPC_2017-02-27_3\n",
            "DD_2017-05-12_2\n",
            "DD_2017-05-12_3\n",
            "ADM_2017-06-01_2\n",
            "BRK_2017-06-30_2\n",
            "UPS_2017-08-28_2\n",
            "NSC_2017-09-14_2\n",
            "NSC_2017-09-14_3\n",
            "NSC_2017-09-14_4\n",
            "NSC_2017-09-14_5\n",
            "NSC_2017-09-14_6\n",
            "NSC_2017-09-14_7\n",
            "NSC_2017-09-14_8\n",
            "NSC_2017-09-14_9\n",
            "NSC_2017-09-14_10\n",
            "KSU_2017-09-18_2\n",
            "KSU_2017-09-18_3\n",
            "KSU_2017-09-18_4\n",
            "KSU_2017-09-18_5\n",
            "KSU_2017-09-18_6\n",
            "KSU_2017-09-18_7\n",
            "KSU_2017-09-18_8\n",
            "KSU_2017-09-18_9\n",
            "KSU_2017-09-18_10\n",
            "KSU_2017-09-18_11\n",
            "MPC_2017-09-26_2\n",
            "BRK_2017-09-28_2\n",
            "BRK_2017-09-28_3\n",
            "BRK_2017-09-28_4\n",
            "BRK_2017-09-28_5\n",
            "BRK_2017-09-28_6\n",
            "BRK_2017-09-28_7\n",
            "BRK_2017-09-28_8\n",
            "BRK_2017-09-28_9\n",
            "BRK_2017-09-28_10\n",
            "BRK_2017-09-28_11\n",
            "BRK_2017-09-28_12\n",
            "BRK_2017-09-28_13\n",
            "BRK_2017-09-28_14\n",
            "BRK_2017-09-28_15\n",
            "BRK_2017-09-28_16\n",
            "BRK_2017-09-28_17\n",
            "BRK_2017-09-28_18\n",
            "BRK_2017-09-28_19\n",
            "BRK_2017-09-28_20\n",
            "BRK_2017-09-28_21\n",
            "BRK_2017-09-28_22\n",
            "BRK_2017-09-28_23\n",
            "BRK_2017-09-28_24\n",
            "BRK_2017-09-28_25\n",
            "BRK_2017-09-28_26\n",
            "BRK_2017-09-28_27\n",
            "BRK_2017-09-28_28\n",
            "BRK_2017-09-28_29\n",
            "BRK_2017-09-28_30\n",
            "BRK_2017-09-28_31\n",
            "BRK_2017-09-28_32\n",
            "BRK_2017-09-28_33\n",
            "BRK_2017-09-28_34\n",
            "BRK_2017-09-28_35\n",
            "BRK_2017-09-28_36\n",
            "BRK_2017-09-28_37\n",
            "BRK_2017-09-28_38\n",
            "BRK_2017-09-28_39\n",
            "BRK_2017-09-28_40\n",
            "BRK_2017-09-28_41\n",
            "BRK_2017-09-28_42\n",
            "BRK_2017-09-28_43\n",
            "BRK_2017-09-28_44\n",
            "BRK_2017-09-28_45\n",
            "BRK_2017-09-28_46\n",
            "BRK_2017-09-28_47\n",
            "BRK_2017-09-28_48\n",
            "BRK_2017-09-28_49\n",
            "BRK_2017-09-28_50\n",
            "BRK_2017-09-28_51\n",
            "BRK_2017-09-28_52\n",
            "BRK_2017-09-28_53\n",
            "BRK_2017-09-28_54\n",
            "BRK_2017-09-28_55\n",
            "BRK_2017-09-28_56\n",
            "BRK_2017-09-28_57\n",
            "BRK_2017-09-28_58\n",
            "BRK_2017-09-28_59\n",
            "BRK_2017-09-28_60\n",
            "BRK_2017-09-28_61\n",
            "BRK_2017-09-28_62\n",
            "BRK_2017-09-28_63\n",
            "BRK_2017-09-28_64\n",
            "BRK_2017-09-28_65\n",
            "BRK_2017-09-28_66\n",
            "BRK_2017-09-28_67\n",
            "BRK_2017-09-28_68\n",
            "BRK_2017-09-28_69\n",
            "BRK_2017-09-28_70\n",
            "BRK_2017-09-28_71\n",
            "BRK_2017-09-28_72\n",
            "BRK_2017-09-28_73\n",
            "BRK_2017-09-28_74\n",
            "BRK_2017-09-28_75\n",
            "BRK_2017-09-28_76\n",
            "BRK_2017-09-28_77\n",
            "BRK_2017-09-28_78\n",
            "BRK_2017-09-28_79\n",
            "BRK_2017-09-28_80\n",
            "BRK_2017-09-28_81\n",
            "BRK_2017-09-28_82\n",
            "BRK_2017-09-28_83\n",
            "BRK_2017-09-28_84\n",
            "BRK_2017-09-28_85\n",
            "BRK_2017-09-28_86\n",
            "BRK_2017-09-28_87\n",
            "BRK_2017-09-28_88\n",
            "BRK_2017-09-28_89\n",
            "BRK_2017-09-28_90\n",
            "BRK_2017-09-28_91\n",
            "BRK_2017-09-28_92\n",
            "BRK_2017-09-28_93\n",
            "BRK_2017-09-28_94\n",
            "BRK_2017-09-28_95\n",
            "BRK_2017-09-28_96\n",
            "BRK_2017-09-28_97\n",
            "BRK_2017-09-28_98\n",
            "BRK_2017-09-28_99\n",
            "CSX_2017-09-28_2\n",
            "CSX_2017-09-28_3\n",
            "CSX_2017-09-28_4\n",
            "CSX_2017-09-28_5\n",
            "CSX_2017-09-28_6\n",
            "CSX_2017-09-28_7\n",
            "CSX_2017-09-28_8\n",
            "CSX_2017-09-28_9\n",
            "CSX_2017-09-28_10\n",
            "CSX_2017-09-28_11\n",
            "CSX_2017-09-28_12\n",
            "CSX_2017-09-28_13\n",
            "CSX_2017-09-28_14\n",
            "CSX_2017-09-28_15\n",
            "CSX_2017-09-28_16\n",
            "CSX_2017-09-28_17\n",
            "CSX_2017-09-28_18\n",
            "CSX_2017-09-28_19\n",
            "CSX_2017-09-28_20\n",
            "CSX_2017-09-28_21\n",
            "CSX_2017-09-28_22\n",
            "CSX_2017-09-28_23\n",
            "CSX_2017-09-28_24\n",
            "CSX_2017-09-28_25\n",
            "CSX_2017-09-28_26\n",
            "CSX_2017-09-28_27\n",
            "CSX_2017-09-28_28\n",
            "CSX_2017-09-28_29\n",
            "CSX_2017-09-28_30\n",
            "CSX_2017-09-28_31\n",
            "CSX_2017-09-28_32\n",
            "CSX_2017-09-28_33\n",
            "CSX_2017-09-28_34\n",
            "CSX_2017-09-28_35\n",
            "CSX_2017-09-28_36\n",
            "CSX_2017-09-28_37\n",
            "CSX_2017-09-28_38\n",
            "CSX_2017-09-28_39\n",
            "CSX_2017-09-28_40\n",
            "CSX_2017-09-28_41\n",
            "CSX_2017-09-28_42\n",
            "CSX_2017-09-28_43\n",
            "CSX_2017-09-28_44\n",
            "CSX_2017-09-28_45\n",
            "CSX_2017-09-28_46\n",
            "CSX_2017-09-28_47\n",
            "CSX_2017-09-28_48\n",
            "CSX_2017-09-28_49\n",
            "CSX_2017-09-28_50\n",
            "CSX_2017-09-28_51\n",
            "CSX_2017-09-28_52\n",
            "CSX_2017-09-28_53\n",
            "CSX_2017-09-28_54\n",
            "CSX_2017-09-28_55\n",
            "CSX_2017-09-28_56\n",
            "CSX_2017-09-28_57\n",
            "CSX_2017-09-28_58\n",
            "CSX_2017-09-28_59\n",
            "CSX_2017-09-28_60\n",
            "CSX_2017-09-28_61\n",
            "CSX_2017-09-28_62\n",
            "CSX_2017-09-28_63\n",
            "CSX_2017-09-28_64\n",
            "CSX_2017-09-28_65\n",
            "CSX_2017-09-28_66\n",
            "CSX_2017-09-28_67\n",
            "CSX_2017-09-28_68\n",
            "CSX_2017-09-28_69\n",
            "CSX_2017-09-28_70\n",
            "CSX_2017-09-28_71\n",
            "CSX_2017-09-28_72\n",
            "CSX_2017-09-28_73\n",
            "CSX_2017-09-28_74\n",
            "CSX_2017-09-28_75\n",
            "CSX_2017-09-28_76\n",
            "CSX_2017-09-28_77\n",
            "CSX_2017-09-28_78\n",
            "CSX_2017-09-28_79\n",
            "CSX_2017-09-28_80\n",
            "CSX_2017-09-28_81\n",
            "CSX_2017-09-28_82\n",
            "CSX_2017-09-28_83\n",
            "CSX_2017-09-28_84\n",
            "CSX_2017-09-28_85\n",
            "NSC_2017-09-29_2\n",
            "NSC_2017-09-29_3\n",
            "NSC_2017-09-29_4\n",
            "NSC_2017-09-29_5\n",
            "NSC_2017-09-29_6\n",
            "NSC_2017-09-29_7\n",
            "NSC_2017-09-29_8\n",
            "NSC_2017-09-29_9\n",
            "NSC_2017-09-29_10\n",
            "NSC_2017-09-29_11\n",
            "NSC_2017-09-29_12\n",
            "NSC_2017-09-29_13\n",
            "NSC_2017-09-29_14\n",
            "NSC_2017-09-29_15\n",
            "NSC_2017-09-29_16\n",
            "NSC_2017-09-29_17\n",
            "NSC_2017-09-29_18\n",
            "NSC_2017-09-29_19\n",
            "NSC_2017-09-29_20\n",
            "NSC_2017-09-29_21\n",
            "NSC_2017-09-29_22\n",
            "NSC_2017-09-29_23\n",
            "NSC_2017-09-29_24\n",
            "NSC_2017-09-29_25\n",
            "NSC_2017-09-29_26\n",
            "NSC_2017-09-29_27\n",
            "NSC_2017-09-29_28\n",
            "NSC_2017-09-29_29\n",
            "NSC_2017-09-29_30\n",
            "NSC_2017-09-29_31\n",
            "NSC_2017-09-29_32\n",
            "NSC_2017-09-29_33\n",
            "NSC_2017-09-29_34\n",
            "NSC_2017-09-29_35\n",
            "NSC_2017-09-29_36\n",
            "NSC_2017-09-29_37\n",
            "NSC_2017-09-29_38\n",
            "NSC_2017-09-29_39\n",
            "NSC_2017-09-29_40\n",
            "UNP_2017-09-29_2\n",
            "UNP_2017-09-29_3\n",
            "UNP_2017-09-29_4\n",
            "UNP_2017-09-29_5\n",
            "UNP_2017-09-29_6\n",
            "UNP_2017-09-29_7\n",
            "UNP_2017-09-29_8\n",
            "UNP_2017-09-29_9\n",
            "UNP_2017-09-29_10\n",
            "UNP_2017-09-29_11\n",
            "UNP_2017-09-29_12\n",
            "UNP_2017-09-29_13\n",
            "UNP_2017-09-29_14\n",
            "UNP_2017-09-29_15\n",
            "UNP_2017-09-29_16\n",
            "UNP_2017-09-29_17\n",
            "UNP_2017-09-29_18\n",
            "UNP_2017-09-29_19\n",
            "UNP_2017-09-29_20\n",
            "UNP_2017-09-29_21\n",
            "UNP_2017-09-29_22\n",
            "UNP_2017-09-29_23\n",
            "UNP_2017-09-29_24\n",
            "UNP_2017-09-29_25\n",
            "UNP_2017-09-29_26\n",
            "UNP_2017-09-29_27\n",
            "UNP_2017-09-29_28\n",
            "UNP_2017-09-29_29\n",
            "UNP_2017-09-29_30\n",
            "UNP_2017-09-29_31\n",
            "UNP_2017-09-29_32\n",
            "UNP_2017-09-29_33\n",
            "UNP_2017-09-29_34\n",
            "UNP_2017-09-29_35\n",
            "UNP_2017-09-29_36\n",
            "UNP_2017-09-29_37\n",
            "UNP_2017-09-29_38\n",
            "UNP_2017-09-29_39\n",
            "UNP_2017-09-29_40\n",
            "UNP_2017-09-29_41\n",
            "UNP_2017-09-29_42\n",
            "UNP_2017-09-29_43\n",
            "UNP_2017-09-29_44\n",
            "UNP_2017-09-29_45\n",
            "UNP_2017-09-29_46\n",
            "UNP_2017-09-29_47\n",
            "UNP_2017-09-29_48\n",
            "UNP_2017-09-29_49\n",
            "UNP_2017-09-29_50\n",
            "UNP_2017-09-29_51\n",
            "UNP_2017-09-29_52\n",
            "UNP_2017-09-29_53\n",
            "UNP_2017-09-29_54\n",
            "UNP_2017-09-29_55\n",
            "UNP_2017-09-29_56\n",
            "UNP_2017-09-29_57\n",
            "UNP_2017-09-29_58\n",
            "UNP_2017-09-29_59\n",
            "UNP_2017-09-29_60\n",
            "UNP_2017-09-29_61\n",
            "UNP_2017-09-29_62\n",
            "UNP_2017-09-29_63\n",
            "UNP_2017-09-29_64\n",
            "UNP_2017-09-29_65\n",
            "UNP_2017-09-29_66\n",
            "UNP_2017-09-29_67\n",
            "UNP_2017-09-29_68\n",
            "UNP_2017-09-29_69\n",
            "UNP_2017-09-29_70\n",
            "UNP_2017-09-29_71\n",
            "UNP_2017-09-29_72\n",
            "UNP_2017-09-29_73\n",
            "UNP_2017-09-29_74\n",
            "UNP_2017-09-29_75\n",
            "UNP_2017-09-29_76\n",
            "UNP_2017-09-29_77\n",
            "UNP_2017-09-29_78\n",
            "UNP_2017-09-29_79\n",
            "UNP_2017-09-29_80\n",
            "UNP_2017-09-29_81\n",
            "UNP_2017-09-29_82\n",
            "UNP_2017-09-29_83\n",
            "UNP_2017-09-29_84\n",
            "UNP_2017-09-29_85\n",
            "UNP_2017-09-29_86\n",
            "UNP_2017-09-29_87\n",
            "UNP_2017-09-29_88\n",
            "UNP_2017-09-29_89\n",
            "UNP_2017-09-29_90\n",
            "UNP_2017-09-29_91\n",
            "UNP_2017-09-29_92\n",
            "UNP_2017-09-29_93\n",
            "UNP_2017-09-29_94\n",
            "UNP_2017-09-29_95\n",
            "UNP_2017-09-29_96\n",
            "UNP_2017-09-29_97\n",
            "UNP_2017-09-29_98\n",
            "UNP_2017-09-29_99\n",
            "WHR_2017-09-25_2\n",
            "BAC_2017-12-04_2\n",
            "WFC_2017-02-14_2\n",
            "KMI_2017-06-12_2\n",
            "KMI_2017-07-26_2\n",
            "KMI_2017-08-02_2\n",
            "KMI_2017-08-02_3\n",
            "KMI_2017-08-02_4\n",
            "KMI_2017-08-02_5\n",
            "KMI_2017-07-27_2\n",
            "KMI_2017-04-17_2\n",
            "KMI_2017-04-13_2\n",
            "KMI_2017-07-20_2\n",
            "KMI_2017-05-18_2\n",
            "KMI_2017-06-02_2\n",
            "KMI_2017-03-02_2\n",
            "KMI_2017-03-10_2\n",
            "KMI_2017-09-25_2\n",
            "KMI_2017-08-02_6\n",
            "KMI_2017-08-02_7\n",
            "KMI_2017-04-17_3\n",
            "KMI_2017-08-02_8\n",
            "KMI_2017-07-26_3\n",
            "KMI_2017-05-24_2\n",
            "KMI_2017-01-10_2\n",
            "KMI_2017-08-03_2\n",
            "KMI_2017-06-01_2\n",
            "KMI_2017-04-06_2\n",
            "KMI_2017-05-09_2\n",
            "WFC_2017-05-04_3\n",
            "BAC_2017-01-18_2\n",
            "BAC_2017-06-01_2\n",
            "BAC_2017-03-16_2\n",
            "BAC_2017-04-17_3\n",
            "BAC_2017-04-20_2\n",
            "BAC_2017-05-04_2\n",
            "BAC_2017-11-30_2\n",
            "BAC_2017-02-08_2\n",
            "BAC_2017-07-19_2\n",
            "BAC_2017-09-25_2\n",
            "BAC_2017-06-29_2\n",
            "WFC_2017-06-22_2\n",
            "WFC_2017-01-12_2\n",
            "WFC_2017-07-13_2\n",
            "WFC_2017-06-13_2\n",
            "WFC_2017-04-26_2\n",
            "WFC_2017-03-02_2\n",
            "WFC_2017-01-03_2\n",
            "WFC_2017-05-04_4\n",
            "WFC_2017-02-08_2\n",
            "WFC_2017-05-04_5\n",
            "PGR_2017-10-14_2\n",
            "BAC_2017-07-19_3\n",
            "BAC_2017-01-04_2\n",
            "BAC_2017-05-02_2\n",
            "BAC_2017-12-13_2\n",
            "BAC_2017-11-02_2\n",
            "BAC_2017-03-09_2\n",
            "PSX_2017-01-11_2\n",
            "PSX_2017-09-15_2\n",
            "PSX_2017-01-25_2\n",
            "PSX_2017-09-30_2\n",
            "PSX_2017-08-10_2\n",
            "PSX_2017-11-28_2\n",
            "PSX_2017-06-05_2\n",
            "PSX_2017-10-27_2\n",
            "PSX_2017-03-03_2\n",
            "PSX_2017-04-13_2\n",
            "PSX_2017-11-07_2\n",
            "PSX_2017-06-13_2\n",
            "PSX_2017-10-27_3\n",
            "PSX_2017-04-26_2\n",
            "JPM_2017-12-29_2\n",
            "JPM_2017-06-06_2\n",
            "JPM_2017-06-05_2\n",
            "JPM_2017-02-07_2\n",
            "DLTR_2017-10-30_2\n",
            "DLTR_2017-10-25_2\n",
            "DLTR_2017-10-23_2\n",
            "DLTR_2017-12-07_2\n",
            "DLTR_2017-07-21_2\n",
            "C_2017-10-13_2\n",
            "WFC_2017-03-10_2\n",
            "WFC_2017-10-26_2\n",
            "WFC_2017-04-18_2\n",
            "WFC_2017-05-22_2\n",
            "WFC_2017-10-18_2\n",
            "JPM_2017-01-10_2\n",
            "JPM_2017-12-21_2\n",
            "JPM_2017-01-10_3\n",
            "USB_2017-05-04_2\n",
            "WFC_2017-02-02_2\n",
            "WFC_2017-04-20_2\n",
            "WFC_2017-10-18_3\n",
            "WFC_2017-04-25_2\n",
            "WFC_2017-06-28_2\n",
            "WFC_2017-01-27_2\n",
            "WFC_2017-06-09_2\n",
            "WFC_2017-06-22_3\n",
            "WFC_2017-02-14_3\n",
            "WFC_2017-10-19_2\n",
            "WFC_2017-01-20_2\n",
            "WFC_2017-04-03_2\n",
            "WFC_2017-01-26_2\n",
            "BAC_2017-09-14_2\n",
            "BAC_2017-05-02_3\n",
            "BAC_2017-05-04_3\n",
            "BAC_2017-12-19_2\n",
            "BAC_2017-12-04_3\n",
            "BAC_2017-07-06_2\n",
            "BAC_2017-02-08_3\n",
            "BAC_2017-04-13_2\n",
            "BAC_2017-02-10_2\n",
            "BAC_2017-06-05_2\n",
            "BAC_2017-12-12_2\n",
            "BAC_2017-02-06_2\n",
            "BAC_2017-03-16_3\n",
            "BAC_2017-05-04_4\n",
            "BAC_2017-03-30_2\n",
            "BAC_2017-04-17_4\n",
            "BAC_2017-03-09_3\n",
            "BAC_2017-12-07_2\n",
            "IP_2017-09-26_2\n",
            "IP_2017-10-25_2\n",
            "IP_2017-01-27_2\n",
            "IP_2017-12-15_2\n",
            "BAC_2017-04-17_5\n",
            "BAC_2017-07-28_2\n",
            "BAC_2017-09-14_3\n",
            "BAC_2017-06-13_2\n",
            "BAC_2017-07-12_2\n",
            "BAC_2017-04-28_2\n",
            "BAC_2017-02-09_2\n",
            "BAC_2017-09-14_4\n",
            "BAC_2017-10-10_2\n",
            "BAC_2017-06-05_3\n",
            "BAC_2017-12-26_2\n",
            "ULTA_2017-06-20_2\n",
            "C_2017-11-20_2\n",
            "C_2017-10-23_2\n",
            "GE_2017-01-06_2\n",
            "GE_2017-05-25_2\n",
            "GE_2017-12-21_2\n",
            "GE_2017-11-15_3\n",
            "GE_2017-05-04_2\n",
            "GE_2017-05-02_2\n",
            "GE_2017-03-09_2\n",
            "GE_2017-03-29_2\n",
            "GE_2017-10-30_2\n",
            "GE_2017-10-30_3\n",
            "GE_2017-10-30_4\n",
            "IP_2017-10-27_2\n",
            "BRK_2017-12-15_2\n",
            "BMY_2017-07-24_2\n",
            "DVA_2017-12-14_2\n",
            "UPS_2017-03-29_2\n",
            "UPS_2017-08-30_2\n",
            "UPS_2017-09-27_2\n",
            "TGT_2017-05-23_2\n",
            "WBA_2017-01-19_2\n",
            "WBA_2017-04-20_2\n",
            "WBA_2017-01-19_3\n",
            "BAC_2017-06-13_3\n",
            "T_2017-03-14_2\n",
            "For ticker None we have 104 NaN values     in the window\n",
            "AAP_2017-04-03_2\n",
            "AAP_2017-04-03_3\n",
            "AAP_2017-04-03_4\n",
            "AAP_2017-04-03_5\n",
            "AAP_2017-04-03_6\n",
            "AAP_2017-04-03_7\n",
            "AAP_2017-04-02_2\n",
            "AAP_2017-04-02_3\n",
            "AAP_2017-04-02_4\n",
            "AAP_2017-04-02_5\n",
            "ABC_2017-04-01_2\n",
            "ABC_2017-04-01_3\n",
            "ABC_2017-04-01_4\n",
            "ABC_2017-04-01_5\n",
            "ABC_2017-04-01_6\n",
            "ABC_2017-04-01_7\n",
            "ABC_2017-04-01_8\n",
            "ABC_2017-04-01_9\n",
            "CAH_2017-03-31_2\n",
            "CAH_2017-03-31_3\n",
            "CAH_2017-03-31_4\n",
            "CAH_2017-03-31_5\n",
            "CAH_2017-03-31_6\n",
            "CAH_2017-03-31_7\n",
            "CHTR_2017-03-31_2\n",
            "CHTR_2017-03-31_3\n",
            "CHTR_2017-03-31_4\n",
            "CHTR_2017-03-30_2\n",
            "CHTR_2017-03-30_3\n",
            "CHTR_2017-03-30_4\n",
            "CHTR_2017-03-30_5\n",
            "CHTR_2017-03-30_6\n",
            "CHTR_2017-03-30_7\n",
            "CI_2017-03-30_2\n",
            "CI_2017-03-30_3\n",
            "CI_2017-03-29_2\n",
            "CI_2017-03-29_3\n",
            "CI_2017-03-29_4\n",
            "CI_2017-03-29_5\n",
            "CI_2017-03-29_6\n",
            "CI_2017-03-29_7\n",
            "CI_2017-03-28_2\n",
            "CI_2017-03-28_3\n",
            "CI_2017-03-28_4\n",
            "CI_2017-03-28_5\n",
            "CI_2017-03-28_6\n",
            "CI_2017-03-28_7\n",
            "CI_2017-03-28_8\n",
            "CI_2017-03-28_9\n",
            "CI_2017-03-28_10\n",
            "CI_2017-03-28_11\n",
            "CI_2017-03-28_12\n",
            "CI_2017-03-28_13\n",
            "CI_2017-03-28_14\n",
            "CI_2017-03-27_2\n",
            "CI_2017-03-27_3\n",
            "CI_2017-03-27_4\n",
            "CI_2017-03-27_5\n",
            "CI_2017-03-27_6\n",
            "CI_2017-03-27_7\n",
            "CI_2017-03-27_8\n",
            "CI_2017-03-27_9\n",
            "CI_2017-03-27_10\n",
            "CI_2017-03-26_2\n",
            "CI_2017-03-26_3\n",
            "CI_2017-03-26_4\n",
            "CI_2017-03-26_5\n",
            "CI_2017-03-26_6\n",
            "CI_2017-03-25_2\n",
            "CI_2017-03-25_3\n",
            "CI_2017-03-25_4\n",
            "CI_2017-03-25_5\n",
            "CI_2017-03-25_6\n",
            "CI_2017-03-25_7\n",
            "CI_2017-03-25_8\n",
            "CI_2017-03-25_9\n",
            "CI_2017-03-25_10\n",
            "CI_2017-03-25_11\n",
            "CI_2017-03-25_12\n",
            "CI_2017-03-25_13\n",
            "COP_2017-03-24_2\n",
            "COP_2017-03-24_3\n",
            "COP_2017-03-24_4\n",
            "COP_2017-03-24_5\n",
            "COP_2017-03-24_6\n",
            "COP_2017-03-24_7\n",
            "COP_2017-03-24_8\n",
            "COP_2017-03-23_2\n",
            "COP_2017-03-23_3\n",
            "COP_2017-03-23_4\n",
            "COP_2017-03-23_5\n",
            "COP_2017-03-23_6\n",
            "COP_2017-03-23_7\n",
            "COP_2017-03-23_8\n",
            "COP_2017-03-23_9\n",
            "COP_2017-03-23_10\n",
            "COP_2017-03-23_11\n",
            "COP_2017-03-23_12\n",
            "COP_2017-03-23_13\n",
            "COP_2017-03-23_14\n",
            "COP_2017-03-23_15\n",
            "COP_2017-03-23_16\n",
            "COP_2017-03-23_17\n",
            "COP_2017-03-23_18\n",
            "COP_2017-03-22_2\n",
            "COP_2017-03-22_3\n",
            "DLTR_2017-03-17_2\n",
            "JNJ_2017-03-15_2\n",
            "JNJ_2017-03-15_3\n",
            "JNJ_2017-03-15_4\n",
            "JNJ_2017-03-14_2\n",
            "JNJ_2017-03-14_3\n",
            "JNJ_2017-03-14_4\n",
            "JNJ_2017-03-14_5\n",
            "JNJ_2017-03-14_6\n",
            "JNJ_2017-03-14_7\n",
            "JNJ_2017-03-14_8\n",
            "JNJ_2017-03-14_9\n",
            "JNJ_2017-03-14_10\n",
            "JNJ_2017-03-14_11\n",
            "JNJ_2017-03-13_2\n",
            "JNJ_2017-03-13_3\n",
            "JNJ_2017-03-13_4\n",
            "JNJ_2017-03-13_5\n",
            "JNJ_2017-03-13_6\n",
            "JNJ_2017-03-13_7\n",
            "JNJ_2017-03-13_8\n",
            "JNJ_2017-03-13_9\n",
            "SPG_2017-03-05_2\n",
            "SPG_2017-03-05_3\n",
            "GIS_2017-02-26_2\n",
            "GM_2017-02-24_2\n",
            "GM_2017-02-24_3\n",
            "GM_2017-02-24_4\n",
            "GM_2017-02-24_5\n",
            "GM_2017-02-24_6\n",
            "GM_2017-02-24_7\n",
            "GM_2017-02-24_8\n",
            "GM_2017-02-24_9\n",
            "GM_2017-02-23_2\n",
            "GM_2017-02-23_3\n",
            "GM_2017-02-23_4\n",
            "GM_2017-02-23_5\n",
            "GM_2017-02-23_6\n",
            "GM_2017-02-23_7\n",
            "GM_2017-02-23_8\n",
            "GM_2017-02-22_2\n",
            "GM_2017-02-22_3\n",
            "GM_2017-02-22_4\n",
            "GM_2017-02-22_5\n",
            "GM_2017-02-22_6\n",
            "GM_2017-02-22_7\n",
            "GM_2017-02-22_8\n",
            "GM_2017-02-21_2\n",
            "GM_2017-02-21_3\n",
            "AMZN_2017-02-19_2\n",
            "AMZN_2017-02-19_3\n",
            "AMZN_2017-02-19_4\n",
            "AMZN_2017-02-19_5\n",
            "AMZN_2017-02-19_6\n",
            "AMZN_2017-02-18_2\n",
            "AMZN_2017-02-18_3\n",
            "AMZN_2017-02-18_4\n",
            "AMZN_2017-02-18_5\n",
            "AMZN_2017-02-18_6\n",
            "AMZN_2017-02-18_7\n",
            "AMZN_2017-02-18_8\n",
            "AMZN_2017-02-18_9\n",
            "AMZN_2017-02-18_10\n",
            "AMZN_2017-02-18_11\n",
            "AMZN_2017-02-18_12\n",
            "UAL_2017-05-30_2\n",
            "UNH_2017-02-23_2\n",
            "ANTM_2017-07-20_2\n",
            "ANTM_2017-07-20_3\n",
            "BRK_2017-12-19_2\n",
            "AIG_2017-02-24_2\n",
            "UNH_2017-04-27_2\n",
            "UNH_2017-03-01_2\n",
            "UNH_2017-03-01_3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>FCX</th>\n",
              "      <th>FCX_2020-01-08</th>\n",
              "      <th>FCX_2020-01-09</th>\n",
              "      <th>FCX_2020-01-09_2</th>\n",
              "      <th>FCX_2020-01-09_3</th>\n",
              "      <th>MOS</th>\n",
              "      <th>FCX_2020-01-12</th>\n",
              "      <th>FCX_2020-01-12_2</th>\n",
              "      <th>FCX_2020-01-08_2</th>\n",
              "      <th>FCX_2020-01-09_4</th>\n",
              "      <th>FCX_2020-01-09_5</th>\n",
              "      <th>FCX_2020-01-27</th>\n",
              "      <th>DHI</th>\n",
              "      <th>DLTR</th>\n",
              "      <th>ITW</th>\n",
              "      <th>CPB</th>\n",
              "      <th>BRK</th>\n",
              "      <th>PHM</th>\n",
              "      <th>DG</th>\n",
              "      <th>UPS</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>CAT</th>\n",
              "      <th>MAR</th>\n",
              "      <th>WRK</th>\n",
              "      <th>DOV</th>\n",
              "      <th>AZO</th>\n",
              "      <th>DLTR_2020-02-24</th>\n",
              "      <th>CVS</th>\n",
              "      <th>TMO</th>\n",
              "      <th>SYY</th>\n",
              "      <th>DE</th>\n",
              "      <th>ORLY</th>\n",
              "      <th>KMB</th>\n",
              "      <th>FDX</th>\n",
              "      <th>SYY_2020-02-07</th>\n",
              "      <th>ALK</th>\n",
              "      <th>HRL</th>\n",
              "      <th>BLL</th>\n",
              "      <th>DLTR_2020-02-03</th>\n",
              "      <th>...</th>\n",
              "      <th>CI_2017-08-24</th>\n",
              "      <th>TRV_2017-07-18</th>\n",
              "      <th>ALL_2017-04-27</th>\n",
              "      <th>CI_2017-08-03</th>\n",
              "      <th>CVS_2017-01-05</th>\n",
              "      <th>HIG_2017-01-10</th>\n",
              "      <th>AIG_2017-01-17</th>\n",
              "      <th>HUM_2017-01-23</th>\n",
              "      <th>AIG_2017-02-13</th>\n",
              "      <th>CI_2017-04-06</th>\n",
              "      <th>CNC_2017-04-24</th>\n",
              "      <th>AIG_2017-05-03</th>\n",
              "      <th>AIZ_2017-05-05</th>\n",
              "      <th>CVS_2017-05-09</th>\n",
              "      <th>TRV_2017-05-12</th>\n",
              "      <th>L_2017-05-15</th>\n",
              "      <th>AIG_2017-05-19</th>\n",
              "      <th>L_2017-06-27</th>\n",
              "      <th>HIG_2017-07-06</th>\n",
              "      <th>HUM_2017-08-21</th>\n",
              "      <th>TRV_2017-08-22</th>\n",
              "      <th>UNH_2017-08-31</th>\n",
              "      <th>MMC_2017-10-02</th>\n",
              "      <th>CNC_2017-12-20</th>\n",
              "      <th>SHW_2017-12-28</th>\n",
              "      <th>UNH_2017-03-01_2</th>\n",
              "      <th>UNH_2017-03-01_3</th>\n",
              "      <th>PGR_2017-08-01</th>\n",
              "      <th>CVS_2017-08-18</th>\n",
              "      <th>PGR_2017-10-02</th>\n",
              "      <th>HIG_2017-11-01</th>\n",
              "      <th>CVS_2017-11-29</th>\n",
              "      <th>BRK_2017-04-12</th>\n",
              "      <th>WMT_2017-05-26</th>\n",
              "      <th>UNH_2017-06-29</th>\n",
              "      <th>CI_2017-07-31</th>\n",
              "      <th>HIG_2017-08-30</th>\n",
              "      <th>AIG_2017-09-22</th>\n",
              "      <th>HIG_2017-11-20</th>\n",
              "      <th>AFL_2017-11-29</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 5987 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  FCX  ... HIG_2017-11-20 AFL_2017-11-29\n",
              "0    2016-01-04  NaN  ...            NaN            NaN\n",
              "1    2016-01-05  NaN  ...            NaN            NaN\n",
              "2    2016-01-06  NaN  ...            NaN            NaN\n",
              "3    2016-01-07  NaN  ...            NaN            NaN\n",
              "4    2016-01-08  NaN  ...            NaN            NaN\n",
              "...         ...  ...  ...            ...            ...\n",
              "1254 2020-12-24  NaN  ...            NaN            NaN\n",
              "1255 2020-12-28  NaN  ...            NaN            NaN\n",
              "1256 2020-12-29  NaN  ...            NaN            NaN\n",
              "1257 2020-12-30  NaN  ...            NaN            NaN\n",
              "1258 2020-12-31  NaN  ...            NaN            NaN\n",
              "\n",
              "[1259 rows x 5987 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdLEnk70rA8B",
        "outputId": "584ac255-d7f4-4bc8-fb82-91a2100066f5"
      },
      "source": [
        "AR = AR_ev_es_vec(df, events[events.major_industry == 'retailing'], 200, [-20,20], minNA=100, gap_=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DLTR_2020-01-07_2\n",
            "DLTR_2019-11-25_2\n",
            "DLTR_2019-11-07_2\n",
            "DLTR_2019-10-08_2\n",
            "DLTR_2019-09-20_2\n",
            "DLTR_2019-09-05_2\n",
            "WMT_2019-08-20_2\n",
            "TGT_2019-08-12_2\n",
            "DLTR_2019-04-30_2\n",
            "DLTR_2019-03-28_2\n",
            "WMT_2019-04-30_2\n",
            "AMZN_2019-02-28_2\n",
            "AMZN_2019-04-29_2\n",
            "AMZN_2019-06-10_2\n",
            "AMZN_2019-05-22_2\n",
            "DG_2019-01-14_2\n",
            "DLTR_2019-07-25_2\n",
            "DLTR_2019-04-09_2\n",
            "DLTR_2019-01-12_2\n",
            "DLTR_2019-05-17_2\n",
            "DLTR_2019-05-17_3\n",
            "DLTR_2019-06-26_2\n",
            "DLTR_2019-08-26_2\n",
            "DLTR_2019-07-12_2\n",
            "KR_2019-03-27_2\n",
            "KR_2019-01-19_2\n",
            "KR_2019-06-03_2\n",
            "KR_2019-04-19_2\n",
            "KR_2019-01-10_2\n",
            "KR_2019-06-28_2\n",
            "KR_2019-07-11_2\n",
            "KR_2019-07-11_3\n",
            "KR_2019-02-21_2\n",
            "KR_2019-05-28_2\n",
            "WBA_2019-01-22_2\n",
            "WBA_2019-01-22_3\n",
            "WMT_2019-06-20_2\n",
            "WBA_2019-09-10_2\n",
            "WBA_2019-09-10_3\n",
            "WBA_2019-09-10_4\n",
            "WBA_2019-09-10_5\n",
            "WBA_2019-09-10_6\n",
            "WBA_2019-09-09_2\n",
            "WBA_2019-09-09_3\n",
            "WBA_2019-09-09_4\n",
            "WBA_2019-09-09_5\n",
            "WBA_2019-09-09_6\n",
            "WMT_2019-09-09_2\n",
            "WMT_2019-09-09_3\n",
            "WMT_2019-09-07_2\n",
            "WMT_2019-09-06_2\n",
            "WMT_2019-09-06_3\n",
            "WMT_2019-09-06_4\n",
            "WMT_2019-09-05_2\n",
            "WMT_2019-09-05_3\n",
            "WMT_2019-09-05_4\n",
            "WMT_2019-09-05_5\n",
            "WMT_2019-09-05_6\n",
            "WMT_2019-09-05_7\n",
            "WMT_2019-09-05_8\n",
            "WMT_2019-09-05_9\n",
            "WMT_2019-09-04_2\n",
            "WMT_2019-09-04_3\n",
            "WMT_2019-09-03_2\n",
            "WMT_2019-09-03_3\n",
            "WMT_2019-09-03_4\n",
            "WMT_2019-09-03_5\n",
            "WMT_2019-09-01_2\n",
            "WMT_2019-08-31_2\n",
            "WMT_2019-08-31_3\n",
            "WMT_2019-08-30_2\n",
            "WMT_2019-08-30_3\n",
            "WMT_2019-08-30_4\n",
            "WMT_2019-08-29_2\n",
            "WMT_2019-08-29_3\n",
            "WMT_2019-08-29_4\n",
            "WMT_2019-08-29_5\n",
            "WMT_2019-08-29_6\n",
            "WMT_2019-08-29_7\n",
            "WMT_2019-08-29_8\n",
            "WMT_2019-08-29_9\n",
            "WMT_2019-08-29_10\n",
            "WMT_2019-08-29_11\n",
            "WMT_2019-08-29_12\n",
            "WMT_2019-08-28_2\n",
            "WMT_2019-08-28_3\n",
            "DG_2019-10-09_2\n",
            "DG_2019-10-09_3\n",
            "DG_2019-10-09_4\n",
            "DG_2019-10-09_5\n",
            "DG_2019-10-09_6\n",
            "DG_2019-10-09_7\n",
            "DG_2019-10-09_8\n",
            "DG_2019-10-09_9\n",
            "DG_2019-10-08_2\n",
            "DG_2019-10-08_3\n",
            "DG_2019-10-08_4\n",
            "DG_2019-10-08_5\n",
            "DG_2019-10-08_6\n",
            "DG_2019-10-07_2\n",
            "WMT_2019-10-03_2\n",
            "WMT_2019-10-03_3\n",
            "WMT_2019-10-03_4\n",
            "WMT_2019-10-03_5\n",
            "WMT_2019-10-03_6\n",
            "WMT_2019-10-02_2\n",
            "WMT_2019-10-02_3\n",
            "WMT_2019-10-02_4\n",
            "WMT_2019-10-01_2\n",
            "WMT_2019-10-01_3\n",
            "WMT_2019-10-01_4\n",
            "WMT_2019-10-01_5\n",
            "WMT_2019-10-01_6\n",
            "WMT_2019-10-01_7\n",
            "WMT_2019-10-02_5\n",
            "WMT_2019-09-12_2\n",
            "WMT_2019-10-23_2\n",
            "WMT_2019-10-02_6\n",
            "WMT_2019-09-09_4\n",
            "WMT_2019-10-11_2\n",
            "WMT_2019-10-02_7\n",
            "WMT_2019-10-01_8\n",
            "WMT_2019-11-15_2\n",
            "WMT_2019-11-14_2\n",
            "WMT_2019-10-28_2\n",
            "WMT_2019-10-28_3\n",
            "WMT_2019-10-28_4\n",
            "WMT_2019-10-28_5\n",
            "DLTR_2019-09-24_2\n",
            "DLTR_2019-11-05_2\n",
            "DG_2019-04-26_2\n",
            "CVS_2019-04-02_2\n",
            "CVS_2019-04-15_2\n",
            "CVS_2019-06-21_2\n",
            "DLTR_2018-11-02_2\n",
            "WMT_2018-05-22_2\n",
            "DLTR_2018-04-16_2\n",
            "DLTR_2018-03-20_2\n",
            "M_2018-06-01_2\n",
            "M_2018-05-29_2\n",
            "M_2018-05-23_2\n",
            "HD_2018-06-18_2\n",
            "HD_2018-06-21_2\n",
            "HD_2018-06-21_3\n",
            "HD_2018-06-21_4\n",
            "HD_2018-06-26_2\n",
            "HD_2018-06-27_2\n",
            "HD_2018-06-29_2\n",
            "HD_2018-06-29_3\n",
            "HD_2018-07-05_2\n",
            "HD_2018-07-13_2\n",
            "CVS_2018-10-10_2\n",
            "TGT_2018-12-11_2\n",
            "AMZN_2018-09-07_2\n",
            "AMZN_2018-09-06_2\n",
            "AMZN_2018-09-06_3\n",
            "AMZN_2018-09-06_4\n",
            "AMZN_2018-09-06_5\n",
            "AMZN_2018-09-06_6\n",
            "AMZN_2018-09-06_7\n",
            "AMZN_2018-09-05_2\n",
            "AMZN_2018-09-05_3\n",
            "AMZN_2018-09-05_4\n",
            "AMZN_2018-09-05_5\n",
            "AMZN_2018-09-05_6\n",
            "AMZN_2018-09-05_7\n",
            "AMZN_2018-09-05_8\n",
            "AMZN_2018-09-05_9\n",
            "CVS_2018-06-28_2\n",
            "CVS_2018-12-12_2\n",
            "DG_2017-04-11_2\n",
            "WMT_2017-06-22_2\n",
            "HD_2017-04-13_2\n",
            "HD_2017-05-05_2\n",
            "HD_2017-02-08_2\n",
            "HD_2017-04-27_2\n",
            "HD_2017-06-02_2\n",
            "HD_2017-02-08_3\n",
            "DLTR_2017-10-30_2\n",
            "DLTR_2017-10-25_2\n",
            "DLTR_2017-10-23_2\n",
            "DLTR_2017-12-07_2\n",
            "DLTR_2017-07-21_2\n",
            "ULTA_2017-06-20_2\n",
            "TGT_2017-05-23_2\n",
            "WBA_2017-01-19_2\n",
            "WBA_2017-04-20_2\n",
            "WBA_2017-01-19_3\n",
            "AAP_2017-04-03_2\n",
            "AAP_2017-04-03_3\n",
            "AAP_2017-04-03_4\n",
            "AAP_2017-04-03_5\n",
            "AAP_2017-04-03_6\n",
            "AAP_2017-04-03_7\n",
            "AAP_2017-04-02_2\n",
            "AAP_2017-04-02_3\n",
            "AAP_2017-04-02_4\n",
            "AAP_2017-04-02_5\n",
            "DLTR_2017-03-17_2\n",
            "AMZN_2017-02-19_2\n",
            "AMZN_2017-02-19_3\n",
            "AMZN_2017-02-19_4\n",
            "AMZN_2017-02-19_5\n",
            "AMZN_2017-02-19_6\n",
            "AMZN_2017-02-18_2\n",
            "AMZN_2017-02-18_3\n",
            "AMZN_2017-02-18_4\n",
            "AMZN_2017-02-18_5\n",
            "AMZN_2017-02-18_6\n",
            "AMZN_2017-02-18_7\n",
            "AMZN_2017-02-18_8\n",
            "AMZN_2017-02-18_9\n",
            "AMZN_2017-02-18_10\n",
            "AMZN_2017-02-18_11\n",
            "AMZN_2017-02-18_12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-YErKV8tEOQ"
      },
      "source": [
        "AR_len = []\n",
        "for i in range(AR.shape[0]):\n",
        "  AR_len.append(len(AR.iloc[i,:][AR.iloc[i,:].notna()]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBh5LPG9xCHL"
      },
      "source": [
        "  AR_t = pd.DataFrame()\n",
        "  AR_t['AR_t'] = AR.iloc[:, 1::].sum(axis=1)/AR_len\n",
        "  AR_t['date'] = AR['date']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-rU8i_SxXgb",
        "outputId": "f4fca770-e922-4997-e43a-dc3236b80325"
      },
      "source": [
        "AR_t[AR_t.AR_t>0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AR_t</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>0.001125</td>\n",
              "      <td>2016-12-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>0.000719</td>\n",
              "      <td>2016-12-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>0.008501</td>\n",
              "      <td>2016-12-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>0.007275</td>\n",
              "      <td>2016-12-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>0.002310</td>\n",
              "      <td>2016-12-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1132</th>\n",
              "      <td>0.007215</td>\n",
              "      <td>2020-07-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1133</th>\n",
              "      <td>0.003227</td>\n",
              "      <td>2020-07-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1135</th>\n",
              "      <td>0.024935</td>\n",
              "      <td>2020-07-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1137</th>\n",
              "      <td>0.008194</td>\n",
              "      <td>2020-07-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1138</th>\n",
              "      <td>0.003800</td>\n",
              "      <td>2020-07-13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>467 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          AR_t       date\n",
              "232   0.001125 2016-12-02\n",
              "234   0.000719 2016-12-06\n",
              "235   0.008501 2016-12-07\n",
              "237   0.007275 2016-12-09\n",
              "239   0.002310 2016-12-13\n",
              "...        ...        ...\n",
              "1132  0.007215 2020-07-02\n",
              "1133  0.003227 2020-07-06\n",
              "1135  0.024935 2020-07-08\n",
              "1137  0.008194 2020-07-10\n",
              "1138  0.003800 2020-07-13\n",
              "\n",
              "[467 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5oqU6vpTv0E",
        "outputId": "be24230f-02af-4cbe-f571-7751e26e907e"
      },
      "source": [
        "AR = es_AR(df, events[events.major_industry == 'retailing'], 200, [-20,20], minNA=100, gap_=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Something is wrong with WBA in 2019-09-10 00:00:00\n",
            "Something is wrong with WMT in 2019-08-31 00:00:00\n",
            "Something is wrong with WMT in 2019-08-28 00:00:00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzFgRmSeUT-i",
        "outputId": "b85a832d-f049-427c-8add-f2838df9181d"
      },
      "source": [
        "AR"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>HD</th>\n",
              "      <th>DG_2020-01-28</th>\n",
              "      <th>WBA</th>\n",
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              "      <th>DLTR_2019-11-25</th>\n",
              "      <th>...</th>\n",
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              "      <th>AMZN_2017-02-18_8</th>\n",
              "      <th>AMZN_2017-02-18_9</th>\n",
              "      <th>AMZN_2017-02-18_10</th>\n",
              "      <th>AMZN_2017-02-18_11</th>\n",
              "      <th>AMZN_2017-02-18_12</th>\n",
              "      <th>ULTA_2017-01-19</th>\n",
              "      <th>KR_2017-11-17</th>\n",
              "      <th>LOW_2017-07-06</th>\n",
              "      <th>CVS_2017-10-25</th>\n",
              "      <th>CVS_2017-10-31</th>\n",
              "      <th>CVS_2017-12-26</th>\n",
              "      <th>CVS_2017-06-16</th>\n",
              "      <th>CVS_2017-07-13</th>\n",
              "      <th>CVS_2017-10-12</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>2016-01-05</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-06</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 981 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  DLTR  DG  ...  CVS_2017-08-18  CVS_2017-11-29  WMT_2017-05-26\n",
              "0    2016-01-04   NaN NaN  ...             NaN             NaN             NaN\n",
              "1    2016-01-05   NaN NaN  ...             NaN             NaN             NaN\n",
              "2    2016-01-06   NaN NaN  ...             NaN             NaN             NaN\n",
              "3    2016-01-07   NaN NaN  ...             NaN             NaN             NaN\n",
              "4    2016-01-08   NaN NaN  ...             NaN             NaN             NaN\n",
              "...         ...   ...  ..  ...             ...             ...             ...\n",
              "1254 2020-12-24   NaN NaN  ...             NaN             NaN             NaN\n",
              "1255 2020-12-28   NaN NaN  ...             NaN             NaN             NaN\n",
              "1256 2020-12-29   NaN NaN  ...             NaN             NaN             NaN\n",
              "1257 2020-12-30   NaN NaN  ...             NaN             NaN             NaN\n",
              "1258 2020-12-31   NaN NaN  ...             NaN             NaN             NaN\n",
              "\n",
              "[1259 rows x 981 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    }
  ]
}