TechfestWeldRight.ipynb

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      "authorship_tag": "ABX9TyOCJk/k/YOPIl3qedT73QlF",
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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/PrakritiShetty/AeroRust.github.io/blob/main/TechfestWeldRight.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "ySpV4VygIu0W"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/WeldRightDataCSV.csv\")\n",
        "print(df)"
      ],
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        "outputId": "4c2ed71e-389f-49cd-c897-498f3e662d52",
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        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (2,3,22,27,32,37,42,47,52,57,62,67,72,77,82,87) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "        Employee Code Machine Production Order Operation No        Date  \\\n",
            "0              382617  TWLD23  E15002965                240  10/09/2022   \n",
            "1              382617  TWLD23  E15002965                240  10/09/2022   \n",
            "2              382617  TWLD23  E15002965                240  10/09/2022   \n",
            "3              382617  TWLD23  E15002965                240  10/09/2022   \n",
            "4              382617  TWLD23  E15002965                240  10/09/2022   \n",
            "...               ...     ...        ...                ...         ...   \n",
            "827529         391342  TWLD23   15003062                240  29/08/2022   \n",
            "827530         391342  TWLD23   15003062                240  29/08/2022   \n",
            "827531         391342  TWLD23   15003062                240  29/08/2022   \n",
            "827532         391342  TWLD23   15003062                240  29/08/2022   \n",
            "827533         391342  TWLD23   15003062                240  29/08/2022   \n",
            "\n",
            "               Time  Current  Humidity  Temperature  Flow  ...  Unnamed: 78  \\\n",
            "0       7:32:28:527     1.13        74           23  0.01  ...          NaN   \n",
            "1        7:32:29:40     1.82        74           23  0.00  ...          NaN   \n",
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            "3       7:32:30:166     0.04        74           23  0.00  ...          NaN   \n",
            "4       7:32:30:696     2.51        74           23  0.00  ...          NaN   \n",
            "...             ...      ...       ...          ...   ...  ...          ...   \n",
            "827529  23:2:13:452     0.33        58           24  0.00  ...          NaN   \n",
            "827530   23:2:14:46     0.34        58           24  0.00  ...          NaN   \n",
            "827531  23:2:14:552    -0.86        58           24  0.06  ...          NaN   \n",
            "827532  23:2:15:143     0.32        58           24  0.00  ...          NaN   \n",
            "827533  23:2:15:646     0.74        57           24  0.04  ...          NaN   \n",
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            "        Unnamed: 79 Unnamed: 80  Unnamed: 81  Unnamed: 82  Unnamed: 83  \\\n",
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            "...             ...         ...          ...          ...          ...   \n",
            "827529          NaN         NaN          NaN          NaN          NaN   \n",
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        "df.shape"
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              "(827534, 88)"
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          "metadata": {},
          "execution_count": 44
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        "df.head()"
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      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   Employee Code Machine Production Order Operation No        Date  \\\n",
              "0         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "1         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "2         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "3         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "4         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "\n",
              "          Time  Current  Humidity  Temperature  Flow  ...  Unnamed: 78  \\\n",
              "0  7:32:28:527     1.13        74           23  0.01  ...          NaN   \n",
              "1   7:32:29:40     1.82        74           23  0.00  ...          NaN   \n",
              "2  7:32:29:677     2.91        74           23  0.00  ...          NaN   \n",
              "3  7:32:30:166     0.04        74           23  0.00  ...          NaN   \n",
              "4  7:32:30:696     2.51        74           23  0.00  ...          NaN   \n",
              "\n",
              "   Unnamed: 79 Unnamed: 80  Unnamed: 81  Unnamed: 82  Unnamed: 83  \\\n",
              "0          NaN         NaN          NaN          NaN          NaN   \n",
              "1          NaN         NaN          NaN          NaN          NaN   \n",
              "2          NaN         NaN          NaN          NaN          NaN   \n",
              "3          NaN         NaN          NaN          NaN          NaN   \n",
              "4          NaN         NaN          NaN          NaN          NaN   \n",
              "\n",
              "   Unnamed: 84  Unnamed: 85  Unnamed: 86  Unnamed: 87  \n",
              "0          NaN          NaN          NaN          NaN  \n",
              "1          NaN          NaN          NaN          NaN  \n",
              "2          NaN          NaN          NaN          NaN  \n",
              "3          NaN          NaN          NaN          NaN  \n",
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              "\n",
              "[5 rows x 88 columns]"
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              "      <th>Employee Code</th>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 45
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        "df.drop(df.columns[13:88], axis=1, inplace= True)\n"
      ],
      "metadata": {
        "id": "RwYjCMYkf7g0"
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      "execution_count": 46,
      "outputs": []
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        "id": "UylsnYT5OhG5",
        "outputId": "a78eca54-48a9-4cc4-b8c4-6146f20e83fc"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Employee Code Machine Production Order Operation No        Date  \\\n",
              "0              382617  TWLD23  E15002965                240  10/09/2022   \n",
              "1              382617  TWLD23  E15002965                240  10/09/2022   \n",
              "2              382617  TWLD23  E15002965                240  10/09/2022   \n",
              "3              382617  TWLD23  E15002965                240  10/09/2022   \n",
              "4              382617  TWLD23  E15002965                240  10/09/2022   \n",
              "...               ...     ...        ...                ...         ...   \n",
              "827529         391342  TWLD23   15003062                240  29/08/2022   \n",
              "827530         391342  TWLD23   15003062                240  29/08/2022   \n",
              "827531         391342  TWLD23   15003062                240  29/08/2022   \n",
              "827532         391342  TWLD23   15003062                240  29/08/2022   \n",
              "827533         391342  TWLD23   15003062                240  29/08/2022   \n",
              "\n",
              "               Time  Current  Humidity  Temperature  Flow  Job Temp  Voltage  \\\n",
              "0       7:32:28:527     1.13        74           23  0.01      29.3     15.2   \n",
              "1        7:32:29:40     1.82        74           23  0.00      29.3      0.0   \n",
              "2       7:32:29:677     2.91        74           23  0.00      29.3      0.0   \n",
              "3       7:32:30:166     0.04        74           23  0.00      29.3      0.0   \n",
              "4       7:32:30:696     2.51        74           23  0.00      27.8      0.0   \n",
              "...             ...      ...       ...          ...   ...       ...      ...   \n",
              "827529  23:2:13:452     0.33        58           24  0.00      34.0      0.0   \n",
              "827530   23:2:14:46     0.34        58           24  0.00      34.0      0.0   \n",
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          "metadata": {},
          "execution_count": 47
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    {
      "cell_type": "code",
      "source": [
        "df.head()"
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      "metadata": {
        "id": "kiW-Cm83x8u9",
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          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "adf4c4c5-6505-4822-ef5e-1b856ae3d7ad"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Employee Code Machine Production Order Operation No        Date  \\\n",
              "0         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "1         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "2         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "3         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "4         382617  TWLD23  E15002965                240  10/09/2022   \n",
              "\n",
              "          Time  Current  Humidity  Temperature  Flow  Job Temp  Voltage  \\\n",
              "0  7:32:28:527     1.13        74           23  0.01      29.3     15.2   \n",
              "1   7:32:29:40     1.82        74           23  0.00      29.3      0.0   \n",
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              "\n",
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              "2           No Defect  \n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-71679aa6-2cb5-4a27-a110-926e8652a00a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-71679aa6-2cb5-4a27-a110-926e8652a00a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "HANDLING MISSING VALUES"
      ],
      "metadata": {
        "id": "fz0CmjFE8qli"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df.columns:\n",
        "    print(df[i].value_counts())\n"
      ],
      "metadata": {
        "id": "5a4E6wB6yE1o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfb2a21e-3248-404e-8add-5547c1e76b76"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "391342    256884\n",
            "394965    174985\n",
            "97346     117426\n",
            "394269    114821\n",
            "0          66820\n",
            "382617     36765\n",
            "383138     27295\n",
            "394365     23922\n",
            "387268      8616\n",
            "Name: Employee Code, dtype: int64\n",
            "TWLD23    827534\n",
            "Name: Machine, dtype: int64\n",
            "#NAME?         278167\n",
            "Test coopan     71734\n",
            "E15003202       49607\n",
            "-15003217       47336\n",
            "E15002881       46235\n",
            "E15002966       44189\n",
            "-150033027      43272\n",
            "E15003200       40890\n",
            "E15003219       29137\n",
            "E10002436       26472\n",
            "E15003220       24526\n",
            "E15003201       24411\n",
            "E10002437       20787\n",
            "15003062        16030\n",
            "E50006711       15473\n",
            "E15002963       14979\n",
            "E15003056       10618\n",
            "E15002965       10153\n",
            "15003062         6863\n",
            "-15003217        5902\n",
            "-                 740\n",
            "E15003253          13\n",
            "Name: Production, dtype: int64\n",
            "240     484464\n",
            "180     128647\n",
            "1        69444\n",
            "180-     53238\n",
            "40       47259\n",
            "130      16711\n",
            "30       15473\n",
            "240       7006\n",
            "1         2290\n",
            "-240      1315\n",
            "130        947\n",
            "-          740\n",
            "Name: Order Operation No, dtype: int64\n",
            "14/09/2022    91059\n",
            "06/09/2022    49879\n",
            "07/09/2022    47588\n",
            "08/09/2022    42636\n",
            "26/08/2022    42282\n",
            "10/09/2022    40992\n",
            "25/08/2022    40701\n",
            "16/09/2022    40188\n",
            "29/08/2022    38333\n",
            "24/08/2022    37024\n",
            "15/09/2022    34591\n",
            "05/09/2022    34100\n",
            "12/09/2022    33063\n",
            "19/09/2022    32315\n",
            "17/09/2022    32166\n",
            "27/08/2022    31925\n",
            "09/09/2022    31699\n",
            "13/09/2022    28888\n",
            "03/09/2022    27593\n",
            "01/09/2022    24855\n",
            "30/08/2022    18165\n",
            "22/08/2022    14681\n",
            "02/09/2022    10682\n",
            "23/08/2022     2129\n",
            "Name: Date, dtype: int64\n",
            "14:22:47:641    3\n",
            "13:24:34:777    3\n",
            "21:48:39:305    3\n",
            "9:28:22:761     3\n",
            "10:15:34:433    3\n",
            "               ..\n",
            "8:53:33:961     1\n",
            "8:53:34:580     1\n",
            "8:53:35:82      1\n",
            "8:53:35:679     1\n",
            "23:2:15:646     1\n",
            "Name: Time, Length: 821555, dtype: int64\n",
            "10.11    10184\n",
            "0.43      1989\n",
            "0.40      1950\n",
            "0.55      1932\n",
            "0.58      1897\n",
            "         ...  \n",
            "60.13        1\n",
            "58.68        1\n",
            "59.02        1\n",
            "59.98        1\n",
            "79.33        1\n",
            "Name: Current, Length: 6818, dtype: int64\n",
            "78      78463\n",
            "77      75309\n",
            "79      74813\n",
            "76      68446\n",
            "75      55442\n",
            "80      45667\n",
            "72      38935\n",
            "81      38067\n",
            "71      37882\n",
            "73      35520\n",
            "70      29976\n",
            "83      29169\n",
            "74      29034\n",
            "84      27846\n",
            "82      22352\n",
            "67      17907\n",
            "69      17308\n",
            "66      16579\n",
            "68      14765\n",
            "65      12923\n",
            "85       9877\n",
            "64       7721\n",
            "0        7184\n",
            "61       6352\n",
            "63       5149\n",
            "62       4496\n",
            "87       4390\n",
            "86       3876\n",
            "58       2770\n",
            "60       2450\n",
            "57       2021\n",
            "59       1905\n",
            "88       1239\n",
            "90        951\n",
            "89        631\n",
            "99         44\n",
            "37         11\n",
            "39         10\n",
            "52          6\n",
            "2444        4\n",
            "40          4\n",
            "314         4\n",
            "38          4\n",
            "35          4\n",
            "33          4\n",
            "118         4\n",
            "53          4\n",
            "171         3\n",
            "149         3\n",
            "158         3\n",
            "30          3\n",
            "31          3\n",
            "56          1\n",
            "Name: Humidity, dtype: int64\n",
            " 24     359642\n",
            " 25     214928\n",
            " 23     141152\n",
            " 22      51097\n",
            " 26      43121\n",
            " 27       7321\n",
            " 0        7184\n",
            " 21       2389\n",
            "-13        436\n",
            " 129       172\n",
            " 12         40\n",
            "-12          9\n",
            "-11          8\n",
            " 11          6\n",
            " 13          4\n",
            " 96          4\n",
            " 828         4\n",
            " 49          4\n",
            " 48          3\n",
            " 31          3\n",
            " 50          3\n",
            " 54          3\n",
            " 64          1\n",
            "Name: Temperature, dtype: int64\n",
            "0.00    257174\n",
            "0.01     10126\n",
            "0.02      8747\n",
            "0.03      7422\n",
            "0.04      6202\n",
            "         ...  \n",
            "7.89         1\n",
            "7.91         1\n",
            "7.90         1\n",
            "6.79         1\n",
            "7.88         1\n",
            "Name: Flow, Length: 1009, dtype: int64\n",
            "31.2      62292\n",
            "56.8      34418\n",
            "3274.3    22484\n",
            "26.6      19591\n",
            "30.9      10962\n",
            "          ...  \n",
            "387.9         2\n",
            "814.4         1\n",
            "3270.3        1\n",
            "200.3         1\n",
            "797.6         1\n",
            "Name: Job Temp, Length: 1173, dtype: int64\n",
            "0.000     578018\n",
            "12.408      3743\n",
            "11.874       441\n",
            "9.235        227\n",
            "8.948        214\n",
            "           ...  \n",
            "23.451         1\n",
            "20.653         1\n",
            "17.706         1\n",
            "3.920          1\n",
            "5.896          1\n",
            "Name: Voltage, Length: 12624, dtype: int64\n",
            "No Defect               819552\n",
            "Tungsten Inclusion        4600\n",
            "Porosity                  1138\n",
            "No Defect77                220\n",
            "No Defect78                211\n",
            "No Defect76                158\n",
            "No Defect79                144\n",
            "No Defect75                140\n",
            "No Defect80                121\n",
            "No Defect73                108\n",
            "No Defect0                 106\n",
            "No Defect72                104\n",
            "No Defect81                102\n",
            "No Defect71                 93\n",
            "No Defect74                 81\n",
            "No Defect83                 77\n",
            "No Defect70                 67\n",
            "No Defect84                 62\n",
            "No Defect67                 59\n",
            "No Defect65                 51\n",
            "No Defect82                 48\n",
            "No Defect68                 46\n",
            "No Defect69                 45\n",
            "No Defect66                 40\n",
            "No Defect64                 31\n",
            "No Defect63                 24\n",
            "No Defect85                 21\n",
            "No Defect61                 15\n",
            "No Defect58                  9\n",
            "No Defect86                  7\n",
            "No Defect62                  7\n",
            "No Defect87                  7\n",
            "No Defect90                  7\n",
            "No Defect57                  6\n",
            "No Defect60                  4\n",
            "No Defect59                  4\n",
            "No Defect88                  2\n",
            "Tungsten Inclusion78         2\n",
            "Tungsten Inclusion77         2\n",
            "Porosity86                   1\n",
            "Porosity71                   1\n",
            "Tungsten Inclusion76         1\n",
            "Tungsten Inclusion75         1\n",
            "Tungsten Inclusion67         1\n",
            "Tungsten Inclusion72         1\n",
            "Tungsten Inclusion71         1\n",
            "Tungsten Inclusion80         1\n",
            "Porosity76                   1\n",
            "No Defect40                  1\n",
            "Porosity80                   1\n",
            "Porosity75                   1\n",
            "No Defect99                  1\n",
            "Name: Defect, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df.columns:\n",
        "    print(df[i].isnull().value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FvnBH5NCUyhC",
        "outputId": "5a11d165-11e4-495f-807a-d171b5beed5e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False    827534\n",
            "Name: Employee Code, dtype: int64\n",
            "False    827534\n",
            "Name: Machine, dtype: int64\n",
            "False    827534\n",
            "Name: Production, dtype: int64\n",
            "False    827534\n",
            "Name: Order Operation No, dtype: int64\n",
            "False    827534\n",
            "Name: Date, dtype: int64\n",
            "False    827534\n",
            "Name: Time, dtype: int64\n",
            "False    827531\n",
            "True          3\n",
            "Name: Current, dtype: int64\n",
            "False    827534\n",
            "Name: Humidity, dtype: int64\n",
            "False    827534\n",
            "Name: Temperature, dtype: int64\n",
            "False    827534\n",
            "Name: Flow, dtype: int64\n",
            "False    827534\n",
            "Name: Job Temp, dtype: int64\n",
            "False    827534\n",
            "Name: Voltage, dtype: int64\n",
            "False    827534\n",
            "Name: Defect, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "ddTWzNFs8Y-F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d4b5dd5-62b1-4724-f06c-a816ede792aa"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Employee Code', 'Machine', 'Production', 'Order Operation No', 'Date',\n",
              "       'Time', 'Current', 'Humidity', 'Temperature', 'Flow', 'Job Temp',\n",
              "       'Voltage', 'Defect'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# imputing current\n",
        "mode = df['Current'].value_counts().values[0]\n",
        "df['Current'].fillna(mode, inplace=True)\n",
        "\n",
        "#imputing Production\n",
        "# for i in df['Production']:\n",
        "#   # print(i)\n",
        "#   if( i== \"#NAME?\"):\n",
        "\n",
        "df.loc[df['Production']==\"#NAME?\"] = \"E15003253\"\n",
        "\n",
        "# most_frequent_10 = df['Production'].value_counts().head(10).index.values\n",
        "# freq_of_top_10 = df['Production'].value_counts().head(10).values\n",
        "# production_probabilities= list(map(lambda value: (1/sum(freq_of_top_10))*value ,freq_of_top_10))\n",
        "# df['Production']= df['Production'].apply( lambda value: value if not np.isna(value) else np.random.choice(most_frequent_10, p= production_probabilities))\n",
        "\n",
        "df['Production'].value_counts()\n"
      ],
      "metadata": {
        "id": "YD0je5ur8UhI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Current'].isnull().value_counts()"
      ],
      "metadata": {
        "id": "a8OTFcSD8iEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df.columns:\n",
        "    print(i)\n",
        "    print(df[i].value_counts())"
      ],
      "metadata": {
        "id": "OS5ErlY4PBxo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LZnlm4jJ8ofy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}