YP_week_1_session_0.ipynb

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        "<a href=\"https://colab.research.google.com/github/YaPineiro/Fin420/blob/main/YP_week_1_session_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Week 1 Session 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTA9yX83MPG6"
      },
      "source": [
        "# Do Higher Levels of Institutional Ownership Increase Management Effectiveness?\n",
        "\n",
        "Imagine you arrive at work on a Monday morning and this is the question your boss asks you.  Theory would prompt you to answer 'yes', however you'll want some data to back up your answer.  In this session we'll gather some relevant data and provide an answer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMLOrR09kNeX"
      },
      "source": [
        "Load the Pandas library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFfLhrAccCX9"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1WEydy2gPFJ"
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      "source": [
        "### Get institutional ownership data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gd8Dc9J_kU5v"
      },
      "source": [
        "The URL of an Excel spreadsheet which contains the amount of Institutional and Insider ownership by industry."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jydsNjYeEH5"
      },
      "source": [
        "inst_own_url = \"http://www.stern.nyu.edu/~adamodar/pc/datasets/inshold.xls\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below we are going to import the data from the url using the `read_excel` method.  This attempts to extablish a SSL connection, and as of writing this the required certificates were not on the server.  The following code tells python to ignore the lack of valid certs.  It is not the safest approach, though this is the benefit of working on a notebook in the cloud."
      ],
      "metadata": {
        "id": "mJBqMKF7pnhs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import ssl\n",
        "ssl._create_default_https_context = ssl._create_unverified_context"
      ],
      "metadata": {
        "id": "QQ6wsHvJrVLf"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3sByMNH3gv-p"
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      "source": [
        "Note the data starts on row 8, so we skip the first 7 rows when creating the Pandas `DataFrame`.  Also, the data is in sheet 2 (which in Python is 1)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjOTPe4sgN2A"
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      "source": [
        "inst_own = pd.read_excel(inst_own_url, skiprows = 7, sheet_name=1)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### Inspecting Data"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "hm91_KL5hEy2"
      },
      "source": [
        "Often the first step will be to inspect the data.  We want to pay particular attention to missing data and the type of each variable (column).  We can first view the data with:"
      ]
    },
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      "execution_count": 5,
      "outputs": [
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          "data": {
            "text/plain": [
              "                        Industry Name  Number of Firms  CEO Holding  \\\n",
              "0                         Advertising               54     0.099896   \n",
              "1                   Aerospace/Defense               67     0.034654   \n",
              "2                       Air Transport               24     0.030976   \n",
              "3                             Apparel               37     0.053675   \n",
              "4                        Auto & Truck               34     0.096935   \n",
              "..                                ...              ...          ...   \n",
              "91                           Trucking               24     0.054088   \n",
              "92                  Utility (General)               14     0.001355   \n",
              "93                    Utility (Water)               15     0.005923   \n",
              "94                       Total Market             6062     0.048202   \n",
              "95  Total Market (without financials)             4935     0.048431   \n",
              "\n",
              "    Corporate Holdings  Institutional Holdings  Insider Holdings  \n",
              "0             0.152666                0.352435          0.233282  \n",
              "1             0.180971                0.528903          0.088726  \n",
              "2             0.261742                0.418113          0.137485  \n",
              "3             0.108202                0.514791          0.170123  \n",
              "4             0.195019                0.230086          0.174605  \n",
              "..                 ...                     ...               ...  \n",
              "91            0.340488                0.583142          0.168315  \n",
              "92            0.000000                0.857136          0.004635  \n",
              "93            0.276800                0.556169          0.059443  \n",
              "94            0.141806                0.484652          0.127259  \n",
              "95            0.141046                0.496171          0.126508  \n",
              "\n",
              "[96 rows x 6 columns]"
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              "      <th>Industry Name</th>\n",
              "      <th>Number of Firms</th>\n",
              "      <th>CEO Holding</th>\n",
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              "      <th>0</th>\n",
              "      <td>Advertising</td>\n",
              "      <td>54</td>\n",
              "      <td>0.099896</td>\n",
              "      <td>0.152666</td>\n",
              "      <td>0.352435</td>\n",
              "      <td>0.233282</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aerospace/Defense</td>\n",
              "      <td>67</td>\n",
              "      <td>0.034654</td>\n",
              "      <td>0.180971</td>\n",
              "      <td>0.528903</td>\n",
              "      <td>0.088726</td>\n",
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              "      <th>2</th>\n",
              "      <td>Air Transport</td>\n",
              "      <td>24</td>\n",
              "      <td>0.030976</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.418113</td>\n",
              "      <td>0.137485</td>\n",
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              "      <th>3</th>\n",
              "      <td>Apparel</td>\n",
              "      <td>37</td>\n",
              "      <td>0.053675</td>\n",
              "      <td>0.108202</td>\n",
              "      <td>0.514791</td>\n",
              "      <td>0.170123</td>\n",
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              "      <th>4</th>\n",
              "      <td>Auto &amp; Truck</td>\n",
              "      <td>34</td>\n",
              "      <td>0.096935</td>\n",
              "      <td>0.195019</td>\n",
              "      <td>0.230086</td>\n",
              "      <td>0.174605</td>\n",
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              "      <th>91</th>\n",
              "      <td>Trucking</td>\n",
              "      <td>24</td>\n",
              "      <td>0.054088</td>\n",
              "      <td>0.340488</td>\n",
              "      <td>0.583142</td>\n",
              "      <td>0.168315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Utility (General)</td>\n",
              "      <td>14</td>\n",
              "      <td>0.001355</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.857136</td>\n",
              "      <td>0.004635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Utility (Water)</td>\n",
              "      <td>15</td>\n",
              "      <td>0.005923</td>\n",
              "      <td>0.276800</td>\n",
              "      <td>0.556169</td>\n",
              "      <td>0.059443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Total Market</td>\n",
              "      <td>6062</td>\n",
              "      <td>0.048202</td>\n",
              "      <td>0.141806</td>\n",
              "      <td>0.484652</td>\n",
              "      <td>0.127259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Total Market (without financials)</td>\n",
              "      <td>4935</td>\n",
              "      <td>0.048431</td>\n",
              "      <td>0.141046</td>\n",
              "      <td>0.496171</td>\n",
              "      <td>0.126508</td>\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_14dae5c6-fab7-4dd8-bf03-36ba88ee7e96 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('inst_own');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "inst_own",
              "summary": "{\n  \"name\": \"inst_own\",\n  \"rows\": 96,\n  \"fields\": [\n    {\n      \"column\": \"Industry Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 96,\n        \"samples\": [\n          \"Shoe\",\n          \"Semiconductor\",\n          \"Retail (Grocery and Food)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 789,\n        \"min\": 1,\n        \"max\": 6062,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          17,\n          333,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CEO Holding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03360049494760392,\n        \"min\": 0.00035000000000000005,\n        \"max\": 0.1663356,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.00944857143,\n          0.017195000000000002,\n          0.1085007692\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corporate Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10010370183473834,\n        \"min\": 0.0,\n        \"max\": 0.5834,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          0.1211093333,\n          0.14885500000000002,\n          0.337705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13959774177935172,\n        \"min\": 0.23008615379999997,\n        \"max\": 0.9653,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.6980344444000001,\n          0.6106276786,\n          0.44433799999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05954576258908088,\n        \"min\": 0.0032400000000000003,\n        \"max\": 0.2973314286,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.055155,\n          0.07960122807,\n          0.2015371429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbeYo80BibG2"
      },
      "source": [
        "EDIT:  There is no longer a blank column, so no need to run the next line of code.  And it looks like we have a blank column (Unnamed: 5).  We can remove it with:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiEzyN8hikNb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "c4ae5970-c2ae-4b32-c45d-b0dbf95fcb7c"
      },
      "source": [
        "inst_own.drop(columns=[\"Unnamed: 5\"], inplace=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['Unnamed: 5'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-e6cd2a1a48f5>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minst_own\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Unnamed: 5\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5579\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5580\u001b[0m         \"\"\"\n\u001b[0;32m-> 5581\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5582\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5583\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4786\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4787\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4788\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4789\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4790\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4828\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4829\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4830\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4831\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   7068\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7069\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7070\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask].tolist()} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7071\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7072\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Unnamed: 5'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3hDz99W6YtV"
      },
      "source": [
        "Now let's see how many rows and columns we have (the shape of our data frame):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2sVkKFPFhl-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a171ccd-c3b1-4b02-bed0-e4e873cf5cc7"
      },
      "source": [
        "inst_own.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(96, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbAw9mN8t_t9"
      },
      "source": [
        "Or just get the rows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqyohlhBuCVz",
        "outputId": "58349c00-4d18-4da2-d0c2-cc4ad3116e55"
      },
      "source": [
        "inst_own.shape[0]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0zQQyunW4v8"
      },
      "source": [
        "And the type of each column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAQxRyvhW73X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "e3676443-5eea-445b-885c-97b867d28d14"
      },
      "source": [
        "inst_own.dtypes"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Industry Name              object\n",
              "Number of Firms             int64\n",
              "CEO Holding               float64\n",
              "Corporate Holdings        float64\n",
              "Institutional Holdings    float64\n",
              "Insider Holdings          float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Industry Name</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Firms</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CEO Holding</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Corporate Holdings</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insider Holdings</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKeVwMVwqO6d"
      },
      "source": [
        "Or we can also use `info()`.  This prints a collection of useful information about a `DataFrame`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRXvIp-fqOck",
        "outputId": "c844be3a-50b2-43e9-cdfb-5f928a0f4035"
      },
      "source": [
        "inst_own.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 96 entries, 0 to 95\n",
            "Data columns (total 6 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   Industry Name           96 non-null     object \n",
            " 1   Number of Firms         96 non-null     int64  \n",
            " 2   CEO Holding             96 non-null     float64\n",
            " 3   Corporate Holdings      96 non-null     float64\n",
            " 4   Institutional Holdings  96 non-null     float64\n",
            " 5   Insider Holdings        96 non-null     float64\n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 4.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gB5gbF8_YBin"
      },
      "source": [
        "And so the Number of Firms is an integer type. With the remaining columns being floats (numbers with decimal portions).  The `Object` for Industry Name in Pandas means it is a string. These columns have been imported correctly.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPZMMlmo7T5X"
      },
      "source": [
        "A common problem when importing numeric column, particularly in large data sets, is there may be a character in one cell.  In this case the whole column may be imported as strings (\"123\" instead of 123)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eIRsJtRly1j"
      },
      "source": [
        "### Example: Importing Data with Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNk7L5xGl5xj"
      },
      "source": [
        "inst_own_with_error = pd.read_excel(\"https://github.com/FinancialMarkets/industry_instown_with_data_error_to_clean/blob/master/which_column_has_bad_data.xls?raw=true\", skiprows = 7)\n",
        "inst_own_with_error.drop(columns=[\"Unnamed: 5\"], inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
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      "source": [
        "inst_own_with_error.dtypes"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Industry Name              object\n",
              "Number of Firms             int64\n",
              "CEO Holding               float64\n",
              "Institutional Holdings     object\n",
              "Insider Holdings          float64\n",
              "dtype: object"
            ],
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          "data": {
            "text/plain": [
              "                        Industry Name  Number of Firms  CEO Holding  \\\n",
              "0                         Advertising               47     0.050409   \n",
              "1                   Aerospace/Defense               77     0.030481   \n",
              "2                       Air Transport               18     0.017713   \n",
              "3                             Apparel               51     0.091342   \n",
              "4                        Auto & Truck               13     0.069599   \n",
              "..                                ...              ...          ...   \n",
              "91                           Trucking               33     0.020479   \n",
              "92                  Utility (General)               16     0.001173   \n",
              "93                    Utility (Water)               17     0.021316   \n",
              "94                       Total Market             7053     0.047442   \n",
              "95  Total Market (without financials)             5878     0.049319   \n",
              "\n",
              "   Institutional Holdings  Insider Holdings  \n",
              "0                0.375451          0.161023  \n",
              "1                0.571001          0.099429  \n",
              "2                0.730171          0.042613  \n",
              "3                0.505583          0.180427  \n",
              "4                0.606275          0.095710  \n",
              "..                    ...               ...  \n",
              "91                0.55906          0.157253  \n",
              "92               0.803944          0.005601  \n",
              "93               0.499558          0.052463  \n",
              "94               0.485737          0.129176  \n",
              "95               0.500262          0.129637  \n",
              "\n",
              "[96 rows x 5 columns]"
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              "type": "dataframe",
              "variable_name": "inst_own_with_error",
              "summary": "{\n  \"name\": \"inst_own_with_error\",\n  \"rows\": 96,\n  \"fields\": [\n    {\n      \"column\": \"Industry Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 96,\n        \"samples\": [\n          \"Shoe\",\n          \"Semiconductor\",\n          \"Retail (Grocery and Food)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 926,\n        \"min\": 2,\n        \"max\": 7053,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          13,\n          17,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CEO Holding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03444435700914534,\n        \"min\": 0.001173125,\n        \"max\": 0.14436875000000002,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.009682857139999999,\n          0.024688125,\n          0.09716833333000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.7812125000000001,\n          0.5881685714,\n          0.477076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05808445696612681,\n        \"min\": 0.0056012499999999995,\n        \"max\": 0.2528118919,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.06596714286,\n          0.08935123077,\n          0.1616555556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2UNOons5nJNl"
      },
      "source": [
        "Now we see `Institutional Holdings` is a string, and not a number.  Let's take a look and try to figure out where the error is:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onY8PJmIncUF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1d75c45b-b067-406f-e346-30f4bf34c66a"
      },
      "source": [
        "pd.set_option('display.max_rows', 1000)\n",
        "inst_own_with_error[\"Institutional Holdings\"]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          0.375451\n",
              "1          0.571001\n",
              "2          0.730171\n",
              "3          0.505583\n",
              "4          0.606275\n",
              "5          0.599107\n",
              "6          0.584515\n",
              "7          0.354313\n",
              "8           0.45555\n",
              "9          0.261087\n",
              "10         0.587724\n",
              "11         0.656569\n",
              "12         0.685724\n",
              "13         0.524726\n",
              "14          0.57085\n",
              "15          0.37529\n",
              "16          0.79305\n",
              "17         0.574201\n",
              "18         0.362273\n",
              "19         0.549416\n",
              "20         0.457172\n",
              "21         0.727958\n",
              "22         0.540199\n",
              "23         0.361361\n",
              "24         0.272346\n",
              "25    5g3.92631818%\n",
              "26         0.406755\n",
              "27         0.281636\n",
              "28         0.449221\n",
              "29         0.676232\n",
              "30         0.439051\n",
              "31         0.455902\n",
              "32         0.440725\n",
              "33         0.388296\n",
              "34         0.497551\n",
              "35          0.62787\n",
              "36         0.425788\n",
              "37         0.342021\n",
              "38         0.461281\n",
              "39         0.491228\n",
              "40         0.505673\n",
              "41         0.768542\n",
              "42         0.537949\n",
              "43         0.629228\n",
              "44         0.338093\n",
              "45         0.621787\n",
              "46         0.733088\n",
              "47         0.657938\n",
              "48         0.568681\n",
              "49         0.470696\n",
              "50         0.673124\n",
              "51         0.208205\n",
              "52         0.602455\n",
              "53           0.7296\n",
              "54         0.377124\n",
              "55         0.565526\n",
              "56          0.52961\n",
              "57         0.694487\n",
              "58          0.75416\n",
              "59         0.709458\n",
              "60          0.20258\n",
              "61          0.56615\n",
              "62         0.765564\n",
              "63         0.308433\n",
              "64         0.530997\n",
              "65          0.34201\n",
              "66         0.518088\n",
              "67          0.89815\n",
              "68         0.597804\n",
              "69         0.741948\n",
              "70         0.775873\n",
              "71          0.60425\n",
              "72         0.728622\n",
              "73         0.477076\n",
              "74         0.423614\n",
              "75         0.674289\n",
              "76         0.467243\n",
              "77         0.588169\n",
              "78         0.640928\n",
              "79         0.384601\n",
              "80         0.781213\n",
              "81         0.366207\n",
              "82         0.466025\n",
              "83         0.480943\n",
              "84         0.667756\n",
              "85          0.49328\n",
              "86         0.460804\n",
              "87          0.38819\n",
              "88         0.416315\n",
              "89         0.824517\n",
              "90          0.63909\n",
              "91          0.55906\n",
              "92         0.803944\n",
              "93         0.499558\n",
              "94         0.485737\n",
              "95         0.500262\n",
              "Name: Institutional Holdings, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Institutional Holdings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.375451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.571001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.730171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.505583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.606275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.599107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.584515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.354313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.45555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.261087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.587724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.656569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.685724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.524726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.57085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.37529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.79305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.574201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.362273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.549416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.457172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.727958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.540199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.361361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.272346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5g3.92631818%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.406755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.281636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.449221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.676232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.439051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.455902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.440725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.388296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.497551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.62787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.425788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.342021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.461281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.491228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.505673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.768542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.537949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.629228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.338093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.621787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.733088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.657938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.568681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.470696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.673124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.208205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0.602455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.7296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0.377124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0.565526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.52961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.694487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.75416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.709458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.20258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>0.56615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>0.765564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.308433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>0.530997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>0.34201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0.518088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>0.89815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0.597804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>0.741948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>0.775873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>0.60425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.728622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0.477076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>0.423614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>0.674289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>0.467243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>0.588169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.640928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>0.384601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>0.781213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>0.366207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>0.466025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>0.480943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>0.667756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>0.49328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>0.460804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>0.38819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>0.416315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>0.824517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>0.63909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>0.55906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>0.803944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>0.499558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>0.485737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>0.500262</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6K9-JrTT3_bd"
      },
      "source": [
        "We see in row 25 there is a letter `g` in the number.  We have two choices at this point, we can (1) remove the row, or (2) if we are sure the number is otherwise correct we can remove the `g`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9O20GHFeXkf",
        "outputId": "7359f1dc-e62f-4477-9a3b-b96029876121"
      },
      "source": [
        "inst_own_with_error.index"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=96, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k66-dYDRcUrj"
      },
      "source": [
        "#### Drop a Row\n",
        "\n",
        "We can drop a row with the following.  Note the `inplace=True` is commented.  We want the error to stay in the DataFrame for the second posssible solution.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuumMZPReIu6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4d4b9333-ded1-4d8a-8bc2-09571aa5b3f3"
      },
      "source": [
        "inst_own_with_error.drop(axis=0, index=25) #, inplace=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             Industry Name  Number of Firms  CEO Holding  \\\n",
              "0                              Advertising               47     0.050409   \n",
              "1                        Aerospace/Defense               77     0.030481   \n",
              "2                            Air Transport               18     0.017713   \n",
              "3                                  Apparel               51     0.091342   \n",
              "4                             Auto & Truck               13     0.069599   \n",
              "5                               Auto Parts               46     0.029056   \n",
              "6                      Bank (Money Center)                7     0.001516   \n",
              "7                         Banks (Regional)              611     0.017899   \n",
              "8                     Beverage (Alcoholic)               21     0.016225   \n",
              "9                          Beverage (Soft)               34     0.040421   \n",
              "10                            Broadcasting               27     0.075388   \n",
              "11          Brokerage & Investment Banking               39     0.045112   \n",
              "12                      Building Materials               42     0.018934   \n",
              "13            Business & Consumer Services              165     0.072431   \n",
              "14                                Cable TV               14     0.005457   \n",
              "15                        Chemical (Basic)               43     0.085014   \n",
              "16                  Chemical (Diversified)                6     0.014690   \n",
              "17                    Chemical (Specialty)               94     0.020830   \n",
              "18                   Coal & Related Energy               22     0.028429   \n",
              "19                       Computer Services              106     0.073701   \n",
              "20                   Computers/Peripherals               48     0.036865   \n",
              "21                   Construction Supplies               44     0.037227   \n",
              "22                             Diversified               23     0.022122   \n",
              "23                   Drugs (Biotechnology)              503     0.033630   \n",
              "24                  Drugs (Pharmaceutical)              267     0.048045   \n",
              "26                    Electrical Equipment              113     0.050020   \n",
              "27         Electronics (Consumer & Office)               20     0.086656   \n",
              "28                   Electronics (General)              153     0.039852   \n",
              "29                Engineering/Construction               54     0.069114   \n",
              "30                           Entertainment              107     0.092871   \n",
              "31          Environmental & Waste Services               82     0.074181   \n",
              "32                     Farming/Agriculture               31     0.046266   \n",
              "33  Financial Svcs. (Non-bank & Insurance)              232     0.037064   \n",
              "34                         Food Processing               88     0.050342   \n",
              "35                        Food Wholesalers               17     0.048782   \n",
              "36                   Furn/Home Furnishings               35     0.059996   \n",
              "37                Green & Renewable Energy               22     0.098471   \n",
              "38                     Healthcare Products              242     0.041659   \n",
              "39             Healthcare Support Services              128     0.065537   \n",
              "40    Heathcare Information and Technology              129     0.074792   \n",
              "41                            Homebuilding               32     0.075656   \n",
              "42         Hospitals/Healthcare Facilities               36     0.042355   \n",
              "43                            Hotel/Gaming               65     0.052368   \n",
              "44                      Household Products              127     0.126213   \n",
              "45                    Information Services               69     0.061668   \n",
              "46                     Insurance (General)               19     0.021413   \n",
              "47                        Insurance (Life)               24     0.007723   \n",
              "48                   Insurance (Prop/Cas.)               51     0.041256   \n",
              "49          Investments & Asset Management              192     0.107877   \n",
              "50                               Machinery              120     0.026871   \n",
              "51                         Metals & Mining               92     0.131931   \n",
              "52             Office Equipment & Services               22     0.035822   \n",
              "53                    Oil/Gas (Integrated)                4     0.002785   \n",
              "54    Oil/Gas (Production and Exploration)              269     0.064955   \n",
              "55                    Oil/Gas Distribution               24     0.066219   \n",
              "56                    Oilfield Svcs/Equip.              136     0.045425   \n",
              "57                   Packaging & Container               24     0.010357   \n",
              "58                   Paper/Forest Products               15     0.005661   \n",
              "59                                   Power               52     0.008871   \n",
              "60                         Precious Metals               83     0.057095   \n",
              "61                 Publishing & Newspapers               31     0.020642   \n",
              "62                                R.E.I.T.              234     0.015122   \n",
              "63               Real Estate (Development)               20     0.135526   \n",
              "64       Real Estate (General/Diversified)               12     0.009882   \n",
              "65     Real Estate (Operations & Services)               57     0.133494   \n",
              "66                              Recreation               63     0.100167   \n",
              "67                             Reinsurance                2     0.002610   \n",
              "68                       Restaurant/Dining               77     0.042689   \n",
              "69                     Retail (Automotive)               26     0.075498   \n",
              "70                Retail (Building Supply)               17     0.013185   \n",
              "71                   Retail (Distributors)               80     0.057704   \n",
              "72                        Retail (General)               18     0.008865   \n",
              "73               Retail (Grocery and Food)               13     0.097168   \n",
              "74                         Retail (Online)               70     0.090229   \n",
              "75                  Retail (Special Lines)               89     0.034434   \n",
              "76                           Rubber& Tires                4     0.044497   \n",
              "77                           Semiconductor               72     0.024688   \n",
              "78                     Semiconductor Equip               39     0.020794   \n",
              "79                   Shipbuilding & Marine               10     0.121509   \n",
              "80                                    Shoe               11     0.009683   \n",
              "81                Software (Entertainment)               86     0.084948   \n",
              "82                     Software (Internet)               30     0.144369   \n",
              "83         Software (System & Application)              363     0.065429   \n",
              "84                                   Steel               32     0.011503   \n",
              "85                      Telecom (Wireless)               18     0.035370   \n",
              "86                      Telecom. Equipment               91     0.055862   \n",
              "87                       Telecom. Services               67     0.057453   \n",
              "88                                 Tobacco               17     0.027034   \n",
              "89                          Transportation               18     0.025404   \n",
              "90              Transportation (Railroads)                8     0.010728   \n",
              "91                                Trucking               33     0.020479   \n",
              "92                       Utility (General)               16     0.001173   \n",
              "93                         Utility (Water)               17     0.021316   \n",
              "94                            Total Market             7053     0.047442   \n",
              "95       Total Market (without financials)             5878     0.049319   \n",
              "\n",
              "   Institutional Holdings  Insider Holdings  \n",
              "0                0.375451          0.161023  \n",
              "1                0.571001          0.099429  \n",
              "2                0.730171          0.042613  \n",
              "3                0.505583          0.180427  \n",
              "4                0.606275          0.095710  \n",
              "5                0.599107          0.113179  \n",
              "6                0.584515          0.070661  \n",
              "7                0.354313          0.120805  \n",
              "8                 0.45555          0.096732  \n",
              "9                0.261087          0.087070  \n",
              "10               0.587724          0.241610  \n",
              "11               0.656569          0.135872  \n",
              "12               0.685724          0.052678  \n",
              "13               0.524726          0.201807  \n",
              "14                0.57085          0.061341  \n",
              "15                0.37529          0.164593  \n",
              "16                0.79305          0.035155  \n",
              "17               0.574201          0.087711  \n",
              "18               0.362273          0.097948  \n",
              "19               0.549416          0.156700  \n",
              "20               0.457172          0.120422  \n",
              "21               0.727958          0.057176  \n",
              "22               0.540199          0.043077  \n",
              "23               0.361361          0.093564  \n",
              "24               0.272346          0.133264  \n",
              "26               0.406755          0.177506  \n",
              "27               0.281636          0.208095  \n",
              "28               0.449221          0.144184  \n",
              "29               0.676232          0.105965  \n",
              "30               0.439051          0.206593  \n",
              "31               0.455902          0.166875  \n",
              "32               0.440725          0.153260  \n",
              "33               0.388296          0.125903  \n",
              "34               0.497551          0.130462  \n",
              "35                0.62787          0.178509  \n",
              "36               0.425788          0.200338  \n",
              "37               0.342021          0.166328  \n",
              "38               0.461281          0.116914  \n",
              "39               0.491228          0.176483  \n",
              "40               0.505673          0.172344  \n",
              "41               0.768542          0.141928  \n",
              "42               0.537949          0.140837  \n",
              "43               0.629228          0.151742  \n",
              "44               0.338093          0.228510  \n",
              "45               0.621787          0.128884  \n",
              "46               0.733088          0.121641  \n",
              "47               0.657938          0.038045  \n",
              "48               0.568681          0.146039  \n",
              "49               0.470696          0.154067  \n",
              "50               0.673124          0.109743  \n",
              "51               0.208205          0.210495  \n",
              "52               0.602455          0.084564  \n",
              "53                 0.7296          0.012653  \n",
              "54               0.377124          0.139174  \n",
              "55               0.565526          0.098708  \n",
              "56                0.52961          0.115139  \n",
              "57               0.694487          0.074581  \n",
              "58                0.75416          0.032181  \n",
              "59               0.709458          0.050637  \n",
              "60                0.20258          0.190125  \n",
              "61                0.56615          0.099606  \n",
              "62               0.765564          0.036724  \n",
              "63               0.308433          0.241273  \n",
              "64               0.530997          0.209057  \n",
              "65                0.34201          0.252812  \n",
              "66               0.518088          0.196893  \n",
              "67                0.89815          0.019575  \n",
              "68               0.597804          0.112961  \n",
              "69               0.741948          0.112152  \n",
              "70               0.775873          0.096234  \n",
              "71                0.60425          0.127875  \n",
              "72               0.728622          0.064081  \n",
              "73               0.477076          0.161656  \n",
              "74               0.423614          0.210139  \n",
              "75               0.674289          0.109561  \n",
              "76               0.467243          0.045578  \n",
              "77               0.588169          0.089351  \n",
              "78               0.640928          0.086716  \n",
              "79               0.384601          0.181002  \n",
              "80               0.781213          0.065967  \n",
              "81               0.366207          0.189351  \n",
              "82               0.466025          0.212108  \n",
              "83               0.480943          0.161489  \n",
              "84               0.667756          0.056544  \n",
              "85                0.49328          0.094157  \n",
              "86               0.460804          0.146245  \n",
              "87                0.38819          0.173874  \n",
              "88               0.416315          0.072561  \n",
              "89               0.824517          0.062299  \n",
              "90                0.63909          0.028092  \n",
              "91                0.55906          0.157253  \n",
              "92               0.803944          0.005601  \n",
              "93               0.499558          0.052463  \n",
              "94               0.485737          0.129176  \n",
              "95               0.500262          0.129637  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3aa3779d-d812-4d93-a72c-d785d1ce8e69\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry Name</th>\n",
              "      <th>Number of Firms</th>\n",
              "      <th>CEO Holding</th>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <th>Insider Holdings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Advertising</td>\n",
              "      <td>47</td>\n",
              "      <td>0.050409</td>\n",
              "      <td>0.375451</td>\n",
              "      <td>0.161023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aerospace/Defense</td>\n",
              "      <td>77</td>\n",
              "      <td>0.030481</td>\n",
              "      <td>0.571001</td>\n",
              "      <td>0.099429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Air Transport</td>\n",
              "      <td>18</td>\n",
              "      <td>0.017713</td>\n",
              "      <td>0.730171</td>\n",
              "      <td>0.042613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apparel</td>\n",
              "      <td>51</td>\n",
              "      <td>0.091342</td>\n",
              "      <td>0.505583</td>\n",
              "      <td>0.180427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Auto &amp; Truck</td>\n",
              "      <td>13</td>\n",
              "      <td>0.069599</td>\n",
              "      <td>0.606275</td>\n",
              "      <td>0.095710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Auto Parts</td>\n",
              "      <td>46</td>\n",
              "      <td>0.029056</td>\n",
              "      <td>0.599107</td>\n",
              "      <td>0.113179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bank (Money Center)</td>\n",
              "      <td>7</td>\n",
              "      <td>0.001516</td>\n",
              "      <td>0.584515</td>\n",
              "      <td>0.070661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Banks (Regional)</td>\n",
              "      <td>611</td>\n",
              "      <td>0.017899</td>\n",
              "      <td>0.354313</td>\n",
              "      <td>0.120805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Beverage (Alcoholic)</td>\n",
              "      <td>21</td>\n",
              "      <td>0.016225</td>\n",
              "      <td>0.45555</td>\n",
              "      <td>0.096732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Beverage (Soft)</td>\n",
              "      <td>34</td>\n",
              "      <td>0.040421</td>\n",
              "      <td>0.261087</td>\n",
              "      <td>0.087070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Broadcasting</td>\n",
              "      <td>27</td>\n",
              "      <td>0.075388</td>\n",
              "      <td>0.587724</td>\n",
              "      <td>0.241610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Brokerage &amp; Investment Banking</td>\n",
              "      <td>39</td>\n",
              "      <td>0.045112</td>\n",
              "      <td>0.656569</td>\n",
              "      <td>0.135872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Building Materials</td>\n",
              "      <td>42</td>\n",
              "      <td>0.018934</td>\n",
              "      <td>0.685724</td>\n",
              "      <td>0.052678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Business &amp; Consumer Services</td>\n",
              "      <td>165</td>\n",
              "      <td>0.072431</td>\n",
              "      <td>0.524726</td>\n",
              "      <td>0.201807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Cable TV</td>\n",
              "      <td>14</td>\n",
              "      <td>0.005457</td>\n",
              "      <td>0.57085</td>\n",
              "      <td>0.061341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Chemical (Basic)</td>\n",
              "      <td>43</td>\n",
              "      <td>0.085014</td>\n",
              "      <td>0.37529</td>\n",
              "      <td>0.164593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Chemical (Diversified)</td>\n",
              "      <td>6</td>\n",
              "      <td>0.014690</td>\n",
              "      <td>0.79305</td>\n",
              "      <td>0.035155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Chemical (Specialty)</td>\n",
              "      <td>94</td>\n",
              "      <td>0.020830</td>\n",
              "      <td>0.574201</td>\n",
              "      <td>0.087711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Coal &amp; Related Energy</td>\n",
              "      <td>22</td>\n",
              "      <td>0.028429</td>\n",
              "      <td>0.362273</td>\n",
              "      <td>0.097948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Computer Services</td>\n",
              "      <td>106</td>\n",
              "      <td>0.073701</td>\n",
              "      <td>0.549416</td>\n",
              "      <td>0.156700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Computers/Peripherals</td>\n",
              "      <td>48</td>\n",
              "      <td>0.036865</td>\n",
              "      <td>0.457172</td>\n",
              "      <td>0.120422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Construction Supplies</td>\n",
              "      <td>44</td>\n",
              "      <td>0.037227</td>\n",
              "      <td>0.727958</td>\n",
              "      <td>0.057176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Diversified</td>\n",
              "      <td>23</td>\n",
              "      <td>0.022122</td>\n",
              "      <td>0.540199</td>\n",
              "      <td>0.043077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Drugs (Biotechnology)</td>\n",
              "      <td>503</td>\n",
              "      <td>0.033630</td>\n",
              "      <td>0.361361</td>\n",
              "      <td>0.093564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Drugs (Pharmaceutical)</td>\n",
              "      <td>267</td>\n",
              "      <td>0.048045</td>\n",
              "      <td>0.272346</td>\n",
              "      <td>0.133264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Electrical Equipment</td>\n",
              "      <td>113</td>\n",
              "      <td>0.050020</td>\n",
              "      <td>0.406755</td>\n",
              "      <td>0.177506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Electronics (Consumer &amp; Office)</td>\n",
              "      <td>20</td>\n",
              "      <td>0.086656</td>\n",
              "      <td>0.281636</td>\n",
              "      <td>0.208095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Electronics (General)</td>\n",
              "      <td>153</td>\n",
              "      <td>0.039852</td>\n",
              "      <td>0.449221</td>\n",
              "      <td>0.144184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Engineering/Construction</td>\n",
              "      <td>54</td>\n",
              "      <td>0.069114</td>\n",
              "      <td>0.676232</td>\n",
              "      <td>0.105965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Entertainment</td>\n",
              "      <td>107</td>\n",
              "      <td>0.092871</td>\n",
              "      <td>0.439051</td>\n",
              "      <td>0.206593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Environmental &amp; Waste Services</td>\n",
              "      <td>82</td>\n",
              "      <td>0.074181</td>\n",
              "      <td>0.455902</td>\n",
              "      <td>0.166875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Farming/Agriculture</td>\n",
              "      <td>31</td>\n",
              "      <td>0.046266</td>\n",
              "      <td>0.440725</td>\n",
              "      <td>0.153260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Financial Svcs. (Non-bank &amp; Insurance)</td>\n",
              "      <td>232</td>\n",
              "      <td>0.037064</td>\n",
              "      <td>0.388296</td>\n",
              "      <td>0.125903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Food Processing</td>\n",
              "      <td>88</td>\n",
              "      <td>0.050342</td>\n",
              "      <td>0.497551</td>\n",
              "      <td>0.130462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Food Wholesalers</td>\n",
              "      <td>17</td>\n",
              "      <td>0.048782</td>\n",
              "      <td>0.62787</td>\n",
              "      <td>0.178509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Furn/Home Furnishings</td>\n",
              "      <td>35</td>\n",
              "      <td>0.059996</td>\n",
              "      <td>0.425788</td>\n",
              "      <td>0.200338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Green &amp; Renewable Energy</td>\n",
              "      <td>22</td>\n",
              "      <td>0.098471</td>\n",
              "      <td>0.342021</td>\n",
              "      <td>0.166328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Healthcare Products</td>\n",
              "      <td>242</td>\n",
              "      <td>0.041659</td>\n",
              "      <td>0.461281</td>\n",
              "      <td>0.116914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Healthcare Support Services</td>\n",
              "      <td>128</td>\n",
              "      <td>0.065537</td>\n",
              "      <td>0.491228</td>\n",
              "      <td>0.176483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Heathcare Information and Technology</td>\n",
              "      <td>129</td>\n",
              "      <td>0.074792</td>\n",
              "      <td>0.505673</td>\n",
              "      <td>0.172344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Homebuilding</td>\n",
              "      <td>32</td>\n",
              "      <td>0.075656</td>\n",
              "      <td>0.768542</td>\n",
              "      <td>0.141928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Hospitals/Healthcare Facilities</td>\n",
              "      <td>36</td>\n",
              "      <td>0.042355</td>\n",
              "      <td>0.537949</td>\n",
              "      <td>0.140837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Hotel/Gaming</td>\n",
              "      <td>65</td>\n",
              "      <td>0.052368</td>\n",
              "      <td>0.629228</td>\n",
              "      <td>0.151742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Household Products</td>\n",
              "      <td>127</td>\n",
              "      <td>0.126213</td>\n",
              "      <td>0.338093</td>\n",
              "      <td>0.228510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Information Services</td>\n",
              "      <td>69</td>\n",
              "      <td>0.061668</td>\n",
              "      <td>0.621787</td>\n",
              "      <td>0.128884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Insurance (General)</td>\n",
              "      <td>19</td>\n",
              "      <td>0.021413</td>\n",
              "      <td>0.733088</td>\n",
              "      <td>0.121641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Insurance (Life)</td>\n",
              "      <td>24</td>\n",
              "      <td>0.007723</td>\n",
              "      <td>0.657938</td>\n",
              "      <td>0.038045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Insurance (Prop/Cas.)</td>\n",
              "      <td>51</td>\n",
              "      <td>0.041256</td>\n",
              "      <td>0.568681</td>\n",
              "      <td>0.146039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Investments &amp; Asset Management</td>\n",
              "      <td>192</td>\n",
              "      <td>0.107877</td>\n",
              "      <td>0.470696</td>\n",
              "      <td>0.154067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Machinery</td>\n",
              "      <td>120</td>\n",
              "      <td>0.026871</td>\n",
              "      <td>0.673124</td>\n",
              "      <td>0.109743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Metals &amp; Mining</td>\n",
              "      <td>92</td>\n",
              "      <td>0.131931</td>\n",
              "      <td>0.208205</td>\n",
              "      <td>0.210495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Office Equipment &amp; Services</td>\n",
              "      <td>22</td>\n",
              "      <td>0.035822</td>\n",
              "      <td>0.602455</td>\n",
              "      <td>0.084564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Oil/Gas (Integrated)</td>\n",
              "      <td>4</td>\n",
              "      <td>0.002785</td>\n",
              "      <td>0.7296</td>\n",
              "      <td>0.012653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Oil/Gas (Production and Exploration)</td>\n",
              "      <td>269</td>\n",
              "      <td>0.064955</td>\n",
              "      <td>0.377124</td>\n",
              "      <td>0.139174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Oil/Gas Distribution</td>\n",
              "      <td>24</td>\n",
              "      <td>0.066219</td>\n",
              "      <td>0.565526</td>\n",
              "      <td>0.098708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Oilfield Svcs/Equip.</td>\n",
              "      <td>136</td>\n",
              "      <td>0.045425</td>\n",
              "      <td>0.52961</td>\n",
              "      <td>0.115139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Packaging &amp; Container</td>\n",
              "      <td>24</td>\n",
              "      <td>0.010357</td>\n",
              "      <td>0.694487</td>\n",
              "      <td>0.074581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Paper/Forest Products</td>\n",
              "      <td>15</td>\n",
              "      <td>0.005661</td>\n",
              "      <td>0.75416</td>\n",
              "      <td>0.032181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Power</td>\n",
              "      <td>52</td>\n",
              "      <td>0.008871</td>\n",
              "      <td>0.709458</td>\n",
              "      <td>0.050637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Precious Metals</td>\n",
              "      <td>83</td>\n",
              "      <td>0.057095</td>\n",
              "      <td>0.20258</td>\n",
              "      <td>0.190125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Publishing &amp; Newspapers</td>\n",
              "      <td>31</td>\n",
              "      <td>0.020642</td>\n",
              "      <td>0.56615</td>\n",
              "      <td>0.099606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>R.E.I.T.</td>\n",
              "      <td>234</td>\n",
              "      <td>0.015122</td>\n",
              "      <td>0.765564</td>\n",
              "      <td>0.036724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Real Estate (Development)</td>\n",
              "      <td>20</td>\n",
              "      <td>0.135526</td>\n",
              "      <td>0.308433</td>\n",
              "      <td>0.241273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Real Estate (General/Diversified)</td>\n",
              "      <td>12</td>\n",
              "      <td>0.009882</td>\n",
              "      <td>0.530997</td>\n",
              "      <td>0.209057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Real Estate (Operations &amp; Services)</td>\n",
              "      <td>57</td>\n",
              "      <td>0.133494</td>\n",
              "      <td>0.34201</td>\n",
              "      <td>0.252812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Recreation</td>\n",
              "      <td>63</td>\n",
              "      <td>0.100167</td>\n",
              "      <td>0.518088</td>\n",
              "      <td>0.196893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Reinsurance</td>\n",
              "      <td>2</td>\n",
              "      <td>0.002610</td>\n",
              "      <td>0.89815</td>\n",
              "      <td>0.019575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Restaurant/Dining</td>\n",
              "      <td>77</td>\n",
              "      <td>0.042689</td>\n",
              "      <td>0.597804</td>\n",
              "      <td>0.112961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Retail (Automotive)</td>\n",
              "      <td>26</td>\n",
              "      <td>0.075498</td>\n",
              "      <td>0.741948</td>\n",
              "      <td>0.112152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Retail (Building Supply)</td>\n",
              "      <td>17</td>\n",
              "      <td>0.013185</td>\n",
              "      <td>0.775873</td>\n",
              "      <td>0.096234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Retail (Distributors)</td>\n",
              "      <td>80</td>\n",
              "      <td>0.057704</td>\n",
              "      <td>0.60425</td>\n",
              "      <td>0.127875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Retail (General)</td>\n",
              "      <td>18</td>\n",
              "      <td>0.008865</td>\n",
              "      <td>0.728622</td>\n",
              "      <td>0.064081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Retail (Grocery and Food)</td>\n",
              "      <td>13</td>\n",
              "      <td>0.097168</td>\n",
              "      <td>0.477076</td>\n",
              "      <td>0.161656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Retail (Online)</td>\n",
              "      <td>70</td>\n",
              "      <td>0.090229</td>\n",
              "      <td>0.423614</td>\n",
              "      <td>0.210139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Retail (Special Lines)</td>\n",
              "      <td>89</td>\n",
              "      <td>0.034434</td>\n",
              "      <td>0.674289</td>\n",
              "      <td>0.109561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Rubber&amp; Tires</td>\n",
              "      <td>4</td>\n",
              "      <td>0.044497</td>\n",
              "      <td>0.467243</td>\n",
              "      <td>0.045578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Semiconductor</td>\n",
              "      <td>72</td>\n",
              "      <td>0.024688</td>\n",
              "      <td>0.588169</td>\n",
              "      <td>0.089351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Semiconductor Equip</td>\n",
              "      <td>39</td>\n",
              "      <td>0.020794</td>\n",
              "      <td>0.640928</td>\n",
              "      <td>0.086716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Shipbuilding &amp; Marine</td>\n",
              "      <td>10</td>\n",
              "      <td>0.121509</td>\n",
              "      <td>0.384601</td>\n",
              "      <td>0.181002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Shoe</td>\n",
              "      <td>11</td>\n",
              "      <td>0.009683</td>\n",
              "      <td>0.781213</td>\n",
              "      <td>0.065967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Software (Entertainment)</td>\n",
              "      <td>86</td>\n",
              "      <td>0.084948</td>\n",
              "      <td>0.366207</td>\n",
              "      <td>0.189351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Software (Internet)</td>\n",
              "      <td>30</td>\n",
              "      <td>0.144369</td>\n",
              "      <td>0.466025</td>\n",
              "      <td>0.212108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Software (System &amp; Application)</td>\n",
              "      <td>363</td>\n",
              "      <td>0.065429</td>\n",
              "      <td>0.480943</td>\n",
              "      <td>0.161489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Steel</td>\n",
              "      <td>32</td>\n",
              "      <td>0.011503</td>\n",
              "      <td>0.667756</td>\n",
              "      <td>0.056544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Telecom (Wireless)</td>\n",
              "      <td>18</td>\n",
              "      <td>0.035370</td>\n",
              "      <td>0.49328</td>\n",
              "      <td>0.094157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Telecom. Equipment</td>\n",
              "      <td>91</td>\n",
              "      <td>0.055862</td>\n",
              "      <td>0.460804</td>\n",
              "      <td>0.146245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Telecom. Services</td>\n",
              "      <td>67</td>\n",
              "      <td>0.057453</td>\n",
              "      <td>0.38819</td>\n",
              "      <td>0.173874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Tobacco</td>\n",
              "      <td>17</td>\n",
              "      <td>0.027034</td>\n",
              "      <td>0.416315</td>\n",
              "      <td>0.072561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Transportation</td>\n",
              "      <td>18</td>\n",
              "      <td>0.025404</td>\n",
              "      <td>0.824517</td>\n",
              "      <td>0.062299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Transportation (Railroads)</td>\n",
              "      <td>8</td>\n",
              "      <td>0.010728</td>\n",
              "      <td>0.63909</td>\n",
              "      <td>0.028092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Trucking</td>\n",
              "      <td>33</td>\n",
              "      <td>0.020479</td>\n",
              "      <td>0.55906</td>\n",
              "      <td>0.157253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Utility (General)</td>\n",
              "      <td>16</td>\n",
              "      <td>0.001173</td>\n",
              "      <td>0.803944</td>\n",
              "      <td>0.005601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Utility (Water)</td>\n",
              "      <td>17</td>\n",
              "      <td>0.021316</td>\n",
              "      <td>0.499558</td>\n",
              "      <td>0.052463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Total Market</td>\n",
              "      <td>7053</td>\n",
              "      <td>0.047442</td>\n",
              "      <td>0.485737</td>\n",
              "      <td>0.129176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Total Market (without financials)</td>\n",
              "      <td>5878</td>\n",
              "      <td>0.049319</td>\n",
              "      <td>0.500262</td>\n",
              "      <td>0.129637</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-3aa3779d-d812-4d93-a72c-d785d1ce8e69 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-3aa3779d-d812-4d93-a72c-d785d1ce8e69');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2d15b51c-1901-420b-8b8f-7e9fba1c9d58 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"inst_own_with_error\",\n  \"rows\": 95,\n  \"fields\": [\n    {\n      \"column\": \"Industry Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 95,\n        \"samples\": [\n          \"Retail (Automotive)\",\n          \"Diversified\",\n          \"Retail (Grocery and Food)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 931,\n        \"min\": 2,\n        \"max\": 7053,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          13,\n          35,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CEO Holding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.034522078676472626,\n        \"min\": 0.001173125,\n        \"max\": 0.14436875000000002,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.07549789474,\n          0.02212181818,\n          0.09716833333000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.20258035710000002,\n        \"max\": 0.89815,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.7419478947,\n          0.5401992308,\n          0.477076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05838947183711692,\n        \"min\": 0.0056012499999999995,\n        \"max\": 0.2528118919,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.11215249999999999,\n          0.043077272730000005,\n          0.1616555556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "cZWUhknY_fF-",
        "outputId": "6c17ba2d-70d3-44bb-857a-8a71506868e0"
      },
      "source": [
        "inst_own_with_error.dtypes"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Industry Name              object\n",
              "Number of Firms             int64\n",
              "CEO Holding               float64\n",
              "Institutional Holdings     object\n",
              "Insider Holdings          float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
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              "      <th>CEO Holding</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <td>object</td>\n",
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              "    <tr>\n",
              "      <th>Insider Holdings</th>\n",
              "      <td>float64</td>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWYv-ASmi4Da"
      },
      "source": [
        "#### Changing the Data in Row 25 of Institutional Holdings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7oSslVQY3Aw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8370c098-d208-44bc-9bcc-ffc5f53ae274"
      },
      "source": [
        "inst_own_with_error[\"Institutional Holdings\"].loc[25] = 0.539263\n",
        "#inst_own_with_error[\"Institutional Holdings\"].loc[25]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-4c4355c97a23>:1: 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",
            "  inst_own_with_error[\"Institutional Holdings\"].loc[25] = 0.539263\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIBy7gUufHqd"
      },
      "source": [
        "inst_own_with_error[\"Institutional Holdings\"] = pd.to_numeric(inst_own_with_error[\"Institutional Holdings\"])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YzM9owYcL9a"
      },
      "source": [
        "And we see below Institutional Holdings is a float."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJwPYD-javSR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "37744cb5-b7ff-40fc-c54d-6d226175ed1e"
      },
      "source": [
        "inst_own_with_error.dtypes"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Industry Name              object\n",
              "Number of Firms             int64\n",
              "CEO Holding               float64\n",
              "Institutional Holdings    float64\n",
              "Insider Holdings          float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
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              "      <th>Number of Firms</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CEO Holding</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insider Holdings</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJaaveDKpASt"
      },
      "source": [
        "### Selecting Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGMlIPWOo_W9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "0c575caa-a48e-49f6-9d18-75f0c07d1f9e"
      },
      "source": [
        "pd.set_option('display.max_rows', 10)\n",
        "inst_own[\"Industry Name\"]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                           Advertising\n",
              "1                     Aerospace/Defense\n",
              "2                         Air Transport\n",
              "3                               Apparel\n",
              "4                          Auto & Truck\n",
              "                    ...                \n",
              "91                             Trucking\n",
              "92                    Utility (General)\n",
              "93                      Utility (Water)\n",
              "94                         Total Market\n",
              "95    Total Market (without financials)\n",
              "Name: Industry Name, Length: 96, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    </tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Air Transport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apparel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Auto &amp; Truck</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Trucking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Utility (General)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Utility (Water)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Total Market</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Total Market (without financials)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eubPoYe18POi"
      },
      "source": [
        "### Adding Columns\n",
        "\n",
        "Lets say we want to create a column which is the sum of Institutional and Insider Holdings.  Let's call it `II_Holdings`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luzJE7ct8jmz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "f918d515-0ce0-4cf9-c286-7d6d7680ab2e"
      },
      "source": [
        "inst_own[\"II_Holdings\"] = inst_own[\"Institutional Holdings\"] + inst_own[\"Insider Holdings\"]\n",
        "inst_own"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Industry Name  Number of Firms  CEO Holding  \\\n",
              "0                         Advertising               54     0.099896   \n",
              "1                   Aerospace/Defense               67     0.034654   \n",
              "2                       Air Transport               24     0.030976   \n",
              "3                             Apparel               37     0.053675   \n",
              "4                        Auto & Truck               34     0.096935   \n",
              "..                                ...              ...          ...   \n",
              "91                           Trucking               24     0.054088   \n",
              "92                  Utility (General)               14     0.001355   \n",
              "93                    Utility (Water)               15     0.005923   \n",
              "94                       Total Market             6062     0.048202   \n",
              "95  Total Market (without financials)             4935     0.048431   \n",
              "\n",
              "    Corporate Holdings  Institutional Holdings  Insider Holdings  II_Holdings  \n",
              "0             0.152666                0.352435          0.233282     0.585717  \n",
              "1             0.180971                0.528903          0.088726     0.617629  \n",
              "2             0.261742                0.418113          0.137485     0.555598  \n",
              "3             0.108202                0.514791          0.170123     0.684914  \n",
              "4             0.195019                0.230086          0.174605     0.404692  \n",
              "..                 ...                     ...               ...          ...  \n",
              "91            0.340488                0.583142          0.168315     0.751457  \n",
              "92            0.000000                0.857136          0.004635     0.861771  \n",
              "93            0.276800                0.556169          0.059443     0.615613  \n",
              "94            0.141806                0.484652          0.127259     0.611911  \n",
              "95            0.141046                0.496171          0.126508     0.622678  \n",
              "\n",
              "[96 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>Industry Name</th>\n",
              "      <th>Number of Firms</th>\n",
              "      <th>CEO Holding</th>\n",
              "      <th>Corporate Holdings</th>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <th>Insider Holdings</th>\n",
              "      <th>II_Holdings</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Advertising</td>\n",
              "      <td>54</td>\n",
              "      <td>0.099896</td>\n",
              "      <td>0.152666</td>\n",
              "      <td>0.352435</td>\n",
              "      <td>0.233282</td>\n",
              "      <td>0.585717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aerospace/Defense</td>\n",
              "      <td>67</td>\n",
              "      <td>0.034654</td>\n",
              "      <td>0.180971</td>\n",
              "      <td>0.528903</td>\n",
              "      <td>0.088726</td>\n",
              "      <td>0.617629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Air Transport</td>\n",
              "      <td>24</td>\n",
              "      <td>0.030976</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.418113</td>\n",
              "      <td>0.137485</td>\n",
              "      <td>0.555598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apparel</td>\n",
              "      <td>37</td>\n",
              "      <td>0.053675</td>\n",
              "      <td>0.108202</td>\n",
              "      <td>0.514791</td>\n",
              "      <td>0.170123</td>\n",
              "      <td>0.684914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Auto &amp; Truck</td>\n",
              "      <td>34</td>\n",
              "      <td>0.096935</td>\n",
              "      <td>0.195019</td>\n",
              "      <td>0.230086</td>\n",
              "      <td>0.174605</td>\n",
              "      <td>0.404692</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Trucking</td>\n",
              "      <td>24</td>\n",
              "      <td>0.054088</td>\n",
              "      <td>0.340488</td>\n",
              "      <td>0.583142</td>\n",
              "      <td>0.168315</td>\n",
              "      <td>0.751457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Utility (General)</td>\n",
              "      <td>14</td>\n",
              "      <td>0.001355</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.857136</td>\n",
              "      <td>0.004635</td>\n",
              "      <td>0.861771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Utility (Water)</td>\n",
              "      <td>15</td>\n",
              "      <td>0.005923</td>\n",
              "      <td>0.276800</td>\n",
              "      <td>0.556169</td>\n",
              "      <td>0.059443</td>\n",
              "      <td>0.615613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Total Market</td>\n",
              "      <td>6062</td>\n",
              "      <td>0.048202</td>\n",
              "      <td>0.141806</td>\n",
              "      <td>0.484652</td>\n",
              "      <td>0.127259</td>\n",
              "      <td>0.611911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Total Market (without financials)</td>\n",
              "      <td>4935</td>\n",
              "      <td>0.048431</td>\n",
              "      <td>0.141046</td>\n",
              "      <td>0.496171</td>\n",
              "      <td>0.126508</td>\n",
              "      <td>0.622678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 7 columns</p>\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"inst_own\",\n  \"rows\": 96,\n  \"fields\": [\n    {\n      \"column\": \"Industry Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 96,\n        \"samples\": [\n          \"Shoe\",\n          \"Semiconductor\",\n          \"Retail (Grocery and Food)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 789,\n        \"min\": 1,\n        \"max\": 6062,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          17,\n          333,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CEO Holding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03360049494760392,\n        \"min\": 0.00035000000000000005,\n        \"max\": 0.1663356,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.00944857143,\n          0.017195000000000002,\n          0.1085007692\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corporate Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10010370183473834,\n        \"min\": 0.0,\n        \"max\": 0.5834,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          0.1211093333,\n          0.14885500000000002,\n          0.337705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13959774177935172,\n        \"min\": 0.23008615379999997,\n        \"max\": 0.9653,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.6980344444000001,\n          0.6106276786,\n          0.44433799999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05954576258908088,\n        \"min\": 0.0032400000000000003,\n        \"max\": 0.2973314286,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.055155,\n          0.07960122807,\n          0.2015371429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"II_Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11724696609341481,\n        \"min\": 0.4046915705,\n        \"max\": 0.96935,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          0.7531894444,\n          0.69022890667,\n          0.6458751428999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xO8uE7SSm7kK"
      },
      "source": [
        "# Which Industries have the Highest Levels of Institutional Ownership?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moiI5dY0nLLZ"
      },
      "source": [
        "We can sort the `DataFrame` on `Institutional Ownership`, and print the first 10 rows, with:"
      ]
    },
    {
      "cell_type": "code",
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        "id": "htcl-_L1rJw6",
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        },
        "outputId": "fe4ce381-8ea2-4b82-cbbc-f161ee11f263"
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      "source": [
        "inst_own.sort_values(\"Institutional Holdings\", ascending=False).head(10)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Industry Name  Number of Firms  CEO Holding  \\\n",
              "67                 Reinsurance                1     0.000350   \n",
              "92           Utility (General)               14     0.001355   \n",
              "12          Building Materials               39     0.018672   \n",
              "74              Retail (REITs)               28     0.005538   \n",
              "90  Transportation (Railroads)                4     0.001335   \n",
              "59                       Power               48     0.007155   \n",
              "41                Homebuilding               30     0.079001   \n",
              "5                   Auto Parts               33     0.051528   \n",
              "78         Semiconductor Equip               30     0.017968   \n",
              "80                        Shoe               12     0.009449   \n",
              "\n",
              "    Corporate Holdings  Institutional Holdings  Insider Holdings  II_Holdings  \n",
              "67            0.000000                0.965300          0.004050     0.969350  \n",
              "92            0.000000                0.857136          0.004635     0.861771  \n",
              "12            0.102658                0.818994          0.052210     0.871204  \n",
              "74            0.027237                0.770551          0.027786     0.798337  \n",
              "90            0.000010                0.768775          0.003240     0.772015  \n",
              "59            0.058685                0.764030          0.023714     0.787744  \n",
              "41            0.069467                0.756331          0.153598     0.909929  \n",
              "5             0.133344                0.711526          0.093646     0.805172  \n",
              "78            0.052257                0.701565          0.086590     0.788155  \n",
              "80            0.087100                0.698034          0.055155     0.753189  "
            ],
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              "      <th>Industry Name</th>\n",
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              "      <th>Corporate Holdings</th>\n",
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              "      <th>67</th>\n",
              "      <td>Reinsurance</td>\n",
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              "      <th>92</th>\n",
              "      <td>Utility (General)</td>\n",
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              "      <th>74</th>\n",
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              "      <th>90</th>\n",
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              "      <td>4</td>\n",
              "      <td>0.001335</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.768775</td>\n",
              "      <td>0.003240</td>\n",
              "      <td>0.772015</td>\n",
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              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Power</td>\n",
              "      <td>48</td>\n",
              "      <td>0.007155</td>\n",
              "      <td>0.058685</td>\n",
              "      <td>0.764030</td>\n",
              "      <td>0.023714</td>\n",
              "      <td>0.787744</td>\n",
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              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Homebuilding</td>\n",
              "      <td>30</td>\n",
              "      <td>0.079001</td>\n",
              "      <td>0.069467</td>\n",
              "      <td>0.756331</td>\n",
              "      <td>0.153598</td>\n",
              "      <td>0.909929</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>33</td>\n",
              "      <td>0.051528</td>\n",
              "      <td>0.133344</td>\n",
              "      <td>0.711526</td>\n",
              "      <td>0.093646</td>\n",
              "      <td>0.805172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Semiconductor Equip</td>\n",
              "      <td>30</td>\n",
              "      <td>0.017968</td>\n",
              "      <td>0.052257</td>\n",
              "      <td>0.701565</td>\n",
              "      <td>0.086590</td>\n",
              "      <td>0.788155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Shoe</td>\n",
              "      <td>12</td>\n",
              "      <td>0.009449</td>\n",
              "      <td>0.087100</td>\n",
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              "      <td>0.055155</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"inst_own\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Industry Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Semiconductor Equip\",\n          \"Utility (General)\",\n          \"Power\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 48,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          33,\n          14,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CEO Holding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02591188079351119,\n        \"min\": 0.00035000000000000005,\n        \"max\": 0.07900125,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.01796785714,\n          0.001355,\n          0.00715538462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corporate Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04652072920699531,\n        \"min\": 0.0,\n        \"max\": 0.1333442857,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.05225666666999999,\n          0.1026581818,\n          0.06946666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08190863917473422,\n        \"min\": 0.6980344444000001,\n        \"max\": 0.9653,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.701565,\n          0.8571357143,\n          0.7640300000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04892847078067809,\n        \"min\": 0.0032400000000000003,\n        \"max\": 0.1535980769,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.08658964286,\n          0.004635,\n          0.02371428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"II_Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06904376843011237,\n        \"min\": 0.7531894444,\n        \"max\": 0.96935,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.78815464286,\n          0.8617707143,\n          0.7877442857100001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGcdiUsZsAuh"
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      "source": [
        "We can also use `describe()` to get a fell for the data."
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "MHlYwvGUsGIk",
        "outputId": "08ef3418-4eed-464c-be97-5dee64912132"
      },
      "source": [
        "inst_own.describe()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Number of Firms  CEO Holding  Corporate Holdings  \\\n",
              "count        96.000000    96.000000           96.000000   \n",
              "mean        177.697917     0.048738            0.150023   \n",
              "std         789.957589     0.033600            0.100104   \n",
              "min           1.000000     0.000350            0.000000   \n",
              "25%          18.000000     0.019231            0.086743   \n",
              "50%          32.500000     0.049144            0.129665   \n",
              "75%          66.250000     0.065548            0.193874   \n",
              "max        6062.000000     0.166336            0.583400   \n",
              "\n",
              "       Institutional Holdings  Insider Holdings  II_Holdings  \n",
              "count               96.000000         96.000000    96.000000  \n",
              "mean                 0.535886          0.125710     0.661597  \n",
              "std                  0.139598          0.059546     0.117247  \n",
              "min                  0.230086          0.003240     0.404692  \n",
              "25%                  0.428347          0.089808     0.573631  \n",
              "50%                  0.532624          0.128464     0.675344  \n",
              "75%                  0.645234          0.163959     0.744324  \n",
              "max                  0.965300          0.297331     0.969350  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Firms</th>\n",
              "      <th>CEO Holding</th>\n",
              "      <th>Corporate Holdings</th>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <th>Insider Holdings</th>\n",
              "      <th>II_Holdings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>177.697917</td>\n",
              "      <td>0.048738</td>\n",
              "      <td>0.150023</td>\n",
              "      <td>0.535886</td>\n",
              "      <td>0.125710</td>\n",
              "      <td>0.661597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>789.957589</td>\n",
              "      <td>0.033600</td>\n",
              "      <td>0.100104</td>\n",
              "      <td>0.139598</td>\n",
              "      <td>0.059546</td>\n",
              "      <td>0.117247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000350</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.230086</td>\n",
              "      <td>0.003240</td>\n",
              "      <td>0.404692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.019231</td>\n",
              "      <td>0.086743</td>\n",
              "      <td>0.428347</td>\n",
              "      <td>0.089808</td>\n",
              "      <td>0.573631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>32.500000</td>\n",
              "      <td>0.049144</td>\n",
              "      <td>0.129665</td>\n",
              "      <td>0.532624</td>\n",
              "      <td>0.128464</td>\n",
              "      <td>0.675344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>66.250000</td>\n",
              "      <td>0.065548</td>\n",
              "      <td>0.193874</td>\n",
              "      <td>0.645234</td>\n",
              "      <td>0.163959</td>\n",
              "      <td>0.744324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6062.000000</td>\n",
              "      <td>0.166336</td>\n",
              "      <td>0.583400</td>\n",
              "      <td>0.965300</td>\n",
              "      <td>0.297331</td>\n",
              "      <td>0.969350</td>\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"inst_own\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Number of Firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2099.662368764081,\n        \"min\": 1.0,\n        \"max\": 6062.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          177.69791666666666,\n          32.5,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CEO Holding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.921820146303595,\n        \"min\": 0.00035000000000000005,\n        \"max\": 96.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.04873847501806893,\n          0.049144083335000005,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corporate Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.87875442650382,\n        \"min\": 0.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.15002324440749626,\n          0.12966495355,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.766453880905026,\n        \"min\": 0.13959774177935172,\n        \"max\": 96.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.535886371669201,\n          0.53262385645,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.897390611756954,\n        \"min\": 0.0032400000000000003,\n        \"max\": 96.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.12571041459906213,\n          0.12846375745,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"II_Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.73263944830151,\n        \"min\": 0.11724696609341481,\n        \"max\": 96.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.661596786268263,\n          0.67534375625,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzvhtmvKgU9V"
      },
      "source": [
        "## Get accounting returns data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kop2aQvcgfWh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "abcebfa3-281f-4904-edf9-037d2c98f9ed"
      },
      "source": [
        "# returns_url = \"http://www.stern.nyu.edu/~adamodar/pc/datasets/pbvdata.xls\"\n",
        "returns_url = \"https://github.com/FinancialMarkets/industry_instown_with_data_error_to_clean/blob/master/pbvdata.xls?raw=true\"\n",
        "returns = pd.read_excel(returns_url, skiprows = 7)\n",
        "returns"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Industry  Name  Number of firms       PBV       ROE  \\\n",
              "0                         Advertising               61  5.729582  0.029333   \n",
              "1                   Aerospace/Defense               72  4.436925  0.085432   \n",
              "2                       Air Transport               17  3.224150 -0.470269   \n",
              "3                             Apparel               51  4.111694 -0.081881   \n",
              "4                        Auto & Truck               19  7.578762  0.044885   \n",
              "..                                ...              ...       ...       ...   \n",
              "91                           Trucking               35  4.811726 -0.176956   \n",
              "92                  Utility (General)               16  1.840435  0.074857   \n",
              "93                    Utility (Water)               17  3.507385  0.082466   \n",
              "94                       Total Market             7582  3.813854  0.082466   \n",
              "95  Total Market (without financials)             6253  4.750108  0.077751   \n",
              "\n",
              "    EV/ Invested Capital      ROIC  \n",
              "0               7.009509  0.515119  \n",
              "1               4.236895  0.191149  \n",
              "2               1.762879 -0.160654  \n",
              "3               3.071836  0.075423  \n",
              "4               2.583233  0.011709  \n",
              "..                   ...       ...  \n",
              "91              2.568211 -0.040357  \n",
              "92              1.476021  0.067851  \n",
              "93              2.376167  0.060506  \n",
              "94              2.401274  0.060506  \n",
              "95              3.423948  0.105844  \n",
              "\n",
              "[96 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74d0f328-6550-4081-a7f0-3e1450738169\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry  Name</th>\n",
              "      <th>Number of firms</th>\n",
              "      <th>PBV</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EV/ Invested Capital</th>\n",
              "      <th>ROIC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Advertising</td>\n",
              "      <td>61</td>\n",
              "      <td>5.729582</td>\n",
              "      <td>0.029333</td>\n",
              "      <td>7.009509</td>\n",
              "      <td>0.515119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aerospace/Defense</td>\n",
              "      <td>72</td>\n",
              "      <td>4.436925</td>\n",
              "      <td>0.085432</td>\n",
              "      <td>4.236895</td>\n",
              "      <td>0.191149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Air Transport</td>\n",
              "      <td>17</td>\n",
              "      <td>3.224150</td>\n",
              "      <td>-0.470269</td>\n",
              "      <td>1.762879</td>\n",
              "      <td>-0.160654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apparel</td>\n",
              "      <td>51</td>\n",
              "      <td>4.111694</td>\n",
              "      <td>-0.081881</td>\n",
              "      <td>3.071836</td>\n",
              "      <td>0.075423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Auto &amp; Truck</td>\n",
              "      <td>19</td>\n",
              "      <td>7.578762</td>\n",
              "      <td>0.044885</td>\n",
              "      <td>2.583233</td>\n",
              "      <td>0.011709</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Trucking</td>\n",
              "      <td>35</td>\n",
              "      <td>4.811726</td>\n",
              "      <td>-0.176956</td>\n",
              "      <td>2.568211</td>\n",
              "      <td>-0.040357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Utility (General)</td>\n",
              "      <td>16</td>\n",
              "      <td>1.840435</td>\n",
              "      <td>0.074857</td>\n",
              "      <td>1.476021</td>\n",
              "      <td>0.067851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Utility (Water)</td>\n",
              "      <td>17</td>\n",
              "      <td>3.507385</td>\n",
              "      <td>0.082466</td>\n",
              "      <td>2.376167</td>\n",
              "      <td>0.060506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Total Market</td>\n",
              "      <td>7582</td>\n",
              "      <td>3.813854</td>\n",
              "      <td>0.082466</td>\n",
              "      <td>2.401274</td>\n",
              "      <td>0.060506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Total Market (without financials)</td>\n",
              "      <td>6253</td>\n",
              "      <td>4.750108</td>\n",
              "      <td>0.077751</td>\n",
              "      <td>3.423948</td>\n",
              "      <td>0.105844</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 6 columns</p>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('returns')\"\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "returns",
              "summary": "{\n  \"name\": \"returns\",\n  \"rows\": 96,\n  \"fields\": [\n    {\n      \"column\": \"Industry  Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 96,\n        \"samples\": [\n          \"Shoe\",\n          \"Semiconductor\",\n          \"Retail (Grocery and Food)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 991,\n        \"min\": 2,\n        \"max\": 7582,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          38,\n          61,\n          135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PBV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.279367720435728,\n        \"min\": 0.5908804164790717,\n        \"max\": 40.06705255976665,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          7.119657387892159,\n          1.8571825941414872,\n          1.24018893184319\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18221112414454155,\n        \"min\": -0.4702685391404543,\n        \"max\": 0.706385377665926,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          0.14110113703375673,\n          0.10620755186982106,\n          0.012807842501367015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EV/ Invested Capital\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.575836483771459,\n        \"min\": 0.7443535863341569,\n        \"max\": 12.284688343655736,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          11.767773271099621,\n          4.86631497737824,\n          1.645322845558704\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROIC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10637881294358448,\n        \"min\": -0.16065422687831812,\n        \"max\": 0.5151189951700217,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.07240115751477365,\n          0.13444721876605892,\n          0.14673499048151145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBTsBS7ngjyb"
      },
      "source": [
        "### Merge the two data sets on the Industry Name"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJ2Dmz8Ggof7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "8eb3b35a-0b7a-4e9c-f6ed-1aaa0bfe10fc"
      },
      "source": [
        "all_data = pd.merge(inst_own, returns, on = 'Industry Name')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'Industry Name'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-88ac0214d382>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mall_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst_own\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Industry Name'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    168\u001b[0m         )\n\u001b[1;32m    169\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m         op = _MergeOperation(\n\u001b[0m\u001b[1;32m    171\u001b[0m             \u001b[0mleft_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0mright_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[1;32m    792\u001b[0m             \u001b[0mleft_drop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    793\u001b[0m             \u001b[0mright_drop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m         ) = self._get_merge_keys()\n\u001b[0m\u001b[1;32m    795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mleft_drop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1295\u001b[0m                         \u001b[0mrk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mrk\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1297\u001b[0;31m                             \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1298\u001b[0m                         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1299\u001b[0m                             \u001b[0;31m# work-around for merge_asof(right_index=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_label_or_level_values\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1909\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1910\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1911\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1913\u001b[0m         \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Industry Name'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtUU1qNtlnrS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f6bacd3-2671-4070-8761-ee15ec440c90"
      },
      "source": [
        "returns.columns"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Industry  Name', 'Number of firms', 'PBV', 'ROE',\n",
              "       'EV/ Invested Capital', 'ROIC'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfCzHhfTlr16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb58b488-b931-4d76-c68d-c90397453dd5"
      },
      "source": [
        "inst_own.columns"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Industry Name', 'Number of Firms', 'CEO Holding', 'Corporate Holdings',\n",
              "       'Institutional Holdings', 'Insider Holdings', 'II_Holdings'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLdZ35n-kTOt"
      },
      "source": [
        "However this throws an error, specifically `KeyError: 'Industry Name'`.  So it is not finding the same column in each `DataFrame`.\n",
        "\n",
        "With a little inspection, we find that in the returns file, there are two spaces in `Industry  Name` and one space in the institutional ownership file.  \n",
        "\n",
        "Having spaces in column names is not a good practice, but let's just change the column name in the returns file to have one space between Industry and Name."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lj1vDD5NlHZ_"
      },
      "source": [
        "returns.rename(columns={'Industry  Name': 'Industry Name'}, inplace=True)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfvbSeU0l45H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf5211f5-adf6-4baa-f984-422228159fa7"
      },
      "source": [
        "returns.columns"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Industry Name', 'Number of firms', 'PBV', 'ROE',\n",
              "       'EV/ Invested Capital', 'ROIC'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50UqsEflgB2_"
      },
      "source": [
        "all_data = pd.merge(inst_own, returns, on = 'Industry Name')"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwdU-z8jgNTq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "86302a79-1a83-43a2-f634-2687c714e692"
      },
      "source": [
        "all_data"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Industry Name  Number of Firms  CEO Holding  \\\n",
              "0                         Advertising               54     0.099896   \n",
              "1                   Aerospace/Defense               67     0.034654   \n",
              "2                       Air Transport               24     0.030976   \n",
              "3                             Apparel               37     0.053675   \n",
              "4                        Auto & Truck               34     0.096935   \n",
              "..                                ...              ...          ...   \n",
              "90                           Trucking               24     0.054088   \n",
              "91                  Utility (General)               14     0.001355   \n",
              "92                    Utility (Water)               15     0.005923   \n",
              "93                       Total Market             6062     0.048202   \n",
              "94  Total Market (without financials)             4935     0.048431   \n",
              "\n",
              "    Corporate Holdings  Institutional Holdings  Insider Holdings  II_Holdings  \\\n",
              "0             0.152666                0.352435          0.233282     0.585717   \n",
              "1             0.180971                0.528903          0.088726     0.617629   \n",
              "2             0.261742                0.418113          0.137485     0.555598   \n",
              "3             0.108202                0.514791          0.170123     0.684914   \n",
              "4             0.195019                0.230086          0.174605     0.404692   \n",
              "..                 ...                     ...               ...          ...   \n",
              "90            0.340488                0.583142          0.168315     0.751457   \n",
              "91            0.000000                0.857136          0.004635     0.861771   \n",
              "92            0.276800                0.556169          0.059443     0.615613   \n",
              "93            0.141806                0.484652          0.127259     0.611911   \n",
              "94            0.141046                0.496171          0.126508     0.622678   \n",
              "\n",
              "    Number of firms       PBV       ROE  EV/ Invested Capital      ROIC  \n",
              "0                61  5.729582  0.029333              7.009509  0.515119  \n",
              "1                72  4.436925  0.085432              4.236895  0.191149  \n",
              "2                17  3.224150 -0.470269              1.762879 -0.160654  \n",
              "3                51  4.111694 -0.081881              3.071836  0.075423  \n",
              "4                19  7.578762  0.044885              2.583233  0.011709  \n",
              "..              ...       ...       ...                   ...       ...  \n",
              "90               35  4.811726 -0.176956              2.568211 -0.040357  \n",
              "91               16  1.840435  0.074857              1.476021  0.067851  \n",
              "92               17  3.507385  0.082466              2.376167  0.060506  \n",
              "93             7582  3.813854  0.082466              2.401274  0.060506  \n",
              "94             6253  4.750108  0.077751              3.423948  0.105844  \n",
              "\n",
              "[95 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f47983d4-3ec6-4405-82ba-f6e63e049311\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry Name</th>\n",
              "      <th>Number of Firms</th>\n",
              "      <th>CEO Holding</th>\n",
              "      <th>Corporate Holdings</th>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <th>Insider Holdings</th>\n",
              "      <th>II_Holdings</th>\n",
              "      <th>Number of firms</th>\n",
              "      <th>PBV</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EV/ Invested Capital</th>\n",
              "      <th>ROIC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Advertising</td>\n",
              "      <td>54</td>\n",
              "      <td>0.099896</td>\n",
              "      <td>0.152666</td>\n",
              "      <td>0.352435</td>\n",
              "      <td>0.233282</td>\n",
              "      <td>0.585717</td>\n",
              "      <td>61</td>\n",
              "      <td>5.729582</td>\n",
              "      <td>0.029333</td>\n",
              "      <td>7.009509</td>\n",
              "      <td>0.515119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aerospace/Defense</td>\n",
              "      <td>67</td>\n",
              "      <td>0.034654</td>\n",
              "      <td>0.180971</td>\n",
              "      <td>0.528903</td>\n",
              "      <td>0.088726</td>\n",
              "      <td>0.617629</td>\n",
              "      <td>72</td>\n",
              "      <td>4.436925</td>\n",
              "      <td>0.085432</td>\n",
              "      <td>4.236895</td>\n",
              "      <td>0.191149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Air Transport</td>\n",
              "      <td>24</td>\n",
              "      <td>0.030976</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.418113</td>\n",
              "      <td>0.137485</td>\n",
              "      <td>0.555598</td>\n",
              "      <td>17</td>\n",
              "      <td>3.224150</td>\n",
              "      <td>-0.470269</td>\n",
              "      <td>1.762879</td>\n",
              "      <td>-0.160654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apparel</td>\n",
              "      <td>37</td>\n",
              "      <td>0.053675</td>\n",
              "      <td>0.108202</td>\n",
              "      <td>0.514791</td>\n",
              "      <td>0.170123</td>\n",
              "      <td>0.684914</td>\n",
              "      <td>51</td>\n",
              "      <td>4.111694</td>\n",
              "      <td>-0.081881</td>\n",
              "      <td>3.071836</td>\n",
              "      <td>0.075423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Auto &amp; Truck</td>\n",
              "      <td>34</td>\n",
              "      <td>0.096935</td>\n",
              "      <td>0.195019</td>\n",
              "      <td>0.230086</td>\n",
              "      <td>0.174605</td>\n",
              "      <td>0.404692</td>\n",
              "      <td>19</td>\n",
              "      <td>7.578762</td>\n",
              "      <td>0.044885</td>\n",
              "      <td>2.583233</td>\n",
              "      <td>0.011709</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Trucking</td>\n",
              "      <td>24</td>\n",
              "      <td>0.054088</td>\n",
              "      <td>0.340488</td>\n",
              "      <td>0.583142</td>\n",
              "      <td>0.168315</td>\n",
              "      <td>0.751457</td>\n",
              "      <td>35</td>\n",
              "      <td>4.811726</td>\n",
              "      <td>-0.176956</td>\n",
              "      <td>2.568211</td>\n",
              "      <td>-0.040357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Utility (General)</td>\n",
              "      <td>14</td>\n",
              "      <td>0.001355</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.857136</td>\n",
              "      <td>0.004635</td>\n",
              "      <td>0.861771</td>\n",
              "      <td>16</td>\n",
              "      <td>1.840435</td>\n",
              "      <td>0.074857</td>\n",
              "      <td>1.476021</td>\n",
              "      <td>0.067851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Utility (Water)</td>\n",
              "      <td>15</td>\n",
              "      <td>0.005923</td>\n",
              "      <td>0.276800</td>\n",
              "      <td>0.556169</td>\n",
              "      <td>0.059443</td>\n",
              "      <td>0.615613</td>\n",
              "      <td>17</td>\n",
              "      <td>3.507385</td>\n",
              "      <td>0.082466</td>\n",
              "      <td>2.376167</td>\n",
              "      <td>0.060506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Total Market</td>\n",
              "      <td>6062</td>\n",
              "      <td>0.048202</td>\n",
              "      <td>0.141806</td>\n",
              "      <td>0.484652</td>\n",
              "      <td>0.127259</td>\n",
              "      <td>0.611911</td>\n",
              "      <td>7582</td>\n",
              "      <td>3.813854</td>\n",
              "      <td>0.082466</td>\n",
              "      <td>2.401274</td>\n",
              "      <td>0.060506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Total Market (without financials)</td>\n",
              "      <td>4935</td>\n",
              "      <td>0.048431</td>\n",
              "      <td>0.141046</td>\n",
              "      <td>0.496171</td>\n",
              "      <td>0.126508</td>\n",
              "      <td>0.622678</td>\n",
              "      <td>6253</td>\n",
              "      <td>4.750108</td>\n",
              "      <td>0.077751</td>\n",
              "      <td>3.423948</td>\n",
              "      <td>0.105844</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>95 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "all_data",
              "summary": "{\n  \"name\": \"all_data\",\n  \"rows\": 95,\n  \"fields\": [\n    {\n      \"column\": \"Industry Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 95,\n        \"samples\": [\n          \"Restaurant/Dining\",\n          \"Diversified\",\n          \"Retail (General)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 793,\n        \"min\": 1,\n        \"max\": 6062,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          17,\n          333,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CEO Holding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03348044948711151,\n        \"min\": 0.00035000000000000005,\n        \"max\": 0.1663356,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.05673931818,\n          0.03925333333,\n          0.04112304348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corporate Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09982623798937852,\n        \"min\": 0.0,\n        \"max\": 0.5834,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.1211093333,\n          0.14885500000000002,\n          0.337705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13821307482190415,\n        \"min\": 0.23008615379999997,\n        \"max\": 0.9653,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.5860518,\n          0.4343863636,\n          0.6322399999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.058994342499332365,\n        \"min\": 0.0032400000000000003,\n        \"max\": 0.2973314286,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.1454764,\n          0.06829083333,\n          0.1007333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"II_Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11701318791998402,\n        \"min\": 0.4046915705,\n        \"max\": 0.96935,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.7315282000000001,\n          0.50267719693,\n          0.7329733332999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 996,\n        \"min\": 2,\n        \"max\": 7582,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          38,\n          61,\n          278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PBV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.10646382652692,\n        \"min\": 0.5908804164790717,\n        \"max\": 40.06705255976665,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          7.119657387892159,\n          1.8571825941414872,\n          1.24018893184319\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18199838372010826,\n        \"min\": -0.4702685391404543,\n        \"max\": 0.706385377665926,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.14110113703375673,\n          0.10620755186982106,\n          0.012807842501367015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EV/ Invested Capital\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5572083914488926,\n        \"min\": 0.7443535863341569,\n        \"max\": 12.284688343655736,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          5.815193084456048,\n          1.9207391255302322,\n          3.9844271176718435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROIC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10694162861503301,\n        \"min\": -0.16065422687831812,\n        \"max\": 0.5151189951700217,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          0.16352219103562232,\n          0.13444721876605892,\n          0.09010014455324526\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8nSyvAvgRk1"
      },
      "source": [
        "# Exercise:  \n",
        "\n",
        "Select only the columns you want and calculate a correlation matrix.  Calculate the matrix.  What is the correlation coefficient between Institutional Ownership and ROE?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "FfGjtHIfO5Is",
        "outputId": "bec82ae6-20e2-4645-9da9-3eed47fa8044"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Industry Name  Number of Firms  CEO Holding  \\\n",
              "0                         Advertising               54     0.099896   \n",
              "1                   Aerospace/Defense               67     0.034654   \n",
              "2                       Air Transport               24     0.030976   \n",
              "3                             Apparel               37     0.053675   \n",
              "4                        Auto & Truck               34     0.096935   \n",
              "..                                ...              ...          ...   \n",
              "90                           Trucking               24     0.054088   \n",
              "91                  Utility (General)               14     0.001355   \n",
              "92                    Utility (Water)               15     0.005923   \n",
              "93                       Total Market             6062     0.048202   \n",
              "94  Total Market (without financials)             4935     0.048431   \n",
              "\n",
              "    Corporate Holdings  Institutional Holdings  Insider Holdings  II_Holdings  \\\n",
              "0             0.152666                0.352435          0.233282     0.585717   \n",
              "1             0.180971                0.528903          0.088726     0.617629   \n",
              "2             0.261742                0.418113          0.137485     0.555598   \n",
              "3             0.108202                0.514791          0.170123     0.684914   \n",
              "4             0.195019                0.230086          0.174605     0.404692   \n",
              "..                 ...                     ...               ...          ...   \n",
              "90            0.340488                0.583142          0.168315     0.751457   \n",
              "91            0.000000                0.857136          0.004635     0.861771   \n",
              "92            0.276800                0.556169          0.059443     0.615613   \n",
              "93            0.141806                0.484652          0.127259     0.611911   \n",
              "94            0.141046                0.496171          0.126508     0.622678   \n",
              "\n",
              "    Number of firms       PBV       ROE  EV/ Invested Capital      ROIC  \n",
              "0                61  5.729582  0.029333              7.009509  0.515119  \n",
              "1                72  4.436925  0.085432              4.236895  0.191149  \n",
              "2                17  3.224150 -0.470269              1.762879 -0.160654  \n",
              "3                51  4.111694 -0.081881              3.071836  0.075423  \n",
              "4                19  7.578762  0.044885              2.583233  0.011709  \n",
              "..              ...       ...       ...                   ...       ...  \n",
              "90               35  4.811726 -0.176956              2.568211 -0.040357  \n",
              "91               16  1.840435  0.074857              1.476021  0.067851  \n",
              "92               17  3.507385  0.082466              2.376167  0.060506  \n",
              "93             7582  3.813854  0.082466              2.401274  0.060506  \n",
              "94             6253  4.750108  0.077751              3.423948  0.105844  \n",
              "\n",
              "[95 rows x 12 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry Name</th>\n",
              "      <th>Number of Firms</th>\n",
              "      <th>CEO Holding</th>\n",
              "      <th>Corporate Holdings</th>\n",
              "      <th>Institutional Holdings</th>\n",
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              "      <th>Number of firms</th>\n",
              "      <th>PBV</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Advertising</td>\n",
              "      <td>54</td>\n",
              "      <td>0.099896</td>\n",
              "      <td>0.152666</td>\n",
              "      <td>0.352435</td>\n",
              "      <td>0.233282</td>\n",
              "      <td>0.585717</td>\n",
              "      <td>61</td>\n",
              "      <td>5.729582</td>\n",
              "      <td>0.029333</td>\n",
              "      <td>7.009509</td>\n",
              "      <td>0.515119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aerospace/Defense</td>\n",
              "      <td>67</td>\n",
              "      <td>0.034654</td>\n",
              "      <td>0.180971</td>\n",
              "      <td>0.528903</td>\n",
              "      <td>0.088726</td>\n",
              "      <td>0.617629</td>\n",
              "      <td>72</td>\n",
              "      <td>4.436925</td>\n",
              "      <td>0.085432</td>\n",
              "      <td>4.236895</td>\n",
              "      <td>0.191149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Air Transport</td>\n",
              "      <td>24</td>\n",
              "      <td>0.030976</td>\n",
              "      <td>0.261742</td>\n",
              "      <td>0.418113</td>\n",
              "      <td>0.137485</td>\n",
              "      <td>0.555598</td>\n",
              "      <td>17</td>\n",
              "      <td>3.224150</td>\n",
              "      <td>-0.470269</td>\n",
              "      <td>1.762879</td>\n",
              "      <td>-0.160654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apparel</td>\n",
              "      <td>37</td>\n",
              "      <td>0.053675</td>\n",
              "      <td>0.108202</td>\n",
              "      <td>0.514791</td>\n",
              "      <td>0.170123</td>\n",
              "      <td>0.684914</td>\n",
              "      <td>51</td>\n",
              "      <td>4.111694</td>\n",
              "      <td>-0.081881</td>\n",
              "      <td>3.071836</td>\n",
              "      <td>0.075423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Auto &amp; Truck</td>\n",
              "      <td>34</td>\n",
              "      <td>0.096935</td>\n",
              "      <td>0.195019</td>\n",
              "      <td>0.230086</td>\n",
              "      <td>0.174605</td>\n",
              "      <td>0.404692</td>\n",
              "      <td>19</td>\n",
              "      <td>7.578762</td>\n",
              "      <td>0.044885</td>\n",
              "      <td>2.583233</td>\n",
              "      <td>0.011709</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Trucking</td>\n",
              "      <td>24</td>\n",
              "      <td>0.054088</td>\n",
              "      <td>0.340488</td>\n",
              "      <td>0.583142</td>\n",
              "      <td>0.168315</td>\n",
              "      <td>0.751457</td>\n",
              "      <td>35</td>\n",
              "      <td>4.811726</td>\n",
              "      <td>-0.176956</td>\n",
              "      <td>2.568211</td>\n",
              "      <td>-0.040357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Utility (General)</td>\n",
              "      <td>14</td>\n",
              "      <td>0.001355</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.857136</td>\n",
              "      <td>0.004635</td>\n",
              "      <td>0.861771</td>\n",
              "      <td>16</td>\n",
              "      <td>1.840435</td>\n",
              "      <td>0.074857</td>\n",
              "      <td>1.476021</td>\n",
              "      <td>0.067851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Utility (Water)</td>\n",
              "      <td>15</td>\n",
              "      <td>0.005923</td>\n",
              "      <td>0.276800</td>\n",
              "      <td>0.556169</td>\n",
              "      <td>0.059443</td>\n",
              "      <td>0.615613</td>\n",
              "      <td>17</td>\n",
              "      <td>3.507385</td>\n",
              "      <td>0.082466</td>\n",
              "      <td>2.376167</td>\n",
              "      <td>0.060506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Total Market</td>\n",
              "      <td>6062</td>\n",
              "      <td>0.048202</td>\n",
              "      <td>0.141806</td>\n",
              "      <td>0.484652</td>\n",
              "      <td>0.127259</td>\n",
              "      <td>0.611911</td>\n",
              "      <td>7582</td>\n",
              "      <td>3.813854</td>\n",
              "      <td>0.082466</td>\n",
              "      <td>2.401274</td>\n",
              "      <td>0.060506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Total Market (without financials)</td>\n",
              "      <td>4935</td>\n",
              "      <td>0.048431</td>\n",
              "      <td>0.141046</td>\n",
              "      <td>0.496171</td>\n",
              "      <td>0.126508</td>\n",
              "      <td>0.622678</td>\n",
              "      <td>6253</td>\n",
              "      <td>4.750108</td>\n",
              "      <td>0.077751</td>\n",
              "      <td>3.423948</td>\n",
              "      <td>0.105844</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>95 rows × 12 columns</p>\n",
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\"max\": 0.1663356,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.05673931818,\n          0.03925333333,\n          0.04112304348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corporate Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09982623798937852,\n        \"min\": 0.0,\n        \"max\": 0.5834,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.1211093333,\n          0.14885500000000002,\n          0.337705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13821307482190415,\n        \"min\": 0.23008615379999997,\n        \"max\": 0.9653,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.5860518,\n          0.4343863636,\n          0.6322399999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insider Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.058994342499332365,\n        \"min\": 0.0032400000000000003,\n        \"max\": 0.2973314286,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.1454764,\n          0.06829083333,\n          0.1007333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"II_Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11701318791998402,\n        \"min\": 0.4046915705,\n        \"max\": 0.96935,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.7315282000000001,\n          0.50267719693,\n          0.7329733332999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of firms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 996,\n        \"min\": 2,\n        \"max\": 7582,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          38,\n          61,\n          278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PBV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.10646382652692,\n        \"min\": 0.5908804164790717,\n        \"max\": 40.06705255976665,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          7.119657387892159,\n          1.8571825941414872,\n          1.24018893184319\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18199838372010826,\n        \"min\": -0.4702685391404543,\n        \"max\": 0.706385377665926,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.14110113703375673,\n          0.10620755186982106,\n          0.012807842501367015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EV/ Invested Capital\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5572083914488926,\n        \"min\": 0.7443535863341569,\n        \"max\": 12.284688343655736,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          5.815193084456048,\n          1.9207391255302322,\n          3.9844271176718435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROIC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10694162861503301,\n        \"min\": -0.16065422687831812,\n        \"max\": 0.5151189951700217,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          0.16352219103562232,\n          0.13444721876605892,\n          0.09010014455324526\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = pd.merge(inst_own, returns, on='Industry Name')\n",
        "selected_columns = ['Institutional Holdings', 'ROE']\n",
        "selected_data = all_data[selected_columns]\n",
        "selected_data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "XgXRkS5tO9AK",
        "outputId": "faaa8d06-289d-4b18-bc4a-06f61ba9031c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Institutional Holdings       ROE\n",
              "0                 0.352435  0.029333\n",
              "1                 0.528903  0.085432\n",
              "2                 0.418113 -0.470269\n",
              "3                 0.514791 -0.081881\n",
              "4                 0.230086  0.044885\n",
              "..                     ...       ...\n",
              "90                0.583142 -0.176956\n",
              "91                0.857136  0.074857\n",
              "92                0.556169  0.082466\n",
              "93                0.484652  0.082466\n",
              "94                0.496171  0.077751\n",
              "\n",
              "[95 rows x 2 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Institutional Holdings</th>\n",
              "      <th>ROE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.352435</td>\n",
              "      <td>0.029333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.528903</td>\n",
              "      <td>0.085432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.418113</td>\n",
              "      <td>-0.470269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.514791</td>\n",
              "      <td>-0.081881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.230086</td>\n",
              "      <td>0.044885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <th>90</th>\n",
              "      <td>0.583142</td>\n",
              "      <td>-0.176956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>0.857136</td>\n",
              "      <td>0.074857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>0.556169</td>\n",
              "      <td>0.082466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>0.484652</td>\n",
              "      <td>0.082466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>0.496171</td>\n",
              "      <td>0.077751</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>95 rows × 2 columns</p>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_ff01ee94-0f2d-48f4-a110-6f6e8209d53a\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('selected_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ff01ee94-0f2d-48f4-a110-6f6e8209d53a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('selected_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "selected_data",
              "summary": "{\n  \"name\": \"selected_data\",\n  \"rows\": 95,\n  \"fields\": [\n    {\n      \"column\": \"Institutional Holdings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13821307482190415,\n        \"min\": 0.23008615379999997,\n        \"max\": 0.9653,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          0.5860518,\n          0.4343863636,\n          0.6322399999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18199838372010826,\n        \"min\": -0.4702685391404543,\n        \"max\": 0.706385377665926,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.14110113703375673,\n          0.10620755186982106,\n          0.012807842501367015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = selected_data.corr()\n",
        "correlation_matrix\n",
        "print(correlation_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9ZAhiNfOr7N",
        "outputId": "feb138fa-6722-4956-d18f-eb1d2ceebe3d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        Institutional Holdings       ROE\n",
            "Institutional Holdings                1.000000  0.077592\n",
            "ROE                                   0.077592  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_roe_institutional = correlation_matrix.loc['Institutional Holdings', 'ROE']\n",
        "print(f\"Correlation coefficient between Institutional Ownership and ROE: {correlation_roe_institutional}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jooYhjKQwY-",
        "outputId": "efb6aa77-c25e-40ae-d712-b39051358010"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation coefficient between Institutional Ownership and ROE: 0.07759219693460978\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Interpretation:\n",
        "\n",
        "The correlation coefficient between Institutional Ownership and Return on Equity (ROE) is 0.078. This reflects a weak positive correlation. However, since the result is a positive correlation this suggests that as Institutional Ownership increases, ROE may also increase. Nevertheless, since the value is close to zero, this relationship is not strong or significant."
      ],
      "metadata": {
        "id": "ompg1C5H0cLj"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFOo8TTuDYI8"
      },
      "source": [
        "[Hint](https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html?highlight=correlation%20matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "up3kBsbT9qYt"
      },
      "source": [
        "# Addendum\n",
        "\n",
        "At this point you may have noticed we call certain 'functions' with `name(a)` and others with `a.name()` where `a` is some object.  As examples, we have used `dir(module)` and `len(object)` but also use `inst_own.sort_values()` and `inst_own.drop()` above.\n",
        "\n",
        "The difference has to do with the distinction between functions and methods, and this is a topic within Python's class system which we don't want or need to worry about at this point. That said, we use `name(a)` when `name` is a globally defined function.  We use `a.name()` when `name` is a method defined for an object of class (a is an instance of an object of the class).  \n",
        "\n",
        "You can read more in Section 9.3.4 here: https://docs.python.org/3/tutorial/classes.html\n",
        "\n",
        "You can read more here and at the referenced links: https://stackoverflow.com/questions/28703834/why-do-some-methods-use-dot-notation-and-others-dont"
      ]
    }
  ]
}