Copy_of_Likert_June_13_Version.ipynb

{
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/clizarraga-UAD7/Notebooks/blob/main/Copy_of_Likert_June_13_Version.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Online version using Google Colab\n",
        "\n",
        "Needs to upload the Excel data file (FemData_Exp2.xlsx), everytime the session is re-started."
      ],
      "metadata": {
        "id": "NxxzxB-ofVN6"
      },
      "id": "NxxzxB-ofVN6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ac3b2e7e-95f2-4da0-b69a-2880ad26fa72",
      "metadata": {
        "id": "ac3b2e7e-95f2-4da0-b69a-2880ad26fa72"
      },
      "outputs": [],
      "source": [
        "#How to get rid of ONLY the rows with more than 5 missing values?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install necessary libraries (quietly)\n",
        "!pip install --quiet xlrd"
      ],
      "metadata": {
        "id": "-iedrGCDgAfx"
      },
      "id": "-iedrGCDgAfx",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Install Likert library plot-likert\n",
        "!pip install --quiet plot-likert"
      ],
      "metadata": {
        "id": "4I23zLdwwGjg"
      },
      "id": "4I23zLdwwGjg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "54e7c672-7b41-49e0-b05f-4648aa1dee6f",
      "metadata": {
        "id": "54e7c672-7b41-49e0-b05f-4648aa1dee6f"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import xlrd\n",
        "from pandas.api.types import CategoricalDtype\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plot_likert\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "90c00afa-4a14-4f12-a869-ed6d350fc488",
      "metadata": {
        "id": "90c00afa-4a14-4f12-a869-ed6d350fc488"
      },
      "outputs": [],
      "source": [
        "# Not needed here\n",
        "# import os\n",
        "# os.getcwd()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7592248e-90a2-4981-819f-31c933c99230",
      "metadata": {
        "id": "7592248e-90a2-4981-819f-31c933c99230"
      },
      "outputs": [],
      "source": [
        "# No longer needed, using xlrd instead.\n",
        "#pip install openpyxl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61f71107-74ef-453f-83fa-f652e38e39e2",
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        "id": "61f71107-74ef-453f-83fa-f652e38e39e2"
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      "outputs": [],
      "source": [
        "# Read the Excel data file\n",
        "#fem_df = pd.read_excel(\"C:\\\\Users\\\\maisu\\\\OneDrive\\\\Desktop\\\\SES Qual Data\\\\CGT\\\\Exp\\\\Data4Exp\\\\More\\\\Exp\\\\FemData_Exp2.xlsx\")\n",
        "fem_df = pd.read_excel('FemData_Exp2.xlsx')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b6e97ad-260d-4c52-9df4-3c47a4c29d95",
      "metadata": {
        "id": "6b6e97ad-260d-4c52-9df4-3c47a4c29d95",
        "outputId": "b5782fc6-1955-4e32-ac8d-ecb919674e1e"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1354 entries, 0 to 1353\n",
            "Data columns (total 26 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   CaseNum   1348 non-null   object\n",
            " 1   Age       1348 non-null   object\n",
            " 2   SchoolYr  1348 non-null   object\n",
            " 3   Race      1348 non-null   object\n",
            " 4   GCQ3A     1348 non-null   object\n",
            " 5   GCQ3B     1348 non-null   object\n",
            " 6   GCQ3C     1348 non-null   object\n",
            " 7   GCQ3D     1348 non-null   object\n",
            " 8   GCQ3E     1348 non-null   object\n",
            " 9   GCQ3F     1348 non-null   object\n",
            " 10  GCQ3G     1348 non-null   object\n",
            " 11  GCQ3H     1348 non-null   object\n",
            " 12  GCQ3I     1348 non-null   object\n",
            " 13  GCQ3J     1348 non-null   object\n",
            " 14  GCQ3K     1348 non-null   object\n",
            " 15  GCQ3L     1348 non-null   object\n",
            " 16  ESECONVA  1338 non-null   object\n",
            " 17  ESECONVB  1338 non-null   object\n",
            " 18  ESECONVC  1338 non-null   object\n",
            " 19  ESECONVD  1338 non-null   object\n",
            " 20  ESECONVE  1338 non-null   object\n",
            " 21  ESECONVF  1338 non-null   object\n",
            " 22  ESECONVG  1338 non-null   object\n",
            " 23  ESECONVH  1338 non-null   object\n",
            " 24  ESECONVI  1338 non-null   object\n",
            " 25  ESECONVJ  1338 non-null   object\n",
            "dtypes: object(26)\n",
            "memory usage: 275.2+ KB\n"
          ]
        }
      ],
      "source": [
        "fem_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Note:\n",
        "The function `df.info()`, gives a general overview of the dataset.\n",
        "\n",
        "The dataset has 1354 rows of data and 26 columns. Some columns have 1348 rows and other 1338 rows.\n",
        "\n",
        "We can next find out which columns have missing data and how many.\n",
        "\n",
        "Apply: `df.isnull().sum()`"
      ],
      "metadata": {
        "id": "wSJ6-nZDiMyY"
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CaseNum      6\n",
              "Age          6\n",
              "SchoolYr     6\n",
              "Race         6\n",
              "GCQ3A        6\n",
              "GCQ3B        6\n",
              "GCQ3C        6\n",
              "GCQ3D        6\n",
              "GCQ3E        6\n",
              "GCQ3F        6\n",
              "GCQ3G        6\n",
              "GCQ3H        6\n",
              "GCQ3I        6\n",
              "GCQ3J        6\n",
              "GCQ3K        6\n",
              "GCQ3L        6\n",
              "ESECONVA    16\n",
              "ESECONVB    16\n",
              "ESECONVC    16\n",
              "ESECONVD    16\n",
              "ESECONVE    16\n",
              "ESECONVF    16\n",
              "ESECONVG    16\n",
              "ESECONVH    16\n",
              "ESECONVI    16\n",
              "ESECONVJ    16\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "fem_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Note:\n",
        "Need to decide if we eliminate rows of incomplete data and also `age` must be a numeric variable."
      ],
      "metadata": {
        "id": "dobgbqH9kbpK"
      },
      "id": "dobgbqH9kbpK"
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    {
      "cell_type": "code",
      "source": [
        "# Preserve original data frame: fem_df.\n",
        "# Create a new clean dataframe by dropping rows with missing data: fem_dfC\n",
        "fem_df_temp = fem_df.copy()\n",
        "fem_dfC = fem_df_temp.dropna()\n",
        "fem_dfC.info()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awpWiaPjkYi_",
        "outputId": "51400bb0-b35a-4006-df8c-d3b7246dabb1"
      },
      "id": "awpWiaPjkYi_",
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        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1338 entries, 0 to 1353\n",
            "Data columns (total 26 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   CaseNum   1338 non-null   object\n",
            " 1   Age       1338 non-null   object\n",
            " 2   SchoolYr  1338 non-null   object\n",
            " 3   Race      1338 non-null   object\n",
            " 4   GCQ3A     1338 non-null   object\n",
            " 5   GCQ3B     1338 non-null   object\n",
            " 6   GCQ3C     1338 non-null   object\n",
            " 7   GCQ3D     1338 non-null   object\n",
            " 8   GCQ3E     1338 non-null   object\n",
            " 9   GCQ3F     1338 non-null   object\n",
            " 10  GCQ3G     1338 non-null   object\n",
            " 11  GCQ3H     1338 non-null   object\n",
            " 12  GCQ3I     1338 non-null   object\n",
            " 13  GCQ3J     1338 non-null   object\n",
            " 14  GCQ3K     1338 non-null   object\n",
            " 15  GCQ3L     1338 non-null   object\n",
            " 16  ESECONVA  1338 non-null   object\n",
            " 17  ESECONVB  1338 non-null   object\n",
            " 18  ESECONVC  1338 non-null   object\n",
            " 19  ESECONVD  1338 non-null   object\n",
            " 20  ESECONVE  1338 non-null   object\n",
            " 21  ESECONVF  1338 non-null   object\n",
            " 22  ESECONVG  1338 non-null   object\n",
            " 23  ESECONVH  1338 non-null   object\n",
            " 24  ESECONVI  1338 non-null   object\n",
            " 25  ESECONVJ  1338 non-null   object\n",
            "dtypes: object(26)\n",
            "memory usage: 282.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Investigate further\n",
        "fem_dfC['Age'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzjCaf9mqakJ",
        "outputId": "e99abca4-ddf5-435d-e95c-23b4a98b46ba"
      },
      "id": "zzjCaf9mqakJ",
      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18    376\n",
              "19    297\n",
              "20    261\n",
              "21    228\n",
              "22     92\n",
              "23     25\n",
              "24     18\n",
              "26     12\n",
              "25     10\n",
              "27      9\n",
              "17      9\n",
              "        1\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
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    {
      "cell_type": "code",
      "source": [
        "# There is still one row wit no Age information\n",
        "fem_dfC[fem_dfC['Age'] == ' ']"
      ],
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              "      CaseNum Age SchoolYr Race GCQ3A GCQ3B GCQ3C GCQ3D GCQ3E GCQ3F  ...  \\\n",
              "881  21200108            2          2     3     2     2     1     1  ...   \n",
              "\n",
              "    ESECONVA ESECONVB ESECONVC ESECONVD ESECONVE ESECONVF ESECONVG ESECONVH  \\\n",
              "881        2        1        1        1        1        1        1        1   \n",
              "\n",
              "    ESECONVI ESECONVJ  \n",
              "881        1        1  \n",
              "\n",
              "[1 rows x 26 columns]"
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    {
      "cell_type": "code",
      "source": [
        "# Drop the row with no Age information, or keep everything else\n",
        "fem_dfC = fem_dfC[fem_dfC['Age'] != ' ']\n",
        "fem_dfC['Age'].value_counts()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "np8XAbC0ql5F",
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      },
      "id": "np8XAbC0ql5F",
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18    376\n",
              "19    297\n",
              "20    261\n",
              "21    228\n",
              "22     92\n",
              "23     25\n",
              "24     18\n",
              "26     12\n",
              "25     10\n",
              "27      9\n",
              "17      9\n",
              "Name: Age, dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 26
        }
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    {
      "cell_type": "code",
      "source": [
        "# Convert the age variable to numeric and check\n",
        "fem_dfC['Age'] = pd.to_numeric(fem_dfC['Age'])\n",
        "fem_dfC.info()"
      ],
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        "outputId": "b2abf489-e709-428a-8364-7ea8f711dba4"
      },
      "id": "R91CX_6dmkwh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1337 entries, 0 to 1353\n",
            "Data columns (total 26 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   CaseNum   1337 non-null   object\n",
            " 1   Age       1337 non-null   int64 \n",
            " 2   SchoolYr  1337 non-null   object\n",
            " 3   Race      1337 non-null   object\n",
            " 4   GCQ3A     1337 non-null   object\n",
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            " 6   GCQ3C     1337 non-null   object\n",
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            " 9   GCQ3F     1337 non-null   object\n",
            " 10  GCQ3G     1337 non-null   object\n",
            " 11  GCQ3H     1337 non-null   object\n",
            " 12  GCQ3I     1337 non-null   object\n",
            " 13  GCQ3J     1337 non-null   object\n",
            " 14  GCQ3K     1337 non-null   object\n",
            " 15  GCQ3L     1337 non-null   object\n",
            " 16  ESECONVA  1337 non-null   object\n",
            " 17  ESECONVB  1337 non-null   object\n",
            " 18  ESECONVC  1337 non-null   object\n",
            " 19  ESECONVD  1337 non-null   object\n",
            " 20  ESECONVE  1337 non-null   object\n",
            " 21  ESECONVF  1337 non-null   object\n",
            " 22  ESECONVG  1337 non-null   object\n",
            " 23  ESECONVH  1337 non-null   object\n",
            " 24  ESECONVI  1337 non-null   object\n",
            " 25  ESECONVJ  1337 non-null   object\n",
            "dtypes: int64(1), object(25)\n",
            "memory usage: 282.0+ KB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-55fbce1ab24e>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  fem_dfC['Age'] = pd.to_numeric(fem_dfC['Age'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Do basic statistics of the Age distribution\n",
        "fem_dfC['Age'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpVaIF8jnsN_",
        "outputId": "1a43ac21-76d3-4c05-ba09-7a212fb85279"
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      "id": "DpVaIF8jnsN_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1337.000000\n",
              "mean       19.751683\n",
              "std         1.737134\n",
              "min        17.000000\n",
              "25%        18.000000\n",
              "50%        19.000000\n",
              "75%        21.000000\n",
              "max        27.000000\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore different values for SchoolYr\n",
        "fem_dfC['SchoolYr'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8euSvl1y4lA",
        "outputId": "57b0d683-89d2-4047-a706-877cefc23539"
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      "id": "C8euSvl1y4lA",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    470\n",
              "3    318\n",
              "2    295\n",
              "4    247\n",
              "5      4\n",
              "       3\n",
              "Name: SchoolYr, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# There is still one row wit noSchoolYr information\n",
        "fem_dfC[fem_dfC['SchoolYr'] == ' ']"
      ],
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      "id": "4Sipp4DjzGiJ",
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      "outputs": [
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        "# Drop the row with no SchoolYr information, or keep everything else\n",
        "fem_dfC = fem_dfC[fem_dfC['SchoolYr'] != ' ']\n",
        "fem_dfC['SchoolYr'].value_counts()"
      ],
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              "2    295\n",
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              "0      3\n",
              "       3\n",
              "6      2\n",
              "Name: Race, dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 47
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        "# Drop the row with no Race information, or keep everything else\n",
        "fem_dfC = fem_dfC[fem_dfC['Race'] != ' ']\n",
        "fem_dfC['Race'].value_counts()"
      ],
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              "6      2\n",
              "Name: Race, dtype: int64"
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      "source": [
        "# See if missing values in CaseNum variable\n",
        "fem_dfC[fem_dfC['CaseNum'] == ' ']"
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-63e9b033-4085-4143-bd13-e2a0f24716df 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-63e9b033-4085-4143-bd13-e2a0f24716df');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Visualizations\n",
        "\n",
        "Next we can explore the distribution of the first 3 variables: `Age`, `SchoolYr` and `Race`"
      ],
      "metadata": {
        "id": "sjSqL43O1gcM"
      },
      "id": "sjSqL43O1gcM"
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a histogram of Age distribution\n",
        "sns.set_style(\"whitegrid\")\n",
        "ax = sns.histplot(data=fem_dfC, x=\"Age\")\n",
        "ax.set(xlabel='Age',\n",
        "       ylabel='Frequency',\n",
        "       title='Age Distribution')\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "tfk0wm3an5Tf",
        "outputId": "bc60e595-83de-43d7-e779-aa5564197cbd"
      },
      "id": "tfk0wm3an5Tf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show SchoolYr distribution\n",
        "ax = sns.histplot(data=fem_dfC, x=\"SchoolYr\")\n",
        "ax.set(xlabel='School Year',\n",
        "       ylabel='Frequency',\n",
        "       title='School Year Distribution')\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "5TYShraZymWy",
        "outputId": "949a4c55-daa5-4eb6-c110-544b08c6c9de"
      },
      "id": "5TYShraZymWy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a histogram of Race distribution\n",
        "sns.set_style(\"whitegrid\")\n",
        "ax = sns.histplot(data=fem_dfC, x=\"Race\")\n",
        "ax.set(xlabel='Race',\n",
        "       ylabel='Frequency',\n",
        "       title='Race Distribution')\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "AxwzWpLCz4k8",
        "outputId": "ecb758d3-4482-40aa-b110-75af35aae7e4"
      },
      "id": "AxwzWpLCz4k8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Likert Scale plots\n",
        "\n",
        "Will prepare new dataframe for produce Likert plots."
      ],
      "metadata": {
        "id": "H-97mzxe4ndD"
      },
      "id": "H-97mzxe4ndD"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NzsNkbRC0eYw"
      },
      "id": "NzsNkbRC0eYw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop column of index using DataFrame.iloc[] and drop() methods.\n",
        "df_answers0 = fem_dfC.drop(fem_dfC.iloc[:, 0:4],axis = 1)\n",
        "df_answers0.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "eJUVxeRF0eef",
        "outputId": "d5f8c516-7908-4cc3-b224-86f516b4fe25"
      },
      "id": "eJUVxeRF0eef",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  GCQ3A GCQ3B GCQ3C GCQ3D GCQ3E GCQ3F GCQ3G GCQ3H GCQ3I GCQ3J  ... ESECONVA  \\\n",
              "0     3     3     3     3     3     3     3     3     0     3  ...        2   \n",
              "1     2     1     1     1     2     3     2     2     0     3  ...        2   \n",
              "2     2     2     1     2     2     2     2     2     1     2  ...        2   \n",
              "3     3     3     2     3     3     3     3     3     1     2  ...        2   \n",
              "4     3     3     1     2     3     2     3     2     0     3  ...        2   \n",
              "\n",
              "  ESECONVB ESECONVC ESECONVD ESECONVE ESECONVF ESECONVG ESECONVH ESECONVI  \\\n",
              "0        1        1        1        1        1        2        1        1   \n",
              "1        2        1        1        1        1        2        2        1   \n",
              "2        1        1        1        1        1        2        2        2   \n",
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          "metadata": {},
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            " 12  ESECONVA  1333 non-null   object\n",
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            " 20  ESECONVI  1333 non-null   object\n",
            " 21  ESECONVJ  1333 non-null   object\n",
            "dtypes: object(22)\n",
            "memory usage: 239.5+ KB\n"
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    {
      "cell_type": "code",
      "source": [
        "df_answers0.iloc[:,3].value_counts()"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "3    719\n",
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              "1    172\n",
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              "       4\n",
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          "output_type": "execute_result",
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              "1    831\n",
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      "source": [
        "# Create additional No Answer (NA)\n",
        "df_answers01 = df_answers0.copy()\n",
        "df_answers01 = df_answers01.replace(' ', 'NA')"
      ],
      "metadata": {
        "id": "zhoOTrPM78T_"
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      "id": "zhoOTrPM78T_",
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df_answers01.iloc[:,1].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "na054etbHBYW",
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      "id": "na054etbHBYW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3     651\n",
              "2     506\n",
              "1     143\n",
              "0      25\n",
              "NA      8\n",
              "Name: GCQ3B, dtype: int64"
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          "metadata": {},
          "execution_count": 117
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 22 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d336e07-5fd5-45c1-bf2d-db2355ec55f6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-4d336e07-5fd5-45c1-bf2d-db2355ec55f6 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-4d336e07-5fd5-45c1-bf2d-db2355ec55f6');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_answers01[df_answers01 == 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "i00b7yZkXwng",
        "outputId": "30a82c6b-fb72-4201-bcaf-2bfbb86ff365"
      },
      "id": "i00b7yZkXwng",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1349</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1350</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1351</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1352</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1353</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1333 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fe5c9ef-3759-4057-a81f-369c4fee2e86')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4fe5c9ef-3759-4057-a81f-369c4fee2e86 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-4fe5c9ef-3759-4057-a81f-369c4fee2e86');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Convert answers to ordered categorical data\n",
        "\n",
        "**Need table equivalence**"
      ],
      "metadata": {
        "id": "-lFSdtHbK3WW"
      },
      "id": "-lFSdtHbK3WW"
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale: 'Strongly disagree', 'Disagree', 'Neither agree nor disagree', 'Agree', 'Strongly agree'\n",
        "# Another scale ['NA', 0, 1, 2, 3, 4, 5]\n",
        "my_scale=['NA', 0, 1, 2, 3, 4, 5]\n",
        "#df_answers01 = df_answers01.replace(0, 'Strongly disagree')\n",
        "#df_answers01 = df_answers01.replace(1, 'Disagree')\n",
        "#df_answers01 = df_answers01.replace('NA', 'Neither agree nor disagree')\n",
        "#df_answers01 = df_answers01.replace(2, 'Agree')\n",
        "#df_answers01 = df_answers01.replace(3, 'Strongly agree')\n",
        "\n",
        "df_answers01.head()\n"
      ],
      "metadata": {
        "id": "HC0WwGpyLPai",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "c1400d26-baa1-4bd7-8daa-d1b7890a898c"
      },
      "id": "HC0WwGpyLPai",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  GCQ3A GCQ3B GCQ3C GCQ3D GCQ3E GCQ3F GCQ3G GCQ3H GCQ3I GCQ3J  ... ESECONVA  \\\n",
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              "      </button>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CategoricalDtype(['NA', 0, 1, 2, 3, 4, 5], ordered=True)\n",
        "\n",
        "df_cat01 = df_answers01.copy().astype(\"category\")\n",
        "df_cat01.dtypes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddUNlqmBK0rt",
        "outputId": "69080e28-ce01-4990-a7e8-52c6d2ace401"
      },
      "id": "ddUNlqmBK0rt",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GCQ3A       category\n",
              "GCQ3B       category\n",
              "GCQ3C       category\n",
              "GCQ3D       category\n",
              "GCQ3E       category\n",
              "GCQ3F       category\n",
              "GCQ3G       category\n",
              "GCQ3H       category\n",
              "GCQ3I       category\n",
              "GCQ3J       category\n",
              "GCQ3K       category\n",
              "GCQ3L       category\n",
              "ESECONVA    category\n",
              "ESECONVB    category\n",
              "ESECONVC    category\n",
              "ESECONVD    category\n",
              "ESECONVE    category\n",
              "ESECONVF    category\n",
              "ESECONVG    category\n",
              "ESECONVH    category\n",
              "ESECONVI    category\n",
              "ESECONVJ    category\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cat01['ESECONVC']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7sgv-oeK0zV",
        "outputId": "8e00dce4-f983-4fa9-8dd9-043a3907a5d9"
      },
      "id": "U7sgv-oeK0zV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1\n",
              "1       1\n",
              "2       1\n",
              "3       1\n",
              "4       2\n",
              "       ..\n",
              "1349    2\n",
              "1350    4\n",
              "1351    1\n",
              "1352    1\n",
              "1353    1\n",
              "Name: ESECONVC, Length: 1333, dtype: category\n",
              "Categories (6, object): [1, 2, 3, 4, 5, 'NA']"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cat01['ESECONVC'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-aM2ofEdOgjG",
        "outputId": "9fa795f7-5fdb-4daa-a137-fa0296d75366"
      },
      "id": "-aM2ofEdOgjG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     759\n",
              "2     312\n",
              "3     125\n",
              "4      68\n",
              "5      61\n",
              "NA      8\n",
              "Name: ESECONVC, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Likert plots\n",
        "\n",
        "Using [plot_likert library](https://github.com/nmalkin/plot-likert) to visualize results"
      ],
      "metadata": {
        "id": "-xHni49jiuZ8"
      },
      "id": "-xHni49jiuZ8"
    },
    {
      "cell_type": "code",
      "source": [
        "plot_likert.plot_likert(df_cat01, my_scale, colors=plot_likert.colors.likert7, figsize=(10,10));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "GFvASWXYVSGj",
        "outputId": "a8c30fae-ad65-479b-f422-1cc9e8c47970"
      },
      "id": "GFvASWXYVSGj",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create our own color table\n",
        "\n",
        "my_color_scheme =[\n",
        "    plot_likert.colors.TRANSPARENT,\n",
        "    \"brown\",\n",
        "    \"red\",\n",
        "    \"orange\",\n",
        "    \"green\",\n",
        "    \"blue\",\n",
        "    \"violet\",\n",
        "    \"purple\",\n",
        "]"
      ],
      "metadata": {
        "id": "GydAFq6TecKj"
      },
      "id": "GydAFq6TecKj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_likert.plot_likert(df_cat01, my_scale, colors=my_color_scheme, figsize=(10,10));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "Qe5mLDTRVSLD",
        "outputId": "48ce5b8b-92bc-400d-f609-d01fccb7d739"
      },
      "id": "Qe5mLDTRVSLD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_likert.plot_likert(df_cat01, my_scale, colors=my_color_scheme, figsize=(10,10), plot_percentage=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "HUZsA5csVSPK",
        "outputId": "48c89e3a-6a7a-4859-ec58-1d6b3966afe9"
      },
      "id": "HUZsA5csVSPK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_likert.plot_likert(df_cat01, my_scale, colors=my_color_scheme, figsize=(12,12),\n",
        "                        bar_labels=True, bar_labels_color=\"snow\", plot_percentage=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "f4eSwHNOSrWg",
        "outputId": "97941af1-124b-4627-aec3-9e5045983f8b"
      },
      "id": "f4eSwHNOSrWg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WDidjZORSrby"
      },
      "id": "WDidjZORSrby",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Previous work"
      ],
      "metadata": {
        "id": "nTol-asljqym"
      },
      "id": "nTol-asljqym"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "702930aa-eed8-4170-9589-2e070454ed30",
      "metadata": {
        "id": "702930aa-eed8-4170-9589-2e070454ed30",
        "outputId": "29d16701-e33e-4fa7-e04e-ab77bc94507c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\maisu\\AppData\\Local\\Temp\\ipykernel_8388\\887244914.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  fem_df.corr()\n"
          ]
        },
        {
          "data": {
            "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fem_df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e3a5b56-6ff5-432d-88fa-449fb09c79c5",
      "metadata": {
        "id": "7e3a5b56-6ff5-432d-88fa-449fb09c79c5"
      },
      "outputs": [],
      "source": [
        "df_nomiss = fem_df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf66a8e5-8b3a-42e5-88a1-fdad9752edcf",
      "metadata": {
        "id": "cf66a8e5-8b3a-42e5-88a1-fdad9752edcf"
      },
      "outputs": [],
      "source": [
        "x_nomiss = df_nomiss[['GCQ3A', 'GCQ3B', 'GCQ3C', 'GCQ3D', 'GCQ3E', 'GCQ3F', 'GCQ3G', 'GCQ3H', 'GCQ3I', 'GCQ3J', 'GCQ3K', 'GCQ3L']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e0c691e-fa6b-487d-97e9-4b40c0bc3d25",
      "metadata": {
        "id": "7e0c691e-fa6b-487d-97e9-4b40c0bc3d25"
      },
      "outputs": [],
      "source": [
        "y_nomiss = df_nomiss[['ESECONVA', 'ESECONVB', 'ESECONVC', 'ESECONVD', 'ESECONVE', 'ESECONVF', 'ESECONVG', 'ESECONVH', 'ESECONVI', 'ESECONVJ']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "123d5e45-8ee0-49a3-9e67-b7a49f3e6a2b",
      "metadata": {
        "id": "123d5e45-8ee0-49a3-9e67-b7a49f3e6a2b"
      },
      "outputs": [],
      "source": [
        "x_nomiss_train, x_nomiss_test, y_nomiss_train, y_nomiss_test = train_test_split(x_nomiss, y_nomiss, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e28c26a5-3d37-4ee8-af3e-1cdbaceaff88",
      "metadata": {
        "id": "e28c26a5-3d37-4ee8-af3e-1cdbaceaff88",
        "outputId": "e31386fa-1f5d-4e77-cc41-208dbf10cf6e"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "could not convert string to float: ''",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_nomiss_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_nomiss_train\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py:889\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m    859\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m    860\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[0;32m    861\u001b[0m \n\u001b[0;32m    862\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    886\u001b[0m \u001b[38;5;124;03m        Fitted estimator.\u001b[39;00m\n\u001b[0;32m    887\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 889\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    895\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py:186\u001b[0m, in \u001b[0;36mBaseDecisionTree.fit\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m    184\u001b[0m check_X_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(dtype\u001b[38;5;241m=\u001b[39mDTYPE, accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    185\u001b[0m check_y_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 186\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    187\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_separately\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_y_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    188\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n\u001b[0;32m    190\u001b[0m     X\u001b[38;5;241m.\u001b[39msort_indices()\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:560\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    558\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_X_params:\n\u001b[0;32m    559\u001b[0m     check_X_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_X_params}\n\u001b[1;32m--> 560\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    561\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_y_params:\n\u001b[0;32m    562\u001b[0m     check_y_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params}\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py:879\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    877\u001b[0m         array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    878\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 879\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m    881\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    882\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    883\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\_array_api.py:185\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m    182\u001b[0m     xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(array)\n\u001b[0;32m    183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy.array_api\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n\u001b[0;32m    184\u001b[0m     \u001b[38;5;66;03m# Use NumPy API to support order\u001b[39;00m\n\u001b[1;32m--> 185\u001b[0m     array \u001b[38;5;241m=\u001b[39m \u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    186\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m    187\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\generic.py:2070\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2069\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m-> 2070\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
            "\u001b[1;31mValueError\u001b[0m: could not convert string to float: ''"
          ]
        }
      ],
      "source": [
        "model.fit(x_nomiss_train, y_nomiss_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ea041be3-0f21-497b-922e-f11aa19b440a",
      "metadata": {
        "id": "ea041be3-0f21-497b-922e-f11aa19b440a"
      },
      "outputs": [],
      "source": [
        "# Step 1: Identify the rows with missing values\n",
        "missing_values = fem_df.isnull()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38fdfa5a-aa1f-4722-bbec-5d8f788290c1",
      "metadata": {
        "id": "38fdfa5a-aa1f-4722-bbec-5d8f788290c1"
      },
      "outputs": [],
      "source": [
        "# Step 2: Count the number of missing values in each row\n",
        "missing_counts = missing_values.sum(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c0e1f56-258e-45c9-aabd-4b42aa27e2ef",
      "metadata": {
        "id": "0c0e1f56-258e-45c9-aabd-4b42aa27e2ef"
      },
      "outputs": [],
      "source": [
        "# Step 3: Filter the rows\n",
        "condition = missing_counts <= 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ddee96e9-ad2c-4a7e-9143-fccacc7a1edc",
      "metadata": {
        "id": "ddee96e9-ad2c-4a7e-9143-fccacc7a1edc"
      },
      "outputs": [],
      "source": [
        "# Step 4: Remove the filtered rows\n",
        "filt_df = fem_df[condition]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9f7e6002-6eed-41bc-8f8f-403fb4031f91",
      "metadata": {
        "id": "9f7e6002-6eed-41bc-8f8f-403fb4031f91",
        "outputId": "dbc80ac3-0981-4e8a-f643-c69fa6369677"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Rows with more than 5 missing values were removed.\n"
          ]
        }
      ],
      "source": [
        "# Check\n",
        "if len(filt_df) < len(fem_df):\n",
        "    print(\"Rows with more than 5 missing values were removed.\")\n",
        "else:\n",
        "    print(\"No rows were removed.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38c130cf-a232-40dc-8035-209b7ff87261",
      "metadata": {
        "id": "38c130cf-a232-40dc-8035-209b7ff87261"
      },
      "outputs": [],
      "source": [
        "# Would using a placeholder for the other missing values interfere with data analysis?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c31c10d5-9fce-4f50-8a39-4877843a2636",
      "metadata": {
        "id": "c31c10d5-9fce-4f50-8a39-4877843a2636"
      },
      "outputs": [],
      "source": [
        "# How do I deal with the other missing values without imputing or removing the rows?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "47bc6962-ce3c-4917-ab30-4fe0c4485e53",
      "metadata": {
        "id": "47bc6962-ce3c-4917-ab30-4fe0c4485e53"
      },
      "outputs": [],
      "source": [
        "# About 1350 participant responses recorded in the dataset with over 70 of them having one or more missing values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5c10dfb-fcb5-4bb9-bf35-15c0b1571187",
      "metadata": {
        "id": "e5c10dfb-fcb5-4bb9-bf35-15c0b1571187",
        "outputId": "f136eb20-a818-42c6-ac4f-9bae4c4c1d3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1338 entries, 0 to 1353\n",
            "Data columns (total 26 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   CaseNum   1338 non-null   object\n",
            " 1   Age       1338 non-null   object\n",
            " 2   SchoolYr  1338 non-null   object\n",
            " 3   Race      1338 non-null   object\n",
            " 4   GCQ3A     1338 non-null   object\n",
            " 5   GCQ3B     1338 non-null   object\n",
            " 6   GCQ3C     1338 non-null   object\n",
            " 7   GCQ3D     1338 non-null   object\n",
            " 8   GCQ3E     1338 non-null   object\n",
            " 9   GCQ3F     1338 non-null   object\n",
            " 10  GCQ3G     1338 non-null   object\n",
            " 11  GCQ3H     1338 non-null   object\n",
            " 12  GCQ3I     1338 non-null   object\n",
            " 13  GCQ3J     1338 non-null   object\n",
            " 14  GCQ3K     1338 non-null   object\n",
            " 15  GCQ3L     1338 non-null   object\n",
            " 16  ESECONVA  1338 non-null   object\n",
            " 17  ESECONVB  1338 non-null   object\n",
            " 18  ESECONVC  1338 non-null   object\n",
            " 19  ESECONVD  1338 non-null   object\n",
            " 20  ESECONVE  1338 non-null   object\n",
            " 21  ESECONVF  1338 non-null   object\n",
            " 22  ESECONVG  1338 non-null   object\n",
            " 23  ESECONVH  1338 non-null   object\n",
            " 24  ESECONVI  1338 non-null   object\n",
            " 25  ESECONVJ  1338 non-null   object\n",
            "dtypes: object(26)\n",
            "memory usage: 282.2+ KB\n"
          ]
        }
      ],
      "source": [
        "filt_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "893747dc-fadf-48d3-b7ca-34c5d025c809",
      "metadata": {
        "id": "893747dc-fadf-48d3-b7ca-34c5d025c809"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a292009b-cdbd-4e8e-86a6-d484875d6d82",
      "metadata": {
        "id": "a292009b-cdbd-4e8e-86a6-d484875d6d82"
      },
      "outputs": [],
      "source": [
        "x = filt_df[['GCQ3A', 'GCQ3B', 'GCQ3C', 'GCQ3D', 'GCQ3E', 'GCQ3F', 'GCQ3G', 'GCQ3H', 'GCQ3I', 'GCQ3J', 'GCQ3K', 'GCQ3L']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad01c20e-87d0-49a4-b17f-b0eafed9bf6f",
      "metadata": {
        "id": "ad01c20e-87d0-49a4-b17f-b0eafed9bf6f"
      },
      "outputs": [],
      "source": [
        "y = filt_df[['ESECONVA', 'ESECONVB', 'ESECONVC', 'ESECONVD', 'ESECONVE', 'ESECONVF', 'ESECONVG', 'ESECONVH', 'ESECONVI', 'ESECONVJ']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdfd1224-540a-4abe-ba1a-05d6ddea56ac",
      "metadata": {
        "id": "cdfd1224-540a-4abe-ba1a-05d6ddea56ac"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7970b81-7bde-43a6-b25f-a8d24159f3a0",
      "metadata": {
        "id": "e7970b81-7bde-43a6-b25f-a8d24159f3a0",
        "outputId": "8b4db9d4-7119-47ae-e1c1-20fd4a675490"
      },
      "outputs": [
        {
          "data": {
            "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>ESECONVA</th>\n",
              "      <th>ESECONVB</th>\n",
              "      <th>ESECONVC</th>\n",
              "      <th>ESECONVD</th>\n",
              "      <th>ESECONVE</th>\n",
              "      <th>ESECONVF</th>\n",
              "      <th>ESECONVG</th>\n",
              "      <th>ESECONVH</th>\n",
              "      <th>ESECONVI</th>\n",
              "      <th>ESECONVJ</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>571</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1301</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1155</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>982</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     ESECONVA ESECONVB ESECONVC ESECONVD ESECONVE ESECONVF ESECONVG ESECONVH  \\\n",
              "571         2        1        1        1        1        1        1        2   \n",
              "1301        1        1        1        1        1        1        1        1   \n",
              "1155        3        3        2        1        2        1        2        1   \n",
              "982         3        3        3        3        4        3        3        5   \n",
              "495         1        1        1        2        1        1        1        1   \n",
              "\n",
              "     ESECONVI ESECONVJ  \n",
              "571         1        1  \n",
              "1301        1        1  \n",
              "1155        2        2  \n",
              "982         4        2  \n",
              "495         1        1  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df6b3c97-d469-4463-8db5-c51ab9b8b0d9",
      "metadata": {
        "id": "df6b3c97-d469-4463-8db5-c51ab9b8b0d9",
        "outputId": "7f47afbe-ad44-4866-9f2a-14455ecff34b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training set size: 1070\n",
            "Test set size: 268\n"
          ]
        }
      ],
      "source": [
        "print(\"Training set size:\", x_train.shape[0])\n",
        "print(\"Test set size:\", x_test.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d98d0fd-4ab7-4a37-8300-41eaea093480",
      "metadata": {
        "id": "9d98d0fd-4ab7-4a37-8300-41eaea093480",
        "outputId": "fabb3e0c-be1b-4b18-f587-0c4806da4445"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training set size: 1070\n",
            "Test set size: 268\n"
          ]
        }
      ],
      "source": [
        "print(\"Training set size:\", y_train.shape[0])\n",
        "print(\"Test set size:\", y_test.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "64f6cf08-755f-4c57-a2d3-046eb5fe2bc3",
      "metadata": {
        "id": "64f6cf08-755f-4c57-a2d3-046eb5fe2bc3"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "abc45b78-9f15-4ac0-820d-49743ef591e5",
      "metadata": {
        "id": "abc45b78-9f15-4ac0-820d-49743ef591e5"
      },
      "outputs": [],
      "source": [
        "model = DecisionTreeClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5031d7de-ed4d-484b-a496-3ce23a2c94c3",
      "metadata": {
        "id": "5031d7de-ed4d-484b-a496-3ce23a2c94c3",
        "outputId": "cb754de1-89cd-4613-d55a-5b7b04926dc3"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "could not convert string to float: ''",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[24], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py:889\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m    859\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m    860\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[0;32m    861\u001b[0m \n\u001b[0;32m    862\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    886\u001b[0m \u001b[38;5;124;03m        Fitted estimator.\u001b[39;00m\n\u001b[0;32m    887\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 889\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    892\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    893\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    894\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    895\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py:186\u001b[0m, in \u001b[0;36mBaseDecisionTree.fit\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m    184\u001b[0m check_X_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(dtype\u001b[38;5;241m=\u001b[39mDTYPE, accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    185\u001b[0m check_y_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 186\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    187\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_separately\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_y_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    188\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X):\n\u001b[0;32m    190\u001b[0m     X\u001b[38;5;241m.\u001b[39msort_indices()\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py:560\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    558\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_X_params:\n\u001b[0;32m    559\u001b[0m     check_X_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_X_params}\n\u001b[1;32m--> 560\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_X_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    561\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mestimator\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m check_y_params:\n\u001b[0;32m    562\u001b[0m     check_y_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdefault_check_params, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params}\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py:879\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    877\u001b[0m         array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    878\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 879\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m    881\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    882\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    883\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\_array_api.py:185\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m    182\u001b[0m     xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(array)\n\u001b[0;32m    183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy.array_api\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n\u001b[0;32m    184\u001b[0m     \u001b[38;5;66;03m# Use NumPy API to support order\u001b[39;00m\n\u001b[1;32m--> 185\u001b[0m     array \u001b[38;5;241m=\u001b[39m \u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    186\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m    187\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\generic.py:2070\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2069\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m-> 2070\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
            "\u001b[1;31mValueError\u001b[0m: could not convert string to float: ''"
          ]
        }
      ],
      "source": [
        "model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6a47b70-56a4-4b5d-804c-07b73266b3fc",
      "metadata": {
        "id": "b6a47b70-56a4-4b5d-804c-07b73266b3fc"
      },
      "outputs": [],
      "source": [
        "y_pred = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b09a9b2f-f0fb-49d2-984c-902cf91c1492",
      "metadata": {
        "id": "b09a9b2f-f0fb-49d2-984c-902cf91c1492"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7c343833-ae75-4e6a-97f0-e917e63350e3",
      "metadata": {
        "id": "7c343833-ae75-4e6a-97f0-e917e63350e3"
      },
      "outputs": [],
      "source": [
        "# Replace empty strings with NaN\n",
        "fem_df.replace('', np.nan, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b44f4419-a526-4be3-9ca9-1acb243e9014",
      "metadata": {
        "id": "b44f4419-a526-4be3-9ca9-1acb243e9014"
      },
      "outputs": [],
      "source": [
        "df_without_missing = fem_df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a768dfe3-795c-4aa3-a5ef-19a3a23f2625",
      "metadata": {
        "id": "a768dfe3-795c-4aa3-a5ef-19a3a23f2625",
        "outputId": "d4fe873b-e020-4da0-a68b-c53d759d0482"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "could not convert string to float: ''",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[24], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Convert columns to float\u001b[39;00m\n\u001b[0;32m      2\u001b[0m float_columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3A\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3B\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3C\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3D\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3E\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3F\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3G\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3H\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3I\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3J\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3K\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGCQ3L\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 3\u001b[0m df_without_missing[float_columns] \u001b[38;5;241m=\u001b[39m \u001b[43mdf_without_missing\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfloat_columns\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\generic.py:6240\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   6233\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m   6234\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m   6235\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[0;32m   6236\u001b[0m     ]\n\u001b[0;32m   6238\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6239\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 6240\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6241\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6243\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\internals\\managers.py:450\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mastype\u001b[39m(\u001b[38;5;28mself\u001b[39m: T, dtype, copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m--> 450\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\internals\\managers.py:352\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[0;32m    350\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    351\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 352\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mNotImplementedError\u001b[39;00m):\n\u001b[0;32m    354\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ignore_failures:\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:526\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    508\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    509\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[0;32m    510\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    522\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[0;32m    523\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    524\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[1;32m--> 526\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    528\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m    529\u001b[0m newb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_block(new_values)\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:299\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    296\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m    298\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 299\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    300\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m    301\u001b[0m     \u001b[38;5;66;03m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m    302\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[0;32m    303\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:230\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m    227\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m    229\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 230\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    232\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m    233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:170\u001b[0m, in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    166\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m is_object_dtype(arr\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mor\u001b[39;00m is_object_dtype(dtype):\n\u001b[0;32m    169\u001b[0m     \u001b[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001b[39;00m\n\u001b[1;32m--> 170\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    172\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
            "\u001b[1;31mValueError\u001b[0m: could not convert string to float: ''"
          ]
        }
      ],
      "source": [
        "# Convert columns to float\n",
        "float_columns = ['GCQ3A', 'GCQ3B', 'GCQ3C', 'GCQ3D', 'GCQ3E', 'GCQ3F', 'GCQ3G', 'GCQ3H', 'GCQ3I', 'GCQ3J', 'GCQ3K', 'GCQ3L']\n",
        "df_without_missing[float_columns] = df_without_missing[float_columns].astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dce78af3-c40a-4fa0-b61e-ee5854dc31a0",
      "metadata": {
        "id": "dce78af3-c40a-4fa0-b61e-ee5854dc31a0",
        "outputId": "4c6ca1f4-4071-4ce2-aed8-f4326c7a2779"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "could not convert string to float: ''",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df_without_missing[float_columns] \u001b[38;5;241m=\u001b[39m \u001b[43mdf_without_missing\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfloat_columns\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapplymap\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnan\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\frame.py:9650\u001b[0m, in \u001b[0;36mDataFrame.applymap\u001b[1;34m(self, func, na_action, **kwargs)\u001b[0m\n\u001b[0;32m   9647\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer(x, func, ignore_na\u001b[38;5;241m=\u001b[39mignore_na)\n\u001b[0;32m   9648\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer(x\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m)\u001b[38;5;241m.\u001b[39m_values, func, ignore_na\u001b[38;5;241m=\u001b[39mignore_na)\n\u001b[1;32m-> 9650\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43minfer\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplymap\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\frame.py:9565\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[0;32m   9554\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[0;32m   9556\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[0;32m   9557\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   9558\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   9563\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m   9564\u001b[0m )\n\u001b[1;32m-> 9565\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\apply.py:746\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    743\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[0;32m    744\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw()\n\u001b[1;32m--> 746\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\apply.py:873\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    872\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 873\u001b[0m     results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    875\u001b[0m     \u001b[38;5;66;03m# wrap results\u001b[39;00m\n\u001b[0;32m    876\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results(results, res_index)\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\apply.py:889\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    886\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    887\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[0;32m    888\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[1;32m--> 889\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    890\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[0;32m    891\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[0;32m    892\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[0;32m    893\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\core\\frame.py:9648\u001b[0m, in \u001b[0;36mDataFrame.applymap.<locals>.infer\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   9646\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mempty:\n\u001b[0;32m   9647\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer(x, func, ignore_na\u001b[38;5;241m=\u001b[39mignore_na)\n\u001b[1;32m-> 9648\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mobject\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_na\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_na\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\pandas\\_libs\\lib.pyx:2918\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
            "Cell \u001b[1;32mIn[25], line 1\u001b[0m, in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df_without_missing[float_columns] \u001b[38;5;241m=\u001b[39m df_without_missing[float_columns]\u001b[38;5;241m.\u001b[39mapplymap(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39mnan)\n",
            "\u001b[1;31mValueError\u001b[0m: could not convert string to float: ''"
          ]
        }
      ],
      "source": [
        "df_without_missing[float_columns] = df_without_missing[float_columns].applymap(lambda x: float(x) if x else np.nan)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df80b562-1427-4cd7-ad74-8a1f3616e8e3",
      "metadata": {
        "id": "df80b562-1427-4cd7-ad74-8a1f3616e8e3"
      },
      "outputs": [],
      "source": [
        "# Replace empty strings with NaN\n",
        "fem_df.replace('', np.nan, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57afbd4a-5ffe-47ed-8b9a-295c1a6cce4e",
      "metadata": {
        "id": "57afbd4a-5ffe-47ed-8b9a-295c1a6cce4e",
        "outputId": "056b2437-1240-484e-a4ce-5d16a33f0429"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   CaseNum Age SchoolYr Race GCQ3A GCQ3B GCQ3C GCQ3D GCQ3E GCQ3F  ...  \\\n",
            "40  210041  21        4    1     3     2     2     3     2     3  ...   \n",
            "41  210042  19        2    1     3     3     2     3     3     3  ...   \n",
            "42  210043  21        4    1     3     3     3     2     3     2  ...   \n",
            "43  210044  20        2    1     3     3     2     3     3        ...   \n",
            "44  210045  20        3    1     3     3     2     3     3     2  ...   \n",
            "45  210046  21        4    1     3     3     1     2     3     2  ...   \n",
            "46  210047  19        2    1     2     2     1     2     2     2  ...   \n",
            "47  210048  20        3    1     3     3     1     3     3     1  ...   \n",
            "48  210049  21        3    8     3     2     2     1     2     2  ...   \n",
            "49  210050  20        3    1     3     3     3     3     3     2  ...   \n",
            "50  210051  21        4    4     3     2     2     2     2     2  ...   \n",
            "\n",
            "   ESECONVA ESECONVB ESECONVC ESECONVD ESECONVE ESECONVF ESECONVG ESECONVH  \\\n",
            "40                                                                           \n",
            "41        4        4        3        4        3        2        1        1   \n",
            "42        2        1        1        1        2        1        1        1   \n",
            "43        3        2        2        1                 1        2        2   \n",
            "44        1        1        1        1        1        1        1        2   \n",
            "45        5        5        5        2        1        5        5        1   \n",
            "46        5        5        5        1        3        1        5        5   \n",
            "47        5        5        5        2        2        2        5        2   \n",
            "48        2        1        2        5        3        1        2        1   \n",
            "49        2        2        2        2        1        2        3        2   \n",
            "50        3        3        2        3        1        1        2        2   \n",
            "\n",
            "   ESECONVI ESECONVJ  \n",
            "40                    \n",
            "41        4        3  \n",
            "42        1        1  \n",
            "43        2        1  \n",
            "44        1        1  \n",
            "45        5        1  \n",
            "46        2        1  \n",
            "47        5        1  \n",
            "48        4        1  \n",
            "49        2        1  \n",
            "50        2        1  \n",
            "\n",
            "[11 rows x 26 columns]\n"
          ]
        }
      ],
      "source": [
        "print(fem_df.iloc[40:51])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9fc7adcf-c48e-44a4-9e49-d44235a46dd9",
      "metadata": {
        "id": "9fc7adcf-c48e-44a4-9e49-d44235a46dd9"
      },
      "outputs": [],
      "source": [
        "#the empty strings aren't getting replaced with NaN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c89a371-cafe-4657-b138-3ad48d8b8faa",
      "metadata": {
        "id": "8c89a371-cafe-4657-b138-3ad48d8b8faa"
      },
      "outputs": [],
      "source": [
        "fem_df = fem_df.applymap(lambda x: x.strip() if isinstance(x, str) else x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0673d715-ae01-4606-92e9-70b896180ca0",
      "metadata": {
        "id": "0673d715-ae01-4606-92e9-70b896180ca0",
        "outputId": "a3b5ffaa-fc0e-4e63-c04c-c1c6b8f92664"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   CaseNum Age SchoolYr Race GCQ3A GCQ3B GCQ3C GCQ3D GCQ3E GCQ3F  ...  \\\n",
            "40  210041  21        4    1     3     2     2     3     2     3  ...   \n",
            "41  210042  19        2    1     3     3     2     3     3     3  ...   \n",
            "42  210043  21        4    1     3     3     3     2     3     2  ...   \n",
            "43  210044  20        2    1     3     3     2     3     3        ...   \n",
            "44  210045  20        3    1     3     3     2     3     3     2  ...   \n",
            "45  210046  21        4    1     3     3     1     2     3     2  ...   \n",
            "46  210047  19        2    1     2     2     1     2     2     2  ...   \n",
            "47  210048  20        3    1     3     3     1     3     3     1  ...   \n",
            "48  210049  21        3    8     3     2     2     1     2     2  ...   \n",
            "49  210050  20        3    1     3     3     3     3     3     2  ...   \n",
            "50  210051  21        4    4     3     2     2     2     2     2  ...   \n",
            "\n",
            "   ESECONVA ESECONVB ESECONVC ESECONVD ESECONVE ESECONVF ESECONVG ESECONVH  \\\n",
            "40                                                                           \n",
            "41        4        4        3        4        3        2        1        1   \n",
            "42        2        1        1        1        2        1        1        1   \n",
            "43        3        2        2        1                 1        2        2   \n",
            "44        1        1        1        1        1        1        1        2   \n",
            "45        5        5        5        2        1        5        5        1   \n",
            "46        5        5        5        1        3        1        5        5   \n",
            "47        5        5        5        2        2        2        5        2   \n",
            "48        2        1        2        5        3        1        2        1   \n",
            "49        2        2        2        2        1        2        3        2   \n",
            "50        3        3        2        3        1        1        2        2   \n",
            "\n",
            "   ESECONVI ESECONVJ  \n",
            "40                    \n",
            "41        4        3  \n",
            "42        1        1  \n",
            "43        2        1  \n",
            "44        1        1  \n",
            "45        5        1  \n",
            "46        2        1  \n",
            "47        5        1  \n",
            "48        4        1  \n",
            "49        2        1  \n",
            "50        2        1  \n",
            "\n",
            "[11 rows x 26 columns]\n"
          ]
        }
      ],
      "source": [
        "print(fem_df.iloc[40:51])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "053cd762-cdc5-4d7a-a73c-7866ef4351a5",
      "metadata": {
        "id": "053cd762-cdc5-4d7a-a73c-7866ef4351a5"
      },
      "outputs": [],
      "source": [
        "fem_df.loc[40] = fem_df.loc[40].fillna(np.nan)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5b679a5e-bb79-4882-af98-aeba361caf60",
      "metadata": {
        "id": "5b679a5e-bb79-4882-af98-aeba361caf60",
        "outputId": "5792e321-bf66-4748-e5fd-083ef9aeb0be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CaseNum     210041\n",
            "Age             21\n",
            "SchoolYr         4\n",
            "Race             1\n",
            "GCQ3A            3\n",
            "GCQ3B            2\n",
            "GCQ3C            2\n",
            "GCQ3D            3\n",
            "GCQ3E            2\n",
            "GCQ3F            3\n",
            "GCQ3G            3\n",
            "GCQ3H            3\n",
            "GCQ3I            1\n",
            "GCQ3J            2\n",
            "GCQ3K            1\n",
            "GCQ3L            1\n",
            "ESECONVA          \n",
            "ESECONVB          \n",
            "ESECONVC          \n",
            "ESECONVD          \n",
            "ESECONVE          \n",
            "ESECONVF          \n",
            "ESECONVG          \n",
            "ESECONVH          \n",
            "ESECONVI          \n",
            "ESECONVJ          \n",
            "Name: 40, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(fem_df.loc[40])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a93dc743-23c3-42c1-b30d-6c39bfe90266",
      "metadata": {
        "id": "a93dc743-23c3-42c1-b30d-6c39bfe90266"
      },
      "outputs": [],
      "source": [
        "fem_df.loc[40] = fem_df.loc[40].replace('', np.nan)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "905dda71-fcbf-4144-99e1-ddfd03bcd094",
      "metadata": {
        "id": "905dda71-fcbf-4144-99e1-ddfd03bcd094",
        "outputId": "db886476-4817-4207-afb0-0368e4514bee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CaseNum     210041.0\n",
            "Age             21.0\n",
            "SchoolYr         4.0\n",
            "Race             1.0\n",
            "GCQ3A            3.0\n",
            "GCQ3B            2.0\n",
            "GCQ3C            2.0\n",
            "GCQ3D            3.0\n",
            "GCQ3E            2.0\n",
            "GCQ3F            3.0\n",
            "GCQ3G            3.0\n",
            "GCQ3H            3.0\n",
            "GCQ3I            1.0\n",
            "GCQ3J            2.0\n",
            "GCQ3K            1.0\n",
            "GCQ3L            1.0\n",
            "ESECONVA         NaN\n",
            "ESECONVB         NaN\n",
            "ESECONVC         NaN\n",
            "ESECONVD         NaN\n",
            "ESECONVE         NaN\n",
            "ESECONVF         NaN\n",
            "ESECONVG         NaN\n",
            "ESECONVH         NaN\n",
            "ESECONVI         NaN\n",
            "ESECONVJ         NaN\n",
            "Name: 40, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(fem_df.loc[40])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e78ed22e-59c7-45f4-84a5-638f519acdcc",
      "metadata": {
        "id": "e78ed22e-59c7-45f4-84a5-638f519acdcc"
      },
      "outputs": [],
      "source": [
        "# NaN had shown up previously but it's not working this time.\n",
        "# But it worked now after running the 6 lines of code before it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a437f057-a551-47f0-9cbc-efc4c5b7a0bf",
      "metadata": {
        "id": "a437f057-a551-47f0-9cbc-efc4c5b7a0bf"
      },
      "outputs": [],
      "source": [
        "fem_df.replace('', np.nan, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2412d279-35cf-47e1-966a-71d7c892aea5",
      "metadata": {
        "id": "2412d279-35cf-47e1-966a-71d7c892aea5"
      },
      "outputs": [],
      "source": [
        "# Why did decimals show up?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bdc4469b-358a-4cb0-b47a-8183bb82d04d",
      "metadata": {
        "id": "bdc4469b-358a-4cb0-b47a-8183bb82d04d",
        "outputId": "1896ced7-478c-4aff-eca7-8bc2f8778d76"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     CaseNum   Age  SchoolYr  Race  GCQ3A  GCQ3B  GCQ3C  GCQ3D  GCQ3E  GCQ3F  \\\n",
            "40  210041.0  21.0       4.0   1.0    3.0    2.0    2.0    3.0    2.0    3.0   \n",
            "41  210042.0  19.0       2.0   1.0    3.0    3.0    2.0    3.0    3.0    3.0   \n",
            "42  210043.0  21.0       4.0   1.0    3.0    3.0    3.0    2.0    3.0    2.0   \n",
            "43  210044.0  20.0       2.0   1.0    3.0    3.0    2.0    3.0    3.0    NaN   \n",
            "44  210045.0  20.0       3.0   1.0    3.0    3.0    2.0    3.0    3.0    2.0   \n",
            "45  210046.0  21.0       4.0   1.0    3.0    3.0    1.0    2.0    3.0    2.0   \n",
            "46  210047.0  19.0       2.0   1.0    2.0    2.0    1.0    2.0    2.0    2.0   \n",
            "47  210048.0  20.0       3.0   1.0    3.0    3.0    1.0    3.0    3.0    1.0   \n",
            "48  210049.0  21.0       3.0   8.0    3.0    2.0    2.0    1.0    2.0    2.0   \n",
            "49  210050.0  20.0       3.0   1.0    3.0    3.0    3.0    3.0    3.0    2.0   \n",
            "50  210051.0  21.0       4.0   4.0    3.0    2.0    2.0    2.0    2.0    2.0   \n",
            "\n",
            "    ...  ESECONVA  ESECONVB  ESECONVC  ESECONVD  ESECONVE  ESECONVF  ESECONVG  \\\n",
            "40  ...       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
            "41  ...       4.0       4.0       3.0       4.0       3.0       2.0       1.0   \n",
            "42  ...       2.0       1.0       1.0       1.0       2.0       1.0       1.0   \n",
            "43  ...       3.0       2.0       2.0       1.0       NaN       1.0       2.0   \n",
            "44  ...       1.0       1.0       1.0       1.0       1.0       1.0       1.0   \n",
            "45  ...       5.0       5.0       5.0       2.0       1.0       5.0       5.0   \n",
            "46  ...       5.0       5.0       5.0       1.0       3.0       1.0       5.0   \n",
            "47  ...       5.0       5.0       5.0       2.0       2.0       2.0       5.0   \n",
            "48  ...       2.0       1.0       2.0       5.0       3.0       1.0       2.0   \n",
            "49  ...       2.0       2.0       2.0       2.0       1.0       2.0       3.0   \n",
            "50  ...       3.0       3.0       2.0       3.0       1.0       1.0       2.0   \n",
            "\n",
            "    ESECONVH  ESECONVI  ESECONVJ  \n",
            "40       NaN       NaN       NaN  \n",
            "41       1.0       4.0       3.0  \n",
            "42       1.0       1.0       1.0  \n",
            "43       2.0       2.0       1.0  \n",
            "44       2.0       1.0       1.0  \n",
            "45       1.0       5.0       1.0  \n",
            "46       5.0       2.0       1.0  \n",
            "47       2.0       5.0       1.0  \n",
            "48       1.0       4.0       1.0  \n",
            "49       2.0       2.0       1.0  \n",
            "50       2.0       2.0       1.0  \n",
            "\n",
            "[11 rows x 26 columns]\n"
          ]
        }
      ],
      "source": [
        "print(fem_df.loc[40:50])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88ef53e2-aa25-4c91-9f56-72bdb78b3294",
      "metadata": {
        "id": "88ef53e2-aa25-4c91-9f56-72bdb78b3294",
        "outputId": "c4ae0213-a4c1-4a30-9f24-35929964b374"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      CaseNum   Age  SchoolYr  Race  GCQ3A  GCQ3B  GCQ3C  GCQ3D  GCQ3E  GCQ3F  \\\n",
            "135   22004.0  20.0       3.0   1.0    1.0    2.0    2.0    1.0    1.0    1.0   \n",
            "136       NaN   NaN       NaN   NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
            "137       NaN   NaN       0.0   0.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
            "138   22005.0  21.0       4.0   1.0    3.0    3.0    3.0    3.0    3.0    3.0   \n",
            "139   22006.0  18.0       1.0   1.0    2.0    2.0    3.0    3.0    2.0    2.0   \n",
            "140   22007.0  18.0       1.0   8.0    2.0    1.0    1.0    2.0    2.0    2.0   \n",
            "141   22008.0  18.0       1.0   1.0    3.0    3.0    3.0    2.0    2.0    2.0   \n",
            "142   22009.0  18.0       1.0   1.0    3.0    3.0    1.0    3.0    3.0    3.0   \n",
            "143  220010.0  18.0       1.0   1.0    2.0    2.0    3.0    3.0    2.0    2.0   \n",
            "144  220011.0  18.0       1.0   1.0    2.0    2.0    2.0    2.0    1.0    1.0   \n",
            "145  220012.0  18.0       1.0   1.0    3.0    3.0    3.0    3.0    3.0    3.0   \n",
            "\n",
            "     ...  ESECONVA  ESECONVB  ESECONVC  ESECONVD  ESECONVE  ESECONVF  \\\n",
            "135  ...       4.0       4.0       2.0       2.0       2.0       1.0   \n",
            "136  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
            "137  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
            "138  ...       4.0       1.0       1.0       2.0       1.0       1.0   \n",
            "139  ...       1.0       1.0       2.0       1.0       1.0       1.0   \n",
            "140  ...       1.0       2.0       1.0       1.0       1.0       1.0   \n",
            "141  ...       4.0       1.0       3.0       5.0       1.0       1.0   \n",
            "142  ...       2.0       1.0       1.0       1.0       1.0       1.0   \n",
            "143  ...       1.0       1.0       1.0       1.0       1.0       1.0   \n",
            "144  ...       5.0       2.0       2.0       2.0       2.0       2.0   \n",
            "145  ...       1.0       2.0       1.0       1.0       1.0       1.0   \n",
            "\n",
            "     ESECONVG  ESECONVH  ESECONVI  ESECONVJ  \n",
            "135       2.0       1.0       1.0       1.0  \n",
            "136       NaN       NaN       NaN       NaN  \n",
            "137       NaN       NaN       NaN       NaN  \n",
            "138       1.0       1.0       1.0       1.0  \n",
            "139       1.0       1.0       2.0       1.0  \n",
            "140       1.0       1.0       1.0       1.0  \n",
            "141       2.0       1.0       1.0       1.0  \n",
            "142       3.0       1.0       1.0       1.0  \n",
            "143       1.0       1.0       1.0       1.0  \n",
            "144       4.0       1.0       1.0       1.0  \n",
            "145       1.0       1.0       2.0       1.0  \n",
            "\n",
            "[11 rows x 26 columns]\n"
          ]
        }
      ],
      "source": [
        "print(fem_df.loc[135:145])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c3afef1b-f0e1-48ff-bb90-44939fe5bf42",
      "metadata": {
        "id": "c3afef1b-f0e1-48ff-bb90-44939fe5bf42"
      },
      "outputs": [],
      "source": [
        "# NaN are showing up now but what's with the decimals?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9c4e2f7-6311-4df5-9117-018239e7956e",
      "metadata": {
        "id": "f9c4e2f7-6311-4df5-9117-018239e7956e"
      },
      "source": [
        "# Title\n",
        "## Subtitle\n",
        "\n",
        "This is a test.\n",
        "This is **boldface**\n",
        "This is _emphasize_\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0c29413-4e7a-4f7b-af85-657e59f60b6d",
      "metadata": {
        "id": "a0c29413-4e7a-4f7b-af85-657e59f60b6d"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd3f8861-0c32-4994-ba9b-0248028c17ad",
      "metadata": {
        "id": "bd3f8861-0c32-4994-ba9b-0248028c17ad"
      },
      "outputs": [],
      "source": [
        "# Test\n",
        "data = pd.DataFrame({'Q1': {0: 'Strongly disagree', 1: 'Agree'},\n",
        "                     'Q2': {0: 'Disagree', 1: 'Strongly agree'}})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63f78d56-2751-4611-813e-926a7c62b6da",
      "metadata": {
        "id": "63f78d56-2751-4611-813e-926a7c62b6da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "outputId": "6e1b6d9a-9326-4f9d-ff07-d6e52f536669"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Q1              Q2\n",
              "0  Strongly disagree        Disagree\n",
              "1              Agree  Strongly agree"
            ],
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              "      <div>\n",
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              "      <th>0</th>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-664bed48-1128-46dc-9e26-acd62ce1396a 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-664bed48-1128-46dc-9e26-acd62ce1396a');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "data"
      ]
    },
    {
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      "id": "fc2c0616-c81e-4432-abe9-1429990ddd09",
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    },
    {
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    {
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    {
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    {
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