affects_productivity.ipynb

{
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  "metadata": {
    "colab": {
      "name": "affects_productivity.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOO5n3LUQhsSNnCJBSsycKr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexisbianco0919/compsci216-project/blob/main/affects_productivity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfUGdIBWPHDQ"
      },
      "source": [
        "# import needed libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import seaborn as sns\n",
        "from scipy import stats"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "pnbMRma_PJfA",
        "outputId": "1ef53599-7774-42f3-c0af-8e042e420f45"
      },
      "source": [
        "# download data from merged_data.csv\n",
        "cd = pd.read_csv('https://raw.githubusercontent.com/alexisbianco0919/compsci216-project/main/clean_data_4.csv')\n",
        "\n",
        "# visualize data\n",
        "cd"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>self_employeed</th>\n",
              "      <th>company_size</th>\n",
              "      <th>tech_company</th>\n",
              "      <th>tech_prole</th>\n",
              "      <th>mh_benefits</th>\n",
              "      <th>anonymity</th>\n",
              "      <th>leaving_difficulty</th>\n",
              "      <th>disc_mh_coworker</th>\n",
              "      <th>disc_mh_supervisor</th>\n",
              "      <th>reveal_mh_business</th>\n",
              "      <th>reveal_mh_coworkers</th>\n",
              "      <th>affects_productivity</th>\n",
              "      <th>productivity_percent</th>\n",
              "      <th>mention_ph_interview</th>\n",
              "      <th>mention_mh_interview</th>\n",
              "      <th>share_mh_ff</th>\n",
              "      <th>exp_unsupport</th>\n",
              "      <th>reveal_less</th>\n",
              "      <th>family_history</th>\n",
              "      <th>mh_in_past</th>\n",
              "      <th>mh_currently</th>\n",
              "      <th>mh_diagnosed</th>\n",
              "      <th>sought_treatment</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>country_live</th>\n",
              "      <th>state_live</th>\n",
              "      <th>country_work</th>\n",
              "      <th>state_work</th>\n",
              "      <th>year</th>\n",
              "      <th>gender_cleaned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>26-100</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not eligible for coverage / NA</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>6-25</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.0</td>\n",
              "      <td>male</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>6-25</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>Maybe/Not sure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1-25%</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>6-25</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>Yes, I experienced</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>2016</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2953</th>\n",
              "      <td>False</td>\n",
              "      <td>More than 1000</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>Very open</td>\n",
              "      <td>Yes, I observed</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>27.0</td>\n",
              "      <td>male</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2954</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Yes</td>\n",
              "      <td>26-50%</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Somewhat not open</td>\n",
              "      <td>Yes, I observed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>48.0</td>\n",
              "      <td>m</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Louisiana</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Louisiana</td>\n",
              "      <td>2019</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2955</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Unsure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Very open</td>\n",
              "      <td>I've always been self-employed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>50.0</td>\n",
              "      <td>M</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2956</th>\n",
              "      <td>False</td>\n",
              "      <td>More than 1000</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Difficult</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Somewhat not open</td>\n",
              "      <td>Maybe/Not sure</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>30.0</td>\n",
              "      <td>female</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2957</th>\n",
              "      <td>False</td>\n",
              "      <td>More than 1000</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Very open</td>\n",
              "      <td>Maybe/Not sure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>True</td>\n",
              "      <td>24.0</td>\n",
              "      <td>Female (cis)</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>2019</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2958 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      self_employeed    company_size  ...  year gender_cleaned\n",
              "0              False          26-100  ...  2016           male\n",
              "1              False            6-25  ...  2016           male\n",
              "2              False            6-25  ...  2016           male\n",
              "3               True             NaN  ...  2016           male\n",
              "4              False            6-25  ...  2016         female\n",
              "...              ...             ...  ...   ...            ...\n",
              "2953           False  More than 1000  ...  2019           male\n",
              "2954            True             NaN  ...  2019           male\n",
              "2955            True             NaN  ...  2019           male\n",
              "2956           False  More than 1000  ...  2019         female\n",
              "2957           False  More than 1000  ...  2019         female\n",
              "\n",
              "[2958 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4uNzimnPO7v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac42fab2-b650-4938-bd7c-050191212977"
      },
      "source": [
        "cd['affects_productivity'].value_counts(dropna=False)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NaN                     2454\n",
              "Yes                      367\n",
              "Unsure                    60\n",
              "Not applicable to me      51\n",
              "No                        26\n",
              "Name: affects_productivity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_66BIB1PnR5"
      },
      "source": [
        "n = len(cd)\n",
        "#cd['affects_productivity'] = cd['affects_productivity'].replace(np.nan, 'Not applicable to me')\n",
        "#ap_counts = cd.groupby('year')['affects_productivity'].value_counts(dropna = False).unstack()#/n\n",
        "#ap_counts"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOcIwDwNQsgM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b3283166-1024-450b-d023-ef980a5b8115"
      },
      "source": [
        "sns.countplot(data=cd, x='affects_productivity')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f481614ffd0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WC0L_FVdUzmf",
        "outputId": "a94a0c74-01c3-41fd-c7d3-b9d0e2301cde"
      },
      "source": [
        "#cd_edit = cd.copy\n",
        "cd['affects_productivity'] = cd['affects_productivity'].fillna('NaN')\n",
        "blue_p = [\"#185ea8\", \"#3372b5\", \"#568fcc\", \"#81aad6\", \"#a8c3e0\"]\n",
        "#cd_edit['self_employeed'] = cd_edit['self_employeed'].replace(np.nan, 'NaN')\n",
        "sns.countplot(data=cd, x='year', hue='affects_productivity', palette = blue_p)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4815876650>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbFXbr7k2Lw2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "02d95e3d-6d9f-4a39-9b38-201e8d9049f1"
      },
      "source": [
        "sns.countplot(data=cd, x='self_employeed', hue='affects_productivity', palette = blue_p)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4815827110>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "LXkDKZYcU90w",
        "outputId": "6a8ae703-ae96-42d8-9073-887f3b1f02ed"
      },
      "source": [
        "sns.countplot(data=cd[cd['affects_productivity'] != 'NaN'], x='year', hue='affects_productivity', palette = blue_p)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f481579ee10>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "zvT32YHbapLQ",
        "outputId": "fb283850-5712-4728-f6da-04a2621deffb"
      },
      "source": [
        "filt_se = cd[cd['self_employeed'] == True]\n",
        "filt_se.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>self_employeed</th>\n",
              "      <th>company_size</th>\n",
              "      <th>tech_company</th>\n",
              "      <th>tech_prole</th>\n",
              "      <th>mh_benefits</th>\n",
              "      <th>anonymity</th>\n",
              "      <th>leaving_difficulty</th>\n",
              "      <th>disc_mh_coworker</th>\n",
              "      <th>disc_mh_supervisor</th>\n",
              "      <th>reveal_mh_business</th>\n",
              "      <th>reveal_mh_coworkers</th>\n",
              "      <th>affects_productivity</th>\n",
              "      <th>productivity_percent</th>\n",
              "      <th>mention_ph_interview</th>\n",
              "      <th>mention_mh_interview</th>\n",
              "      <th>share_mh_ff</th>\n",
              "      <th>exp_unsupport</th>\n",
              "      <th>reveal_less</th>\n",
              "      <th>family_history</th>\n",
              "      <th>mh_in_past</th>\n",
              "      <th>mh_currently</th>\n",
              "      <th>mh_diagnosed</th>\n",
              "      <th>sought_treatment</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>country_live</th>\n",
              "      <th>state_live</th>\n",
              "      <th>country_work</th>\n",
              "      <th>state_work</th>\n",
              "      <th>year</th>\n",
              "      <th>gender_cleaned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1-25%</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No, because it doesn't matter</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1-25%</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Very open</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No, because it would impact me negatively</td>\n",
              "      <td>No, because it would impact me negatively</td>\n",
              "      <td>Yes</td>\n",
              "      <td>76-100%</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat not open</td>\n",
              "      <td>Yes, I observed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I don't know</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>female</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Washington</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>Washington</td>\n",
              "      <td>2016</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>Not applicable to me</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not applicable to me (I do not have a mental i...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>New York</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>New York</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No, because it doesn't matter</td>\n",
              "      <td>Sometimes, if it comes up</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1-25%</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat open</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    self_employeed company_size tech_company  ...  state_work  year gender_cleaned\n",
              "3             True          NaN          NaN  ...         NaN  2016           male\n",
              "9             True          NaN          NaN  ...    Kentucky  2016           male\n",
              "18            True          NaN          NaN  ...  Washington  2016         female\n",
              "24            True          NaN          NaN  ...    New York  2016           male\n",
              "33            True          NaN          NaN  ...         NaN  2016           male\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3NSyul8U75o"
      },
      "source": [
        "#hypothesis test if self-employed people were more likely to say their mental\n",
        "#health affected productivity\n",
        "n_2016 = len(filt_se[filt_se['year'] == 2016])\n",
        "n_2017 = len(filt_se[filt_se['year'] == 2017])\n",
        "n_2018 = len(filt_se[filt_se['year'] == 2018])\n",
        "n_2019 = len(filt_se[filt_se['year'] == 2019])\n",
        "\n",
        "mu_2016 = len(filt_se[(filt_se['year'] == 2016) & (filt_se['affects_productivity'] == 'Yes')])/n_2016\n",
        "mu_2017 = len(filt_se[(filt_se['year'] == 2017) & (filt_se['affects_productivity'] == 'Yes')])/n_2017\n",
        "mu_2018 = len(filt_se[(filt_se['year'] == 2018) & (filt_se['affects_productivity'] == 'Yes')])/n_2018\n",
        "mu_2019 = len(filt_se[(filt_se['year'] == 2019) & (filt_se['affects_productivity'] == 'Yes')])/n_2019\n",
        "\n",
        "sigma_2016 = np.sqrt(mu_2016*(1-mu_2016))\n",
        "sigma_2017 = np.sqrt(mu_2017*(1-mu_2017))\n",
        "sigma_2018 = np.sqrt(mu_2018*(1-mu_2018))\n",
        "sigma_2019 = np.sqrt(mu_2019*(1-mu_2019))\n",
        "#also test if percentage changed year over year\n",
        "\n",
        "p_1 = stats.ttest_ind_from_stats(mean1 = mu_2016, std1 = sigma_2016, nobs1 = n_2016,\n",
        "                                  mean2 = mu_2017, std2 = sigma_2017, nobs2 = n_2017)[1]\n",
        "\n",
        "p_2 = stats.ttest_ind_from_stats(mean1 = mu_2016, std1 = sigma_2016, nobs1 = n_2016,\n",
        "                                  mean2 = mu_2018, std2 = sigma_2018, nobs2 = n_2018)[1]\n",
        "\n",
        "p_3 = stats.ttest_ind_from_stats(mean1 = mu_2016, std1 = sigma_2016, nobs1 = n_2016,\n",
        "                                  mean2 = mu_2019, std2 = sigma_2019, nobs2 = n_2019)[1]\n",
        "\n",
        "p_4 = stats.ttest_ind_from_stats(mean1 = mu_2017, std1 = sigma_2017, nobs1 = n_2017,\n",
        "                                  mean2 = mu_2018, std2 = sigma_2018, nobs2 = n_2018)[1]\n",
        "\n",
        "p_5 = stats.ttest_ind_from_stats(mean1 = mu_2017, std1 = sigma_2017, nobs1 = n_2017,\n",
        "                                  mean2 = mu_2019, std2 = sigma_2019, nobs2 = n_2019)[1]\n",
        "\n",
        "p_6 = stats.ttest_ind_from_stats(mean1 = mu_2018, std1 = sigma_2018, nobs1 = n_2018,\n",
        "                                  mean2 = mu_2019, std2 = sigma_2019, nobs2 = n_2019)[1]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(p_1)\n",
        "print(p_2)\n",
        "print(p_3)\n",
        "print(p_4)\n",
        "print(p_5)\n",
        "print(p_6)"
      ],
      "metadata": {
        "id": "N9sRNJZkUabw",
        "outputId": "056e68da-b1a3-43c1-e7ac-dfda718aae65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.40510175701560014\n",
            "0.7465812882939566\n",
            "0.39170974313325657\n",
            "0.7782579322754642\n",
            "0.8011343272543386\n",
            "0.6502983154854325\n"
          ]
        }
      ]
    }
  ]
}