reopeningcinemas_EDA.ipynb

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    "colab": {
      "name": "reopeningcinemas_EDA.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNfdsZ+cVuym5HvNNOX7VIC",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Anusha-T8/ProgrammingAssignment2/blob/master/reopeningcinemas_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJNM2mGyn4g7"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline \n",
        "from scipy import stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4uwTDvFoOtO"
      },
      "source": [
        "path=\"/content/audience_cinema_cleaned_dataset\"\n",
        "df=pd.read_csv(path, index_col=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5DRqFLfoiou",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "db3d9779-8d34-4859-962a-6dec8fa0f3bc"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Eager</th>\n",
              "      <th>Location</th>\n",
              "      <th>Implement_safety</th>\n",
              "      <th>alternate_seating</th>\n",
              "      <th>Price_hike</th>\n",
              "      <th>Visit_time</th>\n",
              "      <th>Food_option</th>\n",
              "      <th>Risk_factor</th>\n",
              "      <th>Factors_impacting</th>\n",
              "      <th>Trustworthy</th>\n",
              "      <th>Risk_factor-binned</th>\n",
              "      <th>Cinemas</th>\n",
              "      <th>OTT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Karthik R</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Night time (the last two shows when there is l...</td>\n",
              "      <td>Highly prefer carrying own food</td>\n",
              "      <td>4</td>\n",
              "      <td>To hang out with friends/families</td>\n",
              "      <td>Theatre</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aishwarya E D'Souza</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>Student</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Disagree</td>\n",
              "      <td>Disagree</td>\n",
              "      <td>No</td>\n",
              "      <td>Not visiting cinemas</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>4</td>\n",
              "      <td>Precautions taken by cinemas, Cinemas in conta...</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chandan</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Neutral, depends on the number of people visit...</td>\n",
              "      <td>Disagree</td>\n",
              "      <td>No</td>\n",
              "      <td>Night time (the last two shows when there is l...</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>4</td>\n",
              "      <td>Not interested in visiting cinemas</td>\n",
              "      <td>None</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunaina</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Not visiting cinemas</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>4</td>\n",
              "      <td>Not interested in visiting cinemas</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sunaina</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Not visiting cinemas</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>4</td>\n",
              "      <td>Not interested in visiting cinemas</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Name    Age     Sex  ... Risk_factor-binned Cinemas OTT\n",
              "0             Karthik R  18-28    Male  ...               High       1   0\n",
              "1  Aishwarya E D'Souza   18-28  Female  ...               High       0   1\n",
              "2               Chandan  18-28    Male  ...               High       0   1\n",
              "3               Sunaina  18-28  Female  ...               High       0   1\n",
              "4               Sunaina  18-28  Female  ...               High       0   1\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjjC3OaCpBhP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a688ed3-013e-4d3a-826c-c7ef30c2c9ab"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name                  object\n",
              "Age                   object\n",
              "Sex                   object\n",
              "Occupation            object\n",
              "Eager                 object\n",
              "Location              object\n",
              "Implement_safety      object\n",
              "alternate_seating     object\n",
              "Price_hike            object\n",
              "Visit_time            object\n",
              "Food_option           object\n",
              "Risk_factor            int64\n",
              "Factors_impacting     object\n",
              "Trustworthy           object\n",
              "Risk_factor-binned    object\n",
              "Cinemas                int64\n",
              "OTT                    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoS2DbAbok1M",
        "colab": {
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          "height": 284
        },
        "outputId": "cb4d3c94-ca1a-43d3-ba83-3bb70752a649"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Risk_factor</th>\n",
              "      <th>Cinemas</th>\n",
              "      <th>OTT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>140.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>140.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.678571</td>\n",
              "      <td>0.514286</td>\n",
              "      <td>0.485714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.282241</td>\n",
              "      <td>0.501590</td>\n",
              "      <td>0.501590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Risk_factor     Cinemas         OTT\n",
              "count   140.000000  140.000000  140.000000\n",
              "mean      3.678571    0.514286    0.485714\n",
              "std       1.282241    0.501590    0.501590\n",
              "min       0.000000    0.000000    0.000000\n",
              "25%       3.000000    0.000000    0.000000\n",
              "50%       4.000000    1.000000    0.000000\n",
              "75%       5.000000    1.000000    1.000000\n",
              "max       5.000000    1.000000    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghXIFG3Wy_ok",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "4db462f2-d286-422d-b139-235e1074c8ba"
      },
      "source": [
        "df.describe(include=object)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Eager</th>\n",
              "      <th>Location</th>\n",
              "      <th>Implement_safety</th>\n",
              "      <th>alternate_seating</th>\n",
              "      <th>Price_hike</th>\n",
              "      <th>Visit_time</th>\n",
              "      <th>Food_option</th>\n",
              "      <th>Factors_impacting</th>\n",
              "      <th>Trustworthy</th>\n",
              "      <th>Risk_factor-binned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>132</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Deepthi D H</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Male</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Anytime as per your conveience</td>\n",
              "      <td>Highly prefer carrying own food</td>\n",
              "      <td>Coronavirus cases in the city</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>2</td>\n",
              "      <td>87</td>\n",
              "      <td>76</td>\n",
              "      <td>57</td>\n",
              "      <td>94</td>\n",
              "      <td>128</td>\n",
              "      <td>108</td>\n",
              "      <td>87</td>\n",
              "      <td>105</td>\n",
              "      <td>62</td>\n",
              "      <td>61</td>\n",
              "      <td>24</td>\n",
              "      <td>71</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Name    Age  ... Trustworthy Risk_factor-binned\n",
              "count           140    140  ...         140                140\n",
              "unique          132      4  ...           8                  3\n",
              "top     Deepthi D H  18-28  ...         PVR               High\n",
              "freq              2     87  ...          71                 87\n",
              "\n",
              "[4 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qub76O0lzHoy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791
        },
        "outputId": "c4f1e579-e381-457d-e20c-2abed5410398"
      },
      "source": [
        "categorical_data=df.select_dtypes(include=['object'])\n",
        "categorical_data\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Eager</th>\n",
              "      <th>Location</th>\n",
              "      <th>Implement_safety</th>\n",
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              "      <th>Price_hike</th>\n",
              "      <th>Visit_time</th>\n",
              "      <th>Food_option</th>\n",
              "      <th>Factors_impacting</th>\n",
              "      <th>Trustworthy</th>\n",
              "      <th>Risk_factor-binned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Karthik R</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Night time (the last two shows when there is l...</td>\n",
              "      <td>Highly prefer carrying own food</td>\n",
              "      <td>To hang out with friends/families</td>\n",
              "      <td>Theatre</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aishwarya E D'Souza</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>Student</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Disagree</td>\n",
              "      <td>Disagree</td>\n",
              "      <td>No</td>\n",
              "      <td>Not visiting cinemas</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>Precautions taken by cinemas, Cinemas in conta...</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chandan</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Male</td>\n",
              "      <td>Student</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Neutral, depends on the number of people visit...</td>\n",
              "      <td>Disagree</td>\n",
              "      <td>No</td>\n",
              "      <td>Night time (the last two shows when there is l...</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>Not interested in visiting cinemas</td>\n",
              "      <td>None</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunaina</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Not visiting cinemas</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>Not interested in visiting cinemas</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sunaina</td>\n",
              "      <td>18-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Not visiting cinemas</td>\n",
              "      <td>Not visiting Cinemas</td>\n",
              "      <td>Not interested in visiting cinemas</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Sagar</td>\n",
              "      <td>38-48</td>\n",
              "      <td>Male</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Anytime as per your conveience</td>\n",
              "      <td>Highly prefer carrying own food</td>\n",
              "      <td>Precautions taken by cinemas, Not interested i...</td>\n",
              "      <td>None</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Aishwarya</td>\n",
              "      <td>28-38</td>\n",
              "      <td>Female</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Anytime as per your conveience</td>\n",
              "      <td>Highly prefer carrying own food</td>\n",
              "      <td>Precautions taken by cinemas, Coronavirus case...</td>\n",
              "      <td>None</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Shashi</td>\n",
              "      <td>38-48</td>\n",
              "      <td>Male</td>\n",
              "      <td>Professionals/Government/Private employees</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Anytime as per your conveience</td>\n",
              "      <td>No food should be entertained at cinemas</td>\n",
              "      <td>Coronavirus cases in the city</td>\n",
              "      <td>None</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Kishan</td>\n",
              "      <td>28-38</td>\n",
              "      <td>Male</td>\n",
              "      <td>Business</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Neutral, depends on the number of people visit...</td>\n",
              "      <td>Disagree</td>\n",
              "      <td>No</td>\n",
              "      <td>Anytime as per your conveience</td>\n",
              "      <td>Highly prefer carrying own food</td>\n",
              "      <td>Precautions taken by cinemas</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Lakshmi</td>\n",
              "      <td>28-38</td>\n",
              "      <td>Female</td>\n",
              "      <td>Home maker</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Strongly agree</td>\n",
              "      <td>Agree</td>\n",
              "      <td>No</td>\n",
              "      <td>Anytime as per your conveience</td>\n",
              "      <td>Highly prefer carrying own food</td>\n",
              "      <td>Coronavirus cases in the city</td>\n",
              "      <td>PVR</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>140 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Name    Age  ... Trustworthy Risk_factor-binned\n",
              "0               Karthik R  18-28  ...     Theatre               High\n",
              "1    Aishwarya E D'Souza   18-28  ...         PVR               High\n",
              "2                 Chandan  18-28  ...        None               High\n",
              "3                 Sunaina  18-28  ...         PVR               High\n",
              "4                 Sunaina  18-28  ...         PVR               High\n",
              "..                    ...    ...  ...         ...                ...\n",
              "135                 Sagar  38-48  ...        None               High\n",
              "136             Aishwarya  28-38  ...        None               High\n",
              "137                Shashi  38-48  ...        None               High\n",
              "138                Kishan  28-38  ...         PVR               High\n",
              "139              Lakshmi   28-38  ...         PVR               High\n",
              "\n",
              "[140 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zPKebuM1iTy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2844799b-f4da-46a8-e023-e739527a420d"
      },
      "source": [
        "for columns in categorical_data.columns.values.tolist():\n",
        "    print(columns)\n",
        "    print(categorical_data[columns].value_counts())\n",
        "    print('')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Name\n",
            "Deepthi D H                 2\n",
            "Akshata S Mantorshettar     2\n",
            "Sunaina                     2\n",
            "Lakshmi                     2\n",
            "Paramesh                    2\n",
            "                           ..\n",
            "Roopa J                     1\n",
            "Shashi                      1\n",
            "Jayashree                   1\n",
            "Kavana                      1\n",
            "Sharanya                    1\n",
            "Name: Name, Length: 132, dtype: int64\n",
            "\n",
            "Age\n",
            "18-28       87\n",
            "38-48       24\n",
            "28-38       22\n",
            "Above 48     7\n",
            "Name: Age, dtype: int64\n",
            "\n",
            "Sex\n",
            "Male      76\n",
            "Female    64\n",
            "Name: Sex, dtype: int64\n",
            "\n",
            "Occupation\n",
            "Professionals/Government/Private employees    57\n",
            "Student                                       46\n",
            "Business                                      22\n",
            "Home maker                                     7\n",
            "Unemployed                                     5\n",
            "Retired                                        3\n",
            "Name: Occupation, dtype: int64\n",
            "\n",
            "Eager\n",
            "No     94\n",
            "Yes    46\n",
            "Name: Eager, dtype: int64\n",
            "\n",
            "Location\n",
            "Urban    128\n",
            "Rural     12\n",
            "Name: Location, dtype: int64\n",
            "\n",
            "Implement_safety\n",
            "Strongly agree                                                   108\n",
            "Neutral, depends on the number of people visiting the cinemas     28\n",
            "Disagree                                                           4\n",
            "Name: Implement_safety, dtype: int64\n",
            "\n",
            "alternate_seating\n",
            "Agree       87\n",
            "Disagree    53\n",
            "Name: alternate_seating, dtype: int64\n",
            "\n",
            "Price_hike\n",
            "No     105\n",
            "Yes     35\n",
            "Name: Price_hike, dtype: int64\n",
            "\n",
            "Visit_time\n",
            "Anytime as per your conveience                                                  62\n",
            "Not visiting cinemas                                                            29\n",
            "Day time (any of the three shows when there is high public activity outside)    25\n",
            "Night time (the last two shows when there is low public activity outside)       24\n",
            "Name: Visit_time, dtype: int64\n",
            "\n",
            "Food_option\n",
            "Highly prefer carrying own food                                 61\n",
            "Need food availability at cinemas and options to carry food.    32\n",
            "Not visiting Cinemas                                            22\n",
            "No food should be entertained at cinemas                        16\n",
            "Do not prefer carrying own food                                  9\n",
            "Name: Food_option, dtype: int64\n",
            "\n",
            "Factors_impacting\n",
            "Coronavirus cases in the city                                                                                                                                                                                                                        24\n",
            "Not interested in visiting cinemas                                                                                                                                                                                                                   19\n",
            "Cinemas in containment or safe zone                                                                                                                                                                                                                  16\n",
            "Precautions taken by cinemas                                                                                                                                                                                                                         11\n",
            "To hang out with friends/families                                                                                                                                                                                                                     8\n",
            "Interest in the specific film running at the theatre                                                                                                                                                                                                  6\n",
            "Precautions taken by cinemas, Interest in the specific film running at the theatre, To hang out with friends/families                                                                                                                                 4\n",
            "Precautions taken by cinemas, Coronavirus cases in the city                                                                                                                                                                                           4\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, To hang out with friends/families                                                                                                                                                  4\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Coronavirus cases in the city                                                                                                                                                      4\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Interest in the specific film running at the theatre                                                                                                                               3\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Interest in the specific film running at the theatre, To hang out with friends/families, To get out of home                                                                        2\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Coronavirus cases in the city, Interest in the specific film running at the theatre                                                                                                2\n",
            "Cinemas in containment or safe zone, Interest in the specific film running at the theatre                                                                                                                                                             2\n",
            "Coronavirus cases in the city, Not interested in visiting cinemas                                                                                                                                                                                     2\n",
            "To hang out with friends/families, To get out of home                                                                                                                                                                                                 2\n",
            "Precautions taken by cinemas, Coronavirus cases in the city, To get out of home                                                                                                                                                                       2\n",
            "Interest in the specific film running at the theatre, To hang out with friends/families                                                                                                                                                               2\n",
            "Cinemas in containment or safe zone, Coronavirus cases in the city, Not interested in visiting cinemas                                                                                                                                                2\n",
            "Precautions taken by cinemas, Not interested in visiting cinemas                                                                                                                                                                                      1\n",
            "Cinemas in containment or safe zone, Coronavirus cases in the city, Interest in the specific film running at the theatre                                                                                                                              1\n",
            "Precautions taken by cinemas, Coronavirus cases in the city, Interest in the specific film running at the theatre                                                                                                                                     1\n",
            "Precautions taken by cinemas, Interest in the specific film running at the theatre, To hang out with friends/families, To get out of home                                                                                                             1\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone                                                                                                                                                                                     1\n",
            "To get out of home                                                                                                                                                                                                                                    1\n",
            "Cinemas in containment or safe zone, Not interested in visiting cinemas                                                                                                                                                                               1\n",
            "Precautions taken by cinemas, Interest in the specific film running at the theatre                                                                                                                                                                    1\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Coronavirus cases in the city, Interest in the specific film running at the theatre, To hang out with friends/families, To get out of home, Not interested in visiting cinemas     1\n",
            "Cinemas in containment or safe zone, Coronavirus cases in the city                                                                                                                                                                                    1\n",
            "Interest in the specific film running at the theatre, To hang out with friends/families, To get out of home                                                                                                                                           1\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Coronavirus cases in the city, To hang out with friends/families, To get out of home                                                                                               1\n",
            "Precautions taken by cinemas, To hang out with friends/families                                                                                                                                                                                       1\n",
            "Precautions taken by cinemas, Coronavirus cases in the city, To hang out with friends/families                                                                                                                                                        1\n",
            "Cinemas in containment or safe zone, Coronavirus cases in the city, Interest in the specific film running at the theatre, To hang out with friends/families, To get out of home                                                                       1\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Coronavirus cases in the city, To get out of home, Not interested in visiting cinemas                                                                                              1\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Coronavirus cases in the city, Interest in the specific film running at the theatre, To hang out with friends/families, To get out of home                                         1\n",
            "Cinemas in containment or safe zone, Interest in the specific film running at the theatre, To get out of home                                                                                                                                         1\n",
            "Precautions taken by cinemas, Cinemas in containment or safe zone, Coronavirus cases in the city, Interest in the specific film running at the theatre, To hang out with friends/families                                                             1\n",
            "Precautions taken by cinemas, Coronavirus cases in the city, Interest in the specific film running at the theatre, To hang out with friends/families, To get out of home                                                                              1\n",
            "Coronavirus cases in the city, Interest in the specific film running at the theatre                                                                                                                                                                   1\n",
            "Name: Factors_impacting, dtype: int64\n",
            "\n",
            "Trustworthy\n",
            "PVR            71\n",
            "None           24\n",
            "Inox           19\n",
            "Theatre        13\n",
            "Cinepolis       5\n",
            "SPI Cinemas     4\n",
            "Carnival        3\n",
            "SRS Cinemas     1\n",
            "Name: Trustworthy, dtype: int64\n",
            "\n",
            "Risk_factor-binned\n",
            "High      87\n",
            "Medium    42\n",
            "Low       11\n",
            "Name: Risk_factor-binned, dtype: int64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EP54vgs_2oFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "0b45c0bb-fafd-4307-86fc-8f62c7b6422a"
      },
      "source": [
        "pd.crosstab(index=df['Eager'],columns='count',dropna=True)"
      ],
      "execution_count": null,
      "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>col_0</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Eager</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0  count\n",
              "Eager       \n",
              "No        94\n",
              "Yes       46"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrC3NOzC9zg8"
      },
      "source": [
        "from the table above we can observe that most of the people are not eager to rush into cinemas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMsyBN_86FIT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "7169567b-0c60-4503-e907-9c980b369696"
      },
      "source": [
        "pd.crosstab(index=df['OTT'],columns=df['Eager'],dropna=True)"
      ],
      "execution_count": null,
      "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>Eager</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OTT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Eager  No  Yes\n",
              "OTT           \n",
              "0      32   40\n",
              "1      62    6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjndTE2I-CHL"
      },
      "source": [
        "people who are not eager to visit cinemas have opted more of OTT option to watch new movies.\n",
        "Therefore the people count who are not eager to visit cinemas and prefer OTT platforms is of 62."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Jx9ctcp6TVZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "04d9fedb-54b0-49e1-bb73-69edb1fa3fdd"
      },
      "source": [
        "pd.crosstab(index=df['Cinemas'],columns=df['Eager'],dropna=True)"
      ],
      "execution_count": null,
      "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>Eager</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cinemas</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>62</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Eager    No  Yes\n",
              "Cinemas         \n",
              "0        62    6\n",
              "1        32   40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7hp9JSf-pSk"
      },
      "source": [
        "Similarly, people who are eager to visit cinemas prefer to watch new movies in cinema but not OTT are in 40 numbers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CiTV0hz6w_m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "9ef69b38-aa66-4707-89d2-6c4936df806c"
      },
      "source": [
        "pd.crosstab(index=df['Risk_factor-binned'],columns=df['Eager'],dropna=True)"
      ],
      "execution_count": null,
      "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>Eager</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Risk_factor-binned</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>73</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Eager               No  Yes\n",
              "Risk_factor-binned         \n",
              "High                73   14\n",
              "Low                  5    6\n",
              "Medium              16   26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJfiC2Nh787G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "7bd6d0d0-a7d4-4fb4-cfce-a9b0d2cd87e6"
      },
      "source": [
        "pd.crosstab(index=df['Risk_factor-binned'],columns=df['Eager'],normalize=True, dropna=True)"
      ],
      "execution_count": null,
      "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>Eager</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Risk_factor-binned</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>0.521429</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low</th>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.042857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>0.114286</td>\n",
              "      <td>0.185714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Eager                     No       Yes\n",
              "Risk_factor-binned                    \n",
              "High                0.521429  0.100000\n",
              "Low                 0.035714  0.042857\n",
              "Medium              0.114286  0.185714"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Td5pXY6c88X_"
      },
      "source": [
        "from joint probability we can observe that people who are not eager to rush into cinemas with high risk rate is 0.521429 i.e. 52.14%\n",
        "\n",
        "And also people who are eager to rush into cinemas with high risk rate is about 10%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__LgRIIB8rYj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "8703b280-d838-4215-8d5f-8548e55681df"
      },
      "source": [
        "pd.crosstab(index=df['Cinemas'],columns=df['Eager'],normalize=True, dropna=True)"
      ],
      "execution_count": null,
      "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>Eager</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cinemas</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.442857</td>\n",
              "      <td>0.042857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.228571</td>\n",
              "      <td>0.285714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Eager          No       Yes\n",
              "Cinemas                    \n",
              "0        0.442857  0.042857\n",
              "1        0.228571  0.285714"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29_Skqmw_ToK"
      },
      "source": [
        "From joint probability, people who are willing to visit cinemas and watch new movies in cinemas itself is of 28.5% \n",
        "And people who want it in OTT platform and not eager to visit cinema is of 44.28%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7lm9eou9B6S5"
      },
      "source": [
        "**CONDITIONAL PROBABILITY**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBGSSa2hCCri"
      },
      "source": [
        "Scenario: Given, people are not willing to visit cinemas. What will be the probability of them watching movies on OTT platform."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cD6guBk_AcD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "b966c094-8f34-4bda-9338-e33fd87f099f"
      },
      "source": [
        " pd.crosstab(index=df['OTT'],columns=df['Eager'],normalize='columns',\n",
        "              margins=True,dropna=True)"
      ],
      "execution_count": null,
      "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>Eager</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OTT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.340426</td>\n",
              "      <td>0.869565</td>\n",
              "      <td>0.514286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.659574</td>\n",
              "      <td>0.130435</td>\n",
              "      <td>0.485714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Eager        No       Yes       All\n",
              "OTT                                \n",
              "0      0.340426  0.869565  0.514286\n",
              "1      0.659574  0.130435  0.485714"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ-m5DasDNi5"
      },
      "source": [
        "Therefore from the output, it is 65.9% of the people will watch movies on OTT platform when they are not willing to visit cinemas \n",
        "\n",
        "And on the contrast, we can analyse from the output that 86.9% of the people will watch new movies in cinemas when they are willing or eager to visit cinemas.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNNoI2zeEHJE"
      },
      "source": [
        "Scenario: Given the risk rate to be high to visit cinemas, what is the probability of their eagerness to visit cinemas?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9c7LqRQB11B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "503e5b58-718e-48ca-f6a9-f5017749f197"
      },
      "source": [
        "pd.crosstab(index=df['Risk_factor-binned'],columns=df['Eager'],normalize='index',\n",
        "              margins=True,dropna=True)"
      ],
      "execution_count": null,
      "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>Eager</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Risk_factor-binned</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>0.839080</td>\n",
              "      <td>0.160920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low</th>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.545455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medium</th>\n",
              "      <td>0.380952</td>\n",
              "      <td>0.619048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>0.671429</td>\n",
              "      <td>0.328571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Eager                     No       Yes\n",
              "Risk_factor-binned                    \n",
              "High                0.839080  0.160920\n",
              "Low                 0.454545  0.545455\n",
              "Medium              0.380952  0.619048\n",
              "All                 0.671429  0.328571"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U2LK0SmEWJ2"
      },
      "source": [
        "From the conditional marginal probabilty it is seen that given the high risk rate to visit cinemas, 83.9% of the people are not willing to visit cinemas. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WeuM8uWkI85i"
      },
      "source": [
        "**CORRELATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWtWoaghEAwf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "858f2162-8817-46d2-a012-47e6f4ea87e1"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": null,
      "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>Risk_factor</th>\n",
              "      <th>Cinemas</th>\n",
              "      <th>OTT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Risk_factor</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.32279</td>\n",
              "      <td>0.32279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cinemas</th>\n",
              "      <td>-0.32279</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OTT</th>\n",
              "      <td>0.32279</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Risk_factor  Cinemas      OTT\n",
              "Risk_factor      1.00000 -0.32279  0.32279\n",
              "Cinemas         -0.32279  1.00000 -1.00000\n",
              "OTT              0.32279 -1.00000  1.00000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtihxCHvKWWr"
      },
      "source": [
        "**DATA VISUALIZATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xd79cjq0KVEH"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvQTm3ZtJyUb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "32e72dd5-5f58-46cb-c2ac-160734d377f8"
      },
      "source": [
        "plt.hist(df['Age'])\n",
        "plt.xlabel('AGE GROUP')\n",
        "plt.ylabel('COUNT')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'COUNT')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDWkGWp2ONc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "6f351961-9890-464f-fd76-057d50dbbc67"
      },
      "source": [
        "sns.factorplot(y='Age',hue='Eager',kind='count', data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3704: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f52685cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 412x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUPHfRzIs0Zs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "3034ce0b-432b-4803-ad5d-171192978bea"
      },
      "source": [
        "pd.crosstab(index=df['Price_hike'],columns='count',dropna=True)"
      ],
      "execution_count": null,
      "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>col_0</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price_hike</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0       count\n",
              "Price_hike       \n",
              "No            105\n",
              "Yes            35"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eO0fmRi7QsFO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "f22e64f2-1d61-4c45-bab0-9087887c652e"
      },
      "source": [
        "sns.catplot(x='Price_hike',kind='count', data=df, color='black')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f4d54eda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxZB0cxgR2YJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "4d8416f2-7f8f-428d-febb-d0e463fea4ad"
      },
      "source": [
        "#plt.figure(figsize=(8, 6))\n",
        "splot=sns.catplot(x=\"Risk_factor\",data=df,kind='count')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWMg37i-WRKn"
      },
      "source": [
        "df_group_z=df[['Age','Sex','Risk_factor']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nCBmY7bG4N0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "c4972146-9070-485f-a6c2-2a71c6e4a77a"
      },
      "source": [
        "sns.catplot(x='Sex', data=df, kind='count',color='c' )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f4f477b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0AJkcWQZSk-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "64590c97-9f01-407d-f7a7-04bf42c3e17a"
      },
      "source": [
        "sns.barplot(x='Age',y='Risk_factor', data=df_group_z, hue='Sex')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f4f7e7eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bntLn6-Da_kG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "outputId": "0b90a75f-aa09-47f8-b059-637b6b2bcdc2"
      },
      "source": [
        "df_group_y=df[['Occupation','Sex','Risk_factor']]\n",
        "plt.figure(figsize=(8,12))\n",
        "plot=sns.catplot(x='Occupation',y='Risk_factor', data=df_group_y, hue='Sex')\n",
        "plot.set_xticklabels(rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f4f7ff9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x864 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432.125x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uguja19ycK2E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "8c7bbc1e-9efc-425d-c75e-07fd2b3137c8"
      },
      "source": [
        "g=sns.catplot(x=\"Occupation\", hue=\"Sex\", kind=\"count\",\n",
        "            palette=\"pastel\", edgecolor=\".6\",\n",
        "            data=df)\n",
        "g.set_xticklabels(rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f4f7e7cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432.125x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOfAdDSacSkx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "0326885a-3325-4b5b-c6b7-6613280dc399"
      },
      "source": [
        "sns.catplot(x=\"Location\",hue='Eager' ,kind=\"count\", palette=\"ch:.25\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f525fb978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 412x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRmSNVf6m69L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "036d0dc1-d8a8-4578-ee0c-fa22ce52c905"
      },
      "source": [
        "plt.hist(df['Implement_safety'],\n",
        "         color='brown',\n",
        "         edgecolor='white',\n",
        "         bins=5)\n",
        "plt.title('count of people who look for safety implementation')\n",
        "plt.xlabel('Implement safety')\n",
        "plt.ylabel('Frequency') \n",
        "plt.xticks(rotation=5) \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nm1lWn1MnjlA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "648f03cf-a83a-42c2-aa41-f0f6fa01a271"
      },
      "source": [
        "plt.hist(df['alternate_seating'],\n",
        "         color='orange',\n",
        "         edgecolor='white')\n",
        "plt.title('count of people who agree for alternate seating')\n",
        "plt.xlabel('Alternate seating')\n",
        "plt.ylabel('Frequency') \n",
        "plt.xticks(rotation=45) \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsGufO1dogIK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "60d80541-683a-46a7-83d3-45fc07d67434"
      },
      "source": [
        "f, ax = plt.subplots(figsize=(7, 3))\n",
        "sns.countplot(y=\"Visit_time\", data=df, color=\"c\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f4f5d1e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZZqXt3yo_LN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "c541eea5-ca01-427f-aeb8-2f0309050411"
      },
      "source": [
        "sns.catplot(y=\"Price_hike\", hue=\"Eager\",\n",
        "            col=\"Sex\", aspect=.7, kind=\"count\",\n",
        "             data=df, palette=\"ch:.25\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f4d25a898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 556x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNbhis4Pp4pR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "e7378f9e-3073-42e9-b3cc-5fe19f4fab55"
      },
      "source": [
        "sns.catplot(x=\"Risk_factor-binned\", y=\"Age\", order=[\"Low\", \"Medium\",\"High\"], hue='Sex', data=df, kind=\"swarm\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1296: UserWarning: 26.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1296: UserWarning: 44.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f4d226320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432.125x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xd_qw-o0rX3D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "b40bef94-9ccc-4448-a787-8667143d7c6c"
      },
      "source": [
        "x = sns.violinplot(x=\"Risk_factor\", kind='count', data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZrKYjCvEvtDMbf+kkqqv/yZw5c/jSl77UbceWAhZnxOfz8dFHH/Huu++y7J138Pt8KJuTUN+ziBQMJ+7Jlzlekfai/YYRbd7Js88+S2VlJeXl5d1yXClg0SmhUIgNGzZQXV1NVdVKatbVEI/FUDYn4dyBRPqXEcsZIKNdkVmUIlh2Pvb18/jxT37Ks888jdfr7fLDSgGLE/L5fOzatYtt27axefNmNm7cxNZtW4lFowDorHzCBRXE+pQSyyqEbpw7EyLVtN2Db8gl7N20gLvvvpv77rsPq7VrBxJSwL1QPB7H5/PR3t5Oc3Mzhw4doqmpif3799PQ0EBDQwM7d+3mUNPBI89RNgdRdz6xfiOJeouIZReBzWnwuxAi9WLeYoKDzmPFiveYNWsW3/zmN7v0eFLAGSQYDNLa2kprayvt7e1Hbn6//8gtEAgQDAYJBoNHPvcHAsmvBwgE/AQDgROeeK6cWUTt2cSdfYgPKCPu7kPM3QftzJG5XNErRApHYPEf5MUXX2TQoEFcccUVXXYsKWCDIpEILS0tR0ahJ7o1HTpEa0srkUj45C+oLCibHax2tMWGVjbiFita2dBWO1j7orML0bl2tM151M2NtiduMncrBIQGnYc11Mb999+P3W7n0ksv7ZLjSAF3gtaacDhMIBAgEAgQCoUIBoOEw2GCwSChUOjIqPPwR5/Ph8/nw+/309bWRktrKy0trbS1tRLw+49/IIsV5XATs7qI21xoWx90XjHa7kTbXGhrsjitjmSxJj9KeQqRGhYr/qGXkbVlEffccw9Wq5UpU6ak/DCqM9dAV1ZW6qqqqpSHMK29vZ36+noaGxtpbGxk//79nxiBtra20e5rJ+D3d3oRZ2W1gc2BtjiIW+zEbc6PC9TuShSqzYW2u4jb3WibK3Ghgvy5L4R5sQhZW97E5jvIb37zMOeee+5pvYxSaqXWuvLY+3vVCNjv91NbW8u2bdvYtm0bdXV17Ni5k9aWlk8+0GJFOTzErM7ECNSahfb0RXsP/3mf+BMfqy3x0XL4ozX58eOvy1Y6QmQwqx3fsM/iXfUSy5cvP+0CPpEeW8DxeJydO3dSU1PD+vXrWbd+PTt37Djy5pOyOYm6+xBzFqFLhxF35hB3ZqMdWYlRqIxAhRDQpX+R9pgCjkajbN26lTVr1iRv1fh87QAou4uIpx+xkrHEsvoRd+ehHVlSskIIozK2gA8X7urVqxO3NdUEA8k3tdy5hLNKiBUUEfMWySlUQoi0lDEFHA6H2bx5M9XV1YkRbnU1wUAg8UV3H8LZA4mVFBPzlqAdHrNhhRCiA9KygLXWHDhwgA0bNiTmb9etY+PGjUQikcQDPH0IZw9OFm6xFK4QIiMZL+BIJMKuXbuoq6ujrq6OzZu3sGnTJlpamhMPsFiJe/KI5g0nlrwEVtvdZkMLIUQKdEsBa63ZunUr9fX1NDQ0sG/fPurr69mxcxf7G/d9fG6tUmh3X6KeAmKDziaWVZBYzlAuMBDdzLnzAyz+JtMxOicWRkXDaJsj4xa9j3vyCA06z3SMbtctBVxVVcUPfvCDI/9WNgdxZw5Rh5d40Rji7j7E3X2Ju3KNlm1G/tKJLmH1H0TFIqZjdIrL5WLqF6by+uuvE8ywn+Oo6QCGnLKAlVLfAr4FMGjQoNM6iM/nAyAw9DKi3mJZRUuILjB16lTuuOMOtNbMmTPHdBzRAacsYK31s8CzkLgU+XQOopKngLnrq5IrbWUTd+agXV7irlzizpy0mGbojX8CieNzb3wDW1uD6Rid8vrrr6O1Zv78+aajiA7qlimI8ePHc+ONN1JfX8/ehgb27t1D+/5NHz9AWcCdS8TZh7gnj1hWPnFPvrzZJoyJe/Iy7s/i9liYv732JtrmAW8f03E6Je7JMx3BiG4pYK/Xy2233faJ+9ra2qivr2f37t1s376duro6tmzdRmP9yo8f5PImrmDLKiSWXZh8Q07WVhBdT/4aEt3B2GloXq+XESNGMGLEiE/c39bWxtatW9m0aRMbNmygZt16Du6qA0BZ7USzCohmFxHzFhPLLgCL8TPphBDitKRde3m9XsaNG8e4ceOO3Ld//35qampYu3Ytq9esoa52NXqPRlmsiUL2Jq6Ak0IWQmSSjGirgoICLrnkEi655BIgMUpeu3Ytq1ev5qNVq9i2dQ16z2qwWIll9SOaXZwcIReC1W44vRBCHF9GFPCxvF4v559/Pueffz7wyUJetXo1W7dUo/euSVzY4ckjklWQuKgjqyBxrrEszCOESAMZWcDHOraQfT4f69atOzJtsX79BkKNG4HEPHLM3Zeou++Riz/irlxZnlII0e16RAEfKysri0mTJjFp0iQAYrEYO3fuZOPGjWzevJmtyR0x/EefCmexgjObqM2DdmQltgeyuxIbViYv7dRWJ9qa3PHCagNlldIWQpy2HlnAx7JarZSXl1NeXs5VV10FfLzi2u7du9m1a9eRPeH27dvHvsZGWg5sJxo99ZmgympPbD1ksaGTOxDHk1sTaUtyC6PkppmJveAcyQ01P94TTvaAEyJ9OfasgXiMgoKClL92ryjg41FKUVBQQEFBwSfOuDhMa43P56O5uZn29vYjt8M7Ih+9K3IoFDpyO7Ibst+P3x/A728j0O4nGAycJIwF5XATt7mIWV1HtoiPH960M7lZZ2ILeVfiTA8pbCG6nH3vWpz1K7n88su55pprUv76vbaAT0UpRXZ2NtnZ2Sl5vVgsht/vp7W1NbE9fUsLLS0tNDc3c+jQoSMfDzY10dTUdPIRuMWKsiVG03FlJ249arRtsR/ZLPTozw9Pm2iLPbFLc3I0LqftCXF89oZ1uHZ/yCWXXMpdd92F1Zr65RLkt6+bWK1WvF4vXq+3Q48/egR++Nba2kpraystLS34fD7a29tpa2vD5/fj8/nx+9uTI/MgkXC4Q8dRVjs43EStLuJ2D9pxeK0OL3F3H7QjW0bbotexNW3HtWs5F110ET/5yY+x2bqmKqWA09TRI/DS0tJOPz8ejxMMBo/cAoEAfr8fv9+Pz+ejra2N9vb2j0feBw+yr7GRxsZNRCMfL8OoLDbi7j5EPHnEPf0Sm5p68hLrdwjRA1n8TXi2v8PZI0fy05/+tMvKF6SAeyyLxYLH48Hj6dx2TfF4nEOHDlFfX8/OnTvZsWMH22pr2bhxE/79m4HEes6RrEJi3mKiuQOIu/NklCx6hmiIrG1vkZvj5Z6778bh6NqF7aWAxSdYLBby8/PJz8/nnHPOOXK/1pqGhgbWrVtHdXU1q9esYeeOKpy7q1COLEI5A4j2HUwsp39aLC0qRKfpOJ5tS7BG/fz6nkfo169flx9SClh0iFKKkpISSkpK+MxnPgPAwYMHWbFiBcuXL2f58hUEDmxG2ZyE+wwikj+UmLdYRsYiY9gb1mFt3cP0O++koqKiW44pBSxOW35+PldddRVXXXUV4XCYqqoq3n77bZa98w7BA1vAnUMofxjhgrPB5jIdV4gTsvibcO35iAsvvJCrr766246rtO74JheVlZW6qqqqC+OIniAQCLB06VJenz+fmrVrUVYbofyhhItGo105puMJ8UnxGNkbXyfXFuOPLzxPnz6pX8xeKbVSa1157P0yAhYp53a7ufLKK7nyyiupq6vjb3/7GwsXLcLRuJFI/lmEBoxHOzt2Op4QXc2xZzXKd5Af3nNPl5Tvyci5RKJLlZeXc9ddd/G3v/6VG264AU/rTrJr5uDc8T4qEjQdT/RyFt9BnA3VXHHFFUyePLn7j9/tRxS9Un5+PtOmTeOll15i6uc+h+vAJrzrXsZ2YAt0YhpMiJTRGvfO98nJyeWOO+4wEkEKWHSrgoIC7rzzTmbOnMnIYWfhrnsHz+YFqGCr6Wiil7Ed3IqlvZFp376tw1eoppoUsDBiyJAhPP74Y9x55514Y614N7yK7eA207FEbxEN4amvYsTIkVxxxRXGYkgBC2MsFgtTp07luVmzGHX2cNy1S3HVvQOxyKmfLMQZcO5ZhY4E+c/p07EY3GldClgYV1RUxCOP/I6bb74Z+8GtZG96AxX2mY4leihL4BCOxg184fOfZ/jw4WazGD26EEk2m41vfOMbPHD//bjjfrI3vo7Fd9B0LNEDOXdX4Xa7ufXWW01HkQIW6eVTn/oUTz7xBPleD9mb3sDavMt0JNGDWNsasDXv4t+/+lVyc3NNx5ECFulnyJAhPPvM05xVXoZn22Jsh3aYjiR6Aq1x1a+kT988rr/+etNpAClgkaby8/P57W9/w9nDhuPetgRb03bTkUSGs7bswtK2j6/f8jVcrvRYm0QKWKQtr9fLww/PYOTIEbhrl2BrqjMdSWQqHcddv5KS/gO6dbGdU5ECFmktOzubh2fMoGJUBe66pVhb95iOJDKQ7WAtyn+Ib33z/3bpDhedJQUs0p7H4+H+++9j8KBBZG17S86OEJ2j47gbqikrK2fKlCmm03yCFLDICF6vl4cefJC8Prlkb10kly6LDrM11UGgmVtu+ZrRiy6OJ73SCHEShYWF/ObhGXgcVrK3/gOiIdORRLrTcVx71zBo8GAuuugi02n+hRSwyCiDBw/m3l/fgyXchqd2Kei46UgijdmatqMCzdzytfQb/YIUsMhAY8eOZfr3voe1ZTfO3bJDizgBrXE1rGHgwEFpN/d7mBSwyEif//znufbaa3E01CTWFBbiGLZDO1D+Q9xyy9ewWtNzp24pYJGxvvOd73DuuHF4dryPxS9nRoijaI2roZr+/Qdw8cUXm05zQlLAImPZbDb++xe/oG/fXLK2LZE35cQR1tY9KN8BvvKVG9N29AtSwCLD9enTh7t/9SssYR/uundkeyMBgLOhmr55+Xz2s581HeWkpIBFxquoqOD226dha96Jo2Gt6TjCMEt7I9bWvdz45RtwOBym45yUFLDoEa6//nqmTJmCs34l1rYG03GEQc691WRle5k6darpKKckBSx6BKUUP/zhDykuLsZTtwyisuV9b2TxH8LWvJMvXn8dHo/HdJxTkgIWPUZWVha/+uUvscaCuGtlPrg3cjRU43A6ue6660xH6RApYNGjDB8+nO/cfju2ll3YG2pMxxHdSIXasTfVcs0XvpAWu110hBSw6HGuvfZaLrzwQlz1K7G0N5qOI7qJo6EGi8XCl770JdNROkwKWPQ4h+eDCwsKyKp9W84P7gVUJIjz4GY+e/nlFBYWmo7TYVLAokfyer386le/xBoN4K5bJvPBPZy9cT06FuXGG280HaVTpIBFjzVixAhuv/12bM27sO9bZzqO6CqxCK79G7jgggsYPHiw6TSdIgUserTrrruOyZMn49pdJecH91D2/ZvQkRBf+cpXTEfpNClg0aMppbjrrrsoKS7GU/s2Kuw3HUmkUjyGq3EdY8eOpaKiwnSaTpMCFj2e1+vl17++BwcxPLVvQ1wWce8p7Ae2QMjHTTfdZDrKaZECFr3CkCFDuOuuH2Jpa8C5+0PTcUQq6DiufTUMP/tsJkyYYDrNaZECFr3GZZddxvXXX49j3zps+zebjiPOkO1gLQRbufmmm1BKmY5zWqSARa8ybdo0xk+YgHvHe1hb95qOI06X1rj2rWVwWRnnn3++6TSnTQpY9Co2m41f/fKXDBxYSta2t1CBFtORxGk4vN3QzTfdlJabbXZU5iYX4jRlZ2fz4AMP4M1yk711ESoSMB1JdEZys82Skv5pvd1QR0gBi16ppKSE++79NY54iKzNC6SEM4jt0HaU7yBf//otab3dUEdIAYteq6Kigvvvvw9HpJ2sLQtlzYhMoOO49qxi4MBBXHbZZabTnDEpYNGrjR8/nnvvvRdbqIWszW9KCac5W1MdKtDMrbd+I+NHvyAFLAQTJ07knrvvxh5qxrvxdVSw1XQkcTw6jnvvKsrKh3DRRReZTpMSUsBCAJ/+9Kf57W9+Q7Ytjnfj61jb9pmOJI5hO7AVAq188//emtFnPhytZ3wXQqTAOeecw9NPPUVxQT6ezQuw71svy1imi1gE995VDD/77Iw+7/dYUsBCHKW0tJSnn3qSiRMm4Nr5AZ7Nb6LCPtOxej1Hw1oI+fjuHXdk7FVvxyMFLMQxcnNzefDBB5g+fTru4EG864n7XG0AAAwlSURBVOYmRsPxmOlovZIKteNqqOGSSy5lzJgxpuOklBSwEMehlOKaa65h1qw/cM7okbh2foB33SvYDm4DLaupdSfn7g+x2Sx8+9u3mY6SclLAQpxEaWkpv/vtb3nwwQcZMqAAd+1SvDUv49hbjYoETcfr8axtDdib6vjKjTdSVFRkOk7K2UwHECLdKaWYNGkSlZWVLFu2jJdffoXq6ipce1YRzh1ENL+caG4pWOTXKaXiMdy7lpOXn8+Xv/xl02m6hPzECNFBFouFiy++mIsvvpja2lpeffVVFr/1Fm1b61A2B+HcgUT7lhHNHSBlnAKOvWtQvoNM/6+7cbvdpuN0CaU7cZpNZWWlrqqq6sI4QmSWaDTK6tWreeutt1i67B187W0oq41w7kAieWcRyx0Alsy/Yqu7WXwHyNrwGp+9/HJ+/OMfm45zxpRSK7XWlf9yvxSwEKlxuIyXLl3Kkrffpr2tDWV3Euo7hEjhCOLuvqYjZoZ4lOz1r5LvsfLC88/j9XpNJzpjUsBCdKNoNEpVVRULFy5k2bJ3iEYjxHJKCBVWEOszEHrQuayp5ty5Ase+Gh566CEmTpxoOk5KnKiAZaJKiC5gs9k477zzOO+882hubuaNN97g5Vde4cDWf6Cz+hHofy6xXCniY9kObMWxr4Zrrrmmx5TvycgIWIhuEo1GWbx4MbOee559DXuJZxcSGPgp4tkFpqOlBWtbA1mb3+ScMaOZMWMGdrvddKSUOdEIWM4DFqKb2Gw2rrjiCl6c/Sd+8IMfkGePkLXhNZx17/T6BeFVsJWsbW9RUlLM3Xff3aPK92SkgIXoZjabjauvvpoXZ8/mhhtuwHWoFm/Ny4mdmnvh4j8q2Er2loVkuew8+MAD5OTkmI7UbaSAhTAkKyuLadOm8dysWVSMHI57+z8Ti/+E2kxH6zbWtn14N75Otl3zwP33U1paajpSt5ICFsKwwYMH8+gjjzB9+nQ84aaPF//p4aNh28FtZG1eQHFBPk8/9RQVFRWmI3U7KWAh0oDFYuGaa67hjy+8wPhxY5NLYS7okaNhS6AZ9+aFuGuXUlExiqeferLXjXwPk7MghEgzWmvmz5/P4088QSgcJdB/PJGikaAyeLykNRb/QewHNuPYvxm3y8Utt3yN6667rle84SbnAQuRIZRSTJ06lYkTJ/Lwww+zYsVyHE3bCJRdQNyTbzrex7RO3I4+lTkeBx1FxaJYQm1YQq1Y/IdwtO6CYBsWq5XPXf05br31Vvr2lSsDZQQsRBrTWrNkyRJ+98ijtLa2EO43nHD/cWiHp0uPq8J+LP6DWALNWILNWELt2OIhVCSIjoYSayJ3sDvsdgfjJ4zn4ilTuOCCC3rVWQ6HyQhYiAyklOLSSy+lsrKS559/nrnz5uFs2kawaDSRwlFouys1xwm1YWvdk1h/17cfjtoZOje3D6WDS8nr25fc3Fy8Xi82mw2r1Xpkc8zDAzmHw4HT6cThcFBcXExpaSkFBQU9Ygv5riAjYCEyyO7du5k5cyZvv/02ymIl3LeMcMEI4tmFnbqsWUUCWFv3Ym3bi6O9AQItAOT26cM5Y8YwZswYRo4cSVlZWY9YDMc0WYxHiB6krq6OefPmseDNNwkGAiiHm3BWETFvMXGnF21zoe0uVDyGioZQ0SCWYEvijbBA05HCdbndnDv2XCZOrGTChAkMHjy4R216mS6kgIXogfx+P8uWLWPVqlWs/OgjDuzff9LHFxQWcfbwYYwcOZLx48czbNgwbDaZiexqUsBC9AKNjY3s37+flpYWWlpacDgc5OTk4PV6GTBggEwnGCJvwgnRCxQWFlJYWGg6huigDD6zWwghMpsUsBBCGCIFLIQQhkgBCyGEIVLAQghhiBSwEEIYIgUshBCGSAELIYQhUsBCCGGIFLAQQhgiBSyEEIZIAQshhCFSwEIIYYgUsBBCGCIFLIQQhkgBCyGEIVLAQghhiBSwEEIYIgUshBCGdGpTTqXUfmBHJ4/RDzjQyeeYJHm7luTtWpK3a51u3sFa64Jj7+xUAZ8OpVTV8XYDTVeSt2tJ3q4lebtWqvPKFIQQQhgiBSyEEIZ0RwE/2w3HSCXJ27Ukb9eSvF0rpXm7fA5YCCHE8ckUhBBCGCIFLIQQhnRLASulvqSUWqeUiiul0vKUE6XUlUqpTUqprUqp/zKd51SUUrOUUo1KqRrTWTpCKTVQKbVEKbU++bPwH6YznYxSyqWUWqGUWpPM+0vTmU5FKWVVSq1SSr1uOktHKKW2K6XWKqVWK6WqTOc5FaVUH6XU35VSG5VSG5RSnz7T1+yuEXANcB2wrJuO1ylKKSvwBHAVMAq4USk1ymyqU3oeuNJ0iE6IAt/XWo8CzgO+k+b/jUPApVrrscC5wJVKqfMMZzqV/wA2mA7RSZdorc/NkHOBHwEWaK1HAGNJwX/rbilgrfUGrfWm7jjWaZoEbNVa12qtw8BfgGsMZzoprfUyoMl0jo7SWu/VWn+U/LyNxA/vALOpTkwntCf/aU/e0vYda6VUKXA1MNN0lp5IKZULXAT8AUBrHdZaN5/p68occMIAYNdR/95NGpdDplNKlQHjgOVmk5xc8k/61UAjsEhrnc55fwf8EIibDtIJGliolFqplPqW6TCnUA7sB55LTvPMVEplnemLpqyAlVL/UErVHOeW1iNJ0b2UUtnAHOB7WutW03lORmsd01qfC5QCk5RSo01nOh6l1FSgUWu90nSWTpqstR5PYurvO0qpi0wHOgkbMB54Sms9DvABZ/xeke1MX+AwrfVnUvVaBtQDA4/6d2nyPpFCSik7ifJ9UWv9suk8HaW1blZKLSEx556Ob3peAHxBKfU5wAXkKKVma63/3XCuk9Ja1yc/NiqlXiExFZiW7xOR+Kt491F/Bf2dFBSwTEEkfAgMU0qVK6UcwJeBVw1n6lGUUorE/NkGrfVvTOc5FaVUgVKqT/JzN3A5sNFsquPTWv9Ia12qtS4j8bP7VrqXr1IqSynlPfw58FnS839uAGitG4BdSqmzk3ddBqw/09ftrtPQrlVK7QY+DcxXSr3ZHcftKK11FLgDeJPEm0N/01qvM5vq5JRSfwbeB85WSu1WSt1qOtMpXADcBFyaPO1odXLElq5KgCVKqWoS/4NepLXOiNO7MkQR8E+l1BpgBTBfa73AcKZT+S7wYvJn4lzg3jN9QbkUWQghDJEpCCGEMEQKWAghDJECFkIIQ6SAhRDCEClgIYQwRApYCCEMkQIWXUopFUue81ujlHrtqIsb+iul/n6S55V1ZqlNpdSflVLVSqnpnczXRyl1e2eeI0SqSAGLrhZILjc4msTqbd8B0Frv0Vp/MRUHUEoVAxO11udorX/byaf3ATpVwEqplF3CL3o3KWDRnd4nucrc0SNcpVRFcvHz1clR7LCjn6SUGpJcgWriCV53ITAg+fwLlVLfVEp9mFxMfY5SypN8nSKl1CvJ+9copc4H7gfOSj73IZXwUHLEvlYpdUPyuRcrpd5RSr1KCi5BFQJSuBiPECeTXPT+MpLrqR7j28AjWusXk2txWElcqkry2vu/ALdordec4OW/ALyeXLkMpdR6rfXvk5/fA9wKPAY8CizVWl+bzJNNYkGV0Uc993oSl5mOBfoBHyqlDi8QMz752Loz+E8hxBFSwKKruZNr6g4gsc7GouM85n3gJ8lFxV/WWm9JrN1DATAPuE5r3ZlR5+hk8fYhUbKH1x65FLgZEktNAi1Kqb7HPHcy8Ofk1/cppZYCE4FWYIWUr0glmYIQXS2QHF0OBhTJOeCjaa1fIjGKDQBvKKUuTX6pBdhJohQ743ngDq31GOCXJJZoTAVfil5HCEAKWHQTrbUf+H/A9499E0spNQSo1Vo/SmLEe07yS2HgWuBmpdRXOnE4L7A3uf7wV4+6fzEwLXlMa3Kbmbbk4w97B7gh+fUCEtvQrOjEsYXoMClg0W201quAauDGY770f4Ca5FTFaOCPRz3HB0wFpiulvtDBQ/2MxHZH7/LJNXz/A7hEKbUWWAmM0lofBN5Nvun2EPBKMuMa4C3gh8m1YIVIOVmOUgghDJERsBBCGCJnQYiMoZS6AnjgmLvrtNbXmsgjxJmSKQghhDBEpiCEEMIQKWAhhDBEClgIIQyRAhZCCEP+Pw84Vop88CdfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfryWQu0sbre",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "62119d9a-d134-4447-dde0-069a774eb9ed"
      },
      "source": [
        "u=sns.catplot(x=\"Trustworthy\",kind=\"count\", palette=\"ch:.25\", data=df)\n",
        "u.set_xticklabels(rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0f4f3ead68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33DBT9zYurwG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "b7b1f620-6790-4144-9dec-545c148f383c"
      },
      "source": [
        "sns.stripplot(x=\"Age\",y='Trustworthy', data=df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f4f314a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9ComMsovAz6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}