Stack_Overflow2018.ipynb

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    "colab": {
      "provenance": []
    },
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# EDA ON STACK OVERFLOW 2018 DATA"
      ],
      "metadata": {
        "id": "I0-wmKWr9L2v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA LOADING**"
      ],
      "metadata": {
        "id": "g4EeBybM95Zo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Importing modules*"
      ],
      "metadata": {
        "id": "q4uFubUDAPQN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "PofFmURLASrI"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Loading 2018 stackoverflow data*"
      ],
      "metadata": {
        "id": "6nuRfrESAoQC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/survey_results_sample_2018.csv\")"
      ],
      "metadata": {
        "id": "YreYMLwKAW3p"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Initial exploration*"
      ],
      "metadata": {
        "id": "Rm63VMBVA2rm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#First few rows of the data\n",
        "df.head(10)"
      ],
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        "outputId": "2a7a3c35-52c4-4540-c663-ed4aa65d99b8"
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      "execution_count": 4,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Respondent Hobby OpenSource         Country         Student  \\\n",
              "0           1   Yes         No           Kenya              No   \n",
              "1           3   Yes        Yes  United Kingdom              No   \n",
              "2           4   Yes        Yes   United States              No   \n",
              "3           5    No         No   United States              No   \n",
              "4           7   Yes         No    South Africa  Yes, part-time   \n",
              "5           8   Yes         No  United Kingdom              No   \n",
              "6           9   Yes        Yes   United States              No   \n",
              "7          10   Yes        Yes         Nigeria              No   \n",
              "8          11   Yes        Yes   United States              No   \n",
              "9          16    No        Yes           India              No   \n",
              "\n",
              "           Employment                                    FormalEducation  \\\n",
              "0  Employed part-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "1  Employed full-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "2  Employed full-time                                   Associate degree   \n",
              "3  Employed full-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "4  Employed full-time  Some college/university study without earning ...   \n",
              "5  Employed full-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "6  Employed full-time  Some college/university study without earning ...   \n",
              "7  Employed full-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "8  Employed full-time  Some college/university study without earning ...   \n",
              "9  Employed full-time         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "\n",
              "                                      UndergradMajor  \\\n",
              "0                          Mathematics or statistics   \n",
              "1  A natural science (ex. biology, chemistry, phy...   \n",
              "2  Computer science, computer engineering, or sof...   \n",
              "3  Computer science, computer engineering, or sof...   \n",
              "4  Computer science, computer engineering, or sof...   \n",
              "5  Computer science, computer engineering, or sof...   \n",
              "6  Computer science, computer engineering, or sof...   \n",
              "7  Computer science, computer engineering, or sof...   \n",
              "8  Fine arts or performing arts (ex. graphic desi...   \n",
              "9  Computer science, computer engineering, or sof...   \n",
              "\n",
              "                CompanySize  \\\n",
              "0        20 to 99 employees   \n",
              "1  10,000 or more employees   \n",
              "2        20 to 99 employees   \n",
              "3      100 to 499 employees   \n",
              "4  10,000 or more employees   \n",
              "5        10 to 19 employees   \n",
              "6  10,000 or more employees   \n",
              "7        10 to 19 employees   \n",
              "8      100 to 499 employees   \n",
              "9      500 to 999 employees   \n",
              "\n",
              "                                             DevType  ...  \\\n",
              "0                               Full-stack developer  ...   \n",
              "1  Database administrator;DevOps specialist;Full-...  ...   \n",
              "2           Engineering manager;Full-stack developer  ...   \n",
              "3                               Full-stack developer  ...   \n",
              "4  Data or business analyst;Desktop or enterprise...  ...   \n",
              "5  Back-end developer;Database administrator;Fron...  ...   \n",
              "6  Back-end developer;Front-end developer;Full-st...  ...   \n",
              "7  Designer;Front-end developer;QA or test developer  ...   \n",
              "8  Back-end developer;C-suite executive (CEO, CTO...  ...   \n",
              "9                                           Designer  ...   \n",
              "\n",
              "                     Exercise  Gender         SexualOrientation  \\\n",
              "0        3 - 4 times per week    Male  Straight or heterosexual   \n",
              "1   Daily or almost every day    Male  Straight or heterosexual   \n",
              "2                         NaN     NaN                       NaN   \n",
              "3  I don't typically exercise    Male  Straight or heterosexual   \n",
              "4        3 - 4 times per week    Male  Straight or heterosexual   \n",
              "5        1 - 2 times per week    Male  Straight or heterosexual   \n",
              "6  I don't typically exercise    Male  Straight or heterosexual   \n",
              "7        1 - 2 times per week  Female                       NaN   \n",
              "8  I don't typically exercise    Male  Straight or heterosexual   \n",
              "9                         NaN     NaN                       NaN   \n",
              "\n",
              "                                    EducationParents  \\\n",
              "0         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "1         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "2                                                NaN   \n",
              "3  Some college/university study without earning ...   \n",
              "4  Some college/university study without earning ...   \n",
              "5  Secondary school (e.g. American high school, G...   \n",
              "6      Master‚Äôs degree (MA, MS, M.Eng., MBA, etc.)   \n",
              "7                          Primary/elementary school   \n",
              "8  Some college/university study without earning ...   \n",
              "9                                                NaN   \n",
              "\n",
              "                  RaceEthnicity                Age Dependents  MilitaryUS  \\\n",
              "0   Black or of African descent  25 - 34 years old        Yes         NaN   \n",
              "1  White or of European descent  35 - 44 years old        Yes         NaN   \n",
              "2                           NaN                NaN        NaN         NaN   \n",
              "3  White or of European descent  35 - 44 years old         No          No   \n",
              "4  White or of European descent  18 - 24 years old        Yes         NaN   \n",
              "5  White or of European descent  18 - 24 years old         No         NaN   \n",
              "6  White or of European descent  18 - 24 years old         No          No   \n",
              "7   Black or of African descent  25 - 34 years old         No         NaN   \n",
              "8  White or of European descent  35 - 44 years old        Yes          No   \n",
              "9                           NaN                NaN        NaN         NaN   \n",
              "\n",
              "                          SurveyTooLong          SurveyEasy  \n",
              "0  The survey was an appropriate length           Very easy  \n",
              "1  The survey was an appropriate length       Somewhat easy  \n",
              "2                                   NaN                 NaN  \n",
              "3  The survey was an appropriate length       Somewhat easy  \n",
              "4  The survey was an appropriate length       Somewhat easy  \n",
              "5  The survey was an appropriate length       Somewhat easy  \n",
              "6  The survey was an appropriate length       Somewhat easy  \n",
              "7               The survey was too long  Somewhat difficult  \n",
              "8  The survey was an appropriate length           Very easy  \n",
              "9                                   NaN                 NaN  \n",
              "\n",
              "[10 rows x 129 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Respondent</th>\n",
              "      <th>Hobby</th>\n",
              "      <th>OpenSource</th>\n",
              "      <th>Country</th>\n",
              "      <th>Student</th>\n",
              "      <th>Employment</th>\n",
              "      <th>FormalEducation</th>\n",
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              "      <th>CompanySize</th>\n",
              "      <th>DevType</th>\n",
              "      <th>...</th>\n",
              "      <th>Exercise</th>\n",
              "      <th>Gender</th>\n",
              "      <th>SexualOrientation</th>\n",
              "      <th>EducationParents</th>\n",
              "      <th>RaceEthnicity</th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>MilitaryUS</th>\n",
              "      <th>SurveyTooLong</th>\n",
              "      <th>SurveyEasy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Kenya</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed part-time</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Mathematics or statistics</td>\n",
              "      <td>20 to 99 employees</td>\n",
              "      <td>Full-stack developer</td>\n",
              "      <td>...</td>\n",
              "      <td>3 - 4 times per week</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Black or of African descent</td>\n",
              "      <td>25 - 34 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Very easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
              "      <td>...</td>\n",
              "      <td>Daily or almost every day</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>White or of European descent</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>United States</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Associate degree</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>20 to 99 employees</td>\n",
              "      <td>Engineering manager;Full-stack developer</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>United States</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>100 to 499 employees</td>\n",
              "      <td>Full-stack developer</td>\n",
              "      <td>...</td>\n",
              "      <td>I don't typically exercise</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>White or of European descent</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Yes, part-time</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Data or business analyst;Desktop or enterprise...</td>\n",
              "      <td>...</td>\n",
              "      <td>3 - 4 times per week</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>White or of European descent</td>\n",
              "      <td>18 - 24 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>10 to 19 employees</td>\n",
              "      <td>Back-end developer;Database administrator;Fron...</td>\n",
              "      <td>...</td>\n",
              "      <td>1 - 2 times per week</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Secondary school (e.g. American high school, G...</td>\n",
              "      <td>White or of European descent</td>\n",
              "      <td>18 - 24 years old</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>United States</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Back-end developer;Front-end developer;Full-st...</td>\n",
              "      <td>...</td>\n",
              "      <td>I don't typically exercise</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Master‚Äôs degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
              "      <td>White or of European descent</td>\n",
              "      <td>18 - 24 years old</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>10 to 19 employees</td>\n",
              "      <td>Designer;Front-end developer;QA or test developer</td>\n",
              "      <td>...</td>\n",
              "      <td>1 - 2 times per week</td>\n",
              "      <td>Female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Primary/elementary school</td>\n",
              "      <td>Black or of African descent</td>\n",
              "      <td>25 - 34 years old</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was too long</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>United States</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>Fine arts or performing arts (ex. graphic desi...</td>\n",
              "      <td>100 to 499 employees</td>\n",
              "      <td>Back-end developer;C-suite executive (CEO, CTO...</td>\n",
              "      <td>...</td>\n",
              "      <td>I don't typically exercise</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>White or of European descent</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Very easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>16</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>India</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>500 to 999 employees</td>\n",
              "      <td>Designer</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "<p>10 rows × 129 columns</p>\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6b6b7fbd-fc96-4f60-bc36-f5a95879ae72 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6b6b7fbd-fc96-4f60-bc36-f5a95879ae72');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  </button>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ac305f5-cb1d-49f3-9e16-b71edfc530b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dimensions of the data\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pk727hn8BAtt",
        "outputId": "d679067c-11a7-4d2a-c094-a3dc62b8ec2b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 129)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#datatypes of the data\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJa9xMhaBN0h",
        "outputId": "809bc6cf-e913-4486-cf76-240056a82af6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Respondent        int64\n",
              "Hobby            object\n",
              "OpenSource       object\n",
              "Country          object\n",
              "Student          object\n",
              "                  ...  \n",
              "Age              object\n",
              "Dependents       object\n",
              "MilitaryUS       object\n",
              "SurveyTooLong    object\n",
              "SurveyEasy       object\n",
              "Length: 129, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYgb3H0fBXSM",
        "outputId": "86a3960c-cb0c-463e-b6a1-faf60a7a49a0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Respondent', 'Hobby', 'OpenSource', 'Country', 'Student', 'Employment',\n",
              "       'FormalEducation', 'UndergradMajor', 'CompanySize', 'DevType',\n",
              "       ...\n",
              "       'Exercise', 'Gender', 'SexualOrientation', 'EducationParents',\n",
              "       'RaceEthnicity', 'Age', 'Dependents', 'MilitaryUS', 'SurveyTooLong',\n",
              "       'SurveyEasy'],\n",
              "      dtype='object', length=129)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA** **CLEANING**"
      ],
      "metadata": {
        "id": "JcsoF8mUC3FW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for the missing or null values\n",
        "missing_values=df.isnull().sum()\n",
        "missing_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oq3xNHCbC0dw",
        "outputId": "760f4a2a-0b86-4966-cde7-64120d6e98fe"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Respondent        0\n",
              "Hobby             0\n",
              "OpenSource        0\n",
              "Country           0\n",
              "Student           1\n",
              "                 ..\n",
              "Age              32\n",
              "Dependents       32\n",
              "MilitaryUS       80\n",
              "SurveyTooLong    32\n",
              "SurveyEasy       32\n",
              "Length: 129, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By this we can get to the conclusion that there exist missing values"
      ],
      "metadata": {
        "id": "2XZHexqvDe_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating percentage of missing values\n",
        "total_cells=np.product(df.shape)\n",
        "total_missing=missing_values.sum()\n",
        "percent=(total_missing/total_cells)*100"
      ],
      "metadata": {
        "id": "GUvMjmK8Dbhf"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Total number of cells: ', total_cells)\n",
        "print('Total number of missing values: ', total_missing)\n",
        "print('Missing Percentage: ', percent, '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzfvQT1kDxYW",
        "outputId": "550042b8-7523-4924-925b-a585bcd9e60f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of cells:  12771\n",
            "Total number of missing values:  4421\n",
            "Missing Percentage:  34.61749275702764 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "approximately 35% of data is missing we need to handle the missing values"
      ],
      "metadata": {
        "id": "wpCPQqvTEiPZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**cleaning the gender column**"
      ],
      "metadata": {
        "id": "JocVQX4iFe7X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the missing percentage of the 'Gender' column\n",
        "df['Gender'].isnull().sum()\n",
        "missing_percentage_gender = (df['Gender'].isnull().sum() / len(df['Gender'])) * 100\n",
        "\n",
        "print(\"Missing percentage of the 'Gender' column:\", missing_percentage_gender, \"%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsT298IwEf-8",
        "outputId": "3027b990-d126-4f60-a169-a79370878369"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing percentage of the 'Gender' column: 32.323232323232325 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "32% of the gender data is missing lets categorize them"
      ],
      "metadata": {
        "id": "j9z63fRMF_BT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOL_vo90F812",
        "outputId": "604ef82e-c325-4f4d-f153-660465f8ad58"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Male', nan, 'Female',\n",
              "       'Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming',\n",
              "       'Female;Male',\n",
              "       'Male;Non-binary, genderqueer, or gender non-conforming'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#counting the number of each gender\n",
        "df.groupby('Gender')['Gender'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WThNvO9dGNJr",
        "outputId": "79435260-cd2a-4b2e-ef55-5874094efd63"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "Female                                                                        5\n",
              "Female;Male                                                                   1\n",
              "Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming     1\n",
              "Male                                                                         59\n",
              "Male;Non-binary, genderqueer, or gender non-conforming                        1\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filling the missing values and reducing the number of divisions to male,female and non confirmed only to get easy analysis"
      ],
      "metadata": {
        "id": "N5mSGdaFHQ7C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'] = df['Gender'].fillna('Non-binary, genderqueer, or gender non-conforming')\n",
        "df['Gender'].replace('Female;Male;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
        "df['Gender'].replace('Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
        "df['Gender'].replace('Female;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
        "df['Gender'].replace('Female;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
        "df['Gender'].replace('Male;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
        "df['Gender'].replace('Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
        "df['Gender'].replace('Transgender;Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) ##not sure\n",
        "df['Gender'].replace('Female;Male', 'Female', inplace =True)\n",
        "df['Gender'].replace('Female;Male;Transgender', 'Female', inplace =True)\n",
        "df['Gender'].replace('Female;Transgender', 'Female', inplace =True)\n",
        "df['Gender'].replace('Male;Transgender', 'Female', inplace =True)\n",
        "df['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) #\n",
        "df['Gender'].replace('Transgender', 'Male', inplace =True)"
      ],
      "metadata": {
        "id": "mr6IrwZxGWvH"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Gender')['Gender'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyI5oGQJHPVt",
        "outputId": "a73003d3-59e2-42b0-fd28-df476679d9bb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "Female             7\n",
              "Male              60\n",
              "Non-conforming    32\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wIFGzW0HcDS",
        "outputId": "a724cd6b-795a-45ee-cd39-5ded3adb5db9"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 129)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()['Gender']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEYOPjlNHw-I",
        "outputId": "d41f7fe8-2535-4c2c-ebbf-e38520bd7d2d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in gender column\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "6h37X59cH0ne"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**cleaning the country column**"
      ],
      "metadata": {
        "id": "Y7lg2qNyIHRT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Country')['Country'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUTBusH2HzGg",
        "outputId": "f32f20f9-1110-49ad-84f2-7eccbbd58c24"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "Algeria                1\n",
              "Argentina              2\n",
              "Australia              1\n",
              "Belgium                1\n",
              "Brazil                 1\n",
              "Bulgaria               1\n",
              "Chile                  1\n",
              "China                  2\n",
              "Colombia               1\n",
              "Croatia                1\n",
              "Denmark                1\n",
              "Dominican Republic     1\n",
              "Finland                1\n",
              "France                 3\n",
              "Germany                5\n",
              "Greece                 1\n",
              "India                 16\n",
              "Indonesia              2\n",
              "Ireland                1\n",
              "Israel                 1\n",
              "Japan                  1\n",
              "Kenya                  1\n",
              "Latvia                 1\n",
              "Netherlands            1\n",
              "Nigeria                1\n",
              "Poland                 3\n",
              "Romania                1\n",
              "Russian Federation     4\n",
              "South Africa           1\n",
              "Spain                  2\n",
              "Sweden                 3\n",
              "Ukraine                1\n",
              "United Kingdom         7\n",
              "United States         28\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Country'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ds9Hf2hgIn8d",
        "outputId": "3afea5a2-2a96-4eb1-fad0-5ed0a4d8ae66"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling null values\n",
        "df['Country'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "8XoIM4LoIreR"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Country'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvZcOHoAIv79",
        "outputId": "384f0346-a962-4850-de64-53751ad54399"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in country column"
      ],
      "metadata": {
        "id": "oMNRkkg8JKSP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**cleaning the hobbies column**"
      ],
      "metadata": {
        "id": "J34nhG7BJXUD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Hobby'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LXVD5rzJEuX",
        "outputId": "c8269b20-8789-4259-adde-9b1352091813"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Hobby')['Hobby'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-Z7I4JrJhRo",
        "outputId": "25abd92b-b696-4090-abb0-bb28d58df519"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hobby\n",
              "No     20\n",
              "Yes    79\n",
              "Name: Hobby, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As the hobbies are categorized only to yes or not further we dont need to categorize them."
      ],
      "metadata": {
        "id": "DIRvLcbQJ-qO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Hobby'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3_uu36PJ8Wm",
        "outputId": "41568282-f651-4fe7-f632-5f6cb181f411"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in hobbies column"
      ],
      "metadata": {
        "id": "rqM9IL9AKKbS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**cleaning the undergradmajor column**"
      ],
      "metadata": {
        "id": "lNfYL_EYKU10"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2xoCi7mKIgO",
        "outputId": "f83fa113-0e5f-4a19-9861-5c933ba3697a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_jhcUTzKdKE",
        "outputId": "656569ce-cea0-43c0-a378-99208f5c05da"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UndergradMajor\n",
              "Computer science, computer engineering, or software engineering          56\n",
              "A natural science (ex. biology, chemistry, physics)                       7\n",
              "Another engineering discipline (ex. civil, electrical, mechanical)        7\n",
              "A business discipline (ex. accounting, finance, marketing)                5\n",
              "Fine arts or performing arts (ex. graphic design, music, studio art)      4\n",
              "Information systems, information technology, or system administration     3\n",
              "Mathematics or statistics                                                 2\n",
              "Web development or web design                                             2\n",
              "A social science (ex. anthropology, psychology, political science)        1\n",
              "A humanities discipline (ex. literature, history, philosophy)             1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def refactor_major(df):\n",
        "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'),\n",
        "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
        "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'),\n",
        "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
        "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)')\n",
        "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'),\n",
        "                        (df['UndergradMajor'] == 'Web development or web design'),\n",
        "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'),\n",
        "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
        "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
        "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)'),\n",
        "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
        "\n",
        "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
        "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
        "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
        "    return df"
      ],
      "metadata": {
        "id": "u3dNbovyKfnF"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "the values in the \"UndergradMajor\" column are classified based on certain conditions."
      ],
      "metadata": {
        "id": "ilV2dwqCMcsM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = refactor_major(df)"
      ],
      "metadata": {
        "id": "CRTeAPqlKuzm"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].replace('nan', 'No major', inplace=True)"
      ],
      "metadata": {
        "id": "nHySDU3cM66h"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDnUZGWMNJDA",
        "outputId": "7a3aada2-3d81-4dce-c7d4-0ed1cea11684"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UndergradMajor\n",
              "Computer Science    56\n",
              "No major            11\n",
              "Other Science        7\n",
              "Engineering          7\n",
              "Arts and Science     6\n",
              "Business             5\n",
              "Info Systems         3\n",
              "Math/Stat            2\n",
              "Web Design/Dev       2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7thqzYMnNPDu",
        "outputId": "9075fc05-1240-4451-872c-829dc560f6e3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['UndergradMajor'], inplace=True)"
      ],
      "metadata": {
        "id": "3VIx65RZNlNY"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DYmuQj-Nnoi",
        "outputId": "74c4fad3-28c6-45b4-b9b1-9199e69b92b4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we assigned the remaining null values to the category of no major\n",
        "Hence, there are no existing null values in the undergradmajor column"
      ],
      "metadata": {
        "id": "ku9qgKCENWC6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**cleaning the jobsearchstatus column**"
      ],
      "metadata": {
        "id": "WL4YcRevN0PL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSearchStatus'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzXzkzVsNUPH",
        "outputId": "e5d6578b-b528-4569-88d7-58970570dbd6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "JobSearchStatus\n",
              "I‚Äôm not actively looking, but I am open to new opportunities    54\n",
              "I am not interested in new job opportunities                      18\n",
              "I am actively looking for a job                                   11\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping null values\n",
        "df.dropna(subset=['JobSearchStatus'], inplace=True)"
      ],
      "metadata": {
        "id": "vszoQOfKNymE"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Categorizing those into groups: the ones who are seeking for job and the ones who are not seeking for job and others"
      ],
      "metadata": {
        "id": "1ey3HKcNlSTt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def refactor_job(df):\n",
        "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
        "\n",
        "    conditions_job = [(df['JobSearchStatus'] == 'I am actively looking for a job'),\n",
        "                    (df['JobSearchStatus'] == 'I am not interested in new job opportunities')\n",
        "                    | (df['JobSearchStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
        "\n",
        "    choices_job = ['Seeking', 'Not seeking']\n",
        "\n",
        "    df['JobSearchStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
        "\n",
        "    return df\n"
      ],
      "metadata": {
        "id": "1FiRCkUMlGYX"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = refactor_job(df)"
      ],
      "metadata": {
        "id": "w6eyQW0KlfFA"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSearchStatus'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tjcirO5lhYz",
        "outputId": "b1dfcca2-9e72-4c61-d532-621120c88e33"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "JobSearchStatus\n",
              "nan            54\n",
              "Not seeking    18\n",
              "Seeking        11\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSearchStatus'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZhqXlToljTS",
        "outputId": "4ae49700-1b6a-4ef6-86ca-fc136cba0061"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the jobsearchstatus column"
      ],
      "metadata": {
        "id": "jEXRkQselt_c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**cleaning the Employement column**"
      ],
      "metadata": {
        "id": "dbccv75Pl7dH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HN5Hw7Jslsn2",
        "outputId": "40dfeb73-d5cb-40ab-d776-b17861c13e8f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Employment\n",
              "Employed full-time    77\n",
              "Employed part-time     6\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dB_IwQPmHfa",
        "outputId": "2072d63a-28f1-4b58-f543-fca4bf9f98d5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "e8soU9WymLEm"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tc7hG1v4mULw",
        "outputId": "fa86a7b9-6faa-4c5e-aaa2-3cc77f55e3df"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     Employed part-time\n",
              "1     Employed full-time\n",
              "2     Employed full-time\n",
              "3     Employed full-time\n",
              "4     Employed full-time\n",
              "             ...        \n",
              "93    Employed full-time\n",
              "94    Employed full-time\n",
              "95    Employed full-time\n",
              "97    Employed full-time\n",
              "98    Employed full-time\n",
              "Name: Employment, Length: 83, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4dwu9zemeg2",
        "outputId": "d52d81bf-c2f7-497c-ba5e-55f5df8d262e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the employment column"
      ],
      "metadata": {
        "id": "6sCzyYBnmuNR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Job** **Satisfaction**"
      ],
      "metadata": {
        "id": "gGEvRbeAmzSY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSatisfaction'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZBwz9J5OmpJX",
        "outputId": "8341bfd8-05ff-4d42-a1ab-f6fc1c98d897"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "JobSatisfaction\n",
              "Moderately satisfied                  26\n",
              "Slightly satisfied                    17\n",
              "Neither satisfied nor dissatisfied    11\n",
              "Extremely satisfied                    9\n",
              "Slightly dissatisfied                  9\n",
              "Moderately dissatisfied                6\n",
              "Extremely dissatisfied                 2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSatisfaction'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDnUpweIm66o",
        "outputId": "f6b76f5e-ea29-4d5f-d825-55f9928c3f4f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling the null values\n",
        "df['JobSatisfaction'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "eMrI20RNm_Am"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSatisfaction'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0Qg8298nCCC",
        "outputId": "52e46af1-95da-403a-ad73-2328b9721577"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the job satisfaction column"
      ],
      "metadata": {
        "id": "agn4hD9KnKLJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Race Ethnicity**"
      ],
      "metadata": {
        "id": "D_K5_TEFnUQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('RaceEthnicity')['RaceEthnicity'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8BUpHtAnTrh",
        "outputId": "8b102d3a-91e9-4f78-b858-339e3aafa07b"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RaceEthnicity\n",
              "Black or of African descent                                                                                                                                                              3\n",
              "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent     1\n",
              "Black or of African descent;Hispanic or Latino/Latina                                                                                                                                    1\n",
              "East Asian                                                                                                                                                                               2\n",
              "Hispanic or Latino/Latina                                                                                                                                                                1\n",
              "Hispanic or Latino/Latina;White or of European descent                                                                                                                                   1\n",
              "South Asian                                                                                                                                                                              8\n",
              "White or of European descent                                                                                                                                                            41\n",
              "Name: RaceEthnicity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "combining the given Ethnicity by str.match which means if each string starts with a match of a regular expression pattern then they will be categorized.\n"
      ],
      "metadata": {
        "id": "0LS8bEhVn30b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['RaceEthnicity'].str.match('Biracial') == True, 'RaceEthnicity'] = 'Biracial'\n",
        "df.loc[df['RaceEthnicity'].str.match('Black or of African descent') == True, 'RaceEthnicity'] = 'Black or African descent'\n",
        "df.loc[df['RaceEthnicity'].str.match('East Asian') == True, 'RaceEthnicity'] = 'East Asian'\n",
        "df.loc[df['RaceEthnicity'].str.match('Hispanic or Latino') == True, 'RaceEthnicity'] = 'Hispanic or Latino'\n",
        "df.loc[df['RaceEthnicity'].str.match('Indigenous') == True, 'RaceEthnicity'] = 'Indigenous'\n",
        "df.loc[df['RaceEthnicity'].str.match('Middle Eastern') == True, 'RaceEthnicity'] = 'Middle Eastern'\n",
        "df.loc[df['RaceEthnicity'].str.match('South') == True, 'RaceEthnicity'] = 'South Asian'\n",
        "df.loc[df['RaceEthnicity'].str.match('White or of European descent') == True, 'RaceEthnicity'] = 'White or European descent'\n",
        "df.loc[df['RaceEthnicity'].str.match('Multiracial') == True, 'RaceEthnicity'] = 'Multiracial'\n",
        "df.loc[df['RaceEthnicity'].str.match('Native American') == True, 'RaceEthnicity'] = 'Native American'"
      ],
      "metadata": {
        "id": "FH4ji11wnIFd"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('RaceEthnicity')['RaceEthnicity'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJUNzLQJomx-",
        "outputId": "2bcb98d7-df46-41d2-e13b-f4fcd95f9eb5"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RaceEthnicity\n",
              "Black or African descent      5\n",
              "East Asian                    2\n",
              "Hispanic or Latino            2\n",
              "South Asian                   8\n",
              "White or European descent    41\n",
              "Name: RaceEthnicity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['RaceEthnicity'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIwYiTOSorvU",
        "outputId": "fb688028-fa6a-4bc5-ba0b-ad3b17c4337a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#This code performs forward and backward filling of missing values in the RaceEthnicity column based on the values in the Country column.\n",
        "df['RaceEthnicity']=df.groupby(['Country'])['RaceEthnicity'].bfill().ffill()"
      ],
      "metadata": {
        "id": "ko6zkKW4o5V2"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['RaceEthnicity'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_q1T5xRo9H3",
        "outputId": "f50c6efe-3053-4aa6-9206-500c19683c13"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the race ethnicity column"
      ],
      "metadata": {
        "id": "ta2DJio-poxZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DevType**"
      ],
      "metadata": {
        "id": "8DUVlql_pz1T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['DevType'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbxq2hzxpnep",
        "outputId": "5a21e6b9-1c69-433d-c112-51a17e0e78ff"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['DevType'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "SYNuGwm-p-rh"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('DevType')['DevType'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OQ3ubLwqA8k",
        "outputId": "4aafa34e-e88e-4ed4-8bc6-d0f1e52def06"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DevType\n",
              "Back-end developer                                                                                                                                                                       2\n",
              "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Database administrator;DevOps specialist;Engineering manager;Full-stack developer;System administrator    1\n",
              "Back-end developer;C-suite executive (CEO, CTO, etc.);Database administrator;Designer;Front-end developer;Full-stack developer;Mobile developer                                          1\n",
              "Back-end developer;C-suite executive (CEO, CTO, etc.);Database administrator;DevOps specialist;Engineering manager;Full-stack developer;System administrator                             1\n",
              "Back-end developer;Data or business analyst;Database administrator;DevOps specialist;Front-end developer;Full-stack developer;System administrator                                       1\n",
              "Back-end developer;Data or business analyst;Designer;Front-end developer;Game or graphics developer;Mobile developer;Student                                                             1\n",
              "Back-end developer;Data scientist or machine learning specialist;Desktop or enterprise applications developer;Front-end developer;Full-stack developer                                   1\n",
              "Back-end developer;Data scientist or machine learning specialist;DevOps specialist;Educator or academic researcher;QA or test developer;System administrator                             1\n",
              "Back-end developer;Data scientist or machine learning specialist;Full-stack developer;Game or graphics developer;Student                                                                 1\n",
              "Back-end developer;Database administrator;Designer;Desktop or enterprise applications developer;Front-end developer;Full-stack developer                                                 1\n",
              "Back-end developer;Database administrator;Designer;Front-end developer;Full-stack developer;Mobile developer;System administrator                                                        1\n",
              "Back-end developer;Database administrator;Desktop or enterprise applications developer;DevOps specialist;Full-stack developer;QA or test developer                                       1\n",
              "Back-end developer;Database administrator;DevOps specialist;Front-end developer;Full-stack developer;Mobile developer                                                                    1\n",
              "Back-end developer;Database administrator;DevOps specialist;Front-end developer;Full-stack developer;QA or test developer;System administrator                                           1\n",
              "Back-end developer;Database administrator;Front-end developer                                                                                                                            1\n",
              "Back-end developer;Database administrator;Front-end developer;Full-stack developer                                                                                                       1\n",
              "Back-end developer;Database administrator;Front-end developer;Full-stack developer;Mobile developer                                                                                      1\n",
              "Back-end developer;Database administrator;Front-end developer;Student;System administrator                                                                                               1\n",
              "Back-end developer;Designer;Educator or academic researcher;Front-end developer                                                                                                          1\n",
              "Back-end developer;Designer;Front-end developer;Full-stack developer;Marketing or sales professional;Mobile developer                                                                    1\n",
              "Back-end developer;Desktop or enterprise applications developer;Embedded applications or devices developer                                                                               2\n",
              "Back-end developer;Desktop or enterprise applications developer;Embedded applications or devices developer;Front-end developer;Full-stack developer;System administrator                 1\n",
              "Back-end developer;Desktop or enterprise applications developer;Front-end developer;Full-stack developer                                                                                 2\n",
              "Back-end developer;Desktop or enterprise applications developer;Front-end developer;Full-stack developer;Game or graphics developer;Student                                              1\n",
              "Back-end developer;Desktop or enterprise applications developer;QA or test developer                                                                                                     1\n",
              "Back-end developer;DevOps specialist                                                                                                                                                     2\n",
              "Back-end developer;DevOps specialist;Front-end developer;Full-stack developer;Mobile developer                                                                                           1\n",
              "Back-end developer;Embedded applications or devices developer                                                                                                                            1\n",
              "Back-end developer;Embedded applications or devices developer;Full-stack developer                                                                                                       1\n",
              "Back-end developer;Engineering manager                                                                                                                                                   1\n",
              "Back-end developer;Front-end developer                                                                                                                                                   1\n",
              "Back-end developer;Front-end developer;Full-stack developer                                                                                                                              5\n",
              "Back-end developer;Front-end developer;Full-stack developer;Mobile developer                                                                                                             1\n",
              "Back-end developer;Front-end developer;Student                                                                                                                                           2\n",
              "Back-end developer;Full-stack developer                                                                                                                                                  5\n",
              "Back-end developer;Full-stack developer;QA or test developer                                                                                                                             1\n",
              "Back-end developer;Full-stack developer;System administrator                                                                                                                             1\n",
              "Data or business analyst;Data scientist or machine learning specialist;Database administrator;DevOps specialist                                                                          1\n",
              "Data or business analyst;Database administrator;DevOps specialist;System administrator                                                                                                   1\n",
              "Data or business analyst;Desktop or enterprise applications developer;Game or graphics developer;QA or test developer;Student                                                            1\n",
              "Data scientist or machine learning specialist                                                                                                                                            1\n",
              "Database administrator;DevOps specialist;Full-stack developer;System administrator                                                                                                       1\n",
              "Database administrator;Full-stack developer;Mobile developer                                                                                                                             1\n",
              "Designer;Front-end developer                                                                                                                                                             2\n",
              "Designer;Front-end developer;Marketing or sales professional                                                                                                                             1\n",
              "Designer;Front-end developer;QA or test developer                                                                                                                                        1\n",
              "Desktop or enterprise applications developer;Embedded applications or devices developer;Full-stack developer;Game or graphics developer;Mobile developer                                 1\n",
              "Desktop or enterprise applications developer;Front-end developer;Product manager                                                                                                         1\n",
              "Embedded applications or devices developer                                                                                                                                               1\n",
              "Embedded applications or devices developer;Engineering manager                                                                                                                           1\n",
              "Engineering manager;Full-stack developer                                                                                                                                                 1\n",
              "Engineering manager;Mobile developer                                                                                                                                                     1\n",
              "Front-end developer                                                                                                                                                                      1\n",
              "Front-end developer;Full-stack developer                                                                                                                                                 1\n",
              "Front-end developer;Student                                                                                                                                                              1\n",
              "Full-stack developer                                                                                                                                                                     8\n",
              "Full-stack developer;Product manager                                                                                                                                                     1\n",
              "Mobile developer                                                                                                                                                                         2\n",
              "QA or test developer                                                                                                                                                                     1\n",
              "Student                                                                                                                                                                                  2\n",
              "Name: DevType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "combining the given DevType by str.match which means if each string starts with a match of a regular expression pattern then they will be categorized."
      ],
      "metadata": {
        "id": "a7Vr5H6yqqKK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#categorizing the given fields into developer,manager,student,non developer roles\n",
        "df.loc[df['DevType'].str.match('Back-end developer') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Student') == True, 'DevType'] = 'Student'\n",
        "df.loc[df['DevType'].str.match('QA or test developer') == True, 'DevType'] = 'Non developer'\n",
        "df.loc[df['DevType'].str.match('Product manager') == True, 'DevType'] = 'Manager'\n",
        "df.loc[df['DevType'].str.match('Mobile developer') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Marketing or sales professional') == True, 'DevType'] = 'Non developer'\n",
        "df.loc[df['DevType'].str.match('System administrator') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Game or graphics developer') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Full-stack developer') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Front-end developer') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Engineering manager') == True, 'DevType'] = 'Manager'\n",
        "df.loc[df['DevType'].str.match('Embedded applications or devices developer') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Educator or academic researcher') == True, 'DevType'] = 'Student'\n",
        "df.loc[df['DevType'].str.match('DevOps specialist') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Desktop or enterprise applications developer') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Designer') == True, 'DevType'] = 'Non developer'\n",
        "df.loc[df['DevType'].str.match('Database administrator') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Data scientist or machine learning specialist') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('Data or business analyst') == True, 'DevType'] = 'Developer'\n",
        "df.loc[df['DevType'].str.match('C-suite executive') == True, 'DevType'] = 'Developer'\n"
      ],
      "metadata": {
        "id": "yhGUOZDrqDSJ"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('DevType')['DevType'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Uq3rID4rLYy",
        "outputId": "e9a7c890-14de-48c6-8496-53ddfab1202d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DevType\n",
              "Developer        74\n",
              "Manager           2\n",
              "Non developer     5\n",
              "Student           2\n",
              "Name: DevType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Language worked with**"
      ],
      "metadata": {
        "id": "0oM_tG12riOE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#this return tha 3 most frequent entries or the 3 most used languages\n",
        "df['LanguageWorkedWith'].value_counts().nlargest(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XA6DoJOrd1F",
        "outputId": "769df166-e8e2-4124-bdd0-7ce5780f9659"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LanguageWorkedWith\n",
              "Java;JavaScript;PHP;SQL;TypeScript;HTML;CSS    2\n",
              "JavaScript;PHP;HTML;CSS                        2\n",
              "C;F#;Haskell;Python;Scala                      1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageWorkedWith'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bzonouh4rnpz",
        "outputId": "06949423-b338-470c-cfd7-05e6c038ed83"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values\n",
        "df['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "AnfM5PJJsEow"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageWorkedWith'].value_counts().nlargest(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGdafg4HsG7-",
        "outputId": "8cc4c79b-3456-44fb-a414-d861cbf18f2c"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LanguageWorkedWith\n",
              "Java;JavaScript;PHP;VB.NET;HTML;CSS                3\n",
              "Assembly;C;C++;Java;Python;Delphi/Object Pascal    2\n",
              "Java;JavaScript;PHP;SQL;TypeScript;HTML;CSS        2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageWorkedWith'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKmOpf2TsP6n",
        "outputId": "7d2c32a5-57fe-4913-edf9-d5a448797537"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the language work with column"
      ],
      "metadata": {
        "id": "BcCtC4FesbbH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Language desirednext year**"
      ],
      "metadata": {
        "id": "WqnvC05gsybz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#this return tha 3 most frequent entries that may appear the next year.\n",
        "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4UcToECsTjv",
        "outputId": "f4a7d684-3ba7-46e3-a73e-a5cb1734a22f"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LanguageDesireNextYear\n",
              "C#;JavaScript;PHP;SQL;HTML;CSS    2\n",
              "JavaScript;Python;HTML;CSS        1\n",
              "C#;JavaScript;TypeScript          1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageDesireNextYear'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUG2NWJ8s4H8",
        "outputId": "e193682b-b9f7-47bd-efce-9f320879d7e0"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values\n",
        "df['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "y2uYLmpxtFcn"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLb6YvEBtMSf",
        "outputId": "2fe0ed67-c11d-4d35-8858-b25c72845596"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LanguageDesireNextYear\n",
              "Java;Python                                    4\n",
              "JavaScript;PHP;SQL;Swift;CSS;Bash/Shell        2\n",
              "C#;Java;JavaScript;Ruby;TypeScript;HTML;CSS    2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageDesireNextYear'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wEXX5jztUPN",
        "outputId": "4330cb1e-41df-4e0f-e61c-31e118ad8e2c"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the language Desire next year column"
      ],
      "metadata": {
        "id": "IOB02QJptbuC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Yearscodingprof**"
      ],
      "metadata": {
        "id": "DpIvYka_tl7Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#this returns the the top 10 most coding proficient ones\n",
        "df['YearsCodingProf'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTVOvj1itYyG",
        "outputId": "28b28493-24d1-41c6-8c68-2d25ffe1c7bc"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsCodingProf\n",
              "3-5 years      26\n",
              "0-2 years      20\n",
              "6-8 years      12\n",
              "9-11 years      9\n",
              "12-14 years     6\n",
              "18-20 years     3\n",
              "21-23 years     2\n",
              "24-26 years     2\n",
              "15-17 years     1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hptn0b6Ut3kL",
        "outputId": "f77083fd-d246-44c2-9d0e-4edb1932c8ad"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "xI1uiR84t-AE"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOeODKlBuI6-",
        "outputId": "cafb7ce1-4645-4d8a-a182-be3890b9e445"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsCodingProf\n",
              "3-5 years      27\n",
              "0-2 years      21\n",
              "6-8 years      12\n",
              "9-11 years      9\n",
              "12-14 years     6\n",
              "18-20 years     3\n",
              "21-23 years     2\n",
              "24-26 years     2\n",
              "15-17 years     1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGiq0aEouASu",
        "outputId": "802c8767-313f-4da3-848c-b7dd2d087098"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the yearscodingprof column"
      ],
      "metadata": {
        "id": "cjOfpbY4uOjB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#the top 10 years coding ones\n",
        "df['YearsCoding'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWblz6_9uCZR",
        "outputId": "096eb6ac-019e-497a-e44d-1d3f523dadc0"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsCoding\n",
              "6-8 years           17\n",
              "3-5 years           15\n",
              "9-11 years          15\n",
              "0-2 years           11\n",
              "15-17 years          7\n",
              "12-14 years          6\n",
              "24-26 years          5\n",
              "18-20 years          4\n",
              "30 or more years     3\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCoding'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5hqJopHuWkK",
        "outputId": "c3cacd20-713f-4093-975e-409fa0131072"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values\n",
        "df['YearsCoding'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "t_c4jz26ug5q"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCoding'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtrovjDBuxcb",
        "outputId": "32cbfcd0-549d-408b-e674-8fcf6e071bf5"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsCoding\n",
              "6-8 years           17\n",
              "3-5 years           15\n",
              "9-11 years          15\n",
              "0-2 years           11\n",
              "15-17 years          7\n",
              "12-14 years          6\n",
              "24-26 years          5\n",
              "18-20 years          4\n",
              "30 or more years     3\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCoding'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZB_36JfBukFp",
        "outputId": "f78aac0f-06ca-41d4-9812-07983198fd31"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the yearscoding column"
      ],
      "metadata": {
        "id": "A7C6tWmJu2sG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Operating System**"
      ],
      "metadata": {
        "id": "TnxIzjxCu6QO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#top 10 most used operating systems\n",
        "df['OperatingSystem'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yj_CIgyOuqL5",
        "outputId": "332ab12a-de40-4409-af9d-75f8e3f516df"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OperatingSystem\n",
              "Windows        32\n",
              "MacOS          20\n",
              "Linux-based    15\n",
              "BSD/Unix        1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['OperatingSystem'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfIBSbjNu-6f",
        "outputId": "a1fcd7b1-bcb3-438d-c43b-9295a032256b"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling values\n",
        "df['OperatingSystem'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "HEz3Vxr9vPcu"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['OperatingSystem'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeSMf-SIvTwZ",
        "outputId": "41995c08-0fba-4da4-ea5e-fe98234e7c1b"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OperatingSystem\n",
              "Windows        44\n",
              "MacOS          21\n",
              "Linux-based    17\n",
              "BSD/Unix        1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['OperatingSystem'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FofzEDQkvWll",
        "outputId": "7577bd58-594f-4605-d187-a96fcde66640"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the operating system column"
      ],
      "metadata": {
        "id": "cCjVGXBtwNrC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Salary Type**"
      ],
      "metadata": {
        "id": "9BviI_lAwZpR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#top 10 frequently used salary types\n",
        "df['SalaryType'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EjK1EthMvZmc",
        "outputId": "a687b413-53ef-4f3a-e898-2f73ffd5ba80"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalaryType\n",
              "Monthly    25\n",
              "Yearly     22\n",
              "Weekly      1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5qYCnc2wn4T",
        "outputId": "f2fefea0-c426-412e-c3d3-37ee1ffa0ad5"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "-x9tHjLQwq5t"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnB1-anYws9D",
        "outputId": "8adcc589-028f-4f8c-d25c-125961fbcda2"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalaryType\n",
              "Monthly    42\n",
              "Yearly     39\n",
              "Weekly      2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_EK-CI5wvUc",
        "outputId": "7df61c2e-940a-495f-a789-97fb9625cb63"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the salary type column"
      ],
      "metadata": {
        "id": "cSybCP-aw5LC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Currency**"
      ],
      "metadata": {
        "id": "WQmFdwV3w-_L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#top 10 used currency\n",
        "df['Currency'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g21xj2qGwycm",
        "outputId": "12045289-1f13-4d9c-8dc8-e54711697b17"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Currency\n",
              "U.S. dollars ($)                20\n",
              "Euros (‚Ç¨)                     11\n",
              "British pounds sterling (¬£)     7\n",
              "Indian rupees (‚Çπ)              7\n",
              "Swedish kroner (SEK)             3\n",
              "Russian rubles (‚ÇΩ)             3\n",
              "Polish z≈Çoty (z≈Ç)              2\n",
              "Chinese yuan renminbi (¬•)       2\n",
              "South African rands (R)          1\n",
              "Australian dollars (A$)          1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CFcuvPTWxIO_",
        "outputId": "25844916-8d3e-4146-ff12-72c5045246e5"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "tRct3FX2xLlp"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['Currency'], inplace = True)"
      ],
      "metadata": {
        "id": "siI0zCLQxN2E"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slvL5FMsxWCx",
        "outputId": "a5d0c36c-a058-43cf-8be0-7ae599156fd9"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Currency\n",
              "U.S. dollars ($)                27\n",
              "Euros (‚Ç¨)                     16\n",
              "British pounds sterling (¬£)    10\n",
              "Indian rupees (‚Çπ)              8\n",
              "Chinese yuan renminbi (¬•)       5\n",
              "Swedish kroner (SEK)             4\n",
              "Russian rubles (‚ÇΩ)             4\n",
              "Polish z≈Çoty (z≈Ç)              2\n",
              "Brazilian reais (R$)             2\n",
              "South African rands (R)          1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5A2t9JscxYgE",
        "outputId": "2287575f-6df5-4fb1-ae47-5936b3e5f2d4"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the currency column"
      ],
      "metadata": {
        "id": "1SwoIy1nxclx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "renaming the column for our convinience"
      ],
      "metadata": {
        "id": "5ZN2w7IQ6oDj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "col=['Age','ConvertedSalary','Country','Currency','DevType','Employment','RaceEthnicity','Gender','SalaryType','Hobby','JobSatisfaction','JobSearchStatus','OperatingSystem','UndergradMajor','YearsCoding','YearsCodingProf','LanguageDesireNextYear','LanguageWorkedWith','FormalEducation']\n",
        "df1=df[col]\n",
        "#renaming the coloumn\n",
        "# 'ConvertedSalary': 'SalaryUSD'\n",
        "df.rename(columns={'ConvertedSalary': 'SalaryUSD' }, inplace =True)\n",
        "df.sort_index(axis=1).head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "RYdkvEksxbOA",
        "outputId": "40835601-abb7-4a4a-d88e-faaa109ec63f"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     AIDangerous  \\\n",
              "1  Increasing automation of jobs   \n",
              "2                            NaN   \n",
              "\n",
              "                                            AIFuture  \\\n",
              "1  I'm excited about the possibilities more than ...   \n",
              "2                                                NaN   \n",
              "\n",
              "                   AIInteresting  \\\n",
              "1  Increasing automation of jobs   \n",
              "2                            NaN   \n",
              "\n",
              "                                  AIResponsible AdBlocker AdBlockerDisable  \\\n",
              "1  The developers or the people creating the AI       Yes              Yes   \n",
              "2                                           NaN       NaN              NaN   \n",
              "\n",
              "                                    AdBlockerReasons AdsActions  \\\n",
              "1  The website I was visiting asked me to disable it        NaN   \n",
              "2                                                NaN        NaN   \n",
              "\n",
              "  AdsAgreeDisagree1           AdsAgreeDisagree2  ...     SurveyEasy  \\\n",
              "1    Somewhat agree  Neither agree nor disagree  ...  Somewhat easy   \n",
              "2               NaN                         NaN  ...            NaN   \n",
              "\n",
              "                          SurveyTooLong  TimeAfterBootcamp  \\\n",
              "1  The survey was an appropriate length                NaN   \n",
              "2                                   NaN                NaN   \n",
              "\n",
              "   TimeFullyProductive    UndergradMajor                             UpdateCV  \\\n",
              "1  One to three months     Other Science  I saw an employer‚Äôs advertisement   \n",
              "2                  NaN  Computer Science                                  NaN   \n",
              "\n",
              "   VersionControl                WakeTime       YearsCoding YearsCodingProf  \n",
              "1  Git;Subversion  Between 6:01 - 7:00 AM  30 or more years     18-20 years  \n",
              "2             NaN                     NaN       24-26 years       6-8 years  \n",
              "\n",
              "[2 rows x 129 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-332043ed-3ee3-4d41-8028-fa027c92218d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AIDangerous</th>\n",
              "      <th>AIFuture</th>\n",
              "      <th>AIInteresting</th>\n",
              "      <th>AIResponsible</th>\n",
              "      <th>AdBlocker</th>\n",
              "      <th>AdBlockerDisable</th>\n",
              "      <th>AdBlockerReasons</th>\n",
              "      <th>AdsActions</th>\n",
              "      <th>AdsAgreeDisagree1</th>\n",
              "      <th>AdsAgreeDisagree2</th>\n",
              "      <th>...</th>\n",
              "      <th>SurveyEasy</th>\n",
              "      <th>SurveyTooLong</th>\n",
              "      <th>TimeAfterBootcamp</th>\n",
              "      <th>TimeFullyProductive</th>\n",
              "      <th>UndergradMajor</th>\n",
              "      <th>UpdateCV</th>\n",
              "      <th>VersionControl</th>\n",
              "      <th>WakeTime</th>\n",
              "      <th>YearsCoding</th>\n",
              "      <th>YearsCodingProf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Increasing automation of jobs</td>\n",
              "      <td>I'm excited about the possibilities more than ...</td>\n",
              "      <td>Increasing automation of jobs</td>\n",
              "      <td>The developers or the people creating the AI</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>The website I was visiting asked me to disable it</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Somewhat agree</td>\n",
              "      <td>Neither agree nor disagree</td>\n",
              "      <td>...</td>\n",
              "      <td>Somewhat easy</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>NaN</td>\n",
              "      <td>One to three months</td>\n",
              "      <td>Other Science</td>\n",
              "      <td>I saw an employer‚Äôs advertisement</td>\n",
              "      <td>Git;Subversion</td>\n",
              "      <td>Between 6:01 - 7:00 AM</td>\n",
              "      <td>30 or more years</td>\n",
              "      <td>18-20 years</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Computer Science</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24-26 years</td>\n",
              "      <td>6-8 years</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 129 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-332043ed-3ee3-4d41-8028-fa027c92218d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-332043ed-3ee3-4d41-8028-fa027c92218d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-332043ed-3ee3-4d41-8028-fa027c92218d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fb82f633-0707-43e6-b8f4-3893f4b0f68a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb82f633-0707-43e6-b8f4-3893f4b0f68a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fb82f633-0707-43e6-b8f4-3893f4b0f68a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#top 10 salaries\n",
        "df['SalaryUSD'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOeLpbNu5xYD",
        "outputId": "a15e2a35-355e-4ef9-ba76-d450eb22ee59"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalaryUSD\n",
              "120000.0    3\n",
              "30000.0     2\n",
              "115000.0    2\n",
              "70841.0     1\n",
              "36000.0     1\n",
              "90000.0     1\n",
              "73428.0     1\n",
              "128507.0    1\n",
              "13212.0     1\n",
              "48955.0     1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryUSD'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViidUYaW62ha",
        "outputId": "e7453826-3b4a-4854-ce38-451df5352e17"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#grouping the salary based on the devtype and their country\n",
        "mean_salary = df.groupby(['DevType','Country'])['SalaryUSD'].mean()"
      ],
      "metadata": {
        "id": "1PuVMW007JQx"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_salary.nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B14qBmwx7Mm2",
        "outputId": "f46363e7-7168-41b5-a69a-caa2f45b4498"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DevType        Country       \n",
              "Developer      United States     146687.5\n",
              "               Ireland           128507.0\n",
              "Non developer  India             123984.0\n",
              "Developer      Australia          95968.0\n",
              "               Colombia           64116.0\n",
              "               Germany            61191.5\n",
              "               Sweden             60257.5\n",
              "               China              52604.0\n",
              "               Greece             51408.0\n",
              "               United Kingdom     48144.8\n",
              "Name: SalaryUSD, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#grouping the salary based on the devtype and their country and results the mean for each\n",
        "means = df.groupby(['YearsCodingProf','DevType', 'Country'])['SalaryUSD'].transform('mean')"
      ],
      "metadata": {
        "id": "vyFU-a0q7hYG"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#filling the null values with the mean values\n",
        "df['SalaryUSD'] = df['SalaryUSD'].fillna(means)"
      ],
      "metadata": {
        "id": "JZiz2R937nDK"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#grouping the mean salry based on the yearscodingprof,devtype and country columns\n",
        "mean_salary = df.groupby(['YearsCodingProf','DevType','Country'])['SalaryUSD'].mean()"
      ],
      "metadata": {
        "id": "Pi_YcfwD8FK5"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_salary.nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNWX4Zok8MRy",
        "outputId": "d0bef784-50b0-478f-b188-bc5218800be9"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearsCodingProf  DevType        Country       \n",
              "21-23 years      Developer      United States     250000.000000\n",
              "0-2 years        Developer      United States     244000.000000\n",
              "15-17 years      Developer      Ireland           128507.000000\n",
              "0-2 years        Non developer  India             123984.000000\n",
              "9-11 years       Developer      United States     115000.000000\n",
              "12-14 years      Developer      Australia          95968.000000\n",
              "18-20 years      Developer      United States      95000.000000\n",
              "6-8 years        Developer      United States      91333.333333\n",
              "3-5 years        Developer      China              85708.000000\n",
              "9-11 years       Developer      United Kingdom     82648.000000\n",
              "Name: SalaryUSD, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['SalaryUSD'], inplace = True)"
      ],
      "metadata": {
        "id": "RV4purXk8YwP"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**AGE**"
      ],
      "metadata": {
        "id": "1zhbNy2S8cf3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#frequently used top 10 ages\n",
        "df['Age'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NO728mSY8bEC",
        "outputId": "651a6720-e530-4e3a-b683-a126dfb43457"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age\n",
              "25 - 34 years old    22\n",
              "35 - 44 years old    13\n",
              "18 - 24 years old    13\n",
              "45 - 54 years old     1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGAuNMgn8muK",
        "outputId": "621e8e5e-dc06-4bf2-e13d-c206896e8e30"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "olP6Db5J8pbD"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bk5V6oIM8u3z",
        "outputId": "d0eef3a7-d251-4191-bdaa-4fbb2c6ea163"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age\n",
              "25 - 34 years old    26\n",
              "35 - 44 years old    16\n",
              "18 - 24 years old    16\n",
              "45 - 54 years old     1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRa2gNv68riy",
        "outputId": "27bbe03b-c55d-423e-b530-8c2cdc53bc09"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in age column"
      ],
      "metadata": {
        "id": "qsC_39Qo83__"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Formal Education**\n"
      ],
      "metadata": {
        "id": "QZEkje_V9AGJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['FormalEducation'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NK5Ynajx8vzu",
        "outputId": "6d9b5b48-a974-4889-bb4a-218be23f9b70"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['FormalEducation'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngTqWgm09HYP",
        "outputId": "287f4077-2189-4657-bace-f14eb6c94d07"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FormalEducation\n",
              "Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)                                            25\n",
              "Master‚Äôs degree (MA, MS, M.Eng., MBA, etc.)                                         17\n",
              "Some college/university study without earning a degree                                12\n",
              "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     3\n",
              "Associate degree                                                                       2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Changing column's name for our convinienccce\n",
        "df.rename(columns={'FormalEducation':'EdLevel'}, inplace =True)"
      ],
      "metadata": {
        "id": "3eoaxYJb9Jo0"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Categorizing the given fields into few optimized fields"
      ],
      "metadata": {
        "id": "NgV5cbDd9Xx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def refactor_ed(df):\n",
        "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
        "    conditions_ed = [(df['EdLevel'] == 'Associate degree'),\n",
        "                    (df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
        "                    (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
        "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),\n",
        "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
        "                    (df['EdLevel'] == 'Some college/university study without earning a degree')\n",
        "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)')\n",
        "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
        "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
        "\n",
        "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
        "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
        "    return df"
      ],
      "metadata": {
        "id": "lhdFv5Ym9QIX"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = refactor_ed(df)"
      ],
      "metadata": {
        "id": "NewXTlW79TrK"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
        "df['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
      ],
      "metadata": {
        "id": "i-KubSAV9odW"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['EdLevel'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTnp5YHk9rjH",
        "outputId": "510b0aee-738f-4c48-c422-40098e033a85"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EdLevel\n",
              "Bachelors    42\n",
              "No Degree    15\n",
              "Associate     2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['EdLevel'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pifoZPD19z-2",
        "outputId": "d9736b79-6218-4775-f7b4-b28bf08aa25d"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, there are no existing null values in the edlevel column"
      ],
      "metadata": {
        "id": "0AywkL0v9y-I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*So far We cleaned a lot of data. Therefore we can check the percent again*"
      ],
      "metadata": {
        "id": "rSs6cDdi-LBn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_2018 = df[df.notnull()]"
      ],
      "metadata": {
        "id": "uyXEowD39uAh"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_2018.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 933
        },
        "id": "uK6hUeN_9xsD",
        "outputId": "d3596b49-d62b-4d40-9100-6049ad3f24c4"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Respondent Hobby OpenSource         Country         Student  \\\n",
              "1            3   Yes        Yes  United Kingdom              No   \n",
              "4            7   Yes         No    South Africa  Yes, part-time   \n",
              "5            8   Yes         No  United Kingdom              No   \n",
              "6            9   Yes        Yes   United States              No   \n",
              "8           11   Yes        Yes   United States              No   \n",
              "13          20    No         No           India              No   \n",
              "14          21    No         No     Netherlands  Yes, full-time   \n",
              "17          27   Yes         No          Sweden              No   \n",
              "18          29   Yes        Yes           India  Yes, full-time   \n",
              "20          33   Yes        Yes       Australia              No   \n",
              "\n",
              "            Employment    EdLevel    UndergradMajor               CompanySize  \\\n",
              "1   Employed full-time  Bachelors     Other Science  10,000 or more employees   \n",
              "4   Employed full-time  No Degree  Computer Science  10,000 or more employees   \n",
              "5   Employed full-time  Bachelors  Computer Science        10 to 19 employees   \n",
              "6   Employed full-time  No Degree  Computer Science  10,000 or more employees   \n",
              "8   Employed full-time  No Degree  Arts and Science      100 to 499 employees   \n",
              "13  Employed full-time  Bachelors       Engineering        20 to 99 employees   \n",
              "14  Employed full-time  No Degree          No major        20 to 99 employees   \n",
              "17  Employed full-time  Bachelors          Business        10 to 19 employees   \n",
              "18  Employed full-time  Bachelors          No major  10,000 or more employees   \n",
              "20  Employed full-time  Bachelors       Engineering  1,000 to 4,999 employees   \n",
              "\n",
              "      DevType  ...                    Exercise          Gender  \\\n",
              "1   Developer  ...   Daily or almost every day            Male   \n",
              "4   Developer  ...        3 - 4 times per week            Male   \n",
              "5   Developer  ...        1 - 2 times per week            Male   \n",
              "6   Developer  ...  I don't typically exercise            Male   \n",
              "8   Developer  ...  I don't typically exercise            Male   \n",
              "13  Developer  ...  I don't typically exercise  Non-conforming   \n",
              "14  Developer  ...   Daily or almost every day            Male   \n",
              "17  Developer  ...        3 - 4 times per week            Male   \n",
              "18  Developer  ...   Daily or almost every day          Female   \n",
              "20  Developer  ...        3 - 4 times per week            Male   \n",
              "\n",
              "           SexualOrientation  \\\n",
              "1   Straight or heterosexual   \n",
              "4   Straight or heterosexual   \n",
              "5   Straight or heterosexual   \n",
              "6   Straight or heterosexual   \n",
              "8   Straight or heterosexual   \n",
              "13                       NaN   \n",
              "14                       NaN   \n",
              "17                       NaN   \n",
              "18                       NaN   \n",
              "20  Straight or heterosexual   \n",
              "\n",
              "                                     EducationParents  \\\n",
              "1          Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "4   Some college/university study without earning ...   \n",
              "5   Secondary school (e.g. American high school, G...   \n",
              "6       Master‚Äôs degree (MA, MS, M.Eng., MBA, etc.)   \n",
              "8   Some college/university study without earning ...   \n",
              "13                                                NaN   \n",
              "14                                   Associate degree   \n",
              "17         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "18  Some college/university study without earning ...   \n",
              "20         Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)   \n",
              "\n",
              "                RaceEthnicity                Age Dependents  MilitaryUS  \\\n",
              "1   White or European descent  35 - 44 years old        Yes         NaN   \n",
              "4   White or European descent  18 - 24 years old        Yes         NaN   \n",
              "5   White or European descent  18 - 24 years old         No         NaN   \n",
              "6   White or European descent  18 - 24 years old         No          No   \n",
              "8   White or European descent  35 - 44 years old        Yes          No   \n",
              "13                South Asian  35 - 44 years old        NaN         NaN   \n",
              "14  White or European descent  18 - 24 years old         No         NaN   \n",
              "17  White or European descent  35 - 44 years old        Yes         NaN   \n",
              "18                South Asian  35 - 44 years old        NaN         NaN   \n",
              "20                South Asian  35 - 44 years old        Yes         NaN   \n",
              "\n",
              "                           SurveyTooLong                  SurveyEasy  \n",
              "1   The survey was an appropriate length               Somewhat easy  \n",
              "4   The survey was an appropriate length               Somewhat easy  \n",
              "5   The survey was an appropriate length               Somewhat easy  \n",
              "6   The survey was an appropriate length               Somewhat easy  \n",
              "8   The survey was an appropriate length                   Very easy  \n",
              "13                                   NaN                         NaN  \n",
              "14  The survey was an appropriate length  Neither easy nor difficult  \n",
              "17               The survey was too long          Somewhat difficult  \n",
              "18               The survey was too long              Very difficult  \n",
              "20               The survey was too long  Neither easy nor difficult  \n",
              "\n",
              "[10 rows x 129 columns]"
            ],
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              "\n",
              "  <div id=\"df-5b952dc3-8cd2-4fdb-8dab-208ac4480d99\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Respondent</th>\n",
              "      <th>Hobby</th>\n",
              "      <th>OpenSource</th>\n",
              "      <th>Country</th>\n",
              "      <th>Student</th>\n",
              "      <th>Employment</th>\n",
              "      <th>EdLevel</th>\n",
              "      <th>UndergradMajor</th>\n",
              "      <th>CompanySize</th>\n",
              "      <th>DevType</th>\n",
              "      <th>...</th>\n",
              "      <th>Exercise</th>\n",
              "      <th>Gender</th>\n",
              "      <th>SexualOrientation</th>\n",
              "      <th>EducationParents</th>\n",
              "      <th>RaceEthnicity</th>\n",
              "      <th>Age</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>MilitaryUS</th>\n",
              "      <th>SurveyTooLong</th>\n",
              "      <th>SurveyEasy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Other Science</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>Daily or almost every day</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>White or European descent</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Yes, part-time</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>No Degree</td>\n",
              "      <td>Computer Science</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>3 - 4 times per week</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>White or European descent</td>\n",
              "      <td>18 - 24 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Computer Science</td>\n",
              "      <td>10 to 19 employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>1 - 2 times per week</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Secondary school (e.g. American high school, G...</td>\n",
              "      <td>White or European descent</td>\n",
              "      <td>18 - 24 years old</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>United States</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>No Degree</td>\n",
              "      <td>Computer Science</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>I don't typically exercise</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Master‚Äôs degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
              "      <td>White or European descent</td>\n",
              "      <td>18 - 24 years old</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Somewhat easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>United States</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>No Degree</td>\n",
              "      <td>Arts and Science</td>\n",
              "      <td>100 to 499 employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>I don't typically exercise</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>White or European descent</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Very easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>20</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>India</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>20 to 99 employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>I don't typically exercise</td>\n",
              "      <td>Non-conforming</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>South Asian</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>21</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Yes, full-time</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>No Degree</td>\n",
              "      <td>No major</td>\n",
              "      <td>20 to 99 employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>Daily or almost every day</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Associate degree</td>\n",
              "      <td>White or European descent</td>\n",
              "      <td>18 - 24 years old</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was an appropriate length</td>\n",
              "      <td>Neither easy nor difficult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>27</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Business</td>\n",
              "      <td>10 to 19 employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>3 - 4 times per week</td>\n",
              "      <td>Male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>White or European descent</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was too long</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>29</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>India</td>\n",
              "      <td>Yes, full-time</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>No major</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>Daily or almost every day</td>\n",
              "      <td>Female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Some college/university study without earning ...</td>\n",
              "      <td>South Asian</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was too long</td>\n",
              "      <td>Very difficult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>33</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Australia</td>\n",
              "      <td>No</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>1,000 to 4,999 employees</td>\n",
              "      <td>Developer</td>\n",
              "      <td>...</td>\n",
              "      <td>3 - 4 times per week</td>\n",
              "      <td>Male</td>\n",
              "      <td>Straight or heterosexual</td>\n",
              "      <td>Bachelor‚Äôs degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>South Asian</td>\n",
              "      <td>35 - 44 years old</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The survey was too long</td>\n",
              "      <td>Neither easy nor difficult</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 129 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b952dc3-8cd2-4fdb-8dab-208ac4480d99')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5b952dc3-8cd2-4fdb-8dab-208ac4480d99 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5b952dc3-8cd2-4fdb-8dab-208ac4480d99');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a987371e-5d3f-471e-8cf3-4e64e8552072\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a987371e-5d3f-471e-8cf3-4e64e8552072')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a987371e-5d3f-471e-8cf3-4e64e8552072 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cleaned_2018"
            }
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating percentage of missing values\n",
        "missing_count = df.isnull().sum() #number of missing\n",
        "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
        "total_missing = missing_count.sum()\n",
        "missing_percent = (total_missing*100)/total_cells"
      ],
      "metadata": {
        "id": "2LyBn6_i-b3o"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Total : ', total_cells)\n",
        "print('Total missing : ', total_missing)\n",
        "print('Missing Percentage: ', missing_percent, '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcVgSusH-5Sh",
        "outputId": "85446a7c-5516-4402-e24f-b1a6dc1d89dc"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total :  7611\n",
            "Total missing :  1487\n",
            "Missing Percentage:  19.537511496518196 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, we reduced the missing data from 35% to 19% which is far way better. And the remaining null values could be present in the columns thatwe are not going to use in our analysis"
      ],
      "metadata": {
        "id": "pwoj3j56-9nh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier detection**"
      ],
      "metadata": {
        "id": "dJH6dMHm_rcr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#detecting outliers using the interquartile range (IQR) method\n",
        "def detect_outliers_iqr(dataframe, fields):\n",
        "    outliers_indices = []\n",
        "    for feature in fields:\n",
        "        # Calculate the first and third quartiles\n",
        "        Q1 = dataframe[feature].quantile(0.25)\n",
        "        Q3 = dataframe[feature].quantile(0.75)\n",
        "        # Calculating the interquartile range (IQR)(diff of remaining)\n",
        "        IQR = Q3 - Q1\n",
        "        # Defining the lower and upper bounds for outlier detection\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "        # Finding outliers based on the lower and upper bounds\n",
        "        outliers = dataframe[(dataframe[feature] < lower_bound) | (dataframe[feature] > upper_bound)].index\n",
        "        # Adding the indices of outliers to the list to know whether they exist or not\n",
        "        outliers_indices.extend(outliers)\n",
        "    # Return indices of outliers\n",
        "    return list(set(outliers_indices))"
      ],
      "metadata": {
        "id": "pheMWmjZ-7gh"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating the outliers for different columns"
      ],
      "metadata": {
        "id": "sWFblaMjCekz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#outliers for Salaryusd\n",
        "cols = [ 'SalaryUSD']\n"
      ],
      "metadata": {
        "id": "ZxPFi0uHCaaz"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outliers_indices_iqr = detect_outliers_iqr(df,cols )"
      ],
      "metadata": {
        "id": "w8cRvgaTAQR1"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Printig the indices of outliers detected using the IQR method\n",
        "print(\"Indices of outliers detected using IQR method:\", outliers_indices_iqr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpKuLqjgASsQ",
        "outputId": "4ce6e58a-d709-4179-9b79-0d73babdd422"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Indices of outliers detected using IQR method: [8, 75, 27]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'Salaryusd' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='SalaryUSD', orient='h')\n",
        "plt.title('Horizontal Box Plot of SalaryUSD')\n",
        "plt.xlabel('Salary (USD)')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-AOrLBl_DXZv",
        "outputId": "49738230-e283-4f74-b4d8-90d9a939c0a5"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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TtWrVVKtWLU2bNk2lS5fW3r17s108++LnqUGDBtmOsWPHjnxdSL5w4cIqX768I/LyI+uLXwX9CAhwo+EMIZAPrVq1kiSNGzfOaXvW27utW7e+rOMmJCTo3Xff1YQJE3T77bdn21+yZEnVrFlTU6dOdYqyTZs2aeHChY55FVRAQECOkefh4ZHtTOL48eMv61Iul5Kenq6FCxfK29vbEditWrVSRkaG3n77baexb7zxhtzc3NSyZUvHbePj4xUeHq4333xTiYmJOnTokOOs1tVQkHXICsP8RHSdOnUUGhqqSZMmOX2ubd68edq6detlv66WLFmS4xnhrM86Zr1FnXU2+e8fDUhISMjzPnK67YoVK5ScnFzg+Xbt2lULFy7UuHHjVLRoUcdaZ6ldu7ZCQ0P1/vvvZ/v83+zZs7Vv3z6n26xfvz7Hqwfs2bNHW7ZsyfVb/383ffp0vf/++4qJidFdd91V4McF3Eg4Qwjkw6233qq4uDhNnjxZJ06cUKNGjfTzzz9r6tSpuueee9SkSZMCH/PIkSPq3bu3qlSpIh8fH3388cdO+9u3b6+AgAC99tpratmypWJiYtSjRw/H5U6Cg4M1dOjQy3o8tWvX1qJFi/T6668rPDxcUVFRqlevnmJjY/XRRx8pODhYVapUUXJyshYtWnTJy7Hkx7x58xxn+g4fPqzp06frl19+0XPPPef4XGObNm3UpEkTvfDCC9q9e7duvfVWLVy4UF9++aWefvppx2VWhg8frnXr1mnx4sUqVKiQatSoocGDB2vQoEHq0KHDZUdyXvK7DrVr15YkvfDCC+rUqZO8vLzUpk2bHM/kenl56dVXX1W3bt3UqFEjde7c2XHZmbJly1525Pbt21dnzpxR+/btFR0drbS0NP3444/69NNPVbZsWXXr1k2S9O9//1ve3t5q06aNevXqpdOnT+u9995TaGio4yxibmJjYzVr1iy1b99erVu3VkpKiiZNmqQqVao4zvrm14MPPqiBAwfqiy++0OOPPy4vLy+n/d7e3hozZozi4uJUt25dPfDAAypatKjWrl2rKVOmqEaNGo5LKElSUlKShgwZorZt2+qOO+5QYGCgdu3apSlTpuj8+fM5/rn5/PPPFRgYqLS0NMffVLJ8+XLdeuutjrfYgZua677gDLhOXpexaNSokdNlZ4wxJj093bz00ksmKirKeHl5mTJlypjnn3/enDt3zmlcZGSkad26dY7HvfiyM1mX48jt5+LLlixatMjUr1/f+Pn5maCgINOmTRuzZcsWp2PndvmMrMd68fG2bdtmGjZsaPz8/Iwkx5yOHz9uunXrZooVK2YCAwNNixYtzLZt27JdLuefXHbG19fX1KxZ00ycONFkZmY6jU9NTTXPPPOMCQ8PN15eXqZixYrmtddec4xbvXq18fT0NH379nW63YULF0zdunVNeHi4OX78eK7zyZr3zJkzLznvnC47Y0z+1sEYY4YNG2ZKlSpl3N3d83UJmk8//dTUqlXL+Pj4mCJFipguXbqY33//3WlMQS47M2/ePNO9e3cTHR1tAgMDjbe3t6lQoYLp27dvtkuvzJkzx9SoUcP4+vqasmXLmldffdVMmTIl27z/ftmZzMxMM3LkSBMZGWl8fHxMrVq1zFdffWXi4uKcLgGT9Vy+9tprl5xzq1atjCTz448/XvJxNWnSxAQFBRkvLy8TFRVl+vXrl23Nd+3aZQYPHmzuuOMOExoaajw9PU3x4sVN69atnS4RZcz//bm5+PVZunRpExsba6ZMmZLtzzdws3IzJh+fNAcA4Cpq3769Nm7cqJ07d7p6KoCV+AwhAMClDhw4oK+//tpx/U0A1x6fIQQAuERKSoqWL1+u999/X15eXurVq5erpwRYizOEAACX+O6779S1a1elpKRo6tSpKlGihKunBFiLzxACAABYjjOEAAAAliMIAQAALHfZXyrJzMzU/v37VahQoQL9VU0AAAC4NowxSk1NVXh4uNzdcz8PeNlBuH//fpUpU+Zybw4AAIBr5LffflPp0qVz3X/ZQZj1l8//9ttvjr96CgAAANePU6dOqUyZMo5uy81lB2HW28RBQUEEIQAAwHUsr4/38aUSAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACW83T1BFzh0KFDOnnypKunIUkKDg5WWFiYq6cBAAAsZl0QHjp0SA91fVjpaeddPRVJkpe3jz7+6EOiEAAAuIx1QXjy5Emlp53X2XKNlOkbnK/buJ89Ib+UZTob1VCZfiFXbC7u505Ku77TyZMnCUIAAOAy1gVhlkzfYGUGFCvYbfxCCnwbAACA6x1fKgEAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWO6GCcJz585px44dOnfunKuncsPiOQQAADm5YYJw7969evTRR7V3715XT+WGxXMIAABycsMEIQAAAK4OghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWM7T1RMArkdpaWn68ssvtX//foWHh6tdu3by9vZ29bQAADewjIwMbdiwQceOHVORIkVUo0YNeXh4uHpakghCIJtJkyZp5syZysjIcNrWsWNHPfbYYy6cGQDgRrVs2TK98847OnjwoGNbiRIl1Lt3bzVs2NCFM/sLbxkDF5k0aZJmzJihoKAgDRgwQP/73/80YMAABQUFacaMGZo0aZKrpwgAuMEsW7ZMQ4YMUbly5TRhwgR98803mjBhgsqVK6chQ4Zo2bJlrp4iQQhkSUtL08yZM1W4cGHNnDlTsbGxKlq0qGJjY522p6WluXqqAIAbREZGht555x3FxMRo+PDhqlq1qvz9/VW1alUNHz5cMTExmjhxotO7Uq6Q77eMz58/r/Pnzzt+P3Xq1FWZUF727Nnj0ttfDddqTtfjY7+efPnll8rIyFCPHj3k6en8R8PT01Pdu3fX2LFj9eWXX6pjx44umiUA4EayYcMGHTx4UC+++KLc3Z3Pw7m7u6tLly7q06ePNmzYoFq1arlolgUIwlGjRumll166mnPJlxEjRrh6ClfczfiYbkT79++XJMXExOS4P2t71jgAAPJy7NgxSVJUVFSO+7O2Z41zlXwH4fPPP69+/fo5fj916pTKlClzVSZ1KS+88IIiIyMv+/Z79uy57gLsnz6m/LoeH/v1JDw8XJKUnJys2NjYbPuTk5OdxgEAkJciRYpIklJSUlS1atVs+1NSUpzGuUq+g9DHx0c+Pj5Xcy75EhkZqUqVKrl6GlfUzfiYbkTt2rXTpEmT9MEHH+juu+92etv4woULmjJlijw8PNSuXTsXzhIAcCOpUaOGSpQooWnTpmn48OFObxtnZmZq2rRpKlmypGrUqOHCWfKlEsDB29tbHTt21PHjx9WxY0fNnTtXR44c0dy5c522cz1CAEB+eXh4qHfv3kpOTtagQYO0efNmnTlzRps3b9agQYOUnJysxx9/3OXXI+Q6hMBFsq4zOHPmTI0dO9ax3cPDQ506deI6hACAAmvYsKFeeuklvfPOO+rTp49je8mSJfXSSy9dF9chJAiBv3nsscfUvXt3/qYSAMAV07BhQ9WvX5+/qQS4kWS9fQwAwJXi4eHh0kvLXAqfIQQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAlrthgjAiIkKTJ09WRESEq6dyw+I5BAAAOfF09QTyy9fXV5UqVXL1NG5oPIcAACAnN8wZQgAAAFwdBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHKerp6Aq7ifO5n/sWdPOP2vK+YAAABwtVgXhMHBwfLy9pF2fVfg2/qlLLvi8/Hy9lFwcPAVPy4AAEB+WReEYWFh+vijD3Xy5PVxdi44OFhhYWGungYAALCYdUEo/RWFRBgAAMBf+FIJAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAliMIAQAALEcQAgAAWI4gBAAAsBxBCAAAYDmCEAAAwHIEIQAAgOUIQgAAAMsRhAAAAJYjCAEAACxHEAIAAFiOIAQAALAcQQgAAGA5ghAAAMByBCEAAIDlCEIAAADLeV7uDY0xkqRTp05dsckAAADgysnqtKxuy81lB2FqaqokqUyZMpd7CAAAAFwDqampCg4OznW/m8krGXORmZmp/fv3q1ChQnJzc7vsCebXqVOnVKZMGf32228KCgq66vcH12PN7cS624c1tw9rfu0YY5Samqrw8HC5u+f+ScHLPkPo7u6u0qVLX+7NL1tQUBAvHsuw5nZi3e3DmtuHNb82LnVmMAtfKgEAALAcQQgAAGC5GyYIfXx8NGTIEPn4+Lh6KrhGWHM7se72Yc3tw5pffy77SyUAAAC4OdwwZwgBAABwdRCEAAAAliMIAQAALEcQAgAAWO6GCcIJEyaobNmy8vX1Vb169fTzzz+7ekrWGzVqlOrWratChQopNDRU99xzj7Zv3+405ty5c+rTp4+KFi2qwMBA3XfffTp06JDTmL1796p169by9/dXaGionn32WV24cMFpzNKlS3XbbbfJx8dHFSpUUGJiYrb55PUayc9cUDCvvPKK3Nzc9PTTTzu2seY3n3379umhhx5S0aJF5efnp+rVq2vVqlWO/cYYDR48WCVLlpSfn5+aNWumX375xekYx44dU5cuXRQUFKSQkBD16NFDp0+fdhqzYcMGNWjQQL6+vipTpoxGjx6dbS4zZ85UdHS0fH19Vb16dX3zzTdO+/MzF+QtIyNDL774oqKiouTn56fy5ctr2LBhTn8fLut+kzE3gBkzZhhvb28zZcoUs3nzZvPII4+YkJAQc+jQIVdPzWotWrQwCQkJZtOmTWbdunWmVatWJiIiwpw+fdox5rHHHjNlypQxixcvNqtWrTJ33HGHufPOOx37L1y4YKpVq2aaNWtm1q5da7755htTrFgx8/zzzzvG7Nq1y/j7+5t+/fqZLVu2mPHjxxsPDw8zf/58x5j8vEbymgsK5ueffzZly5Y1NWrUME899ZRjO2t+czl27JiJjIw08fHxZsWKFWbXrl1mwYIFZufOnY4xr7zyigkODjazZ88269evN23btjVRUVHm7NmzjjF33323ufXWW81PP/1kvv/+e1OhQgXTuXNnx/6TJ0+asLAw06VLF7Np0ybzySefGD8/P/Puu+86xixfvtx4eHiY0aNHmy1btphBgwYZLy8vs3HjxgLNBXkbMWKEKVq0qPnqq69MSkqKmTlzpgkMDDRvvvmmYwzrfnO5IYLw9ttvN3369HH8npGRYcLDw82oUaNcOCv83eHDh40k89133xljjDlx4oTx8vIyM2fOdIzZunWrkWSSk5ONMcZ88803xt3d3Rw8eNAxZuLEiSYoKMicP3/eGGPMwIEDTdWqVZ3u64EHHjAtWrRw/J7XayQ/c0H+paammooVK5qkpCTTqFEjRxCy5jef//znP+Zf//pXrvszMzNNiRIlzGuvvebYduLECePj42M++eQTY4wxW7ZsMZLMypUrHWPmzZtn3NzczL59+4wxxrzzzjumcOHCjtdA1n1XrlzZ8fv9999vWrdu7XT/9erVM7169cr3XJA/rVu3Nt27d3fadu+995ouXboYY1j3m9F1/5ZxWlqaVq9erWbNmjm2ubu7q1mzZkpOTnbhzPB3J0+elCQVKVJEkrR69Wqlp6c7rV10dLQiIiIca5ecnKzq1asrLCzMMaZFixY6deqUNm/e7Bhz8TGyxmQdIz+vkfzMBfnXp08ftW7dOtu6sOY3nzlz5qhOnTrq2LGjQkNDVatWLb333nuO/SkpKTp48KDT8xwcHKx69eo5rXlISIjq1KnjGNOsWTO5u7trxYoVjjENGzaUt7e3Y0yLFi20fft2HT9+3DHmUq+L/MwF+XPnnXdq8eLF2rFjhyRp/fr1+uGHH9SyZUtJrPvNyNPVE8jLkSNHlJGR4fQvD0kKCwvTtm3bXDQr/F1mZqaefvpp1a9fX9WqVZMkHTx4UN7e3goJCXEaGxYWpoMHDzrG5LS2WfsuNebUqVM6e/asjh8/nudrJD9zQf7MmDFDa9as0cqVK7PtY81vPrt27dLEiRPVr18//fe//9XKlSv15JNPytvbW3FxcY7nMqe1uHg9Q0NDnfZ7enqqSJEiTmOioqKyHSNrX+HChXN9XVx8jLzmgvx57rnndOrUKUVHR8vDw0MZGRkaMWKEunTpIil/zzXrfmO57oMQN4Y+ffpo06ZN+uGHH1w9FVxFv/32m5566iklJSXJ19fX1dPBNZCZmak6depo5MiRkqRatWpp06ZNmjRpkuLi4lw8O1wtn332maZNm6bp06eratWqWrdunZ5++mmFh4ez7jep6/4t42LFisnDwyPbNwMPHTqkEiVKuGhWuNgTTzyhr776SkuWLFHp0qUd20uUKKG0tDSdOHHCafzFa1eiRIkc1zZr36XGBAUFyc/PL1+vkfzMBXlbvXq1Dh8+rNtuu02enp7y9PTUd999p7feekuenp4KCwtjzW8yJUuWVJUqVZy23XLLLdq7d6+k/1uzvNbi8OHDTvsvXLigY8eOXZHXxcX785oL8ufZZ5/Vc889p06dOql69erq2rWrnnnmGY0aNUoS634zuu6D0NvbW7Vr19bixYsd2zIzM7V48WLFxMS4cGYwxuiJJ57QF198oW+//Tbbaf/atWvLy8vLae22b9+uvXv3OtYuJiZGGzdudPqHRlJSkoKCghz/EoqJiXE6RtaYrGPk5zWSn7kgb3fddZc2btyodevWOX7q1KmjLl26OP4/a35zqV+/frbLSe3YsUORkZGSpKioKJUoUcLpeT516pRWrFjhtOYnTpzQ6tWrHWO+/fZbZWZmql69eo4xy5YtU3p6umNMUlKSKleurMKFCzvGXOp1kZ+5IH/OnDkjd3fnRPDw8FBmZqYk1v2m5OpvteTHjBkzjI+Pj0lMTDRbtmwxjz76qAkJCXH6liKuvccff9wEBwebpUuXmgMHDjh+zpw54xjz2GOPmYiICPPtt9+aVatWmZiYGBMTE+PYn3UJkn//+99m3bp1Zv78+aZ48eI5XoLk2WefNVu3bjUTJkzI8RIkeb1G8poLLs/F3zI2hjW/2fz888/G09PTjBgxwvzyyy9m2rRpxt/f33z88ceOMa+88ooJCQkxX375pdmwYYNp165djpcfqVWrllmxYoX54YcfTMWKFZ0uP3LixAkTFhZmunbtajZt2mRmzJhh/P39s11+xNPT04wZM8Zs3brVDBkyJMfLj+Q1F+QtLi7OlCpVynHZmVmzZplixYqZgQMHOsaw7jeXGyIIjTFm/PjxJiIiwnh7e5vbb7/d/PTTT66ekvUk5fiTkJDgGHP27FnTu3dvU7hwYePv72/at29vDhw44HSc3bt3m5YtWxo/Pz9TrFgx079/f5Oenu40ZsmSJaZmzZrG29vblCtXzuk+suT1GsnPXFBwfw9C1vzmM3fuXFOtWjXj4+NjoqOjzeTJk532Z2ZmmhdffNGEhYUZHx8fc9ddd5nt27c7jTl69Kjp3LmzCQwMNEFBQaZbt24mNTXVacz69evNv/71L+Pj42NKlSplXnnllWxz+eyzz0ylSpWMt7e3qVq1qvn6668LPBfk7dSpU+app54yERERxtfX15QrV8688MILTpeHYd1vLm7GXHTZcQAAAFjnuv8MIQAAAK4ughAAAMByBCEAAIDlCEIAAADLEYQAAACWIwgBAAAsRxACAABYjiAEAACwHEEI4LqXmJiokJAQl86hYcOGmj59ukvnMH/+fNWsWdPx98kCwJVCEAK4qv744w89/vjjioiIkI+Pj0qUKKEWLVpo+fLlrp5avs2ZM0eHDh1Sp06dHNvc3Nw0e/bsbGPj4+N1zz33OH5PSUnRgw8+qPDwcPn6+qp06dJq166dtm3b5nSsrJ+AgABVrFhR8fHxWr16tdOx7777bnl5eWnatGlX/DECsBtBCOCquu+++7R27VpNnTpVO3bs0Jw5c9S4cWMdPXr0ms4jLS3tsm/71ltvqVu3bnJ3L9g/MtPT09W8eXOdPHlSs2bN0vbt2/Xpp5+qevXqOnHihNPYhIQEHThwQJs3b9aECRN0+vRp1atXTx9++KHTuPj4eL311luX/VgAIEeu/suUAdy8jh8/biSZpUuXXnLc2LFjTbVq1Yy/v78pXbq0efzxx01qaqpjf0JCggkODnb8vnPnTtO2bVsTGhpqAgICTJ06dUxSUpLTMSMjI83LL79sunbtagoVKmTi4uJMkyZNTJ8+fZzGHT582Hh5eZlFixblOLfDhw8bNzc3s2nTJqftkswXX3yRbXxcXJxp166dMcaYtWvXGklm9+7dl3z8uR3r4YcfNoUKFTLHjh1zbNuzZ4+RZHbu3HnJYwJAQXCGEMBVExgYqMDAQM2ePVvnz5/PdZy7u7veeustbd68WVOnTtW3336rgQMH5jr+9OnTatWqlRYvXqy1a9fq7rvvVps2bbR3716ncWPGjNGtt96qtWvX6sUXX1TPnj01ffp0p7l8/PHHKlWqlJo2bZrjff3www/y9/fXLbfcUsBHLxUvXlzu7u76/PPPlZGRUeDbP/PMM0pNTVVSUpJjW0REhMLCwvT9998X+HgAkBuCEMBV4+npqcTERE2dOlUhISGqX7++/vvf/2rDhg1O455++mk1adJEZcuWVdOmTTV8+HB99tlnuR731ltvVa9evVStWjVVrFhRw4YNU/ny5TVnzhyncU2bNlX//v1Vvnx5lS9fXvfee68k6csvv3SMSUxMVHx8vNzc3HK8rz179igsLKzAbxdLUqlSpfTWW29p8ODBKly4sJo2baphw4Zp165d+bp9dHS0JGn37t1O28PDw7Vnz54CzwcAckMQAriq7rvvPu3fv19z5szR3XffraVLl+q2225TYmKiY8yiRYt01113qVSpUipUqJC6du2qo0eP6syZMzke8/Tp0xowYIBuueUWhYSEKDAwUFu3bs12hrBOnTpOv/v6+qpr166aMmWKJGnNmjXatGmT4uPjc53/2bNn5evre3kPXlKfPn108OBBTZs2TTExMZo5c6aqVq3qdNYvN8YYScoWq35+frk+NwBwOQhCAFedr6+vmjdvrhdffFE//vij4uPjNWTIEEl/nf2KjY1VjRo19L///U+rV6/WhAkTJOX+RZABAwboiy++0MiRI/X9999r3bp1ql69erbxAQEB2W7bs2dPJSUl6ffff1dCQoKaNm2qyMjIXOderFgxHT9+PNv2QoUK6eTJk9m2nzhxQsHBwdnGtmnTRiNGjND69evVoEEDDR8+PNf7zLJ161ZJUlRUlNP2Y8eOqXjx4nneHgDyiyAEcM1VqVJFf/75pyRp9erVyszM1NixY3XHHXeoUqVK2r9//yVvv3z5csXHx6t9+/aqXr26SpQoke1t1dxUr15dderU0Xvvvafp06ere/fulxxfq1YtHTx4MFsUVq5cOdtlYTIyMrR+/XpVqlQp1+O5ubkpOjra8fgvZdy4cQoKClKzZs0c286dO6dff/1VtWrVyvP2AJBfnq6eAICb19GjR9WxY0d1795dNWrUUKFChbRq1SqNHj1a7dq1kyRVqFBB6enpGj9+vNq0aaPly5dr0qRJlzxuxYoVNWvWLLVp00Zubm568cUXC3Sx5p49e+qJJ55QQECA2rdvf8mxtWrVUrFixbR8+XLFxsY6tvfr1089evRQdHS0mjdvrj///FPjx4/X8ePH1bNnT0nSunXrNGTIEHXt2lVVqlSRt7e3vvvuO02ZMkX/+c9/nO7nxIkTOnjwoM6fP68dO3bo3Xff1ezZs/Xhhx86XZT7p59+ko+Pj2JiYvL9eAEgT67+mjOAm9e5c+fMc889Z2677TYTHBxs/P39TeXKlc2gQYPMmTNnHONef/11U7JkSePn52datGhhPvzwQyPJHD9+3BiT/bIzKSkppkmTJsbPz8+UKVPGvP3226ZRo0bmqaeecoyJjIw0b7zxRo7zSk1NNf7+/qZ37975ehwDBw40nTp1yrZ92rRppnbt2qZQoUImLCzMtGrVyqxfv96x/48//jBPPvmkqVatmgkMDDSFChUy1atXN2PGjDEZGRmOcZIcP76+vqZ8+fImLi7OrF69Ott9Pvroo6ZXr175mjcA5JebMf//U8sAYIndu3erfPnyWrlypW677bY8xx88eFBVq1bVmjVrLvl5w6vtyJEjqly5slatWpXtc4UA8E8QhACskZ6erqNHj2rAgAFKSUkp0F+fN3v2bBUtWlQNGjS4ijO8tFWrVunXX3/VAw884LI5ALg5EYQArLF06VI1adJElSpV0ueff67q1au7ekoAcF0gCAEAACzHZWcAAAAsRxACAABYjiAEAACwHEEIAABgOYIQAADAcgQhAACA5QhCAAAAyxGEAAAAlvt/Gi817XXmab8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the Box plot, we get to know that the distribution is a bit right skewed. There are potential outliers present here."
      ],
      "metadata": {
        "id": "sTSUF_BQIngx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'YearsCoding' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='YearsCoding', orient='h')\n",
        "plt.title('Horizontal Box Plot of YearsCoding')\n",
        "plt.xlabel('YearsCoding')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "wLIoCAqdDn9g",
        "outputId": "77625d33-8158-4f35-f351-4a9b7d3def26"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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zpTdluclPKki2efPmP7RfpadYsWKSpICAgExD+4cffqhGjRrpf//7n9fwY8eOeW4oy4ro6Gh17txZnTt31qlTp1S/fn0NGTJE3bp1U44cOXTTTTdp4cKF2r17twoVKpRhWVndT5KP2W3btnldVU1rG/4RcXFx2rp1a6rhaQ0D/k3owwpkg5tvvlmS9NJLL3kNT/76/mruPE4pISFBkyZN0vjx41WzZs1U4/Pnz68qVapo6tSpXqFxw4YNmjt3rqdeVys0NDTNEOrn55fq6tErr7xy3X9l6MKFC5o7d64CAwM9HwBuvvlmXbp0Sa+++qrXtGPHjpWPj49atmzpmbdTp06KjY3VuHHjNGXKFB04cECPPvroda1jSlezHZKDa1ZCfvXq1ZUnTx5NnDjR6/Fos2fP1qZNm655v8pInjx51LBhQ02aNEn79+9PNf63337z/D+t/eGDDz7weuRWZg4fPuz1OiwsTCVKlPBa38GDB8vM1KFDB88HmJRWrVqlqVOnSsr6fpL878svv+w13ZXH8B/VvHlzfffdd1qzZo1n2JEjR/T2229f1+UAfzdcYQWyQeXKlRUfH6/Jkyfr2LFjatCggX744QdNnTpVt912mxo1anTVZR46dEjdu3dXuXLlFBQUpOnTp3uNv/322xUaGqpRo0apZcuWql27trp27ep5nFJkZKTX76FfjRtuuEHz58/XmDFjFBsbq6JFi6pWrVpq1aqVpk2bpsjISJUrV07fffed5s+fn+HjnrJi9uzZnitgBw8e1DvvvKMtW7aof//+nj6MrVu3VqNGjTRw4EDt3LlTlStX1ty5c/Xpp5+qT58+nr6Iw4cP15o1a7RgwQKFh4erUqVKevrppzVo0CC1bdv2mkN8ZrK6HW644QZJ0sCBA3X33XcrICBArVu3TvNKeEBAgJ5//nl17txZDRo0UPv27T2PtSpSpMifFsLHjx+vG2+8URUrVtT999+vYsWK6cCBA/ruu++0Z88ez3NWW7Vq5bmSXadOHa1fv15vv/225yptVpQrV04NGzbUDTfcoOjoaK1cuVIffvihevbs6ZmmTp06Gj9+vLp3764yZcp4/dLV4sWL9dlnn2n48OGSsr6fVKlSRe3bt9eECRN0/Phx1alTRwsWLLjuVz6feOIJTZ8+XU2bNlWvXr08j7UqXLiwjhw5clVX3IF/lGx7PgHwD5LZI4IaNGjg9VgrM7MLFy7Y0KFDrWjRohYQEGCFChWyAQMGeD0ayOzy43DSe4xPysdaJT86Kb2/lI/DmT9/vtWtW9dCQkIsIiLCWrdubRs3bvQqO/mxVr/99lua65qyvJ9++snq169vISEhJslTp6NHj1rnzp0tV65cFhYWZs2bN7effvop1eO4/shjrYKDg61KlSr22muvWVJSktf0J0+etEcffdRiY2MtICDASpYsaaNGjfJMt2rVKvP397devXp5zXfx4kWrUaOGxcbG2tGjR9OtT3K9P/jggwzrndZjrcyyth3MzIYNG2YFChQwX1/fLD3a6L333rOqVataUFCQRUdH27333mt79uzxmuZaHmtllva+bGa2bds269ixo+XLl88CAgKsQIEC1qpVK/vwww8905w9e9b69u1r+fPnt5CQEKtbt65999131qBBA2vQoIFnuozadfjw4VazZk2LioqykJAQK1OmjI0YMcLOnz+fatpVq1bZPffc49n+OXPmtMaNG9vUqVO9Hq+V2X6S7MyZM9a7d2+LiYmx0NBQa926te3evTvLj7VK6zi+ct3NzFavXm316tWzoKAgK1iwoI0cOdJefvllk2S//vprqjKAfwMfM3p7AwDgsj59+mjSpEk6deoUP/uKfyX6sAIA4JCUPxMsXe63O23aNN14442EVfxr0YcVAACH1K5dWw0bNlTZsmV14MAB/e9//9OJEyfSfc4t8G9AYAUAwCE333yzPvzwQ02ePFk+Pj6qVq2a/ve//6l+/frZXTUg29CHFQAAAE6jDysAAACcRmAFAACA05zvw5qUlKR9+/YpPDycByYDAAA4yMx08uRJxcbGytf3+l8PdT6w7tu3L9PfggYAAED22717twoWLHjdy3U+sIaHh0u63ADJP7kIAAAAd5w4cUKFChXy5LbrzfnAmtwNICIigsAKAADgsD+r+yY3XQEAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOM0/uyuAv5cDBw7o+PHj2V0NAP9gkZGRyps3b3ZXA4BDCKzIsgMHDui+Dh114fy57K4KgH+wgMAgTZ/2FqEVgAeBFVl2/PhxXTh/TmeKNVBScGR2Vwd/A75njilkxxKdKVpfSSFR2V0d/A34nj0ubf9ax48fJ7AC8CCw4qolBUcqKTRXdlcDfyNJIVHsMwCAa8ZNVwAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKxpOHv2rH7++WedPXs2u6sCAABw1f5pWYbAmoZffvlFDzzwgH755ZfsrgoAAMBV+6dlGQIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwmn92V+BK586d07lz5zyvT5w4kW112bVrV7Yt20W0B4C/Cucb4I/5px1DzgXWkSNHaujQodldDUnSiBEjsrsKAPCvxPkXQErOBdYBAwboscce87w+ceKEChUqlC11GThwoOLi4rJl2S7atWsXbyIA/hKcf4E/5p/2nu1cYA0KClJQUFB2V0OSFBcXp1KlSmV3NQDgX4fzL4CUuOkKAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBNY0FC5cWJMnT1bhwoWzuyoAAABX7Z+WZfyzuwIuCg4OVqlSpbK7GgAAANfkn5ZluMIKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKf5Z3cF8Pfje/Z4dlcBfxO+Z455/QtkhvMLgLQQWJFlkZGRCggMkrZ/nd1Vwd9MyI4l2V0F/I0EBAYpMjIyu6sBwCEEVmRZ3rx5NX3aWzp+nCsgAP48kZGRyps3b3ZXA4BDCKy4Knnz5uWNBAAA/KW46QoAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGkEVgAAADiNwAoAAACnEVgBAADgNAIrAAAAnEZgBQAAgNMIrAAAAHAagRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpBFYAAAA4jcAKAAAApxFYAQAA4DQCKwAAAJxGYAUAAIDTCKwAAABwGoEVAAAATiOwAgAAwGn+2V2BzJiZJOnEiRPZXBMAAACkJTmnJee26835wHry5ElJUqFChbK5JgAAAMjIyZMnFRkZed3L9bE/KwpfJ0lJSdq3b5/Cw8Pl4+PzlyzzxIkTKlSokHbv3q2IiIi/ZJl/B7RL2miX9NE2aaNd0kfbpI12SR9tk7a/ul3MTCdPnlRsbKx8fa9/j1Pnr7D6+vqqYMGC2bLsiIgIdv400C5po13SR9ukjXZJH22TNtolfbRN2v7Kdvkzrqwm46YrAAAAOI3ACgAAAKcRWNMQFBSkwYMHKygoKLur4hTaJW20S/pom7TRLumjbdJGu6SPtknbP61dnL/pCgAAAP9uXGEFAACA0wisAAAAcBqBFQAAAE4jsAIAAMBpVxVYX3vtNVWqVMnzENratWtr9uzZXtOcPXtWPXr0UExMjMLCwvR///d/OnDgwHWt9L/d3r17dd999ykmJkYhISGqWLGiVq5cmeE8v/76qzp06KB8+fIpNDRU1apV00cfffQX1fj6O3nypPr06aO4uDiFhISoTp06WrFiRabzTZ48WQ0bNlRERIR8fHx07NixVNOMGDFCderUUY4cORQVFXX9K/8Xy8pxm5YiRYrIx8fH6++55577C2r81xo/fryKFCmi4OBg1apVSz/88EOG0+/cuVNdu3ZV0aJFFRISouLFi2vw4ME6f/78X1TjP2bJkiVq3bq1YmNj5ePjo08++STVNJ06dUq17Vu0aJFp2b1799YNN9ygoKAgValSJdX4IUOGpCrXx8dHoaGh12HN/ristM2pU6fUs2dPFSxYUCEhISpXrpwmTpyYYblZ3WfWrVunevXqKTg4WIUKFdILL7xwPVfvmmXWLhcuXNCTTz6pihUrKjQ0VLGxserYsaP27duXadmZ7TMpbd26VeHh4U6dl0eOHKkaNWooPDxcefLk0W233abNmzenOa2ZqWXLlunuWyktXrxYt956q/Lnz6/Q0FBVqVJFb7/9dqrpjh07ph49eih//vwKCgpSqVKlNGvWrOuxas65qsBasGBBPffcc1q1apVWrlypm266Sbfeeqt+/PFHzzSPPvqoPv/8c33wwQf6+uuvtW/fPt1xxx3XtdKXLl1SUlLSdS3zapiZLl68mC3LPnr0qOrWrauAgADNnj1bGzdu1OjRo5UzZ8505zl//rw6duyozZs367PPPtP69et1xx13qF27dlq9evVfWPvMZXXbduvWTfPmzdO0adO0fv16NWvWTE2aNNHevXsznO/06dNq0aKFnnrqqXSnOX/+vO688049/PDDV13/v1JWA1JWjtv0PPPMM9q/f7/nr1evXn+02tfdHwmK7733nh577DENHjxYiYmJqly5spo3b66DBw+mO89PP/2kpKQkTZo0ST/++KPGjh2riRMnZrhPZZe02ub3339X5cqVNX78+AznbdGihde2f/fdd7O0zC5duuiuu+5Kc1y/fv28yty/f7/KlSunO++8M0tl/9my0jaPPfaY5syZo+nTp2vTpk3q06ePevbsqc8++yzdebKyz5w4cULNmjVTXFycVq1apVGjRmnIkCGaPHnydV3Ha3Flu1y4cMFr/OnTp5WYmKj//ve/SkxM1Mcff6zNmzerTZs2WSo/o30m2YULF9S+fXvVq1fv2lbiT/L111+rR48e+v777zVr1ixduHBBzZo10++//55q2pdeeinLPzG/bNkyVapUSR999JHWrVunzp07q2PHjvriiy8805w/f15NmzbVzp079eGHH2rz5s16/fXXVaBAgeu2ftfLlfvMNbE/KGfOnPbGG2+YmdmxY8csICDAPvjgA8/4TZs2mST77rvv0i3jyJEj1qFDB4uKirKQkBBr0aKF/fzzz57xCQkJFhkZaZ9++qmVLVvW/Pz8bMeOHanKWbRokUmyOXPmWJUqVSw4ONgaNWpkBw4csFmzZlmZMmUsPDzc2rdvb7///rtnvrNnz1qvXr0sd+7cFhQUZHXr1rUffvghVbmzZs2yatWqWUBAgC1atMguXbpkzz77rBUpUsSCg4OtUqVKXut+paFDh1r58uVTDa9cubINGjTI8/r111+3MmXKWFBQkJUuXdrGjx/vGffkk09awYIFrWTJkhYSEmJFixa1QYMG2fnz5z3TDB482CpXrmyvv/66FSlSxHx8fCw0NNR69uxpFSpUsODgYIuOjjZ/f3979dVX06zr1KlTLTo62s6ePes1/NZbb7X77rvP8/qTTz6xqlWrWlBQkBUtWtSGDBliFy5c8IwfPXq0VahQwXLkyGEFCxa0hx9+2E6ePOkZn962XbRokdWoUcNy5MhhkZGRVqdOHdu5c6eZmZ0+fdr8/Pzsiy++8KpbtWrVbODAgem2f0rJ2/To0aPpTpNct8xcj+1qZvbEE09c9XY1M/vggw+8tmvjxo3t1KlTGdY55XGbnri4OBs7dmxmq+/RuXNnu+WWW7yGnT9/3nLnzu1ZVmbHzMWLF61Lly6e8aVKlbKXXnrJq8z4+Hi79dZbbfjw4ZY/f34rUqSImZmNHz/eSpQoYUFBQZYnTx77v//7v0zrXLNmTevRo4fn9aVLlyw2NtZGjhyZ5fU2M3vhhResaNGi6Y53tW0k2cyZM1PVN7mca5W8r2ZmzZo1JsmWLFmS5vikpCQrXry4jRo1ymv46tWrTZJt2bLFzMyOHj1qXbt2tVy5cll4eLg1atTI1qxZ45l+69at1qZNG8uTJ4+FhoZa9erVbd68eV5lxsXF2TPPPGMdOnSw8PBwk2Tvv/++9ejRw/Lly2dBQUFWuHBhy5Mnjz3zzDNe817NuSfZlfvMhAkTLGfOnHbu3DnPsCeffNJKly7tVNtIskaNGtm5c+dStc2zzz7rmeeHH34wSbZr164stUdm+8wTTzxh9913X6bn5ezcZ+Lj423Pnj0myaKjo73aZfXq1VagQAHbv39/usddZm6++Wbr3Lmz5/Vrr71mxYoV83qfyMipU6csPDw8VU6ZOXOm5ciRw06cOGFmZr/88ovdeeedFhkZaTlz5rQ2bdp4Za4ffvjBmjRpYjExMRYREWH169e3VatWeZUpySZMmGCtW7e2HDly2ODBg+3IkSN2zz33WK5cuSw4ONhKlChhb775ZpbX/5oD68WLF+3dd9+1wMBA+/HHH83MbMGCBWmGgMKFC9uYMWPSLatNmzZWtmxZW7Jkia1Zs8aaN29uJUqU8GyEhIQECwgIsDp16tjSpUvtp59+8gqcyZJDyH/+8x/79ttvLTEx0UqUKGENGjSwZs2aWWJioi1ZssRiYmLsueee88zXu3dvi42NtVmzZtmPP/5o8fHxljNnTjt8+LBXuZUqVbK5c+fa1q1b7fDhwzZ8+HArU6aMzZkzx7Zt22YJCQkWFBRkixcvTnM9d+/ebb6+vl5hODEx0Xx8fGzbtm1mZjZ9+nTLnz+/ffTRR7Z9+3b76KOPLDo62qZMmWJmZmXLlrXatWtbo0aNLCYmxooWLWrh4eH2/PPPe8ocPHiwhYaGWosWLSwxMdHWrl1r9evXNx8fHxs2bJht27bNnn/+eQsMDPQ6QFM6ffq0RUZG2vvvv+8ZduDAAfP397eFCxeamdmSJUssIiLCpkyZYtu2bbO5c+dakSJFbMiQIZ55xo4dawsXLrQdO3bYggULrHTp0vbwww97xqe1bY8fP26RkZHWr18/27p1q23cuNGmTJniOfGdOHHCJNn8+fO96ly3bl1r0KBBmutzpesZWK/HdjUzGzZsmC1dutR27Nhhn332meXNmzfT7bpv3z7z9/e3MWPG2I4dO2zdunU2fvx4rw8FKaV13KYnLi7O8ubNa9HR0ValShV74YUXvD6MXGnp0qXm5+dn+/bt8wz7+OOPLTQ01FOfzI6Z8+fP29NPP20rVqyw7du32/Tp0y1Hjhz23nvvecqMj4+3sLAw69Chg23YsME2bNhgK1asMD8/P3vnnXds586dlpiYaOPGjctw/c6dO2d+fn6p3jg6duxobdq0yXDeKw0cONBuuOGGv13bZBRYIyMjLXfu3FaqVCl76KGH7NChQ1luj6wG1p49e1qpUqUynGbEiBFWrlw5r2G9e/e2+vXre143adLEWrdubStWrLCff/7Z+vbtazExMZ5z+Jo1a2zixIm2fv16+/nnn23QoEEWHBzsFabi4uIsIiLCXnzxRdu6datJsvj4eCtUqJAtWbLEdu7cad988401atTIqlevbnv27LGkpCRbuHChhYWF2ddff53l9jFLvc906NAh1YeEhQsXmiQ7cuSIM20jyV577TUbNWpUqrZ55513PPPMmzfPfHx87Pjx41lqj4z2mQULFljRokXt+PHjWTovZ9c+s3XrVnvyySdNkk2ZMsXTLgkJCVa2bFn75JNPzCz94y4zdevWtb59+3pet2zZ0u699167//77LU+ePFa+fHkbMWKEXbx4Md0y7r//frv55pu9hrVp08Y6duxoZpfPM2XLlrUuXbrYunXrbOPGjXbPPfdY6dKlPR+mFixYYNOmTbNNmzbZxo0brWvXrpY3b15P4E1exzx58tibb75p27Zts127dlmPHj2sSpUqtmLFCtuxY4fNmzfPPvvssyyv/1UH1nXr1lloaKj5+flZZGSkffnll55xb7/9tgUGBqaap0aNGvbEE0+kWd7PP/9skmzp0qWeYYcOHbKQkBBPWEpISDBJ6YarZMkhJGWQGTlypEnyBAczswcffNCaN29uZpc/cQQEBNjbb7/tGX/+/HmLjY21F154wavc5J3N7PJV2Rw5ctiyZcu86tC1a1dr3759unVs2bKlV2Dr1auXNWzY0PO6ePHiXge92eUgU7t2bTMzCwoKsqCgIBswYIAlJibapEmTzN/f33MlxezygR8QEGAHDx70DFu8eLFJMknm7+9vERER9tVXX6VbTzOzhx9+2Fq2bOl5PXr0aCtWrJglJSWZmVnjxo29PlGbmU2bNs3y58+fbpkffPCBxcTEeF6ntW0PHz5sktIN/mZmtWvXtgYNGtjevXvt4sWLNm3aNPP19c30zS/Z9QysZn98u6Zl1KhRXm9oaW3XVatWmSTP1ef0ZHTcpmf06NG2aNEiW7t2rb322msWFRVljz76aIbzlCtXzitkt27d2jp16mRm137M9OjRw+tqaXx8vOXNm9frStRHH31kERERXifMzOzdu9ckparP448/bjVr1sxyOVu2bLGIiAibPHlyhtO52DbpvXG+++679umnn9q6dets5syZVrZsWatRo0aGb4QpZSWwnjlzxnLmzOnVJmnZu3ev+fn52fLly83s8vk5V65cng9733zzjUVERKT6Nqh48eI2adKkdMstX768vfLKK57XcXFxdtttt3leS7Kbb77ZbrrpJs85z+zyturYsaPnXBoYGGhTp07NcB2ulNY+07RpU3vggQe8pvvxxx9Nkm3cuDHNcrKjbZL3mV69eqVqm2RnzpyxatWq2T333JNRM3hJb585dOiQFSpUyPOBICvn5ezaZy5dumRFihSxyMhIr3Z54IEHrGvXrp7X1xJY33vvPQsMDLQNGzZ4hpUuXdqCgoKsS5cutnLlSpsxY4ZFR0d7XTS60vLly70+PCdfiEp+v502bZqVLl3aq/7nzp2zkJCQdDPDpUuXLDw83D7//HOvdezTp4/XdK1bt/a6Qny1rjqwnjt3zrZs2WIrV660/v37W65cuTxXaq4lsH766afm7++f6kRYpUoVGzp0qJld3kEDAwPTPDBSSg4hKd/Q33zzTcuRI4fXdE8//bRVrVrVzMzWrl2b5hv+bbfd5mnY5HL37NnjGb9hwwaTZKGhoV5/AQEBGb7ZffzxxxYVFWVnzpyxc+fOWUxMjL311ltmdjk8S7KQkBCvMpO/xjMzCwgIsJIlS1qdOnUsb968nhDi7+9vZpc/WQYEBHi6ASR/GuzRo4dFRERYSEiI55NlRESErVu3Lt26JiYmmp+fn2e9K1as6PVVWPJl/ZR1DQ4ONkmeK+Dz5s2zm266yWJjYy0sLCzV+PS2badOnSwoKMhatWplL730kteVKbPLX9fUr1/fJJmfn5/VqFHD7r33XitTpoynHVLW68qvpa53YP2j29XMbMaMGV7bNSgoyHLnzu0ZP3jwYCtRooTXci9evGiNGze28PBwa9u2rU2ePDnNqzEZHbcPPvigV73S87///c/8/f1TneRTGjNmjGcb/Prrr+bv7+/5ujerx8yrr75q1apVs1y5cnnG16hRwzM+Pj7emjRp4rXcEydOWMWKFS1Xrlx233332fTp09P8FialrATWzNpmz549Vrx4ca83o79T22T1jXPbtm1eFwNatGjhqeOVV7LMshZY33nnHfP397dff/010+W3adPGHnzwQTO7HMDDw8M96/Dqq6+ar69vqrbz9fX1vO+cPHnS+vbta2XKlLHIyEjP+Mcff9yzjLi4OBs+fLjntSR78cUXLTo62kqWLGm9evWyr776ykaNGmWlSpWyzz77zNauXWuvvPKKhYWFeb4uvtZ95loCa3a0TfI+s2rVqlRtY3Y5HLZu3dqqVq3qdXX1WveZ22+/3Z588knP66yel7Njn3nooYcsf/78FhUV5WmXIUOGWIkSJby+9bryuCtXrpynDi1atEi1LgsXLrQcOXKk+mBUsmRJK1SokFd+Gj16tOXLly/DtqlUqZKn29Po0aOtePHinvfgfv36mZ+fX6q28fHxsQkTJpjZ5fNXt27drESJEhYREeEZn7KbmySbPn2613JnzZplISEhVrlyZXv88ce9LlRmhf/V9nkNDAxUiRIlJEk33HCDVqxYoXHjxmnSpEnKly+fzp8/r2PHjnndxXfgwAHly5fvahflJSQkJMudlQMCAjz/9/Hx8XqdPOxabtpKeSfrqVOnJElffvllqg7OGf1ub+vWrRUUFKSZM2cqMDBQFy5cUNu2bb3KfP3111WrVi2v+fz8/CRJ0dHR2rp1q+Lj49W8eXNFRkbqscce05dffilJeuihh/Trr79q3rx5+vzzzxUbG6tt27Zp/PjxWr9+vY4fP665c+dq5syZOnPmjEaOHKl33nknzbpWrVpVlStX1ltvvaVmzZrpxx9/9Cwnub5Dhw5N86a64OBg7dy5U61atdLDDz+sESNGKDo6Wt9++626du2q8+fPK0eOHJLS3rYJCQnq3bu35syZo/fee0+DBg3SvHnz9J///EeSVLx4cX399df6/fffdeLECeXPn1933XWXihUr5mmHdu3aecqLjY1Nd5tcD390u3733Xe69957NXToUM92nTFjhkaPHu01/ZV3U/v5+WnevHlatmyZ5s6dq1deeUUDBw7U8uXLVbRoUc90GR23zzzzjPr165fpOtaqVUsXL17Uzp07Vbp06TSn6dixo/r376/vvvtOy5YtU9GiRT03SWTlmJkxY4b69eun0aNHq3bt2goPD9eoUaO0fPnyDNshPDxciYmJWrx4sebOnaunn35aQ4YM0YoVK9K9ozhXrlzy8/NL9RSTlOerjNpm3759atSokerUqZOlG2NcbJusKlasmHLlyqWtW7eqcePGeuONN3TmzBlJSnV+zao33nhDrVq1Ut68eTOdtlu3burQoYPGjh2rhIQE3XXXXZ7zx6lTp5Q/f34tXrw41XzJ275fv36aN2+eXnzxRZUoUUIhISFq27ZtqpvSrmy74sWLa8eOHZo9e7bmz5+vO++8UydPntTnn3+uW265RZJUqVIlrVmzRi+++KKaNGlyzftMvnz50twXk8e51jbVqlXzapt27drppptukplp165dWrhwoSIiIjzTX+s+s3DhQn322Wd68cUXJV2+6TkpKUn+/v6aPHmyunTp4kS79OzZU1988YWWLl2qmJgYT7s8++yzOn/+fKrz0P/93/+pXr16Wrx4sedmLeny+2FKX3/9tVq3bq2xY8eqY8eOXuPy58+vgIAAz/uIJJUtW1a//vqrzp8/r8DAwHTbZvz48erfv78SEhLUuXNnz3vwqVOndMMNN6T5RILcuXNLkuLj43X48GGNGzdOcXFxCgoKUu3atTPdZ1q2bKldu3Zp1qxZmjdvnho3bqwePXp4tm2mrirepqFRo0YWHx9vZv//TVcffvihZ/xPP/2U4U1XGXUJSO4YnNVPVGldNUtr3pSf5E6dOmWBgYGpugQUKFDA02k7rXJPnDhhQUFBnqtoV+OJJ56wpk2b2i233JLqE3VsbGyqDv0pVa1a1YKDg72GlS9f3vz8/NJcP7PLXwfrik/pFy9etKCgIPvPf/6TYV0nTJhgpUqVsh49elizZs28xtWpU8e6dOmS7rwffvihBQQE2KVLlzzDhg0b5tWWWd22//nPf6xXr17pjj9y5IhFRkZm+HVOStf7CqvZH9uuL774ohUrVsxrWNeuXb2Wn5WrVhcvXrQCBQrY6NGjM5wu5XGbVdOnTzdfX990+9Mla9eund1///1WoUIFry4jWTlmevbsaTfddJPXsMaNG3utd1ZuCDp16pT5+/vbRx99lOF0NWvWtJ49e3peX7p0yQoUKJDpTVd79uyxkiVL2t13353lr8nN3GsbZfEK6+7du83Hx8c+/fTTTKc1y3xf3b59u/n4+Hh9hZiRixcvWmxsrI0ePdr8/f29rorPnTs33Rtxk1WoUMHr+Dt58qRFRkbaI4884hl25U2GabVNcpul7Ddsdvkr36ZNm2a4DpntM8k3XaW8gWbAgAHp3nSV7K9um/T2mS+++MIkWZkyZby+5cyq9PaZjRs32vr16z1/w4cPt/DwcFu/fn2G56K/ql3GjBljPXr0sNjYWK+bxZO98847Jsm+/fZbzzpIsnHjxtn27dszbJNFixZZaGhoujdHDxgwwOLi4rzeY1966aUMu+WZXX6/DA4OtnHjxpmvr6/t3r3bM27y5MmWM2fODPseh4WFeZ2rfvnlF5OU6fFzpYkTJ1p4eHiG06R0VYG1f//+9vXXX3tu7ujfv7/5+PjY3LlzPdM89NBDVrhwYVu4cKGtXLnSateunWE/PbPLd56XK1fOvvnmG1uzZo21aNEi1U1Xf1ZgNTN75JFHLDY21mbPnu1101XywZBeuBk4cKDFxMTYlClTbOvWrbZq1Sp7+eWXvW6kScvPP/9sfn5+5ufnZ99//73XuNdff91CQkJs3LhxtnnzZlu3bp29+eabngCS3On9rrvusvnz53vu2gwJCUl3/c6fP28FCxa0IkWK2JQpU2zJkiWe+ZK7XaTn2LFjliNHDgsMDLQZM2Z4jZszZ475+/vbkCFDbMOGDbZx40Z79913PXfLJt8B/NJLL9m2bdvsrbfesgIFCmQaWLdv3279+/e3ZcuW2c6dO+2rr76ymJgYz9cRycuePXu2bd++3ebOnWuVK1e2WrVqZXq35P79+2316tX2+uuve+5OXr16taejvZnZrl27bPXq1TZ06FALCwuz1atX2+rVq9O9kSnZH9muyV1j3n33Xdu6dauNGzfOoqOjMw2s33//vY0YMcJWrFhhu3btsvfff98CAwNt1qxZnmmyctxeadmyZTZ27Fhbs2aNbdu2zaZPn265c+f2dMzPyNy5cy0wMND8/Pxs7969XuMyO2bGjRtnERERNmfOHNu8ebMNGjTIIiIiMg1ln3/+uY0bN85Wr15tO3futAkTJpivr69Xf6+0zJgxw4KCgmzKlCm2ceNGe+CBBywqKirDr6n37NljJUqUsMaNG9uePXts//79nr+/Q9uMGTPGfHx87MMPPzRJNmbMGFu9erWn28zJkyetX79+9t1339mOHTts/vz5Vq1aNStZsmSG3UHMLvfNXL16tT344INWqlQpz7GTsk+tmdmgQYMsNjb2qsL+U089ZYGBgVa2bFmv4UlJSXbjjTda5cqV7auvvrIdO3bY0qVL7amnnrIVK1aY2eWvlatUqWKrV6+2NWvWWOvWrS08PDxV+Hjuuec8dZZkbdq0sWeffdYWLFhgmzdvtq5du1pAQICVL1/eFi1aZNu3b7eEhAQLDg72Oj9dKSv7zLFjxyxv3ryeG+ZmzJhhOXLkyNKH8D+7bQoVKmT9+vXztMuYMWPsscces5dfftk2bdpkGzZssMKFC5uvr68lJiZ6rd+V2/5KWd1nkl3NhYS/Yp+pW7euRUZG2uLFi23//v02ePBgmzBhgq1evdqzz+TLl88rVGYlzCV3AxgwYIBXe6Z8r/rll18sPDzcevbsaZs3b7YvvvjC8uTJ49VNIT333HOPBQYGpuqC8Pvvv1vJkiWtYcOGtmTJEtu+fbstWrTIevXq5Qm2VatWtaZNm9rGjRvt+++/t3r16llISEimgfW///2vffLJJ7ZlyxbbsGGDtWrV6qruF7iqwNqlSxeLi4uzwMBAy507tzVu3DjVm96ZM2ese/fuljNnTsuRI4fdfvvtmZ7Ikx9rFRkZaSEhIda8efM0H2uVmWsNrGfOnLFevXpZrly5Mnys1ZWBNSkpyV566SUrXbq0BQQEWO7cua158+ZZulu0Xr16aT4KyexyX+AqVapYYGCg5cyZ0+rXr28ff/yxZ/wdd9xhfn5+JsnCw8OtXbt2mQabWbNmWZ48eczHx8ckWWBgoHXo0CHTeppdvns1rUdcmV0OjnXq1LGQkBCLiIiwmjVret1IMGbMGMufP79nu7711luZBtZff/3VbrvtNsufP78FBgZaXFycPf30014H/HvvvWfFihWzwMBAy5cvn/Xo0cOOHTuW6boMHjzYc/NZyr+EhATPNPHx8WlOs2jRokzL/yPb9fHHH7eYmBgLCwuzu+66y8aOHZvpdt24caM1b97c80i2UqVKed0UYJa14/ZKq1atslq1allkZKQFBwdb2bJl7dlnn800sJhdPi7i4uJS3YmaPC6jY+bs2bPWqVMni4yMtKioKHv44Yetf//+mYayb775xho0aGA5c+a0kJAQq1SpUqqrYOl55ZVXrHDhwhYYGGg1a9ZM9WHjSsk3Cqb1lxkX2qZYsWJp1j35ivvp06etWbNmljt3bgsICLC4uDi7//77s9TXtEGDBmmWnfJK1qVLl6xgwYL21FNPZVpeSsn9aJNvhk3pxIkT1qtXL4uNjbWAgAArVKiQ3XvvvfbLL7+YmdmOHTusUaNGFhISYoUKFbJXX33VGjRokCp89OjRI836J9+omnzsdOrUyWJjYy04ONhKly5to0ePzvAei6zuM2vXrrUbb7zRgoKCrECBAl5Ps8nOtsmbN2+adc+ZM6eFhoZaWFhYuuuX2XkzK/vMlW2Z1cD6V+wz6a13YGCgZ59JTEz0WnZWAmt670NXPgln2bJlVqtWLQsKCrJixYpl+pSAZMlPdUr5JKBk+/fvt44dO3oyUbFixez+++/3XHVNTEy06tWrW3BwsJUsWdI++OCDLH1DMWzYMCtbtqyFhIRYdHS03XrrrZleZU7J5/8rGH8hM1PJkiXVvXt3PfbYY9ldnUw1btxY5cuX18svv5zdVXHa3227/llOnTqlAgUKKCEh4br/aMjfHW1z7b755hs1btxYu3fvzlK/138T2iZttEv6pk2bpkcffVT79u1Lt6+ra676piv8Mb/99ptmzJihX3/9VZ07d87u6mTo6NGjWrx4sRYvXqwJEyZkd3Wc9nfarn+WpKQkHTp0SKNHj1ZUVFSWf+Xm34C2uXbnzp3Tb7/9piFDhujOO+8keKRA26SNdknf6dOntX//fj333HN68MEH/zZhVdIfv+kKV0eS5cqVy+smL1clPxT5yl8MQWp/p+36Z9mxY4dJsoIFC6b6UYd/O9rm2iUkJJivr69Vq1bN69GCoG3SQ7ukb/Dgwebv72833XRTpvdluIYuAQAAAHCab3ZXAAAAAMgIgRUAAABOI7ACAADAaQRWAAAAOI3ACgAAAKcRWAHgb87Hx0effPKJJGnnzp3y8fHRmjVrsrVOAHA9EVgB/KOYmZo0aaLmzZunGjdhwgRFRUVpz5492VAz6aOPPlLDhg0VGRmpsLAwVapUSc8884yOHDly3ZZRqFAh7d+/XxUqVLhuZQJAdiOwAvhH8fHxUUJCgpYvX65JkyZ5hu/YsUNPPPGEXnnlFRUsWPC6LvPChQuZTjNw4EDdddddqlGjhmbPnq0NGzZo9OjRWrt2raZNm3bd6uLn56d8+fLJ358fMgTwz0FgBfCPU6hQIY0bN079+vXTjh07ZGbq2rWrmjVrpqpVq6ply5YKCwtT3rx51aFDBx06dMgz75w5c3TjjTcqKipKMTExatWqlbZt2+YZn/yV+3vvvacGDRooODhYb7/9tnbt2qXWrVsrZ86cCg0NVfny5TVr1ixJ0g8//KBnn31Wo0eP1qhRo1SnTh0VKVJETZs21UcffaT4+HhP+a+99pqKFy+uwMBAlS5dOlWY3bJli+rXr6/g4GCVK1dO8+bN8xp/ZZeAxYsXy8fHRwsWLFD16tWVI0cO1alTR5s3b/aab/jw4cqTJ4/Cw8PVrVs39e/fX1WqVLkemwMA/jACK4B/pPj4eDVu3FhdunTRq6++qg0bNmjSpEm66aabVLVqVa1cuVJz5szRgQMH1K5dO898v//+ux577DGtXLlSCxYskK+vr26//XYlJSV5ld+/f3898sgj2rRpk5o3b64ePXro3LlzWrJkidavX6/nn39eYWFhkqS3335bYWFh6t69e5p1jYqKkiTNnDlTjzzyiPr27asNGzbowQcfVOfOnbVo0SJJUlJSku644w4FBgZq+fLlmjhxop588skstcfAgQM1evRorVy5Uv7+/urSpYtn3Ntvv60RI0bo+eef16pVq1S4cGG99tprWW5rAPjTZe8vwwLAn+fAgQOWK1cu8/X1tZkzZ9qwYcOsWbNmXtPs3r3bJNnmzZvTLOO3334zSbZ+/XozM9uxY4dJspdeeslruooVK9qQIUPSLKNly5ZWqVKlTOtbp04du//++72G3XnnnXbzzTebmdlXX31l/v7+tnfvXs/42bNnmySbOXOmV/1Wr15tZmaLFi0ySTZ//nzPPF9++aVJsjNnzpiZWa1ataxHjx5ey61bt65Vrlw50zoDwF+BK6wA/rHy5MmjBx98UGXLltVtt92mtWvXatGiRQoLC/P8lSlTRpI8X/tv2bJF7du3V7FixRQREaEiRYpIkn755RevsqtXr+71unfv3ho+fLjq1q2rwYMHa926dZ5xZpal+m7atEl169b1Gla3bl1t2rTJM75QoUKKjY31jK9du3aWyq5UqZLn//nz55ckHTx4UJK0efNm1axZ02v6K18DQHYisAL4R/P39/fcgHTq1Cm1bt1aa9as8fpL7hcqSa1bt9aRI0f0+uuva/ny5Vq+fLkk6fz5817lhoaGer3u1q2btm/frg4dOmj9+vWqXr26XnnlFUlSqVKltH379izdnPVnCQgI8Pzfx8dHklJ1cwAAVxFYAfxrVKtWTT/++KOKFCmiEiVKeP2Fhobq8OHD2rx5swYNGqTGjRurbNmyOnr0aJbLL1SokB566CF9/PHH6tu3r15//XVJ0j333KNTp05pwoQJac537NgxSVLZsmW1dOlSr3FLly5VuXLlPON3796t/fv3e8Z///33V9MEaSpdurRWrFjhNezK1wCQnXjuCYB/jR49euj1119X+/bt9cQTTyg6Olpbt27VjBkz9MYbbyhnzpyKiYnR5MmTlT9/fv3yyy/q379/lsru06ePWrZsqVKlSuno0aNatGiRypYtK0mqVauWnnjiCfXt21d79+7V7bffrtjYWG3dulUTJ07UjTfeqEceeUSPP/642rVrp6pVq6pJkyb6/PPP9fHHH2v+/PmSpCZNmqhUqVKKj4/XqFGjdOLECQ0cOPAPt0uvXr10//33q3r16qpTp47ee+89rVu3TsWKFfvDZQPA9cAVVgD/GrGxsVq6dKkuXbqkZs2aqWLFiurTp4+ioqLk6+srX19fzZgxQ6tWrVKFChX06KOPatSoUVkq+9KlS+rRo4fKli2rFi1aqFSpUl5XVJ9//nm98847Wr58uZo3b67y5cvrscceU6VKlTyPtbrttts0btw4vfjiiypfvrwmTZqkhIQENWzYUJLk6+urmTNn6syZM6pZs6a6deumESNG/OF2uffeezVgwAD169dP1apV044dO9SpUycFBwf/4bIB4HrwsazeDQAA+Ndo2rSp8uXLd11/1AAArhVdAgDgX+706dOaOHGimjdvLj8/P7377ruaP39+qh8lAIDswhVWAPiXO3PmjFq3bq3Vq1fr7NmzKl26tAYNGqQ77rgju6sGAJIIrAAAAHAcN10BAADAaQRWAAAAOI3ACgAAAKcRWAEAAOA0AisAAACcRmAFAACA0wisAAAAcBqBFQAAAE77f979uxm9o7E4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the Box plot, we get to know that the distribution is symmetric and positively skewed. There are no potential outliers present here."
      ],
      "metadata": {
        "id": "gKCf7egFNmLC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'YearsCodingProf' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='YearsCodingProf', orient='h')\n",
        "plt.title('Horizontal Box Plot of YearsCodingProf')\n",
        "plt.xlabel('YearsCodingProf')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "k5nQiwb5NfEj",
        "outputId": "d91278eb-29c8-4840-db0b-69ddb1015eaa"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the Box plot, we get to know that the distribution is not symmetric and right skewed. There are no potential outliers present here."
      ],
      "metadata": {
        "id": "-PSi4q3ROTuL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'JobSatisfaction' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='JobSatisfaction', orient='h')\n",
        "plt.title('Horizontal Box Plot of JobSatisfaction')\n",
        "plt.xlabel('JobSatisfaction')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "qHIiS9-oNjPM",
        "outputId": "aa3cc2ed-ce57-4042-9138-98dcbed3f0ab"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the Box plot, we get to know that the distribution is not symmetric and right skewed. There are no potential outliers present here."
      ],
      "metadata": {
        "id": "NI9wmMlRPLc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'age' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='Age', orient='h')\n",
        "plt.title('Horizontal Box Plot of Age')\n",
        "plt.xlabel('Age')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "w-MtFFxDOv43",
        "outputId": "12f053a8-3993-4614-adff-c2e3a1060c15"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the Box plot, we get to know that the distribution is symmetric and positively skewed. There are no potential outliers present here."
      ],
      "metadata": {
        "id": "kSbxaJkzPjU8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'LanguageWorkedWith' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='LanguageWorkedWith', orient='h')\n",
        "plt.title('Horizontal Box Plot of LanguageWorkedWith')\n",
        "plt.xlabel('LanguageWorkedWith')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "VGpsXmZkPhlr",
        "outputId": "4a85447f-801d-43e8-def2-737b1d2782f6"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the Box plot, we get to know that the distribution is symmetric and It is not skewed as well. There are no potential outliers present here."
      ],
      "metadata": {
        "id": "_rGYHfgrQHPj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'OperatingSystem' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='OperatingSystem', orient='h')\n",
        "plt.title('Horizontal Box Plot of OperatingSystem')\n",
        "plt.xlabel('OperatingSystem')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "_2M8dVB1QEgC",
        "outputId": "abdc6fab-8731-4178-911d-3e2d0f874ed3"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the Box plot, we get to know that the distribution is symmetric and It is not skewed as well. There are no potential outliers present here."
      ],
      "metadata": {
        "id": "BY3EFIR8Q70r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers detected for 'DevType ' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=df, x='DevType', orient='h')\n",
        "plt.title('Horizontal Box Plot of DevType ')\n",
        "plt.xlabel('DevType ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "u2d8P2YSQlFv",
        "outputId": "91537098-3852-40d3-dca2-94e9a73051be"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From thi Box plot, We get to know that there are potential outliers."
      ],
      "metadata": {
        "id": "bE9Cf5jmRwLh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*From all the Box plots we did, we can conclude that there are outlier in the DevType and SalaryUSD columns which are to be handled*"
      ],
      "metadata": {
        "id": "L4aBy3o_R6FO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dN_q00K1RP_g"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}