backlog/Stack_Overflow2018 -data visualization.ipynb

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      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
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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": 1,
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Initial exploration*"
      ],
      "metadata": {
        "id": "Rm63VMBVA2rm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#First few rows of the data\n",
        "df.head(10)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NeeuOsj4AzIs",
        "outputId": "0c27b1a1-833a-44d5-a6bc-2602ea42a047"
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      "execution_count": 3,
      "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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              "    <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",
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              "      <th>SexualOrientation</th>\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",
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              "      <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",
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              "    <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",
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              "      <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",
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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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-22f18b71-8606-470a-80a9-74a3a47ae0a8 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-22f18b71-8606-470a-80a9-74a3a47ae0a8');\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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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "  </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",
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              "  .colab-df-quickchart {\n",
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              "    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-78c9332a-f9fc-462f-9cd2-6c2d7a4bacdd 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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dimensions of the data\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pk727hn8BAtt",
        "outputId": "7d1a3ab3-1acc-40e3-b123-c6b15850aa50"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 129)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#datatypes of the data\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJa9xMhaBN0h",
        "outputId": "322aca6c-4c1a-4935-9595-1e85bf183dd2"
      },
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYgb3H0fBXSM",
        "outputId": "84d19026-ad76-49a4-ae6d-46a96ec31a7e"
      },
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "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": "80eafa8c-2af3-4f1d-b7b3-31c5e2785346"
      },
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "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": 8,
      "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": "c2568f5b-da6a-442e-cb3d-a741b55cb816"
      },
      "execution_count": 9,
      "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": "f79b377a-f690-461e-c876-910e2a474dd1"
      },
      "execution_count": 10,
      "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": "36292ed7-3052-4f00-93a7-9f0c17f07c40"
      },
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "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": "35beca82-bf59-491b-a6ac-aa3889500215"
      },
      "execution_count": 12,
      "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": 12
        }
      ]
    },
    {
      "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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Gender')['Gender'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyI5oGQJHPVt",
        "outputId": "7867b364-187a-474b-b08a-cd618aba8276"
      },
      "execution_count": 14,
      "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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wIFGzW0HcDS",
        "outputId": "63317d86-2da2-47dd-ee8b-e0625b5b648d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 129)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()['Gender']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEYOPjlNHw-I",
        "outputId": "83fd8fb9-3b66-4296-e2f3-0a7dc2d89570"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "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": "4bd21ca1-566a-41db-af88-443a3c8ad737"
      },
      "execution_count": 17,
      "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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Country'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ds9Hf2hgIn8d",
        "outputId": "dd01c362-8056-4ebe-daa2-787897a44a88"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling null values\n",
        "df['Country'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "8XoIM4LoIreR"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Country'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvZcOHoAIv79",
        "outputId": "ea920e48-f5c4-4dc3-cdfb-590beb2c929b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "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": "58e97260-e0ee-4561-d64b-095099435c2d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Hobby')['Hobby'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-Z7I4JrJhRo",
        "outputId": "e84ceb6c-dacf-419a-8990-3d1c64614845"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hobby\n",
              "No     20\n",
              "Yes    79\n",
              "Name: Hobby, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "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": "af73cc1c-6281-46c3-ec88-7426474b3354"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "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": "aea25cab-be9a-42d5-c10b-435053089780"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_jhcUTzKdKE",
        "outputId": "7a35514d-908e-4e45-85d7-c68438b34f25"
      },
      "execution_count": 25,
      "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": 25
        }
      ]
    },
    {
      "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": 26,
      "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": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].replace('nan', 'No major', inplace=True)"
      ],
      "metadata": {
        "id": "nHySDU3cM66h"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDnUZGWMNJDA",
        "outputId": "4912b3eb-c9b0-4267-e197-b60907fe16ae"
      },
      "execution_count": 29,
      "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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7thqzYMnNPDu",
        "outputId": "26d58001-7e42-4305-f5b8-0decf6f9160c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['UndergradMajor'], inplace=True)"
      ],
      "metadata": {
        "id": "3VIx65RZNlNY"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['UndergradMajor'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DYmuQj-Nnoi",
        "outputId": "3c54d29b-4037-490e-85e9-7b425bf8cc8c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "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": "d8185d82-ef67-4331-95cd-fd2972383543"
      },
      "execution_count": 33,
      "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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping null values\n",
        "df.dropna(subset=['JobSearchStatus'], inplace=True)"
      ],
      "metadata": {
        "id": "vszoQOfKNymE"
      },
      "execution_count": 34,
      "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": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = refactor_job(df)"
      ],
      "metadata": {
        "id": "w6eyQW0KlfFA"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSearchStatus'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tjcirO5lhYz",
        "outputId": "ffa8281c-1366-433a-9127-2552d24a2bb5"
      },
      "execution_count": 37,
      "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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSearchStatus'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZhqXlToljTS",
        "outputId": "8b285c0e-3029-4cc8-d512-1db91e42d91d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "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": "16a8b626-e528-4b1d-b119-9c8d10f70285"
      },
      "execution_count": 39,
      "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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dB_IwQPmHfa",
        "outputId": "32b8763f-354a-41bd-95fc-7ab79b690cd1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "e8soU9WymLEm"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tc7hG1v4mULw",
        "outputId": "d88bbfd2-483f-4c41-ec74-ff2ff5c66259"
      },
      "execution_count": 42,
      "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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Employment'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4dwu9zemeg2",
        "outputId": "2194127d-59d1-4fa6-ceb0-c9cad62bbf56"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "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": "ccc182ac-8273-49e2-d6ec-43dd4d335cac"
      },
      "execution_count": 44,
      "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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSatisfaction'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDnUpweIm66o",
        "outputId": "fc5c9276-6dc2-45d4-c227-2141e6466c92"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling the null values\n",
        "df['JobSatisfaction'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "eMrI20RNm_Am"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['JobSatisfaction'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0Qg8298nCCC",
        "outputId": "588d2619-ee71-49fe-9ce3-0cd1582ab0ef"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "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": "30609032-2dd0-4c55-fe73-cad5fc3462bf"
      },
      "execution_count": 48,
      "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": 48
        }
      ]
    },
    {
      "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": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('RaceEthnicity')['RaceEthnicity'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJUNzLQJomx-",
        "outputId": "b0ffbb21-8be2-4572-a152-950320536b3d"
      },
      "execution_count": 50,
      "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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['RaceEthnicity'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIwYiTOSorvU",
        "outputId": "02f63317-b6cc-4bf1-c968-2327bc51eda2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "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": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['RaceEthnicity'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_q1T5xRo9H3",
        "outputId": "fce4981b-68b5-4316-8777-ed02e20b78b5"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "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": "7af9275d-7fcb-4ece-9aee-0c9983880e83"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['DevType'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "SYNuGwm-p-rh"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('DevType')['DevType'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OQ3ubLwqA8k",
        "outputId": "8f0ceec0-68ae-4c8a-c973-c0e64cc29eb1"
      },
      "execution_count": 56,
      "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": 56
        }
      ]
    },
    {
      "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": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('DevType')['DevType'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Uq3rID4rLYy",
        "outputId": "354fb59f-c923-4e9e-c434-313dcd06cc6e"
      },
      "execution_count": 58,
      "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": 58
        }
      ]
    },
    {
      "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": "5f0fcb4e-dd3d-42f6-8e71-9a905f128355"
      },
      "execution_count": 59,
      "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": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageWorkedWith'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bzonouh4rnpz",
        "outputId": "be8feeb8-c84a-4ec3-8a23-e39cf6ab6177"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values\n",
        "df['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "AnfM5PJJsEow"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageWorkedWith'].value_counts().nlargest(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGdafg4HsG7-",
        "outputId": "2434b27f-52e5-4a4d-89d8-02d51598d6ca"
      },
      "execution_count": 62,
      "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": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageWorkedWith'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKmOpf2TsP6n",
        "outputId": "20a04ff2-73b8-4f18-d89f-5679c562d1c5"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "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": "b222e4ad-c4d8-4ac6-a999-95332f2a43b7"
      },
      "execution_count": 64,
      "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": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageDesireNextYear'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUG2NWJ8s4H8",
        "outputId": "f8859cad-7ee5-4c93-9026-bf498add649e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values\n",
        "df['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "y2uYLmpxtFcn"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLb6YvEBtMSf",
        "outputId": "b8ba6f0d-a05a-4a69-b491-8352c07c6eb4"
      },
      "execution_count": 67,
      "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": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['LanguageDesireNextYear'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wEXX5jztUPN",
        "outputId": "00cefcfe-08ce-48b2-f366-1d1b8085c78b"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "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": "bf5654ce-3137-4551-e664-2e4f82be3db1"
      },
      "execution_count": 69,
      "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": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hptn0b6Ut3kL",
        "outputId": "9fa2cd48-468d-4abf-fe73-69ea05e4fe81"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "xI1uiR84t-AE"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOeODKlBuI6-",
        "outputId": "48115312-5b7c-4be2-a6af-db612b97fa3a"
      },
      "execution_count": 72,
      "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": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCodingProf'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGiq0aEouASu",
        "outputId": "dfc315c0-2891-4486-dd01-95abf0c6cd13"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "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": "90b6cc80-7a1c-45ae-d87c-ecb1a6b0a109"
      },
      "execution_count": 74,
      "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": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCoding'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5hqJopHuWkK",
        "outputId": "956ef4f9-afe9-457e-8b1f-cd33800d7553"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values\n",
        "df['YearsCoding'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "t_c4jz26ug5q"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCoding'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtrovjDBuxcb",
        "outputId": "019832e8-8b76-415d-e0f0-fb2af84cc26d"
      },
      "execution_count": 77,
      "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": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['YearsCoding'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZB_36JfBukFp",
        "outputId": "d439b466-232a-4f16-83e7-4f35615fbb5d"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "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": "6225b145-b3e6-440e-fe07-70ad65c5b7df"
      },
      "execution_count": 79,
      "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": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['OperatingSystem'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfIBSbjNu-6f",
        "outputId": "7ee3d9bf-8da6-492c-cb31-97b7b689dd89"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling values\n",
        "df['OperatingSystem'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "HEz3Vxr9vPcu"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['OperatingSystem'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeSMf-SIvTwZ",
        "outputId": "db8fb35a-b067-452b-cd74-036addabd605"
      },
      "execution_count": 82,
      "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": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['OperatingSystem'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FofzEDQkvWll",
        "outputId": "f2d79166-2059-4601-d443-930d2dc0cd15"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "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": "a8c0292e-b738-46bd-f2d0-cc608e7f2b7d"
      },
      "execution_count": 84,
      "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": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5qYCnc2wn4T",
        "outputId": "f1d0acc7-885a-4090-c27f-c41baabda4f9"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "-x9tHjLQwq5t"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnB1-anYws9D",
        "outputId": "23c2678a-7000-45e4-a366-b0abfcad2bd6"
      },
      "execution_count": 87,
      "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": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryType'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_EK-CI5wvUc",
        "outputId": "1c5ba00c-fdf6-4d5e-edf1-d19f044ece2d"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "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": "817fcf0b-cb29-43c0-c3fd-cf1cd4fee3e5"
      },
      "execution_count": 89,
      "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": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CFcuvPTWxIO_",
        "outputId": "1ffb5029-f304-447c-8a85-ccbdaa617380"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "tRct3FX2xLlp"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['Currency'], inplace = True)"
      ],
      "metadata": {
        "id": "siI0zCLQxN2E"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slvL5FMsxWCx",
        "outputId": "9022e5fd-c5e1-4ba3-b4d0-b455adbece87"
      },
      "execution_count": 93,
      "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": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Currency'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5A2t9JscxYgE",
        "outputId": "0bcaca21-a9cd-475c-c5b3-85528d3d82d6"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "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": "646d7d92-53c6-4442-f902-f434d1b7bd5c"
      },
      "execution_count": 95,
      "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-6f71d186-77b3-4cfa-8a60-2e40efb7a8a7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <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-6f71d186-77b3-4cfa-8a60-2e40efb7a8a7')\"\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-6f71d186-77b3-4cfa-8a60-2e40efb7a8a7 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-6f71d186-77b3-4cfa-8a60-2e40efb7a8a7');\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-651891e0-9d20-412c-89bf-078107abb509\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-651891e0-9d20-412c-89bf-078107abb509')\"\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",
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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",
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              "      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-651891e0-9d20-412c-89bf-078107abb509 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": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#top 10 salaries\n",
        "df['SalaryUSD'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOeLpbNu5xYD",
        "outputId": "c1a29e62-0a8c-4068-b3fa-fda857c34697"
      },
      "execution_count": 96,
      "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": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalaryUSD'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViidUYaW62ha",
        "outputId": "0f5717e4-893d-49d6-ad45-4b8a9f7fdc89"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "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": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_salary.nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B14qBmwx7Mm2",
        "outputId": "c514d4b8-f7ac-4edc-dad6-8e086b9db26a"
      },
      "execution_count": 99,
      "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": 99
        }
      ]
    },
    {
      "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": 100,
      "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": 101,
      "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": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_salary.nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNWX4Zok8MRy",
        "outputId": "b54ddfbd-9240-46f3-b365-66bdbcce76f4"
      },
      "execution_count": 103,
      "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": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['SalaryUSD'], inplace = True)"
      ],
      "metadata": {
        "id": "RV4purXk8YwP"
      },
      "execution_count": 104,
      "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": "a3674f46-9fe8-4234-a68d-5d3215ce65ef"
      },
      "execution_count": 105,
      "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": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGAuNMgn8muK",
        "outputId": "b7b96e48-baa5-4f41-fc4e-eefcb9b53bef"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].fillna(method='ffill', inplace=True)"
      ],
      "metadata": {
        "id": "olP6Db5J8pbD"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].value_counts().nlargest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bk5V6oIM8u3z",
        "outputId": "2a2555d6-c98e-467e-d87c-df4647df60d6"
      },
      "execution_count": 108,
      "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": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRa2gNv68riy",
        "outputId": "f553e605-ea9f-48b6-9894-29e338ff0445"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "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": "312a5f47-2067-440d-b204-7840d11bf250"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['FormalEducation'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngTqWgm09HYP",
        "outputId": "7794d46e-4d13-49ec-ca35-149ed2cc4b94"
      },
      "execution_count": 111,
      "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": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Changing column's name for our convinienccce\n",
        "df.rename(columns={'FormalEducation':'EdLevel'}, inplace =True)"
      ],
      "metadata": {
        "id": "3eoaxYJb9Jo0"
      },
      "execution_count": 112,
      "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": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = refactor_ed(df)"
      ],
      "metadata": {
        "id": "NewXTlW79TrK"
      },
      "execution_count": 114,
      "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": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['EdLevel'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTnp5YHk9rjH",
        "outputId": "20495f40-3cea-4191-e171-36e2fb427b76"
      },
      "execution_count": 116,
      "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": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['EdLevel'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pifoZPD19z-2",
        "outputId": "2db8caa3-bcdd-46da-a348-f99b04224726"
      },
      "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 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": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_2018.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 933
        },
        "id": "uK6hUeN_9xsD",
        "outputId": "78c5b40c-7491-4f04-e0cc-2ec7bd1d0923"
      },
      "execution_count": 119,
      "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-63129dc6-7e8a-458c-86ee-ed8192db28c3\" 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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              "<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-63129dc6-7e8a-458c-86ee-ed8192db28c3')\"\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-63129dc6-7e8a-458c-86ee-ed8192db28c3 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-63129dc6-7e8a-458c-86ee-ed8192db28c3');\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-b68de340-3557-4020-ba27-f8613a9e8187\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b68de340-3557-4020-ba27-f8613a9e8187')\"\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",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-b68de340-3557-4020-ba27-f8613a9e8187 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": 119
        }
      ]
    },
    {
      "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": 120,
      "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": "da84d3d5-d025-44eb-c590-f4c52f78b531"
      },
      "execution_count": 121,
      "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": 122,
      "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": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outliers_indices_iqr = detect_outliers_iqr(df,cols )"
      ],
      "metadata": {
        "id": "w8cRvgaTAQR1"
      },
      "execution_count": 124,
      "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": "b8acd765-9f35-427f-ab62-d674f3b742f1"
      },
      "execution_count": 125,
      "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": "5f4955c6-958a-4e41-a40e-118aad5c2b03"
      },
      "execution_count": 126,
      "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 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": "9ddf83e8-50b5-4115-da7e-ffdbe1293c1d"
      },
      "execution_count": 127,
      "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": "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": "c85afd3b-27d1-4c97-e9c8-ed0036208294"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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N1+3du1eFCxdWcnKyRowYodGjRyspKcnn/Ne0TjNIa7t/+eWXdffdd6tUqVIqX768br/9drVp08b7Yy5lmzx69GiW3lNWt5OU7+ylpxikdxpLWtI6lJ8rVy798ccfPvWpUaNGquUu7StSFC1aVO+++648Ho9CQkJUsmRJ5c+fP9Vyl7blvn37dOLEiXS3weTkZO3YscN7SB+4HhA08beT1TBz6ahORs6cOaOWLVsqODhY//73vzMMPFmR3jmKdsmFEmnp3r27xo8frx49eqhGjRremz0/+OCD2XqfPz8/P9WvX18jRozQpk2brmjnOHPmTEkXrm7etGnTZYX7ChUqZHiLoOtZyufYq1evdEfdU0LSK6+8on/+85/q2LGjBgwYoNy5c8vPz089evRIc3tIa7uvU6eOtmzZoq+++kqzZs3Se++9p2HDhmnMmDHq3LmzSpQooYCAAK1evTob32X2uJrvUnrCw8OztO1dTh8CXK8ImvjbiI+PV3JysjZt2uQdOZGkPXv26NChQ96rPK/Ek08+qRUrVmjBggXeUaeL1ytduLjoUr/88ovy5s172aNoUvqB+bPPPlO7du00dOhQ77RTp045+Qs0586dkyQdO3ZM0oX3OmfOHB09etRntOqXX37xzk+xatUqvfzyy+rQoYNWrFihzp07a/Xq1YqOjs72el687qx8DpczsnpxuSlXvqfYsGHDVW1X6Uk57BoYGJhp4Pnss89Uv359vf/++z7TDx065L3QKSty586tDh06qEOHDjp27Jjq1Kmj/v37q3PnzgoLC9Ntt92mefPmaceOHYqLi8uwrKxuJynf2S1btviMAqb1GV6N+Ph4bd68OdX0tKZdjXz58iksLCzdbdDPzy/TtgP+ajhHE38bd9xxhyRp+PDhPtNTDnNfzpWsFxs/frzGjh2rUaNGqXr16qnmFypUSJUrV9bEiRN9wt6aNWs0a9Ysb70uV3h4eJrh0d/fP9Vozdtvv53tf5Xl7NmzmjVrloKCgrzB/Y477tD58+c1cuRIn2WHDRsmj8ejpk2bel/bvn17xcbGasSIEZowYYL27Nmjp59+OlvreLHL+RxSAmdWwnnVqlWVP39+jRkzxuc2Wd99953Wr19/xdtVRvLnz6969epp7Nix2r17d6r5KbfYkdLeHj799FOfWy9l5sCBAz7PIyIiVKJECZ/3269fP5mZ2rRp4/3hcbFly5Zp4sSJkrK+naT8+9Zbb/ksd+l3+Go1adJEixcv1ooVK7zTDh48qClTpmTrevz9/dW4cWN99dVXPrdM2rNnj6ZOnapbb70101NjgL8aRjTxt1GpUiW1a9dO48aN06FDh1S3bl39/PPPmjhxolq0aKH69etfdpn79+9Xly5dVLZsWQUHB2vy5Mk+8++55x6Fh4dryJAhatq0qWrUqKFOnTp5b6sTHR3t8/eaL8fNN9+sOXPm6M0331RsbKyKFSumW265Rc2aNdOkSZMUHR2tsmXLavHixZozZ06Gt/3Jiu+++8474rR3715NnTpVmzZt0gsvvODdOTZv3lz169dXnz59tG3bNlWqVEmzZs3SV199pR49enjPtRs4cKBWrFihuXPnKjIyUhUrVtS//vUv9e3bVy1btrzi8J2ZrH4ON998sySpT58+evDBBxUYGKjmzZunOfIcGBio1157TR06dFDdunXVqlUr7+2NihYt6iw8jxo1SrfeeqsqVKigRx55RMWLF9eePXu0ePFi7dy503ufzGbNmnlHjmvWrKnVq1drypQpPhejZKZs2bKqV6+ebr75ZuXOnVtLly7VZ5995vMXa2rWrKlRo0apS5cuKl26tM9fBvr+++81ffp0DRw4UFLWt5PKlSurVatWGj16tA4fPqyaNWtq7ty52T7S+Nxzz2ny5Mlq1KiRunfv7r290Q033KCDBw9e1gh3ZgYOHKjZs2fr1ltvVZcuXRQQEKCxY8fq9OnTev3117NtPUCOcc2udweuUma3iqlbt67P7Y3MzM6ePWsvvfSSFStWzAIDAy0uLs569+7tc4sYswu3RUnvdi4X394o5RYk6T0uvi3KnDlzrFatWhYaGmpRUVHWvHlzW7dunU/ZKbc32rdvX5rv9eLyfvnlF6tTp46FhoaaJG+d/vjjD+vQoYPlzZvXIiIirEmTJvbLL7+kui3T1dzeKCQkxCpXrmzvvPOOJScn+yx/9OhRe/rppy02NtYCAwOtZMmSNmTIEO9yy5Yts4CAAOvevbvP686dO2fVqlWz2NhY++OPP9KtT0q9P/300wzrndbtjcyy9jmYmQ0YMMAKFy5sfn5+WbrV0SeffGI33XSTBQcHW+7cua1169a2c+dOn2Wu5PZGZmlvy2ZmW7ZssbZt21rBggUtMDDQChcubM2aNbPPPvvMu8ypU6esZ8+eVqhQIQsNDbVatWrZ4sWLrW7dula3bl3vchm168CBA6169eoWExNjoaGhVrp0aRs0aJCdOXMm1bLLli2zhx56yPv558qVyxo0aGATJ070uc1SZttJipMnT9qTTz5pefLksfDwcGvevLnt2LEjy7c3Sut7fOl7NzNbvny51a5d24KDg61IkSI2ePBge+utt0yS/f7775l+FpeSZF27dk1zXmJiojVp0sQiIiIsLCzM6tevb4sWLfJZhtsb4XrhMbuKM6IBALhO9ejRQ2PHjtWxY8f+tD9vCVxvOEcTAPC3d/GfQ5UunJc6adIk3XrrrYRM4CpwjiYA4G+vRo0aqlevnsqUKaM9e/bo/fff15EjR9K9TymArCFoAgD+9u644w599tlnGjdunDwej6pUqaL3339fderUudZVA/7SOEcTAAAATnCOJgAAAJwgaAIAAMAJZ+doJicn67ffflNkZGS23uwWAAAA2cPMdPToUcXGxsrPL/vHH50Fzd9++42/2QoAAPAXsGPHDhUpUiTby3UWNCMjIyVdqDh/uxUAACDnOXLkiOLi4ry5Lbs5C5oph8ujoqIImgAAADmYq9McuRgIAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgRMC1rgD+HHv27NHhw4evdTUAXMeio6NVoECBa10NADkIQfNvYM+ePXq4TVudPXP6WlcFwHUsMChYkyd9SNgE4EXQ/Bs4fPiwzp45rZPF6yo5JPpaVyfH8Dt5SKFJC3SyWB0lh8Zc6+oAf2l+pw5LW3/Q4cOHCZoAvAiafyPJIdFKDs97rauR4ySHxtAuAAA4wMVAAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnCJoAAABwgqAJAAAAJwiaAAAAcIKgCQAAACcImgAAAHCCoAkAAAAnrpugeerUKW3cuFGnTp261lUBAAC4Itdbnrluguavv/6qRx99VL/++uu1rgoAAMAVud7yzHUTNAEAAJCzEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOBGRXQadPn9bp06e9z48cOZJdRV+W7du3X5P15mS0CYA/C/0NcHWut+9QtgXNwYMH66WXXsqu4q7YoEGDrnUVAOBviz4YwMWyLWj27t1bzzzzjPf5kSNHFBcXl13FZ1mfPn0UHx//p683J9u+fTudP4A/BX0wcHWut312tgXN4OBgBQcHZ1dxVyw+Pl6lSpW61tUAgL8l+mAAF+NiIAAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAEwRNAAAAOEHQBAAAgBPXTdC84YYbNG7cON1www3XuioAAABX5HrLMwHXugLZJSQkRKVKlbrW1QAAALhi11ueuW5GNAEAAJCzEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4QNAEAAOAEQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwAAAE4EXOsK4M/jd+rwta5CjuJ38pDPvwCuHP0LgLQQNP8GoqOjFRgULG394VpXJUcKTVpwrasAXBcCg4IVHR19rasBIAchaP4NFChQQJMnfajDhxlxAOBOdHS0ChQocK2rASAHIWj+TRQoUIAdAAAA+FNxMRAAAACcIGgCAADACYImAAAAnCBoAgAAwAmCJgAAAJwgaAIAAMAJgiYAAACcIGgCAADACYImAAAAnCBoAgAAwAmCJgAAAJwgaAIAAMAJgiYAAACcIGgCAADACYImAAAAnCBoAgAAwAmCJgAAAJwgaAIAAMAJgiYAAACcIGgCAADACYImAAAAnCBoAgAAwAmCJgAAAJwgaAIAAMAJgiYAAACcIGgCAADACYImAAAAnCBoAgAAwAmCJgAAAJwgaAIAAMAJgiYAAACcIGgCAADACYImAAAAnCBoAgAAwAmCJgAAAJwgaAIAAMAJgiYAAACcIGgCAADACYImAAAAnCBoAgAAwAmCJgAAAJwgaAIAAMCJAFcFm5kk6ciRI65WAQAAgKuQktNSclt2cxY0jx49KkmKi4tztQoAAABkg6NHjyo6Ojrby/WYowibnJys3377TZGRkfJ4PC5W4ePIkSOKi4vTjh07FBUV5Xx9fyW0Tdpol/TRNmmjXdJH26SNdkkfbZO2P7tdzExHjx5VbGys/Pyy/4xKZyOafn5+KlKkiKvi0xUVFcUGmw7aJm20S/pom7TRLumjbdJGu6SPtknbn9kuLkYyU3AxEAAAAJwgaAIAAMCJ6yZoBgcHq1+/fgoODr7WVclxaJu00S7po23SRrukj7ZJG+2SPtombddbuzi7GAgAAAB/b9fNiCYAAAByFoImAAAAnCBoAgAAwAmCJgAAAJy4rKC5YMECNW/eXLGxsfJ4PPryyy9TLXPs2DF169ZNRYoUUWhoqMqWLasxY8ZkWO62bdvUqVMnFStWTKGhobrxxhvVr18/nTlzxme5VatWqXbt2goJCVFcXJxef/31y6n+NTdq1CgVLVpUISEhuuWWW/Tzzz9nuHxW2+Wv7p133lHFihW9N6etUaOGvvvuu0xfV7RoUXk8Hp/Hq6+++ifU+OoNHjxY1apVU2RkpPLnz68WLVpow4YNPsuMGzdO9erVU1RUlDwejw4dOpRpuStXrlSrVq0UFxen0NBQlSlTRiNGjPBZ5n//+59q1aqlPHnyKDQ0VKVLl9awYcOy8+1dlcz6mbNnz+r5559XhQoVFB4ertjYWLVt21a//fZbpmU/+eSTuvnmmxUcHKzKlStnuOzmzZsVGRmpmJiYK38z18CuXbv08MMPez/fChUqaOnSpRm+5vfff1ebNm1UsGBBhYeHq0qVKpo2bdqfVOPsd/ToUfXo0UPx8fEKDQ1VzZo1tWTJkkxfl5Xv3KBBg1SzZk2FhYXl+G0jK/1MCjNT06ZN0923X+z777/X3XffrUKFCik8PFyVK1fWlClTUi136NAhde3aVYUKFVJwcLBKlSqlb7/9Njve2lXLSp5p3759qn3M7bffnmnZmfUz/fv3T1Wux+NReHh4NryznOeygubx48dVqVIljRo1Kt1lnnnmGc2YMUOTJ0/W+vXr1aNHD3Xr1k3Tp09P9zW//PKLkpOTNXbsWK1du1bDhg3TmDFj9OKLL3qXOXLkiBo3bqz4+HgtW7ZMQ4YMUf/+/TVu3LjLeQt/irSC4CeffKJnnnlG/fr1U2JioipVqqQmTZpo79696ZaTlXbJKa4m/BYpUkSvvvqqli1bpqVLl+q2227T3XffrbVr12b62pdfflm7d+/2Prp3737F9XAlrbb54Ycf1LVrV/3444+aPXu2zp49q8aNG+v48ePeZU6cOKHbb7/9sj7vZcuWKX/+/Jo8ebLWrl2rPn36qHfv3ho5cqR3mfDwcHXr1k0LFizQ+vXr1bdvX/Xt2zfHfJcy62dOnDihxMRE/fOf/1RiYqI+//xzbdiwQXfddVeWyu/YsaMeeOCBDJc5e/asWrVqpdq1a192/f8saW1Xf/zxh2rVqqXAwEB99913WrdunYYOHapcuXJlWFbbtm21YcMGTZ8+XatXr9a9996r+++/X8uXL3dV/Sty/vx5JScnZ7pc586dNXv2bE2aNEmrV69W48aN1bBhQ+3atSvD12XlO3fmzBndd999euKJJy67/n+mM2fOZKmfSTF8+PAs/7noRYsWqWLFipo2bZpWrVqlDh06qG3btvr666991t+oUSNt27ZNn332mTZs2KB3331XhQsXzrb3eDWykmck6fbbb/fZx3z00UdZKj+jfqZXr14+Ze7evVtly5bVfffdd9nvw7VsGdiyKyTJvvjii1TTy5UrZy+//LLPtCpVqlifPn0uq/zXX3/dihUr5n0+evRoy5Url50+fdo77fnnn7eEhIQ0X5+cnGw33nijDRkyxGf68uXLTZJt2rTJzMz++OMP69Spk+XNm9ciIyOtfv36tmLFCu/ymzdvtrvuusvy589v4eHhVrVqVZs9e7ZPmfHx8fbyyy9bmzZtLDIy0tq1a2enT5+2rl27WsGCBS04ONiCgoLsH//4h/c158+ft9jYWBs8ePBVtculOnToYHfeeafPtDNnzli+fPnsvffe8677lVdesaJFi1pISIhVrFjRPv30U+/y586ds44dO3rnlypVyoYPH+5TZrt27ezuu++2gQMHWqFChaxo0aJmZjZq1CgrUaKEBQcHW/78+e3//u//Luv9pciVK5e3vumJj4+3YcOGZbnMnNw2e/fuNUn2ww8/pKr3/PnzTZL98ccfWX6vF+vSpYvVr18/w2Xuuecee/jhh9Ocd+zYMYuMjPRpBzOzL774wsLCwuzIkSNmZvbrr7/afffdZ9HR0ZYrVy676667LCkpybv8zz//bA0bNrQ8efJYVFSU1alTx5YtW+ZTpiQbPXq0NW/e3MLCwkySTZo0yR566CHLmzevhYSEWIkSJeyDDz7wed3PP/9skmz79u1ZapN+/fpZpUqV0p3/3HPP2cMPP2zjx4+36OjodJfLaf1MVFSUxcfHZ6kNLhYeHm4ffvihz7TcuXPbu+++m+byEydOtNy5c9upU6d8pt99990+29GXX35pN910kwUHB1uxYsWsf//+dvbsWe/8oUOHWvny5S0sLMyKFCliTzzxhB09etQ7P6X9v/rqKytTpoz5+/tbUlKSzZ8/36pVq2ZhYWEWHR1tNWvWtG3btpmZ2YkTJ8zf39++/vprn7pdzn4oK9+5zLaNFC+99JKVK1cu1fRKlSpZ3759vc/fffddK126tAUHB1tCQoKNGjXKZ/nnnnvOSpYsaaGhoVasWDHr27evnTlzxjs/ZZt+9913rWjRoubxeMzM7NNPP7Xy5ctbSEiIxcTEmCSbMWOGT9nLly+3woUL2+7du9Pdt2fmjjvusA4dOnifv/POO1a8eHGfOmYkJ/YztWrVsrvvvvuy2yJFZv1MihUrVpgkW7BgQZrzc1o/c8MNN9grr7yS5XbI9qD5yCOPWNWqVW3nzp2WnJxs8+bNs4iIiDR3oBnp06eP3Xzzzd7nbdq0SfWBz5s3zyTZwYMH0yxj0KBBVrZsWZ9pTz75pNWpU8f7vGHDhta8eXNbsmSJbdy40Xr27Gl58uSxAwcOmNmFDWDMmDG2evVq27hxo/Xt29dCQkJ8dmjx8fEWFRVlb7zxhm3evNk2b95sQ4YMsbi4OFuwYIFt3LjR/P397emnn/apS9u2be2uu+66qna51MKFC83f399+++0377TPP//cwsPDvR34wIEDrXTp0jZjxgzbsmWLjR8/3oKDg+377783swvh61//+pctWbLEtm7dapMnT7awsDD75JNPvGW2a9fOIiIirE2bNrZmzRpbs2aNLVmyxPz9/W3q1Km2bds2S0xMtBEjRlzW+zt37px99NFHFhQUZGvXrs1w2fj4eCtQoIDlzp3bKleubK+//rrPTuyv1DabNm0ySbZ69epU9b7aoNm6desMA39iYqIVKFAg3VBhduF7fccdd/hMu+uuu6xt27ZmdqFdypQpYx07drRVq1bZunXr7KGHHrKEhATvj8O5c+fapEmTbP369bZu3Trr1KmTFShQwLsDMbvQr+TPn98++OAD27Jli0mypk2bWuXKlW3JkiWWlJRks2fPtunTp/vUZfbs2ebxeOzw4cNZapOMdgBz5861YsWK2eHDh7MUJnJKP7Nt2zaLj4+3pk2bWsuWLS1fvnxWuXJlGzduXKbt0ahRI7vzzjvtwIEDdv78efvoo48sLCzMuwO71IkTJyw6Otr+/e9/e6ft2bPHAgICbN68eWZmtmDBAouKirIJEybYli1bbNasWVa0aFHr37+/9zXDhg2zefPmWVJSks2dO9cSEhLsiSee8M4fP368BQYGWs2aNW3hwoX2yy+/2OHDhy06Otp69eplmzdvtnXr1tmECRO8bXXkyBGTZHPmzPGpc61ataxu3bqZtoVZ9gbNHTt2mJ+fn/3888/eaYmJiebxeGzLli1mZjZ58mQrVKiQTZs2zbZu3WrTpk2z3Llz24QJE7yvGTBggC1cuNCSkpJs+vTpVqBAAXvttde88/v162fh4eF2++23W2Jioq1cudJ+++03CwgIsDfffNOSkpLs66+/Nkn2008/eV93/PhxK1OmjH355Zdmlv6+PTO1atWynj17ep83bdrUWrdubY888ojlz5/fypUrZ4MGDbJz586lW0ZO62duu+02i46Otnz58lmpUqXs8ccft/3792e5TbIaNLt162alSpXKcJmc1M/897//talTp2a5HbI9aJ46dcratm1rkiwgIMCCgoJs4sSJl1X2pk2bLCoqyqeDbNSokT366KM+y61du9Yk2bp169IsZ9euXebv7+/9Up05c8by5s3r/fL+97//taioqFS/ym+88UYbO3ZsuvUrV66cvf32297n8fHx1qJFC59lunfvbrfddpslJyfbrl27TJItWrTIZ5lnn33Wqlevnu56LpVWu6SlbNmyPh1Q8+bNrX379mZ24fMJCwtLVZdOnTpZq1at0i2za9euPmGlXbt2VqBAAZ8R5mnTpllUVJTPFzqrVq1aZeHh4ebv72/R0dH2zTffZPqaoUOH2vz5823lypX2zjvvWExMTKowf6mc2Dbnz5+3O++802rVqpVm+VcTNBcuXGgBAQE2c+bMVPMKFy5sQUFB5ufnl+ooxKV++uknn5CeEipSAvikSZMsISHBkpOTva85ffq0hYaGprluswvvOzIy0v7zn/94p0myHj16+DyvWrWqz0jJpU6ePGlVqlSxhx56KMP3cLH0dgD79++3uLg47w/jrISJnNLPmJkFBwdbcHCw9e7d2xITE23s2LEWEhLiE1jS8scff1jjxo29/XZUVFS6n1uKJ554wpo2bep9PnToUCtevLi3Lg0aNEg16jFp0iQrVKhQumV++umnlidPHu/z8ePHmySfUZkDBw6YJO+2l5YaNWpY3bp1bdeuXXbu3DmbNGmS+fn5ZbozT5GdQdPsQui6OEB3797d6tWr531+4403ptpxDxgwwGrUqJFumUOGDPEZdOjXr58FBgba3r17vdOWLVtmkmzbtm3p9jOPPvqoderUyfv8SoLmJ598YkFBQbZmzRrvtISEBAsODraOHTva0qVL7eOPP7bcuXP7/NC4VE7rZz766CP76quvbNWqVfbFF19YmTJlrFq1ahmG5YtlJWiePHnScuXK5bNfSktO6mcuV7YHzSFDhlipUqVs+vTptnLlSnv77bctIiLCOzz72GOPWXh4uPdxqZ07d9qNN97os+GbXVnQNLvwa+ixxx4zsws7+8jISDt+/LiZmY0cOdL8/Px86hMeHm5+fn723HPPmZnZ0aNHrWfPnla6dGmLjo72zn/22We964iPj7eBAwf6rHfZsmWWO3duK1mypHXo0CHToHml7ZKWN99800qXLm1mZr///rsFBAR4h+TXrFljklK958DAQJ/QO3LkSKtSpYrlzZvXO79atWre+e3atbOGDRv6rPfIkSNWoUIFy5s3rz388MM2efJkb1tn5vTp07Zp0yZbunSpvfDCC5Y3b17viGZmbZPi/ffft4CAgFRftJzeNo8//rjFx8fbjh070qxzeju922+/3VvHS3/pmpmtXr3a8ubNawMGDEiz3K1bt9qqVats3Lhxljt37kx/oVasWNF7qsfQoUPtxhtv9HY8vXr1Mn9//1Rt5/F4bPTo0WZ2ob07d+5sJUqUsKioKO/8iw8RSrLJkyf7PO/bt6+FhoZapUqV7Nlnn7WFCxd65585c8aaN29uN910k89oZmZtk94O4J577rHnn3/e+zyrYSIn9DPdu3e3gICAVOGke/fu3tN2Bg0a5FOHlJGMbt26WfXq1W3OnDm2YsUK69+/v0VHR9uqVavSfc+JiYnm7+9vO3fuNDOzChUq+PxgSTkEefH6QkJCTJK3bVJGjWJjYy0iIiLV/PHjx1tQUFCqHVz79u0tODjYmjVrZsOHD/c5SmF24RBhnTp1TJL5+/tbtWrVrHXr1t7vfnrtkCK7g+bnn39uMTExdvLkSTt9+rTlyZPHe6rCsWPHTJKFhob61CnlFJsUH3/8sdWsWdMKFCjgnZ8vXz7v/H79+lmJEiV81nvu3Dlr0KCBRUZGWvHixS1Pnjw+R02++uorK1GihM/pCpfu28uWLeut0+23357qvc2bN8/CwsJSDSiVLFnS4uLifELZ0KFDrWDBghm2VU7sZ1KkjH6mjJZfaT9zsalTp1pAQID9/vvvGS5nlnP6mcx+hF4qW4PmiRMnLDAwMNW5MZ06dbImTZqY2YVfKJs2bfI+LrZr1y4rWbKktWnTxs6fP+8z70oOnZuZTZ8+3aKjo+3EiRPWrFkz69y5s3feq6++aoULF/apT8pj3759ZnYh5BQvXtw+//xzW7VqlW3atMkqVapkTz31lLec9M4VPHz4sH388cfeoHnxOZpmvofOr7Rd0rJ//34LCgqyRYsW2RtvvGElS5b0zvvxxx+9owGXvudff/3VzC78igsJCbFRo0ZZYmKibdq0yR599FGfL0zKeYiXOnv2rM2ePdueffZZK168uJUoUeKKRuIaNGjg/WGRUdtcLCUo/vLLL+kuk9PapnPnzlakSBHbunVrunVOb6e3c+dOb/1Szk9LsXbtWsufP7+9+OKL6ZZ7sQEDBmQ62vPWW295z4kuX768T2f0+OOPW/Xq1dP8Lh06dMjMzJo0aWJVq1a1b775xtasWWObNm2yvHnz+nx3Lu1XUp7v3bvXJkyYYK1bt7aQkBDr2bOnnTlzxlq0aGEVK1ZMdTgro7YxS38HEB0dbf7+/t6Hn5+fN6y8//776bZNTuhnOnfubB6Px3tOcIrRo0dbbGysmV0YDbx4/WfPnrXNmzebJJ/RKLML38GUnVp6qlSpYq+88ootXbrU/Pz8vN8TM7OQkBB77bXX0nzf58+ft6SkJAsODrYePXrY4sWLbcOGDfb+++/7bOsZhbnExER75ZVXrEaNGhYREWGLFy9OtcyxY8e8IfT+++/3HpZNqx0ult1B8+zZs1agQAGbOnWqffbZZxYVFWUnTpwwswvBKCX4XNpOKf3CokWLzN/f3wYOHOg9LPryyy/7rD+9bTo5Odnuvfdei4qKsoSEBMuXL5+33Keeeso8Ho/PNi/J/Pz8vKcZbNu2zVuflB8VKb7//nsLDw9Pc8SsTp061qBBA59p3377rUnyOdpzqZzWz1wqb968NmbMGDO78n7mYrfddluqEcT05JR+Jjo6+rKuv8jWoHn48GGTZN9++63P9EcffdQaNWqUYXk7d+60kiVL2oMPPpjmsHTKxUAXn1jcu3fvdC8GSnHu3DmLjY21oUOHWkBAgM+o4qxZs7wnl6enfPnyPr/Sjx49atHR0Vn6YC6WkJBgkrznSpw/f94KFy6c6cVAmbVLeu6//3575JFHrHz58j6Hr44cOWLBwcGpTvy/WLdu3ey2227zmdagQYMshamLHTt2zAICAmzatGlZrneK+vXrW7t27S7rNZMnTzY/P78Mf3iY5Yy2OXr0qHk8HsudO7dt3Lgxw2Uv99D5mjVrLH/+/D6/UjPz0ksvZXoRycGDBy0kJMRGjBhhfn5+PiOw48aNs1y5cmV4jmRERIRP2/76668mKUs7gIuNGTPGIiIirEWLFlauXDmfQ4VZld4OYN26dbZ69WrvY+DAgRYZGWmrV6/OcLvKKf1MvXr1fPoZM7MePXpkeAh21apVaR4Zaty4sT3yyCMZrm/06NFWqlQp69q1qzVu3NhnXs2aNa1jx47pvvazzz6zwMBAnx/PAwYMyHLQvNg//vEP6969e7rzDx48aNHR0RkeQrxYdgdNswsX86ScC3vp0bnY2NgMT1954403rHjx4j7TOnXqlGnQTE5Otq5du1psbKxt3LjRzp07Z4ULF7ahQ4eamdnu3bt9tvfVq1ebJBsxYkSGP37NLrRReHi4jRw5Ms35vXv3tvj4eJ/Pd/jw4RmeOmGWs/qZyMhIn2k7duwwj8djX331VYbvIUVmQXPr1q3m8Xh8DutnJKf0MzNmzEjVz2TksoLm0aNHbfny5d4rnd58801bvny5z2GHunXrWrly5Wz+/Pm2detWGz9+vIWEhHiHtdOyc+dOK1GihDVo0MB27txpu3fv9j5SHDp0yAoUKOC9wOLjjz+2sLCwLHUcL774ogUFBVmZMmV8picnJ9utt95qlSpVspkzZ1pSUpItXLjQXnzxRVuyZImZXTiUVrlyZVu+fLmtWLHCmjdvbpGRkZl+MEOHDrWpU6fa+vXrbcOGDVa/fn2TZB988IGtW7fOHn30UYuJiclwuDwr7ZKeWbNmWVBQkPn7+9uuXbt85vXp08fy5MljEyZMsM2bN9uyZcvsrbfe8p7rMWLECIuKirIZM2bYhg0brG/fvhYVFZVpmPrPf/5jI0aMsOXLl9u2bdts9OjR5ufnl2qk5FIvvPCC/fDDD5aUlGSrVq2yF154wTwej82aNSvd1yxatMiGDRtmK1assC1bttjkyZMtX7583pPGc3rb1K5d2yTZ+PHjfT7XlFEOsws7geXLl9u7777rvSJx+fLlGX65V69ebfny5bOHH37Yp9yLw9jIkSNt+vTptnHjRtu4caO99957FhkZmaUrch966CELCgpKdQjt+PHjVrJkSatXr54tWLDAtm7davPnz7fu3bt7dxQ33XSTNWrUyNatW2c//vij1a5d20JDQ1PtAKZOnerTzzRq1MjefPNN++GHH2zNmjV2xx13WExMjBUpUsRWrFjh8z4zGikxu3Ce8/Lly+2xxx6zUqVKedeT3usuJ0zkhH7mrrvuMkk2cOBA27Rpk02ZMsXCwsJ8DhNe6syZM1aiRAmrXbu2/fTTT7Z582Z74403zOPxZHqu9KFDhywsLMyCgoLs448/9pk3Y8YMCwgIsP79+9uaNWts3bp19tFHH3m3s5QrbYcPH25btmyxDz/80AoXLpxp0Ny6dau98MILtmjRItu2bZvNnDnT8uTJ47OPmTFjhn333Xe2detWmzVrllWqVMluueWWTK+Azsp3bvv27bZ8+XJ76aWXLCIiwrsNXXz4OS0pF4X6+/vbjz/+6DPv3XfftdDQUBsxYoRt2LDBVq1aZR988IE3EH711VcWEBBgH330kW3evNlGjBhhuXPnzjRo3nvvvRYSEmJjxoyxJUuW2Lhx4ywwMDDDczCzco5myuHy3r17+3z/Lm6nX3/91SIjI61bt262YcMG+/rrry1//vypDsumJSf0M7fffrsVLFjQFi9ebElJSTZnzhyrUqWKlSxZMsPTs8yy3s/07dvXYmNjL2sQKSf0M506dbKCBQtm6Qir2WUGzZRfeZc+Lh552r17t7Vv395iY2MtJCTEEhISbOjQoRmeRJpywndaj4utXLnSbr31VgsODrbChQvbq6++mqV6p5xX8frrr6ead+TIEevevbvFxsZaYGCgxcXFWevWrb2HgJKSkqx+/foWGhpqcXFxNnLkSKtbt26mH8y4ceOscuXKFh4eblFRUdagQQN77rnn7IYbbrCgoCCrXr16qs7mStslLcnJyRYfH5/qCr6UecOHD7eEhAQLDAy0fPnyWZMmTbwXQJw6dcrat29v0dHRFhMTY0888YS98MILmYap//73v1a3bl3LlSuXhYaGWsWKFX2uxk5Px44dLT4+3oKCgixfvnzWoEGDDEOm2YVzRm655RaLjo62kJAQK1OmjL3yyiuZdgA5pW3S+1zHjx/vfU2/fv0yXeZS6b3m4tHKt956y8qVK2dhYWEWFRVlN910k40ePTpLncbcuXNNks/Vxil2795tbdu2tbx581pwcLAVL17cHnnkEe/oQ2JiolWtWtVCQkKsZMmS9umnn6b67kjyjmpd+vD397fcuXNbo0aN0m2/+fPnZ1j/unXrpvm69EYBLido5pR+Zvjw4Va+fHkLDg620qVLZ+mq840bN9q9995r+fPnt7CwMKtYsWKGI/sXa9OmTZq3OjK7EPhq1qxpoaGhFhUVZdWrV/epz5tvvmmFChWy0NBQa9KkiX344YeZBs3ff//dWrRoYYUKFbKgoCCLj4+3f/3rXz7b7yeffGLFixe3oKAgK1iwoHXt2tV7aDUjWfnOtWvX7oq2PTOz2rVrp3mrIzOzKVOmWOXKlS0oKMhy5cplderUsc8//9w7/9lnn7U8efJYRESEPfDAAzZs2LBMg2ZW+plLZSVoptcGl17Vv2jRIrvlllu8/UFmV52nyAn9TLNmzax27dqWL18+CwwMtPj4eHvkkUeydC5lVvqZ8+fPW5EiRbJ8elOKnNLPJCYmZrnOV3zo/K9kwYIFFhgYmKUN5Hpx9OhRi4qKuqLD1tc72ubKffjhh5YnT55MRw7/jv6O/YzZhXPMMjpsjQtS7oWYMkqJ9NHPpO+v2M8E6Dp2+vRp7du3T/3799d9992nAgUKXOsqOZecnKz9+/dr6NChiomJyfJfS/k7oG2u3IkTJ7R79269+uqreuyxxxQUFHStq5Rj/B37GenCXyH6/vvv9f3332v06NHXujo52r59+/Txxx/r999/V4cOHa51dXIs+pn0/aX7mWuddF0aP368+fn5WZUqVVJdLXe9SkpKMklWpEiRVDcs/rujba5cv379LCAgwG677bZMz0X7u/k79jNm///Gzpf+tRKkJsny5s1rU6ZMudZVydHoZ9L3V+5nPGZm1zjrAgAA4Drkd60rAAAAgOsTQRMAAABOEDQBAADgBEETAAAAThA0AQAA4ARBEwCuksfj0ZdffilJ2rZtmzwej1asWHFN63Q5xo0bp7i4OPn5+Wn48OHXujoAriMETQDZyszUsGFDNWnSJNW80aNHKyYmRjt37rwGNZOmTZumevXqKTo6WhEREapYsaJefvllHTx4MNvWERcXp927d6t8+fLZVqYk9e/fXx6PRx6PRwEBASpatKiefvppHTt27KrKPXLkiLp166bnn39eu3bt0qOPPppNNQYAgiaAbObxeDR+/Hj99NNPGjt2rHd6UlKSnnvuOb399tsqUqRItq7z7NmzmS7Tp08fPfDAA6pWrZq+++47rVmzRkOHDtXKlSs1adKkbKuLv7+/ChYsqICA7P/Da+XKldPu3bu1bds2vfbaaxo3bpx69uyZ5rJnzpzJUpm//vqrzp49qzvvvFOFChVSWFhYdlYZwN8cQRNAtouLi9OIESPUq1cvJSUlyczUqVMnNW7cWDfddJOaNm2qiIgIFShQQG3atNH+/fu9r50xY4ZuvfVWxcTEKE+ePGrWrJm2bNninZ9yaPqTTz5R3bp1FRISoilTpmj79u1q3ry5cuXKpfDwcJUrV07ffvutJOnnn3/WK6+8oqFDh2rIkCGqWbOmihYtqkaNGmnatGlq166dt/x33nlHN954o4KCgpSQkJAqhG7atEl16tRRSEiIypYtq9mzZ/vMv/TQ+ffffy+Px6O5c+eqatWqCgsLU82aNbVhwwaf1w0cOFD58+dXZGSkOnfurBdeeEGVK1f2WSYgIEAFCxZUkSJF9MADD6h169aaPn26pAsjnpUrV9Z7772nYsWKKSQkRNKFIHn33XcrIiJCUVFRuv/++7Vnzx5J0oQJE1ShQgVJUvHixeXxeLRt27bL+agBIEMETQBOtGvXTg0aNFDHjh01cuRIrVmzRmPHjtVtt92mm266SUuXLtWMGTO0Z88e3X///d7XHT9+XM8884yWLl2quXPnys/PT/fcc4+Sk5N9yn/hhRf01FNPaf369WrSpIm6du2q06dPa8GCBVq9erVee+01RURESJKmTJmiiIgIdenSJc26xsTESJK++OILPfXUU+rZs6fWrFmjxx57TB06dND8+fMlScnJybr33nsVFBSkn376SWPGjNHzzz+fpfbo06ePhg4dqqVLlyogIEAdO3b0zpsyZYoGDRqk1157TcuWLdMNN9ygd955J9MyQ0NDfUYuN2/erGnTpunzzz/XihUrlJycrLvvvlsHDx7UDz/8oNmzZ2vr1q164IEHJEkPPPCA5syZI+lCGN+9e7fi4uKy9H4AIEuu7V/ABHA927Nnj+XNm9f8/Pzsiy++sAEDBljjxo19ltmxY4dJsg0bNqRZxr59+0ySrV692sz+/9+sHz58uM9yFSpUsP79+6dZRtOmTa1ixYqZ1rdmzZr2yCOP+Ey777777I477jAzs5kzZ1pAQIDt2rXLO/+7774zSfbFF1/41G/58uVmZjZ//nyTZHPmzPG+5ptvvjFJdvLkSTMzu+WWW6xr164+661Vq5ZVqlTJ+7xfv34+z5cuXWp58+a1li1beucHBgba3r17vcvMmjXL/P397ddff/VOW7t2rUmyn3/+2czMli9fbpIsKSkp0/YBgMvFiCYAZ/Lnz6/HHntMZcqUUYsWLbRy5UrNnz9fERER3kfp0qUlyXt4fNOmTWrVqpWKFy+uqKgoFS1aVNKFQ8AXq1q1qs/zJ598UgMHDlStWrXUr18/rVq1yjvPzLJU3/Xr16tWrVo+02rVqqX169d758fFxSk2NtY7v0aNGlkqu2LFit7/FypUSJK0d+9eSdKGDRtUvXp1n+UvfS5Jq1evVkREhEJDQ1W9enXVqFFDI0eO9M6Pj49Xvnz5fN5PXFyczyhl2bJlFRMT431PAOASQROAUwEBAd4LY44dO6bmzZtrxYoVPo+U8x4lqXnz5jp48KDeffdd/fTTT/rpp58kpb64JTw83Od5586dtXXrVrVp00arV69W1apV9fbbb0uSSpUqpa1bt2bpoiFXAgMDvf/3eDySlOp0gMwkJCRoxYoVWr9+vU6ePKnp06erQIEC3vmXtgkAXGsETQB/mipVqmjt2rUqWrSoSpQo4fMIDw/XgQMHtGHDBvXt21cNGjRQmTJl9Mcff2S5/Li4OD3++OP6/PPP1bNnT7377ruSpIceekjHjh3T6NGj03zdoUOHJEllypTRwoULfeYtXLhQZcuW9c7fsWOHdu/e7Z3/448/Xk4TpCkhIUFLlizxmXbpc0kKCgpSiRIlVLRoUQUFBWVabkp9d+zY4Z22bt06HTp0yPueAMAlgiaAP03Xrl118OBBtWrVSkuWLNGWLVs0c+ZMdejQQefPn1euXLmUJ08ejRs3Tps3b9a8efP0zDPPZKnsHj16aObMmUpKSlJiYqLmz5+vMmXKSJJuueUWPffcc+rZs6eee+45LV68WNu3b9fcuXN13333aeLEiZKkZ599VhMmTNA777yjTZs26c0339Tnn3+uXr16SZIaNmyoUqVKqV27dlq5cqX++9//qk+fPlfdLt27d9f777+viRMnatOmTRo4cKBWrVrlHfm8Ug0bNlSFChXUunVrJSYm6ueff1bbtm1Vt27dVKceAIALBE0Af5rY2FgtXLhQ58+fV+PGjVWhQgX16NFDMTEx8vPzk5+fnz7++GMtW7ZM5cuX19NPP60hQ4Zkqezz58+ra9euKlOmjG6//XaVKlXKZwTztdde09SpU/XTTz+pSZMmKleunJ555hlVrFjRe3ujFi1aaMSIEXrjjTdUrlw5jR07VuPHj1e9evUkSX5+fvriiy908uRJVa9eXZ07d9agQYOuul1at26t3r17q1evXqpSpYqSkpLUvn177y2KrpTH49FXX32lXLlyqU6dOmrYsKGKFy+uTz755KrrDABZ4bGsniUPAPjTNGrUSAULFszWm8kDwJ8t+/90BQDgspw4cUJjxoxRkyZN5O/vr48++khz5sxJdTN4APirYUQTAK6xkydPqnnz5lq+fLlOnTqlhIQE9e3bV/fee++1rhoAXBWCJgAAAJzgYiAAAAA4QdAEAACAEwRNAAAAOEHQBAAAgBMETQAAADhB0AQAAIATBE0AAAA4QdAEAACAE/8PaTIpqd0GyHIAAAAASUVORK5CYII=\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": "40d41870-8d7f-42d5-9625-c72ed2efb414"
      },
      "execution_count": 129,
      "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": "a7e67eee-408e-4dea-f746-f4769b10530d"
      },
      "execution_count": 130,
      "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": "f662f24e-a879-4991-e90f-11782a2a6c73"
      },
      "execution_count": 131,
      "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": "829972be-7e74-43d6-e689-b2f06fa7f482"
      },
      "execution_count": 132,
      "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": "bf41b6ea-6bd2-41f8-93b9-330e583356f1"
      },
      "execution_count": 133,
      "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": "markdown",
      "source": [
        "**HANDLING OUTLIERS**"
      ],
      "metadata": {
        "id": "qu_mr_igE_Wi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Removing the outliers*"
      ],
      "metadata": {
        "id": "dG6YAZuHFdhs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing the outliers using interquartile range method\n"
      ],
      "metadata": {
        "id": "5H7URh84FjpM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_outliers_iqr(data, column):\n",
        "    Q1 = np.percentile(data[column], 25)\n",
        "    Q3 = np.percentile(data[column], 75)\n",
        "    IQR = Q3 - Q1\n",
        "    lower_bound = Q1 - 1.5 * IQR\n",
        "    upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "    # Filter rows where values are within the bounds\n",
        "    filtered_data = data[(data[column] >= lower_bound) & (data[column] <= upper_bound)]\n",
        "\n",
        "    return filtered_data\n",
        "\n",
        "# Assuming 'df' is your DataFrame and 'SalaryUSD' is the column with outliers\n",
        "df = remove_outliers_iqr(df, 'SalaryUSD')\n"
      ],
      "metadata": {
        "id": "dN_q00K1RP_g"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the outliers whether they are removed or not for 'Salaryusd' column with a boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(data=filtered_df, x='SalaryUSD_sqrt', orient='h')\n",
        "plt.title('Horizontal Box Plot of SalaryUSD')\n",
        "plt.xlabel('Salary (USD)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "2eZjNiGjGPus",
        "outputId": "1697c171-0b8f-4c78-9f81-3e891bb74e77"
      },
      "execution_count": 146,
      "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 this we can see that there are no potential outliers left. Hence we successfully removed the outliers"
      ],
      "metadata": {
        "id": "LbC4tUTwKCiO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*By this the data cleaning part is done*"
      ],
      "metadata": {
        "id": "b3UmHkoyKXV6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA VISUALIZATION**"
      ],
      "metadata": {
        "id": "wHIM4gxTL1gK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**UNIVARIENT ANALYSIS**"
      ],
      "metadata": {
        "id": "R7cBpU_8MK_L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*SalaryUSD*"
      ],
      "metadata": {
        "id": "EFEcJiaVM-TI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This histogram shows the distribution of salaries and the bars represent the frequency of salaries."
      ],
      "metadata": {
        "id": "aiErIDcHN-0I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the histogram for SalaryUSD\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(cleaned_2018['SalaryUSD'], bins=30, color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Salary (USD)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of SalaryUSD')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "JIZYMyaUGoIC",
        "outputId": "3f337860-82bb-4a17-b765-1cbbc10e2acf"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the result, we can know that there are many salaries in the lower range [between o to 200000 USD]."
      ],
      "metadata": {
        "id": "lj5wZnLZNrSc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*DevType*"
      ],
      "metadata": {
        "id": "hY9vGCytQH1N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This plots a count plot which shows the distribution of developer type correspoding to the count of respondant"
      ],
      "metadata": {
        "id": "yVS_o2rDQNZF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting a count plot for DevType\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.countplot(y='DevType', data=df)\n",
        "plt.title('Count of Developer Types')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Developer Type')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "Hf7IA9UXMIf0",
        "outputId": "603eddd5-0dbe-4a1c-f80f-2023a3c664b2"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By the result, we can understand that there are extremely high developer categorized ones compared to the non developer and manager."
      ],
      "metadata": {
        "id": "dm8SblT2QieT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Employement*"
      ],
      "metadata": {
        "id": "jM6xnRK_REi3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This plot shows the distribution of different employement statuses among the respondants"
      ],
      "metadata": {
        "id": "4oS0I1UjRQKh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the distribution of employment status\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(data=df, x='Employment', order=df['Employment'].value_counts().index)\n",
        "plt.title('Distribution of Employment Status')\n",
        "plt.xlabel('Employment Status')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "I0SnHfzqPflK",
        "outputId": "79e2d42c-20cc-4d62-f7b3-29ed8320fd7a"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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nzlRsbKz1/rRp0xQXF/ePX6cnfe2tW7fW8ePH1adPHzk6OqpFixYp+hqaNGkiR0dHDR06NMnZIMMwdO3atSee80l/H7Zt2/bQM1GJ13aZz5x5eHg8dF5HR8ckc0yePDnJWaB/qu2ll17Szp07bcZmzpyZZI4Hvyaenp4qWLDgQ1urA0j/aAEOIM1at26dTpw4obi4OIWFhWnr1q3atGmT8ubNq1WrVsnNze2R+w4bNkw7d+5U/fr1lTdvXoWHh2vq1KnKlSuXqlSpIun+H1c+Pj6aPn26MmbMKA8PD1WoUCFJy+jHlTlzZlWpUkXt2rVTWFiYvvzySxUsWNCmTXn79u21fPly1a1bV82aNdPZs2e1YMECm0YKT1rbO++8o+rVq2vAgAE6f/68XnnlFW3cuFErV65Uz549k8ydXB07dtSMGTPUtm1bHTx4UPny5dPy5cu1e/duffnll/94jdi/+euvv2w+9yqRp6enGjdu/BRVJxUTE6OaNWuqWbNmOnnypKZOnaoqVaqoYcOGj9znSV97/fr1lSVLFi1btkz16tWz+dyglPDSSy9pxIgR6t+/v86fP6/GjRsrY8aMOnfunFasWKGOHTvq448/fuI5n+T3oVu3brp7967+85//qGjRooqJidGePXu0ZMkS5cuXz6bpSZkyZbR582ZNmDBB/v7+yp8/vypUqKAGDRpo/vz58vb2VvHixbV3715t3rw5SSv60qVLy9HRUWPHjlVERIRcXV1Vo0YNZc+eXe3bt9eHH36ogIAA1a5dW0eOHNGGDRuUNWtWmzmKFy+uatWqqUyZMsqcObMOHDig5cuXKzg4+Im+TgDSCbv01AOAp5DYqjnx5uLiYvj5+Rm1a9c2vvrqK5tW04kebAG+ZcsWo1GjRoa/v7/h4uJi+Pv7Gy1btjROnTpls9/KlSuN4sWLG05OTjbtj998802jRIkSD63vUS3AFy1aZPTv39/Inj274e7ubtSvX9+4cOFCkv2/+OILI2fOnIarq6vx+uuvGwcOHEgy5z/V9mALcMMwjFu3bhm9evUy/P39DWdnZ6NQoULG559/bm05nUiS0bVr1yQ1Pao1+YPCwsKMdu3aGVmzZjVcXFyMl19++aEto1OqBbj5dQYGBhoeHh5J9n/U9+rBGhJ/rnbs2GF07NjRyJQpk+Hp6Wm0atXKuHbtWpI5H/x+PO5rT9SlSxdDkrFw4cIkjyX+zCxbtsxm/FHt7xN/vh9saf/9998bVapUMTw8PAwPDw+jaNGiRteuXY2TJ0/+69fnYT9Hj/qZe5h169YZH3zwgVG0aFHD09PTcHFxMQoWLGh069bNCAsLs9n2xIkTRtWqVQ13d3dDkvVn7caNG9avqaenp1GnTh3jxIkTD/15nDVrllGgQAHD0dHRph14fHy80a9fPyNr1qxGhgwZjDp16hhnzpxJMseIESOM8uXLGz4+Poa7u7tRtGhRY+TIkQ9tWQ4g/bMYRhq4EhcAgGcsJCRE7dq10/79+63XzTxLvXr10pw5cxQaGqoMGTI88+cDADw+rkkCAOA5i4qK0oIFCxQQEEBAAoBUiGuSAAB4TsLDw7V582YtX75c165dU48ePexdEgDgIQhJAAA8J8ePH1erVq2UPXt2TZo0ydpREQCQunBNEgAAAACYcE0SAAAAAJgQkgAAAADAJN1fk5SQkKDLly8rY8aMslgs9i4HAAAAgJ0YhqFbt27J399fDg6PPl+U7kPS5cuXlTt3bnuXAQAAACCVuHTpknLlyvXIx9N9SMqYMaOk+18ILy8vO1cDAAAAwF4iIyOVO3dua0Z4lHQfkhKX2Hl5eRGSAAAAAPzrZTg0bgAAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwMTJ3gW86Mr0+dbeJQBAijr4eRt7lwAAwFPhTBIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJnYNSUOGDJHFYrG5FS1a1Pp4VFSUunbtqixZssjT01MBAQEKCwuzY8UAAAAA0ju7n0kqUaKErly5Yr3t2rXL+livXr20evVqLVu2TDt27NDly5fVpEkTO1YLAAAAIL1zsnsBTk7y8/NLMh4REaE5c+Zo4cKFqlGjhiRp7ty5KlasmPbt26eKFSs+71IBAAAAvADsfibp9OnT8vf3V4ECBdSqVStdvHhRknTw4EHFxsaqVq1a1m2LFi2qPHnyaO/evY+cLzo6WpGRkTY3AAAAAHhcdg1JFSpUUEhIiNavX69p06bp3LlzeuONN3Tr1i2FhobKxcVFPj4+Nvv4+voqNDT0kXOOHj1a3t7e1lvu3Lmf8asAAAAAkJ7YdbldvXr1rP9fqlQpVahQQXnz5tXSpUvl7u6erDn79++v3r17W+9HRkYSlAAAAAA8NrsvtzPz8fFR4cKFdebMGfn5+SkmJkY3b9602SYsLOyh1zAlcnV1lZeXl80NAAAAAB5XqgpJt2/f1tmzZ5UjRw6VKVNGzs7O2rJli/XxkydP6uLFi6pUqZIdqwQAAACQntl1ud3HH3+sd955R3nz5tXly5c1ePBgOTo6qmXLlvL29lZQUJB69+6tzJkzy8vLS926dVOlSpXobAcAAADgmbFrSPrzzz/VsmVLXbt2TdmyZVOVKlW0b98+ZcuWTZI0ceJEOTg4KCAgQNHR0apTp46mTp1qz5IBAAAApHMWwzAMexfxLEVGRsrb21sRERGp8vqkMn2+tXcJAJCiDn7ext4lAADwUI+bDVLVNUkAAAAAYG+EJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJikmpA0ZswYWSwW9ezZ0zoWFRWlrl27KkuWLPL09FRAQIDCwsLsVyQAAACAdC9VhKT9+/drxowZKlWqlM14r169tHr1ai1btkw7duzQ5cuX1aRJEztVCQAAAOBFYPeQdPv2bbVq1UqzZs1SpkyZrOMRERGaM2eOJkyYoBo1aqhMmTKaO3eu9uzZo3379tmxYgAAAADpmd1DUteuXVW/fn3VqlXLZvzgwYOKjY21GS9atKjy5MmjvXv3PnK+6OhoRUZG2twAAAAA4HE52fPJFy9erEOHDmn//v1JHgsNDZWLi4t8fHxsxn19fRUaGvrIOUePHq2hQ4emdKkAAAAAXhB2O5N06dIl9ejRQ999953c3NxSbN7+/fsrIiLCert06VKKzQ0AAAAg/bNbSDp48KDCw8P12muvycnJSU5OTtqxY4cmTZokJycn+fr6KiYmRjdv3rTZLywsTH5+fo+c19XVVV5eXjY3AAAAAHhcdltuV7NmTR09etRmrF27dipatKj69eun3Llzy9nZWVu2bFFAQIAk6eTJk7p48aIqVapkj5IBAAAAvADsFpIyZsyokiVL2ox5eHgoS5Ys1vGgoCD17t1bmTNnlpeXl7p166ZKlSqpYsWK9igZAAAAwAvAro0b/s3EiRPl4OCggIAARUdHq06dOpo6daq9ywIAAACQjqWqkLR9+3ab+25ubpoyZYqmTJlin4IAAAAAvHDs/jlJAAAAAJCaEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEySFZIKFCiga9euJRm/efOmChQo8NjzTJs2TaVKlZKXl5e8vLxUqVIlrVu3zvp4VFSUunbtqixZssjT01MBAQEKCwtLTskAAAAA8FiSFZLOnz+v+Pj4JOPR0dH666+/HnueXLlyacyYMTp48KAOHDigGjVqqFGjRvr9998lSb169dLq1au1bNky7dixQ5cvX1aTJk2SUzIAAAAAPBanJ9l41apV1v/fsGGDvL29rffj4+O1ZcsW5cuX77Hne+edd2zujxw5UtOmTdO+ffuUK1cuzZkzRwsXLlSNGjUkSXPnzlWxYsW0b98+VaxY8UlKBwAAAIDH8kQhqXHjxpIki8WiwMBAm8ecnZ2VL18+ffHFF8kqJD4+XsuWLdOdO3dUqVIlHTx4ULGxsapVq5Z1m6JFiypPnjzau3fvI0NSdHS0oqOjrfcjIyOTVQ8AAACAF9MThaSEhARJUv78+bV//35lzZr1qQs4evSoKlWqpKioKHl6emrFihUqXry4Dh8+LBcXF/n4+Nhs7+vrq9DQ0EfON3r0aA0dOvSp6wIAAADwYkrWNUnnzp1LkYAkSUWKFNHhw4f1888/q3PnzgoMDNTx48eTPV///v0VERFhvV26dClF6gQAAADwYniiM0lmW7Zs0ZYtWxQeHm49w5Tom2++eex5XFxcVLBgQUlSmTJltH//fn311Vdq3ry5YmJidPPmTZuzSWFhYfLz83vkfK6urnJ1dX2yFwMAAAAA/1+yziQNHTpUb731lrZs2aKrV6/qxo0bNrenkZCQoOjoaJUpU0bOzs7asmWL9bGTJ0/q4sWLqlSp0lM9BwAAAAA8SrLOJE2fPl0hISFq3br1Uz15//79Va9ePeXJk0e3bt3SwoULtX37dmvnvKCgIPXu3VuZM2eWl5eXunXrpkqVKtHZDgAAAMAzk6yQFBMTo8qVKz/1k4eHh6tNmza6cuWKvL29VapUKW3YsEG1a9eWJE2cOFEODg4KCAhQdHS06tSpo6lTpz718wIAAADAo1gMwzCedKd+/frJ09NTAwcOfBY1pajIyEh5e3srIiJCXl5e9i4niTJ9vrV3CQCQog5+3sbeJQAA8FCPmw2SdSYpKipKM2fO1ObNm1WqVCk5OzvbPD5hwoTkTAsAAAAAdpeskPTbb7+pdOnSkqRjx47ZPGaxWJ66KAAAAACwl2SFpG3btqV0HQAAAACQKiSrBTgAAAAApFfJOpNUvXr1f1xWt3Xr1mQXBAAAAAD2lKyQlHg9UqLY2FgdPnxYx44dU2BgYErUBQAAAAB2kayQNHHixIeODxkyRLdv336qggAAAADAnlL0mqT3339f33zzTUpOCQAAAADPVYqGpL1798rNzS0lpwQAAACA5ypZy+2aNGlic98wDF25ckUHDhzQwIEDU6QwAAAAALCHZIUkb29vm/sODg4qUqSIhg0bprfeeitFCgMAAAAAe0hWSJo7d25K1wEAAAAAqUKyQlKigwcP6o8//pAklShRQq+++mqKFAUAAAAA9pKskBQeHq4WLVpo+/bt8vHxkSTdvHlT1atX1+LFi5UtW7aUrBEAAAAAnptkdbfr1q2bbt26pd9//13Xr1/X9evXdezYMUVGRqp79+4pXSMAAAAAPDfJOpO0fv16bd68WcWKFbOOFS9eXFOmTKFxAwAAAIA0LVlnkhISEuTs7Jxk3NnZWQkJCU9dFAAAAADYS7JCUo0aNdSjRw9dvnzZOvbXX3+pV69eqlmzZooVBwAAAADPW7JC0tdff63IyEjly5dPL730kl566SXlz59fkZGRmjx5ckrXCAAAAADPTbKuScqdO7cOHTqkzZs368SJE5KkYsWKqVatWilaHAAAAAA8b090Jmnr1q0qXry4IiMjZbFYVLt2bXXr1k3dunVTuXLlVKJECf33v/99VrUCAAAAwDP3RCHpyy+/VIcOHeTl5ZXkMW9vb3Xq1EkTJkxIseIAAAAA4Hl7opB05MgR1a1b95GPv/XWWzp48OBTFwUAAAAA9vJEISksLOyhrb8TOTk56e+//37qogAAAADAXp4oJOXMmVPHjh175OO//fabcuTI8dRFAQAAAIC9PFFIevvttzVw4EBFRUUleezevXsaPHiwGjRokGLFAQAAAMDz9kQtwD/77DP98MMPKly4sIKDg1WkSBFJ0okTJzRlyhTFx8drwIABz6RQAAAAAHgenigk+fr6as+ePercubP69+8vwzAkSRaLRXXq1NGUKVPk6+v7TAoFAAAAgOfhiT9MNm/evFq7dq1u3LihM2fOyDAMFSpUSJkyZXoW9QEAAADAc/XEISlRpkyZVK5cuZSsBQAAAADs7okaNwAAAABAekdIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBi15A0evRolStXThkzZlT27NnVuHFjnTx50mabqKgode3aVVmyZJGnp6cCAgIUFhZmp4oBAAAApHd2DUk7duxQ165dtW/fPm3atEmxsbF66623dOfOHes2vXr10urVq7Vs2TLt2LFDly9fVpMmTexYNQAAAID0zMmeT75+/Xqb+yEhIcqePbsOHjyoqlWrKiIiQnPmzNHChQtVo0YNSdLcuXNVrFgx7du3TxUrVrRH2QAAAADSsVR1TVJERIQkKXPmzJKkgwcPKjY2VrVq1bJuU7RoUeXJk0d79+596BzR0dGKjIy0uQEAAADA40o1ISkhIUE9e/bU66+/rpIlS0qSQkND5eLiIh8fH5ttfX19FRoa+tB5Ro8eLW9vb+std+7cz7p0AAAAAOlIqglJXbt21bFjx7R48eKnmqd///6KiIiw3i5dupRCFQIAAAB4Edj1mqREwcHBWrNmjXbu3KlcuXJZx/38/BQTE6ObN2/anE0KCwuTn5/fQ+dydXWVq6vrsy4ZAAAAQDpl1zNJhmEoODhYK1as0NatW5U/f36bx8uUKSNnZ2dt2bLFOnby5EldvHhRlSpVet7lAgAAAHgB2PVMUteuXbVw4UKtXLlSGTNmtF5n5O3tLXd3d3l7eysoKEi9e/dW5syZ5eXlpW7duqlSpUp0tgMAAADwTNg1JE2bNk2SVK1aNZvxuXPnqm3btpKkiRMnysHBQQEBAYqOjladOnU0derU51wpAAAAgBeFXUOSYRj/uo2bm5umTJmiKVOmPIeKAAAAALzoUk13OwAAAABIDQhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmdg1JO3fu1DvvvCN/f39ZLBb9+OOPNo8bhqFBgwYpR44ccnd3V61atXT69Gn7FAsAAADghWDXkHTnzh298sormjJlykMfHzdunCZNmqTp06fr559/loeHh+rUqaOoqKjnXCkAAACAF4WTPZ+8Xr16qlev3kMfMwxDX375pT777DM1atRIkvTtt9/K19dXP/74o1q0aPE8SwUAAADwgki11ySdO3dOoaGhqlWrlnXM29tbFSpU0N69ex+5X3R0tCIjI21uAAAAAPC4Um1ICg0NlST5+vrajPv6+lofe5jRo0fL29vbesudO/czrRMAAABA+pJqQ1Jy9e/fXxEREdbbpUuX7F0SAAAAgDQk1YYkPz8/SVJYWJjNeFhYmPWxh3F1dZWXl5fNDQAAAAAeV6oNSfnz55efn5+2bNliHYuMjNTPP/+sSpUq2bEyAAAAAOmZXbvb3b59W2fOnLHeP3funA4fPqzMmTMrT5486tmzp0aMGKFChQopf/78GjhwoPz9/dW4cWP7FQ0AAAAgXbNrSDpw4ICqV69uvd+7d29JUmBgoEJCQtS3b1/duXNHHTt21M2bN1WlShWtX79ebm5u9ioZAAAAQDpnMQzDsHcRz1JkZKS8vb0VERGRKq9PKtPnW3uXAAAp6uDnbexdAgAAD/W42SDVXpMEAAAAAPZASAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmDjZuwAAAF50Zfp8a+8SACBFHfy8jb1LeCqcSQIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMAkTYSkKVOmKF++fHJzc1OFChX0yy+/2LskAAAAAOlUqg9JS5YsUe/evTV48GAdOnRIr7zyiurUqaPw8HB7lwYAAAAgHUr1IWnChAnq0KGD2rVrp+LFi2v69OnKkCGDvvnmG3uXBgAAACAdcrJ3Af8kJiZGBw8eVP/+/a1jDg4OqlWrlvbu3fvQfaKjoxUdHW29HxERIUmKjIx8tsUmU3z0PXuXAAApKrX+e5uacSwAkN6k1mNBYl2GYfzjdqk6JF29elXx8fHy9fW1Gff19dWJEyceus/o0aM1dOjQJOO5c+d+JjUCAGx5T/7Q3iUAAOwstR8Lbt26JW9v70c+nqpDUnL0799fvXv3tt5PSEjQ9evXlSVLFlksFjtWBthPZGSkcufOrUuXLsnLy8ve5QAA7IBjAXD/DNKtW7fk7+//j9ul6pCUNWtWOTo6KiwszGY8LCxMfn5+D93H1dVVrq6uNmM+Pj7PqkQgTfHy8uLACAAvOI4FeNH90xmkRKm6cYOLi4vKlCmjLVu2WMcSEhK0ZcsWVapUyY6VAQAAAEivUvWZJEnq3bu3AgMDVbZsWZUvX15ffvml7ty5o3bt2tm7NAAAAADpUKoPSc2bN9fff/+tQYMGKTQ0VKVLl9b69euTNHMA8Giurq4aPHhwkqWoAIAXB8cC4PFZjH/rfwcAAAAAL5BUfU0SAAAAADxvhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISkAYlJCTYuwQAgJ1xLACenVT/YbIAbCUkJMjB4f77G2vXrtX169cVHx+vOnXqyM/Pz87VAQCeB/OxYOnSpTpz5owcHBxUvnx51ahRw87VAWkfHyYLpFH9+vXTd999p5dfflnHjx9Xnjx51LNnTwUEBNi7NADAc9K3b18tWLBAtWvXVlhYmM6ePasOHTqob9++9i4NSNNYbgekQSEhIVqwYIFWrVqldevWaciQIdqzZ488PDzsXRoA4Dn58ccftXjxYq1YsULz5s1Ty5Yt9eeffyp37tz2Lg1I8whJQBp06tQpNWzYUK+99pqWLFmiXr166euvv1bdunV17949hYaG2rtEAMAzdvbsWZUuXVoVKlTQ999/r27dumnixIlq2bKl7ty5o0OHDtm7RCDNIiQBaUh8fLwk6eLFi8qTJ49+/fVXtW/fXmPGjFHnzp2VkJCg2bNna82aNVzQCwDpVOK/787OzsqXL582btyotm3baty4cfrwww8l3b9mdc2aNbp586YdKwXSLkISkIo9GHQcHR0lSQ0aNNCwYcNUpkwZzZw503pQvHfvntasWaOzZ89aL+gFAKRtDx4LEv99L168uHUVwZQpU6zHgjt37mj27NkKDw+Xj4/P8y4XSBf4KwpIpcydizZv3qxly5bpzz//VExMjBo1aqR27drJz89PXl5eunPnjk6dOqWmTZvq6tWrGj58uJ2rBwCkBMMwrMeCxYsX6+uvv9aaNWt0584d1apVSxMmTJCDg4Nu376tgwcP6sCBA2rSpInCw8P15ZdfWucA8GRoAQ6kUokHxb59+yokJERxcXHy8/PThx9+qA4dOqhHjx6KiYlR48aNlSNHDmXKlEk+Pj7at2+fnJycFB8fbz3zBABImywWiySpf//+mj17try9veXu7q6XX35Z06dPV8+ePXX79m0NGDBArq6uypkzp7JkyaJffvmFYwHwFGgBDqQyhmHIYrHIMAydPn1aH3zwgSZMmKD8+fNr0KBB+uWXX9S0aVP17NlT7u7u2r9/v/7880/5+vqqYsWKcnBwUFxcnJyceA8EANKqxNUEhmEoIiJCbdu21fDhw5UvXz4tWbJEc+bMUfbs2fXtt9/K29tbJ06c0L179+Tu7q7ChQtzLACeEr85QCpiXmJ3584dubm5qUiRInr11Vfl7OysadOmqVevXlq2bJkMw1Dnzp1Vrlw5lStXzmYODooAkHaZjwWXLl1SdHS07t69q+zZsytjxoxq27at3NzcNGXKFLVu3VrffvutihYtmmQOjgVA8nEmCUiFBg8erJUrVyoiIkLZsmXTnj17bA52vXv31u7du1WtWjUNGjSIz0cCgHTo008/VUhIiLJnz64bN27o6NGj8vLykiTFxcVp8eLFmj59ugzD0MaNGzkWACmIxg1AKmDuXLRkyRJNnjxZnTp1UtmyZXXlyhUFBwcrIiLCus2ECRNUokQJXb16VRkyZLBHyQCAFGY+FqxZs0YhISGaOHGiGjduLGdnZ9WsWVPR0dGSJCcnJ7Vo0UJt2rRRyZIl5e7ubq+ygXSJM0mAnSVegyRJP/zwg37//XcVKFBArVq1kiSNHj1aa9asUenSpTV69Gjru4iS7Zr1xDkAAGnb3LlzFR8fr7i4OH344YeKj4/Xtm3b1KdPH7m5uWn79u1ydXWVZLs0z/z/AJ4Ov0mAndSpU0e7du2yhpvDhw9r8ODBGjdunM1B7uOPP1aDBg3066+/6rPPPrM5o+Tg4KCEhAQCEgCkUVWqVNGyZcus969evaqRI0eqY8eOunLliqT7n5FXrVo1jR8/XtHR0apRo4aioqIkyeZ4QUACUg6/TYAdXL58Wa+//rrKly9vHStcuLC6d++uXLlyadq0abp3756k+5+o3qdPHzVq1Ehr167VnDlzbObioAgAadcHH3yghg0bWu9nzZpVP/zwg958800tWbJE165dk3R/eV1iULpw4YK6d+9ur5KBFwLL7QA7+/zzz1WgQAEFBAQoKirK+mGB+fPn17fffmtdZx4bG6vFixfrvffe4zMvACCdGTlypNzc3PTRRx9Jkn7//Xe99957kqT//ve/Ng0bjhw5otKlS3MsAJ4h3oIGnjPz+xLx8fH65ZdfFBgYqJ9++klubm5q3ry5OnfurAsXLqhNmzY2Z5Rat24tR0dHxcfH26t8AMAzEBsbqz59+mjGjBmSpBIlSmjhwoWSpKpVqyoyMlLS/TNKZcqU4VgAPGOEJOA5On36tO7cuSNJGj58uG7evKnx48erdevWatWqldasWSN3d3e99957+vDDD/Xnn3+qfv361m5GiXj3EADSrp9//lnh4eGSpGHDhmnjxo0aMmSIRo8erS5dumjatGmS7gelRYsWycHBQUWKFNHdu3dt5uFYADw7fMoY8JwcOHBAjRs31qhRo7R//35NmTJFzZo1U5EiRdSnTx8lJCTo/fff14IFC9SgQQO1bNlSd+7c0W+//SZnZ2d7lw8ASAGnTp1ScHCwXn75Zbm4uGjmzJk6evSoJKlfv34yDEPBwcGSpM6dO6t48eKaO3euJkyYYO1oB+DZ45ok4Dnq2rWrlixZonv37mnTpk2qXLmy9bH//e9/Gjt2rJYtW6b58+erfv36iomJkbOzsywWC61dASCdmDVrlgYNGqSIiAitXr1aNWvWVFxcnPVDw8eMGaOBAwdq8uTJ+vDDD232jY+P5wwS8BzwFxfwHCSuG69QoYJiY2OVOXNmnTp1Sjdu3LBuU6BAAfXt21fNmzfXO++8o927d8vFxUUWi0WGYRCQACCNS/yw2Hz58snDw0OFChXSkiVL9Ndff8nJycl6rPjkk080YsQIdenSRStWrLCZg4AEPB+cSQKeo7/++kvOzs4aPny41q5dq48//lgtW7aUj4+PdZvw8HDNnj1bffv2tb6rCABIP/7++29J9z9AfN68eSpcuLBGjRolf39/m+0WLlyoZs2acSwA7ICQBDxDhmFYP+jVvJRCkrp06aINGzbok08+UfPmzeXl5aUOHTpo7Nixypw580P3AQCkbYkfAJ54bJg5c6ZCQkJUvHhxDR8+XDly5FBQUJBat26tatWqSeJYANgDIQlIYYMHD1apUqUUEBAgybblt8Vi0b59+5Q/f375+vqqc+fO2rJli6pUqaILFy7o119/VXh4OAdDAEgHzG+USffbfDs5OclisWj79u2KjY1V7dq19fXXX2vJkiW6c+eOvLy8dOLECf35558cCwA74iIHIAUdO3ZM27Zt09SpU7V27VpJsjZdsFgsWrFihSpXrqwjR45IkqZNm6b3339f8fHx8vPzU1hYmM26dABA2nT9+nVrQFq6dKkkWRvxrFixQnXr1tXt27clScHBwerdu7fq1KmjEiVKWAMSxwLAfjiTBKSw7du3a/Lkybp+/bo+/vhj1a9fX5K0ceNG1a1bV9OmTVOnTp1sOhTFxsZa23yzrAIA0raffvpJAwcO1E8//aRx48Zp9uzZ+uOPP5QrVy5t27ZNNWvW1PTp09WxY8dHdi6lix1gX4QkIAX07t1b58+f1w8//CDpflD66quvdPPmTX300Udq0KCBfvjhB8XExKhFixbW/R5civHgfQBA2hMZGakiRYrIyclJkZGR2rlzp1555RVJ9z/u4fDhw2rSpImdqwTwT1huBzylqKgoZcuWTefOnVP79u0lSdWqVVOPHj3k4+OjL774Qps2bVKTJk3UokWLJNcomRGQACDtSkhIkGEY8vLyUtu2bfX3338rb9688vLysrb/LlCgAAEJSAMIScBTcnNzU3BwsIKCgnTgwAEFBQVJ+r+g5O3trVGjRumnn36S9H/XKAEA0hcHBwdZLBaNGTNGhw8f1o4dO3Tnzh21bNlSv//+ux62eIfjAZA6EZKAp5B4UW3GjBnVunVrffDBBzp48KBNUOrZs6f1jFJiMwc+GBYA0g9z0Pn+++81ffp0DRo0SBUqVNAvv/yisLAwdejQQX/88Yd1u6+//loSxwMgteKaJCAFXLp0Sblz59aNGzc0f/58ffPNNypTpozmzJkj6f+aOZw6dUozZsxQ5cqV7VwxACClbdmyRcuXL1f+/PnVt29fRUdHy9XVVTdv3lSZMmWUJUsWffjhh1q+fLlOnjyp06dPE5KAVIrfTOAprVq1Snnz5tWuXbuUKVOmR55R6tixoxo0aKAKFSrYuWIAQEo7efKkunTpom+//VY3b96UJLm6uiomJkY+Pj46dOiQXF1d9c033yg6OlonTpyQg4MDy+2AVIozScBTunDhgj755BOtXr1aGzduVOXKla1nlObOnauyZctq1qxZNvvQ2hUA0p+ffvpJAwYMkMVi0eTJk1WlShVJUkxMjFxcXBQTE6OrV68qR44cslgsfOQDkIpxJgl4TBcuXEgyZhiG8ubNq88//1yNGzdWjRo1tGfPHpszSj/99JNGjx5t3V4SAQkA0rBHnf2pX7++hg0bJicnJ02bNk2//PKLJMnFxUVxcXFycXGRv7+/tYEPAQlIvTiTBDyGWrVqqVSpUvriiy9ksVj09ddfq0aNGipevLj1s40uXbqkTz75RD/88IN27Nih8uXL69q1a9q6dauaNGlCMAKAdMD84a/ffvutjh07JkdHR7311luqXr26pPvNG8aOHatChQqpR48eKl++vD1LBpAMhCTgXwwcOFDffvut9UzSuXPnFBQUpBMnTmjHjh0qVKiQNSidPn1aDRo0UHh4uFatWqU33njDOg9L7AAg/ejXr5/mzp2rBg0a6OTJk7JYLGrcuLE+/vhjSfeD0vjx463dTYsXL27nigE8CZbbAf8iISFB5cqVkySNHj1ae/fu1ZgxY1S2bFnVqlVLp06dsn4IbMGCBfXyyy/Lzc1NAwcOlMQSOwBIb2bMmKGlS5dq7dq1+uabbxQcHKx9+/Zp/vz5GjFihCQpICBAXbt2Va5cuVS0aFE7VwzgSRGSgH9RrFgxbdu2TTVr1tSAAQNUpUoVlS9fXkOGDFHJkiVVu3ZtnTt3TtL/XZy7cOFCbdu2TZKsAQoAkPbFxcXp6tWr6tixo8qWLasVK1aoS5cuGjFihF5++WVNnz5dn3/+uSTp/fff16xZs+hiB6RBLLcDHsOrr76qP/74Q506ddKYMWPk7u4uSTp06JAGDx6szZs367333tORI0fk4OCgvXv3ytHR0WbtOgAg7UlcTm3+//DwcMXGxio2NlZvv/222rdvr969e+vgwYOqXbu2MmbMqM8++0wdOnSw2R9A2sFfb8A/iI+P19WrV5UxY0Z16NBB8+bN08SJExUWFiZJeu211zRr1iz1799fERERKleunHbv3k1AAoB0ICEhwSbgJJ4Nyp49u3LmzKnDhw/LYrGoRYsWkqRbt26pWrVq6tOnj/Vz8ghIQNpE70ngAeYGC46OjsqaNau2bt0qJycn5cqVS6NGjZLFYtEHH3wgX19f+fn5adCgQdaldpL47AsASOMMw7C+0fXFF1/o0KFDCg0NVVBQkCpXrqx8+fJZP/tow4YNql+/vr744gu99NJL6tq1qywWCw17gDSM5XbA/7dgwQK9//77kpJ2ojOfFRo3bpxGjhyp/v37KygoSNmyZbOZh6UVAJC2mf/NHzhwoKZOnap3331XN27c0KZNmxQQEKAePXooZ86c6tixo37++WfFx8fL19dXP//8s5ydnTkWAGkcb3UDut+qtWfPnvr99981evRoOTo62gSlxItuHRwc1LdvX1ksFo0ZM0aRkZHq27evfHx8rHNxUASAtC0xIF25ckW3bt3SihUrVLVqVUnS8uXLNWLECE2dOlVTp07VxIkTdf78eV27dk0NGjSQo6MjqwmAdIDfYEBSpUqV9NFHH2nBggWKj4/XuHHj/jEo9enTR7du3dK+ffvk7e1t5+oBAClt2bJlat68uXLmzKlmzZpZx5s2baq4uDi1bdtWH3zwgcqWLatcuXJZH4+PjycgAekAV5XjhRcXFyd/f391795d7dq109q1azV8+HBJsgalROY2rsOGDdOGDRtksVjEqlUASF8qVKigNm3a6K+//lJoaKgkKTY2VpLUokUL5cqVS/v27UuyH9cgAekDb3XghWYYhvUdv6VLl+rkyZP6+++/NWrUKMXFxWno0KEPPaOUuNY8MSCxxA4A0q6HNVjIkyePRowYodu3b6t9+/bKkyePypYtK0m6fv26EhISlDFjRnuUC+A5oHEDIGnQoEGaMmWKJk6cKEdHR61YsUJHjhxR06ZNNXr0aEkPP4gCANKuf2rYk+jy5cvq0qWLtm/frm7dusnX11fr16/XxYsXdejQIZbWAekUIQkvvPDwcL3zzjvq1KmTPvjgA0n3D4rTp0/X3Llz1bFjRw0cOFCS+OwjAEgnvv/+e3Xq1EkdOnT41zfDLl++rI8++khLlizRe++9p9q1a6t58+Zyc3OjSQOQTvHXHl54np6eun79ui5evGgd8/f3V3BwsHx9fTV27Fh1795dkghIAJBOJDbsWbVqlfr27Ssp6XWoifz9/TV27Fi1bNlS69evV5kyZeTm5qbo6GgCEpBO8ZuNF8qjzgRVrlxZx48f1/nz55UvXz5J9z9RvUKFCnJ1ddXdu3c5iwQA6YS5YY+zs7NCQkKUMWNGDRw4MMl1qIny5MmjcePG6fbt26pdu7bWrVun0qVL2+cFAHjm+IsPLwxzyDl69Kj27t2rS5cuKUOGDOrSpYs2btyoCRMm6OTJk5Kku3fvKiwsTIGBgZo1a5a1YQMAIO36p4Y9gwcPlvToM0o5c+bU9OnTVbRoUQUEBCgmJua51g7g+eGaJLwQzB3oBgwYoKVLl8pisSgqKkqNGjXS6NGjtWvXLgUFBSl//vxydXVVZGSk7t27pyNHjsjR0ZEudgCQjjxNw57Q0FDFxcXZfD4SgPSFkIQXyoQJEzRu3DgtXrxY1apVU/v27fX9999rzZo1ev3113Xo0CHt3btXv/76q3x9fTV06FA5OTnR2Q4A0hEa9gD4N1yThBeCYRhKSEjQrl271K9fP1WrVk2rVq3SsmXLNHbsWL3++uuKiorSq6++qtdee81mXzoXAUD68k8Ne9auXauxY8fq77//1qRJkwhIwAuK33ykWwkJCdb/t1gsio2NVXh4uN58803t2rVLrVq10rhx4/Thhx8qJiZGM2fO1N69e5PMQ0ACgLTLfCwwMzfsSZTYsOeVV16xNuwB8GIiJCHdSnz3LyQkRHfu3JGbm5vy5cunJk2aqE6dOpoyZYo6deokSYqIiNAPP/ygI0eO2LNkAEAKomEPgOTimiSka5GRkcqdO7eCg4M1cuRI/frrr+rWrZsiIiJ09OhRGYahmzdvqlWrVoqIiNDOnTu59ggA0gEa9gB4GpxJQrrm5eWlbt266dixY4qKilLx4sXVsWNHWSwW+fv764033lDdunUVFham7du3P7LtKwAgbUkMNxMmTNCcOXM0a9YsnTp1Sm+99ZYWLFigI0eOqG7dulq9erVatmyp/Pnz66233tLhw4etxwICEvDi4kwS0o1HdSDav3+/qlatqkWLFqlx48aKjY3V5cuXtXjxYhmGoRw5cuj999+Xo6MjTRoAIJ1IbNjz7rvv6o033lCvXr20atUqtW7dWmPHjtWHH36oqKgoubq6JglDHAsAEJKQ7mzcuFEeHh56/fXXrWNdu3bVuXPnNG/ePGXLlu2h+9HmGwDStgffLIuKilKtWrU0adIk3b17V/Xq1dP48ePVqVMnxcTEaPr06SpbtqwqV65sx6oBpEYst0O6YRiGTp48qbZt2yooKEjvvfeefvnlF0VFRalFixY6c+aMwsLCJN1/l/BBBCQASNto2AMgpXAmCWnaw5bYnTp1SqGhoerVq5dcXV3l7Oys8ePHKzAwUK+99poWLFhgp2oBAM8aDXsApARCEtIsc0A6duyYbt68qUKFCsnHx0eurq6KjY3Vzp07NX/+fO3Zs0d3795VTEyMtm/fruLFi9O1CADSqc8++0xHjx7VkiVLZLFYtGTJEo0fP15Xr15VgQIFFBsbq7i4OO3bt0/Ozs4stwaQBCEJaZI54PTv31+LFi1SVFSUoqOj1apVK7Vt21Zly5a1br97924dP35cH330kbp3764RI0bYq3QAQAqhYQ+AZ4WQhDTHfFCcPHmyhg0bpoULF6pYsWJavXq1Fi9eLD8/Pw0aNEglSpSw2XfevHkaN26c1q1bpzx58tijfABACqNhD4CURuMGpBnbtm2TdP/C3Li4OCUkJGjHjh1q3bq1ateurVy5cqlz584KDg7WH3/8ofXr10u6H6oSFS9eXPHx8XwWEgCkAzTsAfCsEJKQJnz11Vfq1KmT5s2bJ0lycnKSxWKRYRi6deuWJFmDz7vvvqvq1atr5syZiouLs1mKceDAAZ07d06urq7P/0UAAJ6a+Y0vi8WiIkWKaPv27Zo5c6ZOnjypnj17qk6dOnJzc5OTk5PGjBkjSSypA/BECElIE6pVq6ZKlSpp9uzZCgkJkXT/4Fi4cGGtWLFC586ds3lHsHjx4vL397c5mN67d09OTk7av3+//P39n/dLAAA8pQcb9uzatUthYWHKmzevqlatqn379mn48OHKnz+/WrVqpcjISG3cuFHHjx+XdP/MEwA8Dq5JQppx/PhxjR49Wv/73//0wQcfKCgoSJJUpUoV/f3331q2bJn8/f3l4eGhBg0aKEuWLFq6dKnNHI+6yBcAkLrRsAfA80RIQpry+++/a8yYMTZBKTw8XC1bttThw4eVJUsWZciQQQkJCTp48KCcnZ1p9Q0AaRwNewA8byzQRar1sLM+JUqUUJ8+ffT5559rzpw5cnJyUmBgoLZs2aJFixbpzp07cnBwUGBgIK1dASCN27Ztm6pXr25t2OPg4GDTsEeSOnfurKxZs2r48OFav369SpQoYXP8oGEPgOTgr0ekSoZhWA9wCxcu1JUrVyRJLVq0UKlSpTRgwACNHDlSM2bMUFxcnIKCgtSyZUubOeLj4wlIAJBGffXVV5oyZYoGDBigwMBAOTk5yTCMJA17HB0d9e6772rXrl2aOXOmevToYfNvPw17ACQHF2cg1TEvj+vdu7eCg4O1cOFCzZo1S8WKFdP333+vokWLqn///ipYsKDmzZunKVOmJJmH1q4AkHbRsAeAPRGSkOokBqQzZ87o1KlT2rp1q3bt2qXDhw+rVatWatOmjTZv3qzixYurT58+8vLy0tGjR+laBADpyCuvvKJ+/fopX758mjVrlubMmSNJGj16tIoWLaq6devqt99+09WrV3Xv3j0tXbpU2bJlk4uLi3UOd3d3BQUFqVSpUvZ6GQDSKBo3IFWaP3++xo8fr4wZM2r16tXy9va2Lr9r3bq1du/erSNHjihjxow6f/688uTJIwcHB5o0AEA6Q8MeAPbABRtIdeLi4nTt2jU5ODjo7NmzypAhgxwcHBQVFSU3Nzd17dpV27Zt0+nTp/Xaa68pX758kmjvDQBpHQ17AKQW/CsCu3vwoOjk5KQOHTrIy8tLgwYN0rvvvqvly5fLzc1N0v3lExaLJUmnIgISAKRdNOwBkJqw3A52ZQ5IO3bskKurq3x8fFS0aFHdvXtX3333nSZPnqysWbNqzJgxunv3rj7//HOFh4fr559/JhgBQDrwYMOekJAQ5c+fX3fu3NHly5c1d+5cBQQE6Pjx4xozZozOnz+v5s2bq2vXrnauHEB6xV+YsKvEkPPJJ5+oYcOGeu+991S9enWtXLlSGTJkUKtWrdSjRw+dPHlSVatW1cyZM1WsWDHt3LlTDg4OfO4FAKQDNOwBkNpwJgl2YX7X8NixY2rTpo1mzJih6OhorVixQl9++aXmz5+v9957T/fu3dOCBQsUEhIiPz8/LVq0SC4uLrp3757c3d3t/EoAACmBhj0AUhMW7uK5My+xi4mJUXR0tGrVqqVy5cpJkkqVKiVnZ2e1bt1aFotFLVu21HvvvaeEhATNmjVLQUFBmjVrFgEJANIJGvYASG0ISXjuEg9ow4YN0549exQaGqqsWbPq1q1bypgxo7y8vDRgwABZLBYFBgbq7t27CgoKUmBgoJydnTVq1CgFBwdr9uzZdn4lAIDkoGEPgNSO5XZ4bswHxcmTJ2vkyJF6//33FRoaqoULF2ry5Mk2F+HeunVL/fr109GjR7V9+3Y5Ojrq3r17WrZsmd544w3lz5/fXi8FAJBMNOwBkBYQkvDcJK4bP3z4sJYsWaIqVaqofv36kqRRo0Zp0KBBmjJlijp16mTd5+7duzbvIDo6OtqrfABACvrkk080bdo0ZcmSRffu3dP06dPVqFEj3b17V4sWLdKgQYN07do1NWnSRP7+/ho+fLjc3d05FgB4Llhuh2fuwIEDKlu2rCwWi3755RdVrFhRLi4uKlWqlHWbTz/9VJIUHBwsBwcHdejQQZKUIUMGSfcDFgdFAEi7HmzYs3HjRm3evNnasKdJkybWhj2J16GGhIQoOjpao0aNomEPgOeKkIRnavr06Ro2bJi2bt2qokWLqnz58po1a5Y6dOigffv2qU6dOsqcObOk+0HJwcFBnTp1Uvbs2dWoUSPrPHQuAoC0i4Y9ANIaQhKemZkzZ6pr165avny5ihYtah0PCgrSnTt31LNnT+XIkUMffvihfHx8JN1ffpEzZ07rMjwAQNpHwx4AaQ3XJOGZmDFjhoKDg7VkyRI1adLEOr5nzx5VrlxZkjRp0iT17NlTo0aNUufOneXt7W0zR1xcnJycyPEAkFbRsAdAWsVfoEhxP/74ozp37qyVK1fqnXfesY43atRImTJlUqlSpeTp6anu3bvLYrGod+/eunXrlj799FN5eHhYtycgAUDalrhU+vDhw7p8+bLmzJljXSlQvHhx9ejRQ05OTtaGPRkzZtT48eNtGva4u7urTZs2dnsNAF5M/BWKFBUdHa0NGzaoQIECOnfunHW8adOmOn36tNauXStPT09rd6Ju3brp5s2bWr9+vUaMGGHHygEAKYWGPQDSOpbbIcVduXJFY8eO1c8//6wWLVpo165dOnXqlFasWKECBQpYOxyZl2Ekjpm7HwEA0p4HG/ZI0pw5c9ShQwd169ZNgwcPtjbskaQxY8bo008/1YoVK2wa9gCAPRGS8EyEhoZq5MiR+umnnxQREaHffvtNOXPmVGxsrJydnSVJ9evXV8mSJTV27FglJCTIYrEQkAAgDZs5c6Y6d+6s5cuX6z//+Y/NY+brUM0NeyRp/vz5atmyJcusAaQahCQ8M2FhYRo1apR2796tFi1a6OOPP5YkxcfHq2HDhjpz5oyOHTtmDU0AgLSLhj0A0hP+JcIz4+vrq/79+2vkyJFatmyZJOnjjz9WkyZNdPbsWWtA4qAIAGkbDXsApDcO9i4A6Zufn58GDBigChUqaMWKFfL19dWJEyd09OhRAhIApAP/1rBnyJAh1oY9ktStWzcNGjRI27dvtzZpAIDUhuV2eC5CQ0PVr18//f3331q5ciUBCQDSERr2AEhvCEl4bm7cuCFvb285ODgQkAAgnaFhD4D0hJCE5878TiIAIP2gYQ+A9IK/VPHcEZAAIH1KbNhTqVIlLVu2TOPHj5ekhzbsAYDUjDNJAAAgRYWGhmrUqFE6ePCgzpw5Ix8fHzqaAkhTCEkAACDF0bAHQFpGSAIAAM8EDXsApFWEJAAA8EzRsAdAWkNIAgAAAAAT3tYBAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQCeibZt26px48b2LgMAgCdGSAKAdKxt27ayWCxJbnXr1rV3aWnK9u3bZbFYdPPmzX/ddtasWXrllVfk6ekpHx8fvfrqqxo9erT18eSGxyFDhqh06dJPvB8A4Mnx0dcAkM7VrVtXc+fOtRlzdXW1UzXp2zfffKOePXtq0qRJevPNNxUdHa3ffvtNx44ds3dpAIAnwJkkAEjnXF1d5efnZ3PLlCmT9XGLxaIZM2aoQYMGypAhg4oVK6a9e/fqzJkzqlatmjw8PFS5cmWdPXvWuk/iWY0ZM2Yod+7cypAhg5o1a6aIiIhH1hEdHa3u3bsre/bscnNzU5UqVbR//35JkmEYKliwoMaPH2+zz+HDh2WxWHTmzJlk1ypJK1eu1GuvvSY3NzcVKFBAQ4cOVVxcnM3XYPbs2frPf/6jDBkyqFChQlq1apUk6fz586pevbokKVOmTLJYLGrbtu1DX+OqVavUrFkzBQUFqWDBgipRooRatmypkSNHWr9u8+bN08qVK61n9bZv3y5J6tevnwoXLqwMGTKoQIECGjhwoGJjYyVJISEhGjp0qI4cOWLdLyQkROfPn5fFYtHhw4etNdy8edNm3hs3bqhVq1bKli2b3N3dVahQoSShGQBgi5AEANDw4cPVpk0bHT58WEWLFtV7772nTp06qX///jpw4IAMw1BwcLDNPmfOnNHSpUu1evVqrV+/Xr/++qu6dOnyyOfo27evvv/+e82bN0+HDh1SwYIFVadOHV2/fl0Wi0UffPBBkj/e586dq6pVq6pgwYLJrvW///2v2rRpox49euj48eOaMWOGQkJCrMEl0dChQ9WsWTP99ttvevvtt9WqVStdv35duXPn1vfffy9JOnnypK5cuaKvvvrqoa/Rz89P+/bt04ULFx76+Mcff6xmzZqpbt26unLliq5cuaLKlStLkjJmzKiQkBAdP35cX331lWbNmqWJEydKkpo3b66PPvpIJUqUsO7XvHnzR36tzQYOHKjjx49r3bp1+uOPPzRt2jRlzZr1sfYFgBeWAQBItwIDAw1HR0fDw8PD5jZy5EjrNpKMzz77zHp/7969hiRjzpw51rFFixYZbm5u1vuDBw82HB0djT///NM6tm7dOsPBwcG4cuWK9bkbNWpkGIZh3L5923B2dja+++476/YxMTGGv7+/MW7cOMMwDOOvv/4yHB0djZ9//tn6eNasWY2QkJCnqrVmzZrGqFGjbL4u8+fPN3LkyPHIeW/fvm1IMtatW2cYhmFs27bNkGTcuHEj6RfZ5PLly0bFihUNSUbhwoWNwMBAY8mSJUZ8fLx1G/PX5Z98/vnnRpkyZaz3Bw8ebLzyyis225w7d86QZPz666/WsRs3bhiSjG3bthmGYRjvvPOO0a5du399PgDA/+GaJABI56pXr65p06bZjGXOnNnmfqlSpaz/7+vrK0l6+eWXbcaioqIUGRkpLy8vSVKePHmUM2dO6zaVKlVSQkKCTp48KT8/P5v5z549q9jYWL3++uvWMWdnZ5UvX15//PGHJMnf31/169fXN998o/Lly2v16tWKjo7Wu++++1S1HjlyRLt377Y5cxQfH6+oqCjdvXtXGTJkSDKvh4eHvLy8FB4erieRI0cO7d27V8eOHdPOnTu1Z88eBQYGavbs2Vq/fr0cHB69gGPJkiWaNGmSzp49q9u3bysuLs76tX4anTt3VkBAgA4dOqS33npLjRs3tp69AgA8HMvtACCd8/DwUMGCBW1uD4YkZ2dn6/9bLJZHjiUkJDzTWtu3b6/Fixfr3r17mjt3rpo3b24NMcmt9fbt2xo6dKgOHz5svR09elSnT5+Wm5vbQ+dNnCe5r7dkyZLq0qWLFixYoE2bNmnTpk3asWPHI7ffu3evWrVqpbfffltr1qzRr7/+qgEDBigmJuYfnycxdBmGYR1LvI4pUb169XThwgX16tVLly9fVs2aNfXxxx8n63UBwIuCM0kAgGS5ePGiLl++LH9/f0nSvn375ODgoCJFiiTZ9qWXXpKLi4t2796tvHnzSrr/x/z+/fvVs2dP63Zvv/22PDw8NG3aNK1fv147d+586jpfe+01nTx50ua6pifl4uIi6f4ZqCdVvHhxSdKdO3escz04z549e5Q3b14NGDDAOvbgdU0P2y9btmySpCtXrujVV1+VJJsmDubtAgMDFRgYqDfeeEN9+vRJ0iQDAPB/CEkAkM5FR0crNDTUZszJyempL953c3NTYGCgxo8fr8jISHXv3l3NmjVLstROun82q3PnzurTp48yZ86sPHnyaNy4cbp7966CgoKs2zk6Oqpt27bq37+/ChUqpEqVKj1VjZI0aNAgNWjQQHny5FHTpk3l4OCgI0eO6NixYxoxYsRjzZE3b15ZLBatWbNGb7/9ttzd3eXp6Zlku86dO8vf3181atRQrly5dOXKFY0YMULZsmWzvpZ8+fJpw4YNOnnypLJkySJvb28VKlRIFy9e1OLFi1WuXDn99NNPWrFihc3c+fLl07lz53T48GHlypVLGTNmlLu7uypWrKgxY8Yof/78Cg8P12effZbk9ZcpU0YlSpRQdHS01qxZo2LFiiXzqwkALwaW2wFAOrd+/XrlyJHD5lalSpWnnrdgwYJq0qSJ3n77bb311lsqVaqUpk6d+sjtx4wZo4CAALVu3Vqvvfaazpw5ow0bNti0I5ekoKAgxcTEqF27dk9doyTVqVNHa9as0caNG1WuXDlVrFhREydOtJ7Rehw5c+bU0KFD9cknn8jX1zdJp79EtWrV0r59+/Tuu++qcOHCCggIkJubm7Zs2aIsWbJIkjp06KAiRYqobNmyypYtm3bv3q2GDRuqV69eCg4OVunSpbVnzx4NHDjQZu6AgADVrVtX1atXV7Zs2bRo0SJJ9z+bKS4uTmXKlFHPnj2TBD8XFxf1799fpUqVUtWqVeXo6KjFixc/yZcQAF44FsO8kBkAgMcwZMgQ/fjjjw9d2vW0/vvf/6pmzZq6dOmStTEDAADPE8vtAACpQnR0tP7++28NGTJE7777LgEJAGA3LLcDAKQKixYtUt68eXXz5k2NGzfO3uUAAF5gLLcDAAAAABPOJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIDJ/wO9A6Cuu+bnMwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By the result, We can clearly see that large number of respondants are employed for full time."
      ],
      "metadata": {
        "id": "yWGex2R2Rt0L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Years of coding profession*"
      ],
      "metadata": {
        "id": "RlVUFYcWSJDK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This plot shows the distribution of years of professional coding experience among the respondants."
      ],
      "metadata": {
        "id": "lFpSRPW5SNvd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the distribution of years of professional coding experience\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(data=df, x='YearsCodingProf', order=df['YearsCodingProf'].value_counts().index)\n",
        "plt.title('Distribution of Years of Professional Coding Experience')\n",
        "plt.xlabel('Years of Professional Coding Experience')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "sWUy4agIRUWk",
        "outputId": "59dc3c47-665f-4e0f-9128-c90e00e7b69f"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By the result, we get to know that there are more people who are experienced for 0-2  years and 3-5 years."
      ],
      "metadata": {
        "id": "amZ9AFIvSrJC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*language worked with*"
      ],
      "metadata": {
        "id": "XkLLqGWUTYJ4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This plot shows the distribution of different languages worked with among the respondents."
      ],
      "metadata": {
        "id": "2QhbMRiBTtdC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the distribution of languages worked with\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.countplot(data=df, x='LanguageWorkedWith', order=df['LanguageWorkedWith'].value_counts().index)\n",
        "plt.title('Distribution of Languages Worked With')\n",
        "plt.xlabel('Languages Worked With')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=90)  # Rotate x-axis labels for better readability\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sXnp1FRmSRiP",
        "outputId": "10cd84a4-e03f-422c-efa1-188bea236836"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By the result, we get to know that all the languages are worked mostly but the first three row's languages are used the most."
      ],
      "metadata": {
        "id": "PYUBvHldXjO5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BIVARIATE ANALYSIS**"
      ],
      "metadata": {
        "id": "hyxbBBmhfBSF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*AGE vs SALARYUSD*"
      ],
      "metadata": {
        "id": "vW75Z-44fS49"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot for Age vs. SalaryUSD\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(df['Age'], df['SalaryUSD'], alpha=0.5)\n",
        "plt.title('Scatter Plot of Age vs. SalaryUSD')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('SalaryUSD')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "FFxMYZ2NTNSV",
        "outputId": "dc8cfeda-4100-4f9b-e3fe-91656933d0d1"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The higher density of dots in the 25-34 years and 35-44 years age ranges indicates that there are more individuals within these age brackets who are paid well."
      ],
      "metadata": {
        "id": "mhLhuf6_hcuk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*DEVTYPE vs SALARYUSD*"
      ],
      "metadata": {
        "id": "VuybtKFGlYdR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set the style of seaborn\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Create the violin plot\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.violinplot(x='SalaryUSD', y='DevType', data=df, inner='quartile')\n",
        "plt.title('Distribution of Salaries by Developer Type')\n",
        "plt.xlabel('Salary (USD)')\n",
        "plt.ylabel('Developer Type')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "8L1bLJYxiH0F",
        "outputId": "f5cd9069-2082-4b0f-c181-5a21d44bdc68"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This tells that the salaries of the developer are much concentrated between 0 to 100000 USD and there is variablity in salaries. and the salaries of the non developer are evenly distributed and less variable and the salaries of the managers are very little variable."
      ],
      "metadata": {
        "id": "mVM6mUsAnGGW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Employment vs SalaryUSD*"
      ],
      "metadata": {
        "id": "XU_XOsMepI9L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a swarm plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.swarmplot(x='Employment', y='SalaryUSD', data=df)\n",
        "plt.title('Employment Status vs. Salary')\n",
        "plt.xlabel('Employment Status')\n",
        "plt.ylabel('Salary (USD)')\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "KxUi1Xl_ilmc",
        "outputId": "48605115-f92d-48bc-fcee-479816b0f95d"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This observation says that the expectation that full-time positions typically offer higher salaries compared to part-time positions is true, resulting in more data points at higher salary ranges for full-time employment.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "leKoKd4cqV-0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*YearsCodingProf vs. SalaryUSD*"
      ],
      "metadata": {
        "id": "iW2l-Jnxqq1U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Bar plot for YearsCodingProf vs. SalaryUSD\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='YearsCodingProf', y='SalaryUSD', data=df, alpha=0.7)\n",
        "plt.title('YearsCodingProf vs. SalaryUSD')\n",
        "plt.xlabel('YearsCodingProf')\n",
        "plt.ylabel('SalaryUSD')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "Le_kPH2ppzzW",
        "outputId": "1b8188e6-82a8-4306-a1fb-34f670667662"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Results seem like individuals with 15-17 years of professional coding experience tend to have relatively higher salaries, with earning up to $120,000. This observation suggests that there may be a positive relation between years of professional coding experience and salary, indicating that more experienced professionals tend to command higher salaries.\n",
        "The 24-26 years again tend to have low salary maybe due to the factor of age and a desire of having young ones to be worked."
      ],
      "metadata": {
        "id": "WAbW8XUWstg6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*LanguageWorkedWith vs. SalaryUSD*"
      ],
      "metadata": {
        "id": "xJZcYFQStZnm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar plot of LanguageWorkedWith vs. SalaryUSD\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.barplot(x='LanguageWorkedWith', y='SalaryUSD', data=df, ci=None)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('LanguageWorkedWith vs. SalaryUSD')\n",
        "plt.xlabel('LanguageWorkedWith')\n",
        "plt.ylabel('SalaryUSD')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_pEWHPhyrNvB",
        "outputId": "8941928e-ccea-44d1-be85-066bb808e847"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-171-ec6b2cfc68a6>:3: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.barplot(x='LanguageWorkedWith', y='SalaryUSD', data=df, ci=None)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The individuals who work with the languages oriented with css,html,php,swift tend to have more pay of salary extending upto 120000 USD."
      ],
      "metadata": {
        "id": "I_mTgBEUvaR3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All these visualizations provide valuable insights into different aspects of the data, such as the distribution of salary, the relationship between different variables like age and salary, and how various factors like employment type and years of coding experience relate to salary which answers many analysis questions."
      ],
      "metadata": {
        "id": "2WdbwBFKwXPF"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CSRjaDcRuk5E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}