14. StackOverFlow Survey 2021 - Analysis/Stackoverflow_Developer_Survey_2021_Analysis.ipynb

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        "## **About Dataset**\n",
        "\n",
        "This dataset has been obtained from stackoverflow.com 2021 developer survey. This dataset has around 84,000 entries and is a great dataset to deal with for Data enthusatics like me and yourself! \n",
        "\n",
        "This dataset has 2 files, one for the data itself, named `survey_df` and the schema file showing the meaning of the columns in the main dataset, named `schema_df`.\n",
        "\n",
        "## **About Notebook**\n",
        "This notebook is divided into 2 sections and 1 bonus section. The first 2 sections are **Data Cleaning** and **Data Visualiztation** respectively. And the **Bonus Section**, that's a surprise, and I'm sure you'll love it! So stay tuned!\n",
        "\n",
        "## **About ME**\n",
        "I'm Muhammad Anas, a 13 year old machine learning maniac & enthusiastic. This is my 13th EDA/Data Analysis Project, and I'm proud to say I enjoyed this project very very much. This was a real life dataset, which needed A LOT of cleaning and really made me grasp the important conceps of Data Cleaning and Wrangling!\n",
        "\n",
        "This is the Github Repo: https://github.com/muhammadanas0716/Data-Science-Projects---EDA\n",
        "\n",
        "I hope you enjoy the project 😎"
      ]
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          "text": [
            "Imported Needed Libraries 😅\n",
            "Data Imported Successfully 🚀\n"
          ]
        }
      ],
      "source": [
        "# Import needed libraries \n",
        "# Importing the needed libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from urllib.request import urlretrieve \n",
        "print(\"Imported Needed Libraries 😅\")\n",
        "\n",
        "# Set Pandas Settings\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "\n",
        "# Get the data\n",
        "URL_1 = \"https://raw.githubusercontent.com/muhammadanas0716/Data-Science-Projects---EDA/main/14.%20StackOverFlow%20Survey%202021%20-%20Analysis/survey_results_public.csv\"\n",
        "URL_2 = \"https://raw.githubusercontent.com/muhammadanas0716/Data-Science-Projects---EDA/main/14.%20StackOverFlow%20Survey%202021%20-%20Analysis/survey_results_schema.csv\"\n",
        "survey_df = pd.read_csv(URL_1)\n",
        "schema_df = pd.read_csv(URL_2)\n",
        "print(\"Data Imported Successfully 🚀\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJTQggRRUXom"
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        "## **Section 1 - Data Exploration**\n",
        "\n",
        "Within this section, I have explored the data, cleaned the data and made it more effecient to further visualize in the next section. There are also filter systems in this section, which I find very cool, so be sure to check em out!\n",
        "\n",
        "This is has been done via Pandas\n"
      ]
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              "   ResponseId  \\\n",
              "0           1   \n",
              "1           2   \n",
              "2           3   \n",
              "\n",
              "                                                                      MainBranch  \\\n",
              "0                                                 I am a developer by profession   \n",
              "1                                         I am a student who is learning to code   \n",
              "2  I am not primarily a developer, but I write code sometimes as part of my work   \n",
              "\n",
              "                                             Employment             Country  \\\n",
              "0  Independent contractor, freelancer, or self-employed            Slovakia   \n",
              "1                                    Student, full-time         Netherlands   \n",
              "2                                    Student, full-time  Russian Federation   \n",
              "\n",
              "  US_State UK_Country  \\\n",
              "0      NaN        NaN   \n",
              "1      NaN        NaN   \n",
              "2      NaN        NaN   \n",
              "\n",
              "                                                                              EdLevel  \\\n",
              "0  Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)   \n",
              "1                                        Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
              "2                                        Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
              "\n",
              "      Age1stCode  \\\n",
              "0  18 - 24 years   \n",
              "1  11 - 17 years   \n",
              "2  11 - 17 years   \n",
              "\n",
              "                                                         LearnCode YearsCode  \\\n",
              "0  Coding Bootcamp;Other online resources (ex: videos, blogs, etc)       NaN   \n",
              "1           Other online resources (ex: videos, blogs, etc);School         7   \n",
              "2     Other online resources (ex: videos, blogs, etc);Online Forum       NaN   \n",
              "\n",
              "   ...              Age Gender Trans                Sexuality  \\\n",
              "0  ...  25-34 years old    Man    No  Straight / Heterosexual   \n",
              "1  ...  18-24 years old    Man    No  Straight / Heterosexual   \n",
              "2  ...  18-24 years old    Man    No        Prefer not to say   \n",
              "\n",
              "                      Ethnicity      Accessibility       MentalHealth  \\\n",
              "0  White or of European descent  None of the above  None of the above   \n",
              "1  White or of European descent  None of the above  None of the above   \n",
              "2             Prefer not to say  None of the above  None of the above   \n",
              "\n",
              "            SurveyLength SurveyEase ConvertedCompYearly  \n",
              "0  Appropriate in length       Easy             62268.0  \n",
              "1  Appropriate in length       Easy                 NaN  \n",
              "2  Appropriate in length       Easy                 NaN  \n",
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      ],
      "source": [
        "# Get forst 5 rows of data from survey_df\n",
        "survey_df.head(3)"
      ]
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              "      <td>&lt;div&gt;&lt;span style=\"font-size:19px;\"&gt;&lt;strong&gt;Hello world! &lt;/strong&gt;&lt;/span&gt;&lt;/div&gt;\\n\\n&lt;div&gt; &lt;/div&gt;\\n\\n&lt;div&gt;Thank you for taking the 2021 Stack Overflow Developer Survey, the longest running survey of software developers (and anyone else who codes!) on Earth. &lt;/div&gt;\\n\\n&lt;div&gt; &lt;/div&gt;\\n\\n&lt;div&gt;As in previous years, anonymized results of the survey will be made publicly available under the Open Database License, where anyone can download and analyze the data. On that note, throughout the survey, certain answers you and your peers give will be treated as personally identifiable information, and therefore kept out of the anonymized results file. We'll call out each of those in the survey with a note saying \"This information will be kept private.\" &lt;/div&gt;\\n\\n&lt;div&gt; &lt;/div&gt;\\n\\n&lt;div&gt;There are six sections in this survey. The 2nd, 3rd, and 4th sections will appear in a random order.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;\\n\\n&lt;div&gt;   1. Basic Information&lt;/div&gt;\\n\\n&lt;div&gt;   2. Education, Work, and Career&lt;/div&gt;\\n\\n&lt;div&gt;   3. Technology and Tech Culture&lt;/div&gt;\\n\\n&lt;div&gt;   4. Stack Overflow Usage + Community&lt;/div&gt;\\n\\n&lt;div&gt;   5. Demographic Information &lt;/div&gt;\\n\\n&lt;div&gt;   6. Final Questions&lt;/div&gt;\\n\\n&lt;div&gt; \\n&lt;div&gt;Most questions in this survey are optional. Required questions are marked with *. This anonymous survey will take about 10 minutes to complete. We encourage you to complete it in one sitting.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;\\n&lt;/div&gt;\\n\\n&lt;div&gt;&lt;strong&gt;If you use security or ad-blocking plugins, you may see error messages&lt;/strong&gt;&lt;/div&gt;\\n\\n&lt;div&gt;Our third-party software provider, Qualtrics, does not work well with certain ad blockers and security software. To avoid error messages that prevent you from taking the survey, please try specifically unblocking Qualtrics in your plugin or pausing the plugin while you take the survey. &lt;/div&gt;\\n\\n&lt;div&gt; &lt;/div&gt;\\n\\n&lt;div&gt;To begin, click &lt;strong&gt;Next.&lt;/strong&gt;&lt;/div&gt;</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MainBranch</td>\n",
              "      <td>Which of the following options best describes you today? Here, by \"developer\" we mean \"someone who writes code.\" &lt;b&gt;*&lt;/b&gt;</td>\n",
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              "      <th>4</th>\n",
              "      <td>Employment</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        qname  \\\n",
              "0          S0   \n",
              "1    MetaInfo   \n",
              "2          S1   \n",
              "3  MainBranch   \n",
              "4  Employment   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              question  \n",
              "0  <div><span style=\"font-size:19px;\"><strong>Hello world! </strong></span></div>\\n\\n<div> </div>\\n\\n<div>Thank you for taking the 2021 Stack Overflow Developer Survey, the longest running survey of software developers (and anyone else who codes!) on Earth. </div>\\n\\n<div> </div>\\n\\n<div>As in previous years, anonymized results of the survey will be made publicly available under the Open Database License, where anyone can download and analyze the data. On that note, throughout the survey, certain answers you and your peers give will be treated as personally identifiable information, and therefore kept out of the anonymized results file. We'll call out each of those in the survey with a note saying \"This information will be kept private.\" </div>\\n\\n<div> </div>\\n\\n<div>There are six sections in this survey. The 2nd, 3rd, and 4th sections will appear in a random order.</div><div><br></div>\\n\\n<div>   1. Basic Information</div>\\n\\n<div>   2. Education, Work, and Career</div>\\n\\n<div>   3. Technology and Tech Culture</div>\\n\\n<div>   4. Stack Overflow Usage + Community</div>\\n\\n<div>   5. Demographic Information </div>\\n\\n<div>   6. Final Questions</div>\\n\\n<div> \\n<div>Most questions in this survey are optional. Required questions are marked with *. This anonymous survey will take about 10 minutes to complete. We encourage you to complete it in one sitting.</div><div><br></div>\\n</div>\\n\\n<div><strong>If you use security or ad-blocking plugins, you may see error messages</strong></div>\\n\\n<div>Our third-party software provider, Qualtrics, does not work well with certain ad blockers and security software. To avoid error messages that prevent you from taking the survey, please try specifically unblocking Qualtrics in your plugin or pausing the plugin while you take the survey. </div>\\n\\n<div> </div>\\n\\n<div>To begin, click <strong>Next.</strong></div>  \n",
              "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Browser Meta Info  \n",
              "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              <span style=\"font-size:22px; font-family: arial,helvetica,sans-serif; font-weight: 700;\">Basic Information</span><br>\\n<br>\\n<p><span style=\"font-size:16px; font-family:arial,helvetica,sans-serif;\">The first section will focus on some basic information about who you are.<br>\\n<br>\\nMost questions in this section are required. Required questions are noted with *.</span></p>  \n",
              "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Which of the following options best describes you today? Here, by \"developer\" we mean \"someone who writes code.\" <b>*</b>  \n",
              "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Which of the following best describes your current <b>employment status</b>?  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get first 5 rows of schema df\n",
        "schema_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTGkME1vVEQo",
        "outputId": "dfcbf8c7-e364-45d9-a383-2c1021028d6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                                                                                                    question\n",
            "3  Which of the following options best describes you today? Here, by \"developer\" we mean \"someone who writes code.\" <b>*</b>\n"
          ]
        }
      ],
      "source": [
        "# Cleaning up schema df and doing some fun with it - look below\n",
        "\n",
        "# 1. Adding Groupby, so I can access the colmun name in survey_df (qname), which then should show me the question if I need to find out meaning. Look below\n",
        "def get_question(column_name):\n",
        "  qname_grp = schema_df.groupby(\"qname\")\n",
        "  print(pd.DataFrame(qname_grp.get_group(column_name)[\"question\"]))\n",
        "\n",
        "# 2. Test it out\n",
        "get_question(\"MainBranch\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvtqxUd_Xi6f",
        "outputId": "128f2721-03df-4e83-918e-a0f247d519ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['ResponseId', 'MainBranch', 'Employment', 'Country', 'US_State',\n",
              "       'UK_Country', 'EdLevel', 'Age1stCode', 'LearnCode', 'YearsCode',\n",
              "       'YearsCodePro', 'DevType', 'OrgSize', 'Currency', 'CompTotal',\n",
              "       'CompFreq', 'LanguageHaveWorkedWith', 'LanguageWantToWorkWith',\n",
              "       'DatabaseHaveWorkedWith', 'DatabaseWantToWorkWith',\n",
              "       'PlatformHaveWorkedWith', 'PlatformWantToWorkWith',\n",
              "       'WebframeHaveWorkedWith', 'WebframeWantToWorkWith',\n",
              "       'MiscTechHaveWorkedWith', 'MiscTechWantToWorkWith',\n",
              "       'ToolsTechHaveWorkedWith', 'ToolsTechWantToWorkWith',\n",
              "       'NEWCollabToolsHaveWorkedWith', 'NEWCollabToolsWantToWorkWith', 'OpSys',\n",
              "       'NEWStuck', 'NEWSOSites', 'SOVisitFreq', 'SOAccount', 'SOPartFreq',\n",
              "       'SOComm', 'NEWOtherComms', 'Age', 'Gender', 'Trans', 'Sexuality',\n",
              "       'Ethnicity', 'Accessibility', 'MentalHealth', 'SurveyLength',\n",
              "       'SurveyEase', 'ConvertedCompYearly'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get column names for survey df\n",
        "survey_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-p2t-L-SZ_Of",
        "outputId": "9cc6b631-c210-42ac-9bf7-84142d400856"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(83439, 48)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get shape of dataset\n",
        "survey_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODbCwFL_aCvn",
        "outputId": "ba7e52f2-59d7-4e30-c96e-6bdb1c2c117d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 83439 entries, 0 to 83438\n",
            "Data columns (total 48 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   ResponseId                    83439 non-null  int64  \n",
            " 1   MainBranch                    83439 non-null  object \n",
            " 2   Employment                    83323 non-null  object \n",
            " 3   Country                       83439 non-null  object \n",
            " 4   US_State                      14920 non-null  object \n",
            " 5   UK_Country                    4418 non-null   object \n",
            " 6   EdLevel                       83126 non-null  object \n",
            " 7   Age1stCode                    83243 non-null  object \n",
            " 8   LearnCode                     82963 non-null  object \n",
            " 9   YearsCode                     81641 non-null  object \n",
            " 10  YearsCodePro                  61216 non-null  object \n",
            " 11  DevType                       66484 non-null  object \n",
            " 12  OrgSize                       60726 non-null  object \n",
            " 13  Currency                      61080 non-null  object \n",
            " 14  CompTotal                     47183 non-null  float64\n",
            " 15  CompFreq                      52150 non-null  object \n",
            " 16  LanguageHaveWorkedWith        82357 non-null  object \n",
            " 17  LanguageWantToWorkWith        76821 non-null  object \n",
            " 18  DatabaseHaveWorkedWith        69546 non-null  object \n",
            " 19  DatabaseWantToWorkWith        58299 non-null  object \n",
            " 20  PlatformHaveWorkedWith        52135 non-null  object \n",
            " 21  PlatformWantToWorkWith        41619 non-null  object \n",
            " 22  WebframeHaveWorkedWith        61707 non-null  object \n",
            " 23  WebframeWantToWorkWith        52095 non-null  object \n",
            " 24  MiscTechHaveWorkedWith        47055 non-null  object \n",
            " 25  MiscTechWantToWorkWith        38021 non-null  object \n",
            " 26  ToolsTechHaveWorkedWith       72537 non-null  object \n",
            " 27  ToolsTechWantToWorkWith       65480 non-null  object \n",
            " 28  NEWCollabToolsHaveWorkedWith  81234 non-null  object \n",
            " 29  NEWCollabToolsWantToWorkWith  73022 non-null  object \n",
            " 30  OpSys                         83294 non-null  object \n",
            " 31  NEWStuck                      83052 non-null  object \n",
            " 32  NEWSOSites                    83171 non-null  object \n",
            " 33  SOVisitFreq                   82413 non-null  object \n",
            " 34  SOAccount                     82525 non-null  object \n",
            " 35  SOPartFreq                    67553 non-null  object \n",
            " 36  SOComm                        82319 non-null  object \n",
            " 37  NEWOtherComms                 82828 non-null  object \n",
            " 38  Age                           82407 non-null  object \n",
            " 39  Gender                        82286 non-null  object \n",
            " 40  Trans                         80678 non-null  object \n",
            " 41  Sexuality                     73366 non-null  object \n",
            " 42  Ethnicity                     79464 non-null  object \n",
            " 43  Accessibility                 77603 non-null  object \n",
            " 44  MentalHealth                  76920 non-null  object \n",
            " 45  SurveyLength                  81711 non-null  object \n",
            " 46  SurveyEase                    81948 non-null  object \n",
            " 47  ConvertedCompYearly           46844 non-null  float64\n",
            "dtypes: float64(2), int64(1), object(45)\n",
            "memory usage: 30.6+ MB\n"
          ]
        }
      ],
      "source": [
        "# Get info on the dataset\n",
        "survey_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "2F9Wl73jaF2o",
        "outputId": "1cbcd734-5a75-4b49-8fb7-b47a76d89b30"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
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              "      <th></th>\n",
              "      <th>ResponseId</th>\n",
              "      <th>CompTotal</th>\n",
              "      <th>ConvertedCompYearly</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>83439.000000</td>\n",
              "      <td>4.718300e+04</td>\n",
              "      <td>4.684400e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41720.000000</td>\n",
              "      <td>2.119407e+69</td>\n",
              "      <td>1.184262e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>24086.908893</td>\n",
              "      <td>4.603702e+71</td>\n",
              "      <td>5.272944e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>20860.500000</td>\n",
              "      <td>1.600000e+04</td>\n",
              "      <td>2.702500e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41720.000000</td>\n",
              "      <td>6.700000e+04</td>\n",
              "      <td>5.621100e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>62579.500000</td>\n",
              "      <td>1.400000e+05</td>\n",
              "      <td>1.000000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83439.000000</td>\n",
              "      <td>1.000000e+74</td>\n",
              "      <td>4.524131e+07</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ],
            "text/plain": [
              "         ResponseId     CompTotal  ConvertedCompYearly\n",
              "count  83439.000000  4.718300e+04         4.684400e+04\n",
              "mean   41720.000000  2.119407e+69         1.184262e+05\n",
              "std    24086.908893  4.603702e+71         5.272944e+05\n",
              "min        1.000000  0.000000e+00         1.000000e+00\n",
              "25%    20860.500000  1.600000e+04         2.702500e+04\n",
              "50%    41720.000000  6.700000e+04         5.621100e+04\n",
              "75%    62579.500000  1.400000e+05         1.000000e+05\n",
              "max    83439.000000  1.000000e+74         4.524131e+07"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get description of the dataset\n",
        "survey_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUvAR442YtDa",
        "outputId": "aa4ee27a-9df1-42e8-a819-b5803c8eb536"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ResponseId                        int64\n",
              "MainBranch                       object\n",
              "Employment                       object\n",
              "Country                          object\n",
              "US_State                         object\n",
              "UK_Country                       object\n",
              "EdLevel                          object\n",
              "Age1stCode                       object\n",
              "LearnCode                        object\n",
              "YearsCode                        object\n",
              "YearsCodePro                     object\n",
              "DevType                          object\n",
              "OrgSize                          object\n",
              "Currency                         object\n",
              "CompTotal                       float64\n",
              "CompFreq                         object\n",
              "LanguageHaveWorkedWith           object\n",
              "LanguageWantToWorkWith           object\n",
              "DatabaseHaveWorkedWith           object\n",
              "DatabaseWantToWorkWith           object\n",
              "PlatformHaveWorkedWith           object\n",
              "PlatformWantToWorkWith           object\n",
              "WebframeHaveWorkedWith           object\n",
              "WebframeWantToWorkWith           object\n",
              "MiscTechHaveWorkedWith           object\n",
              "MiscTechWantToWorkWith           object\n",
              "ToolsTechHaveWorkedWith          object\n",
              "ToolsTechWantToWorkWith          object\n",
              "NEWCollabToolsHaveWorkedWith     object\n",
              "NEWCollabToolsWantToWorkWith     object\n",
              "OpSys                            object\n",
              "NEWStuck                         object\n",
              "NEWSOSites                       object\n",
              "SOVisitFreq                      object\n",
              "SOAccount                        object\n",
              "SOPartFreq                       object\n",
              "SOComm                           object\n",
              "NEWOtherComms                    object\n",
              "Age                              object\n",
              "Gender                           object\n",
              "Trans                            object\n",
              "Sexuality                        object\n",
              "Ethnicity                        object\n",
              "Accessibility                    object\n",
              "MentalHealth                     object\n",
              "SurveyLength                     object\n",
              "SurveyEase                       object\n",
              "ConvertedCompYearly             float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get dtypes for the survey df dataset\n",
        "survey_df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nlI3p-HxZONP",
        "outputId": "caee21a9-bd46-4f73-d212-e03aaa6c8aff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "💡 Your selected dataset has 48 columns\n",
            "💡 Of 48 columns, there are 45 missing columns\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5ba88391-0bd1-47f0-a7fa-a618163a9e77\">\n",
              "    <div 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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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>% of Total Missing Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>UK_Country</th>\n",
              "      <td>79021</td>\n",
              "      <td>94.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>US_State</th>\n",
              "      <td>68519</td>\n",
              "      <td>82.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MiscTechWantToWorkWith</th>\n",
              "      <td>45418</td>\n",
              "      <td>54.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PlatformWantToWorkWith</th>\n",
              "      <td>41820</td>\n",
              "      <td>50.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ConvertedCompYearly</th>\n",
              "      <td>36595</td>\n",
              "      <td>43.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MiscTechHaveWorkedWith</th>\n",
              "      <td>36384</td>\n",
              "      <td>43.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CompTotal</th>\n",
              "      <td>36256</td>\n",
              "      <td>43.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WebframeWantToWorkWith</th>\n",
              "      <td>31344</td>\n",
              "      <td>37.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PlatformHaveWorkedWith</th>\n",
              "      <td>31304</td>\n",
              "      <td>37.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CompFreq</th>\n",
              "      <td>31289</td>\n",
              "      <td>37.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DatabaseWantToWorkWith</th>\n",
              "      <td>25140</td>\n",
              "      <td>30.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrgSize</th>\n",
              "      <td>22713</td>\n",
              "      <td>27.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Currency</th>\n",
              "      <td>22359</td>\n",
              "      <td>26.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YearsCodePro</th>\n",
              "      <td>22223</td>\n",
              "      <td>26.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WebframeHaveWorkedWith</th>\n",
              "      <td>21732</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ToolsTechWantToWorkWith</th>\n",
              "      <td>17959</td>\n",
              "      <td>21.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DevType</th>\n",
              "      <td>16955</td>\n",
              "      <td>20.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOPartFreq</th>\n",
              "      <td>15886</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DatabaseHaveWorkedWith</th>\n",
              "      <td>13893</td>\n",
              "      <td>16.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ToolsTechHaveWorkedWith</th>\n",
              "      <td>10902</td>\n",
              "      <td>13.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWCollabToolsWantToWorkWith</th>\n",
              "      <td>10417</td>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sexuality</th>\n",
              "      <td>10073</td>\n",
              "      <td>12.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LanguageWantToWorkWith</th>\n",
              "      <td>6618</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MentalHealth</th>\n",
              "      <td>6519</td>\n",
              "      <td>7.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accessibility</th>\n",
              "      <td>5836</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ethnicity</th>\n",
              "      <td>3975</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trans</th>\n",
              "      <td>2761</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWCollabToolsHaveWorkedWith</th>\n",
              "      <td>2205</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YearsCode</th>\n",
              "      <td>1798</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SurveyLength</th>\n",
              "      <td>1728</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SurveyEase</th>\n",
              "      <td>1491</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>1153</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOComm</th>\n",
              "      <td>1120</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LanguageHaveWorkedWith</th>\n",
              "      <td>1082</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>1032</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOVisitFreq</th>\n",
              "      <td>1026</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOAccount</th>\n",
              "      <td>914</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWOtherComms</th>\n",
              "      <td>611</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LearnCode</th>\n",
              "      <td>476</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWStuck</th>\n",
              "      <td>387</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EdLevel</th>\n",
              "      <td>313</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEWSOSites</th>\n",
              "      <td>268</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age1stCode</th>\n",
              "      <td>196</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OpSys</th>\n",
              "      <td>145</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Employment</th>\n",
              "      <td>116</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ba88391-0bd1-47f0-a7fa-a618163a9e77')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5ba88391-0bd1-47f0-a7fa-a618163a9e77');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "                              Missing Values  % of Total Missing Values\n",
              "UK_Country                             79021                       94.7\n",
              "US_State                               68519                       82.1\n",
              "MiscTechWantToWorkWith                 45418                       54.4\n",
              "PlatformWantToWorkWith                 41820                       50.1\n",
              "ConvertedCompYearly                    36595                       43.9\n",
              "MiscTechHaveWorkedWith                 36384                       43.6\n",
              "CompTotal                              36256                       43.5\n",
              "WebframeWantToWorkWith                 31344                       37.6\n",
              "PlatformHaveWorkedWith                 31304                       37.5\n",
              "CompFreq                               31289                       37.5\n",
              "DatabaseWantToWorkWith                 25140                       30.1\n",
              "OrgSize                                22713                       27.2\n",
              "Currency                               22359                       26.8\n",
              "YearsCodePro                           22223                       26.6\n",
              "WebframeHaveWorkedWith                 21732                       26.0\n",
              "ToolsTechWantToWorkWith                17959                       21.5\n",
              "DevType                                16955                       20.3\n",
              "SOPartFreq                             15886                       19.0\n",
              "DatabaseHaveWorkedWith                 13893                       16.7\n",
              "ToolsTechHaveWorkedWith                10902                       13.1\n",
              "NEWCollabToolsWantToWorkWith           10417                       12.5\n",
              "Sexuality                              10073                       12.1\n",
              "LanguageWantToWorkWith                  6618                        7.9\n",
              "MentalHealth                            6519                        7.8\n",
              "Accessibility                           5836                        7.0\n",
              "Ethnicity                               3975                        4.8\n",
              "Trans                                   2761                        3.3\n",
              "NEWCollabToolsHaveWorkedWith            2205                        2.6\n",
              "YearsCode                               1798                        2.2\n",
              "SurveyLength                            1728                        2.1\n",
              "SurveyEase                              1491                        1.8\n",
              "Gender                                  1153                        1.4\n",
              "SOComm                                  1120                        1.3\n",
              "LanguageHaveWorkedWith                  1082                        1.3\n",
              "Age                                     1032                        1.2\n",
              "SOVisitFreq                             1026                        1.2\n",
              "SOAccount                                914                        1.1\n",
              "NEWOtherComms                            611                        0.7\n",
              "LearnCode                                476                        0.6\n",
              "NEWStuck                                 387                        0.5\n",
              "EdLevel                                  313                        0.4\n",
              "NEWSOSites                               268                        0.3\n",
              "Age1stCode                               196                        0.2\n",
              "OpSys                                    145                        0.2\n",
              "Employment                               116                        0.1"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get all NAN values\n",
        "def missing_values_table(df):\n",
        "  # Get Missing Values and Missing Values Perecenatage\n",
        "    mis_val = df.isnull().sum()\n",
        "    mis_val_percent = 100 * mis_val / len(df)\n",
        "\n",
        "    # Get them in a table and do some changes\n",
        "    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
        "    mis_val_table_ren_columns = mis_val_table.rename(columns = {0 : 'Missing Values', 1 : '% of Total Missing Values'})\n",
        "    mis_val_table_ren_columns = mis_val_table_ren_columns[mis_val_table_ren_columns.iloc[:,1] != 0].sort_values('% of Total Missing Values', ascending=False).round(1)\n",
        "    \n",
        "    # Show some info of the dataset and missing values\n",
        "    print(f\"💡 Your selected dataset has {df.shape[1]} columns\")\n",
        "    print(f\"💡 Of {df.shape[1]} columns, there are {str(mis_val_table_ren_columns.shape[0])} missing columns\")\n",
        "    \n",
        "    # Retunr the missing values column DataFrame\n",
        "    return mis_val_table_ren_columns\n",
        "\n",
        "# Show Missing Values\n",
        "missing_values_table(survey_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mzStwQPkbB6J",
        "outputId": "5a81cf64-3adc-4b43-9e1a-583b633e021d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Columns Removed Successfully 🚀\n"
          ]
        }
      ],
      "source": [
        "# Deleting the needed items - 50%+ will be DELETED\n",
        "columns_to_be_deleted = [\"UK_Country\", \"US_State\", \"MiscTechWantToWorkWith\", \"PlatformWantToWorkWith\"]\n",
        "survey_df.drop(columns_to_be_deleted, axis = 1, inplace = True) \n",
        "\n",
        "print(\"Columns Removed Successfully 🚀\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDHAVXoEdW7J",
        "outputId": "a71ce88f-a099-4374-9171-29e3783bdb03"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['ResponseId', 'MainBranch', 'Employment', 'Country', 'EdLevel',\n",
              "       'Age1stCode', 'LearnCode', 'YearsCode', 'YearsCodePro', 'DevType',\n",
              "       'OrgSize', 'Currency', 'CompTotal', 'CompFreq',\n",
              "       'LanguageHaveWorkedWith', 'LanguageWantToWorkWith',\n",
              "       'DatabaseHaveWorkedWith', 'DatabaseWantToWorkWith',\n",
              "       'PlatformHaveWorkedWith', 'WebframeHaveWorkedWith',\n",
              "       'WebframeWantToWorkWith', 'MiscTechHaveWorkedWith',\n",
              "       'ToolsTechHaveWorkedWith', 'ToolsTechWantToWorkWith',\n",
              "       'NEWCollabToolsHaveWorkedWith', 'NEWCollabToolsWantToWorkWith', 'OpSys',\n",
              "       'NEWStuck', 'NEWSOSites', 'SOVisitFreq', 'SOAccount', 'SOPartFreq',\n",
              "       'SOComm', 'NEWOtherComms', 'Age', 'Gender', 'Trans', 'Sexuality',\n",
              "       'Ethnicity', 'Accessibility', 'MentalHealth', 'SurveyLength',\n",
              "       'SurveyEase', 'ConvertedCompYearly'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "survey_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "1zAqQaJ0fBLn"
      },
      "outputs": [],
      "source": [
        "# Remove all NAN values. Not good practice but it should do the work for now. Anyways, my computer is lagging cuz of the huge dataset\n",
        "survey_df.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "id": "mhKROyFlfdCo",
        "outputId": "33c49687-5a5b-43f2-ebb6-84874dbceebe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "💡 Your selected dataset has 44 columns\n",
            "💡 Of 44 columns, there are 0 missing columns\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "    <div 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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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>% of Total Missing Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0878dc0c-decd-44db-9056-751e23de6709')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-0878dc0c-decd-44db-9056-751e23de6709 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-0878dc0c-decd-44db-9056-751e23de6709');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Missing Values, % of Total Missing Values]\n",
              "Index: []"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check for missing values\n",
        "missing_values_table(survey_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dC7IJf6feDn",
        "outputId": "35d8ff18-d0a5-4e87-f4d7-456285576330"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9615, 44)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check shape of dataset NOW\n",
        "survey_df.shape # From 83,400 to 9600\n",
        "\n",
        "# Note: This is perfectly fine, I wasnt expecting this number but 9600 data entreis is MORE than enough for EDA. \n",
        "# Otherwise, my computer would've died 😅\n",
        "# We did the absolute right thing. EDA should never be done on such a huge dataset as a whole.\n",
        "# If you don't wish to remove data, then use .sample() to get a sample and then do the EDA on that sample. \n",
        "# Saves time and gives you the insight you need"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "cLIBmMVBgqEg"
      },
      "outputs": [],
      "source": [
        "# Convert all floats to ints\n",
        "survey_df[\"CompTotal\"] = survey_df[\"CompTotal\"].astype(int)\n",
        "survey_df[\"ConvertedCompYearly\"] = survey_df[\"ConvertedCompYearly\"].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "rnwpeWfe4Qdq",
        "outputId": "0ee6e67b-ca6b-45d2-d8fa-713c120a47ad"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The avergae salary in Oman is $3100 and the mean is $2500'"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get the salaries of a specific country\n",
        "country_grp = survey_df.groupby(\"Country\")\n",
        "\n",
        "def get_salary(country):\n",
        "  return f\"The avergae salary in Oman is ${country_grp['CompTotal'].agg(['median', 'mean']).loc[country][1]:.0f} and the mean is ${country_grp['CompTotal'].agg(['median', 'mean']).loc[country][0]:.0f}\"\n",
        "  \n",
        "get_salary(\"Oman\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIAOPx0hYSbn"
      },
      "source": [
        "## **Section 2 - Data Visualiztation - Here comes! My most favourite part of the Data Analysis journey! Let's get graphy!**\n",
        "\n",
        "**NOTE**: This dataset was mainly for data cleaning and data wrangling purposes - not much for data visualization. So I won't be doing very cool charts and stuff - Basic Charts like Bar Graphs should do. For advanced and more fun grahs you could refer to my other notebooks, which contain a lot of complex graphs. \n",
        "\n",
        "This was mainly focused on Data Analysis with Pandas. \n",
        "\n",
        "But wait, towards the end of this notebook - there is a filter system - which you HAVE to check out! It demos both the use of Data Analysis/Filtering/Cleaning and Data Visualization! An awesome way to show what you got!\n",
        "\n",
        "**Be sure to check it out 😎**\n",
        "\n",
        "`Visaluzations done via matplotlib and seaborn`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "KGxQ_7FUQAc3"
      },
      "outputs": [],
      "source": [
        "# Change Matplotlib Settings to show max rows and columns\n",
        "pd.set_option('display.max_rows', 50)\n",
        "pd.set_option('display.max_columns', 10_000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "sVi-_DvzOHSf",
        "outputId": "4c8b71b0-8d04-4f56-ce53-d318c130e2aa"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Frequency of Employment\n",
        "\n",
        "# Set Figure Size\n",
        "plt.figure(figsize=(20,10), dpi=100)\n",
        "\n",
        "# Set Title\n",
        "plt.title(\"Frequency of Employment\")\n",
        "\n",
        "# Plot\n",
        "survey_df[\"Employment\"].value_counts().plot(kind=\"bar\", color=[\"#264653\", \"#2a9d8f\", \"#e9c46a\"])\n",
        "\n",
        "# Show\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=360)\n",
        "plt.show()\n",
        "\n",
        "# Analysis - We obeserve around 80%+ of the devs were employed full-time and a handful self-employed and part-time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "QFT_hlUOmmwt",
        "outputId": "b8f1cd1b-185f-4530-9ebc-b7ba8f801745"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Top 10 Most Participated Countries in the Developer Survey\n",
        "# I won't be doing the top 10 least countries, as they are all equal, meaning the graph is a straight line. You could do that by chaning **nlargest** to\n",
        "# **nsmallest**\n",
        "\n",
        "# Set Figure Size\n",
        "plt.figure(figsize=(20,10), dpi=100)\n",
        "\n",
        "# Set Title\n",
        "plt.title(\"Top 10 Most Participated Countries in the 2021 Developer Survey\")\n",
        "\n",
        "# Plot\n",
        "survey_df[\"Country\"].value_counts().nlargest(10).plot(kind=\"bar\", color=[\"#264653\", \"#2a9d8f\", \"#e9c46a\"])\n",
        "\n",
        "# Show\n",
        "plt.xticks(rotation=360)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Analysis - Around 20% (2000) devs took this Survey from the USA followed by India and the UK"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "AwUadbmOoIe9",
        "outputId": "cd918d3b-345e-4662-bd94-40ab7be9db56"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# At What Age When These Devs First Coded\n",
        "# Set Figure Size\n",
        "plt.figure(figsize=(20,10), dpi=100)\n",
        "\n",
        "# Set Title\n",
        "plt.title(\"At What Age When These Devs First Coded\")\n",
        "\n",
        "# Plot\n",
        "survey_df[\"Age1stCode\"].value_counts().plot(kind=\"bar\", color=[\"#264653\", \"#2a9d8f\", \"#e9c46a\"])\n",
        "\n",
        "# Show\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=360)\n",
        "plt.show()\n",
        "\n",
        "# Analysis - Most of the devs coded their first line of code when they were of ages between 11-17, followed by 18-24."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "81lqdFOipNpM",
        "outputId": "b8819f58-3ec1-4246-e46f-17d447b1a038"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# For How Many Years Have Been These Devs Coding?\n",
        "# Set Figure Size\n",
        "plt.figure(figsize=(20,10))\n",
        "\n",
        "# Set Title\n",
        "plt.title(\"For How Many Years Have Been These Devs Coding?\")\n",
        "\n",
        "# Plot\n",
        "survey_df[\"YearsCode\"].value_counts().plot(kind=\"bar\", color=[\"#264653\", \"#2a9d8f\", \"#e9c46a\"])\n",
        "\n",
        "# Show\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=360)\n",
        "plt.show()\n",
        "\n",
        "# Analysis - Most of the devs who took this survey had around 10 years of experience followed by 8, 15, 7, 6 and 5 years of coding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "id": "Y5WLBhjXqGft",
        "outputId": "399d7ba5-95de-4295-adf0-5b94172e7f95"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# How Often Do These Devs Get Paid?\n",
        "# Set Figure Size\n",
        "plt.figure(figsize=(20,10))\n",
        "\n",
        "# Set Title\n",
        "plt.title(\"How Often Do These Devs Get Paid?\")\n",
        "\n",
        "# Plot\n",
        "survey_df[\"CompFreq\"].value_counts().plot(kind=\"bar\", color=[\"#264653\", \"#2a9d8f\", \"#e9c46a\"])\n",
        "\n",
        "# Show\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=360)\n",
        "plt.show()\n",
        "\n",
        "# Analysis - We obserbe that around 5000 devs get paid yearly and arund 4300 devs get paid monthy and maybe 200-400 get paid weekly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "Y52JEz1Gqnzc",
        "outputId": "fd5ae0f2-0e56-41cb-80ac-255d9c8ef169"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# How Long Was the StackOverFlow 2021 Developer Survey\n",
        "\n",
        "# Set Figure Size\n",
        "plt.figure(figsize=(20,10))\n",
        "\n",
        "# Set Title\n",
        "plt.title(\"How Long Was the StackOverFlow 2021 Developer Survey\")\n",
        "\n",
        "# Plot\n",
        "survey_df[\"SurveyLength\"].value_counts().plot(kind=\"bar\", color=[\"#264653\", \"#2a9d8f\", \"#e9c46a\"])\n",
        "\n",
        "# Show\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=360)\n",
        "plt.show()\n",
        "\n",
        "# Analysis, we can the SurveyLength was fine around 8000+ devs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "s97Bwe7Tq-yt",
        "outputId": "7073ac37-a18f-459b-aa88-60ebbb9fa165"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# How Easy Was the StackOverFlow 2021 Developer Survey\n",
        "\n",
        "# Set Figure Size\n",
        "plt.figure(figsize=(20,10))\n",
        "\n",
        "# Set Title\n",
        "plt.title(\"How Easy Was the StackOverFlow 2021 Developer Survey\")\n",
        "\n",
        "# Plot\n",
        "survey_df[\"SurveyEase\"].value_counts().plot(kind=\"bar\", color=[\"#264653\", \"#2a9d8f\", \"#e9c46a\"])\n",
        "\n",
        "# Show\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=360)\n",
        "plt.show()\n",
        "\n",
        "# Analysis, we can the Survey was easy for around 7800 devs and medium for around 1700 devs and difficult for around 10 poeple 🤣"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCKWmlxws3bQ"
      },
      "source": [
        "## **⤵️ Bonus: FILTER SYSTEM BELOW ⤵️**\n",
        "\n",
        "🚀 I was waiting for this part for so long. This is an example of how you could merge Data Analysis and Data Visualization to output maginificent results! \n",
        "\n",
        "\n",
        "🚀 This Filter System has Filters, which have filters within them. \n",
        "\n",
        "\n",
        "🚀 The first one filters through the whole dataset, outputting the most used tool in the top 5 countries within that useability!\n",
        "\n",
        "\n",
        "🚀 The second one is filtering system that filters through the whole datatset, and return the values of the following columns - in other words the columns\n",
        "which state what the devs out there are using! This is country-specified.\n",
        "\n",
        "**Note**: You have to choose one single column and single country and one single tool. I have listed the columns which you could use below, but for the countries and tools you'll have to find out yourself, consider this an excersize. \n",
        "\n",
        "If you want a hint, here it is. Try doing `survey_df[\"Country\"].value_counts().index.tolist()` to get the **countries' names**. To get the data in the provided columns change **Country** to the requried column. Like this:\n",
        "`survey_df[\"DatabaseHaveWorkedWith\"].value_counts().index.tolist()`\n",
        "\n",
        "#### **🐍 COLUMN LIST 🐍**\n",
        "1. LanguageHaveWorkedWith\n",
        "2. DatabaseHaveWorkedWith\n",
        "3. PlatforomHaveWorkedWith\n",
        "4. WebframeHaveWorkedWith\n",
        "5. MiscTechHaveWorkedWith\n",
        "6. ToolsTechHaveWorkedWith\n",
        "7. NEWCollabToolsHaveWorkedWith\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6MEYkoCErjKN",
        "outputId": "61a293d7-3cdf-412b-ca22-cd086ac40b91"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total Number of Respondents</th>\n",
              "      <th>Have Worked with .Net</th>\n",
              "      <th>Percentage Uses .Net</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>850</td>\n",
              "      <td>112</td>\n",
              "      <td>13.176471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States of America</th>\n",
              "      <td>2024</td>\n",
              "      <td>54</td>\n",
              "      <td>2.667984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>400</td>\n",
              "      <td>39</td>\n",
              "      <td>9.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>213</td>\n",
              "      <td>34</td>\n",
              "      <td>15.962441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pakistan</th>\n",
              "      <td>91</td>\n",
              "      <td>26</td>\n",
              "      <td>28.571429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "    }\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2bb0b7da-fdec-4b44-b209-6a09096bcbe1 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-2bb0b7da-fdec-4b44-b209-6a09096bcbe1');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ],
            "text/plain": [
              "                          Total Number of Respondents  Have Worked with .Net  \\\n",
              "India                                             850                    112   \n",
              "United States of America                         2024                     54   \n",
              "Brazil                                            400                     39   \n",
              "Italy                                             213                     34   \n",
              "Pakistan                                           91                     26   \n",
              "\n",
              "                          Percentage Uses .Net  \n",
              "India                                13.176471  \n",
              "United States of America              2.667984  \n",
              "Brazil                                9.750000  \n",
              "Italy                                15.962441  \n",
              "Pakistan                             28.571429  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Which top 5 countries knows a specific programming language the most?\n",
        "\n",
        "# Create Country Group\n",
        "country_grp = survey_df.groupby(\"Country\")\n",
        "specific_countries_input_column = \"NEWCollabToolsHaveWorkedWith\"  # ⬆️⬆️⬆️ ONE OF THE ABOVE ⬆️⬆️⬆️\n",
        "\n",
        "tool = \".Net\" # Inputted Tool - Make sure this is present in the column you mentioned above ⬆️\n",
        "\n",
        "# Check if tool is C++, (tool with symbols). This is so because Regex doesn't allow `+` symbols\n",
        "if tool == \"C++\":\n",
        "  tool = \"C\\+\\+\"\n",
        "\n",
        "# Get Those Country entries and sum them, with the user-specified-langauge\n",
        "country_uses_language = country_grp[specific_countries_input_column].apply(lambda x: x.str.contains(tool).sum())\n",
        "country_uses_language.head()\n",
        "\n",
        "country_respondents = survey_df['Country'].value_counts()\n",
        "country_respondents\n",
        "\n",
        "# Concat into a DataFrame\n",
        "tool_df = pd.concat([country_respondents, country_uses_language], axis=1, sort=False)\n",
        "\n",
        "# Percentage Using a Specific language\n",
        "tool_df[f'Percentage Uses {tool}'] = (tool_df[specific_countries_input_column] / tool_df['Country']) * 100\n",
        "\n",
        "# Change Column Names\n",
        "language_df = tool_df.rename(columns={\n",
        "    \"Country\" : \"Total Number of Respondents\", \n",
        "    specific_countries_input_column : f\"Have Worked with {tool}\",\n",
        "    \n",
        "})\n",
        "\n",
        "# Show\n",
        "language_df.nlargest(5, columns=[f'Have Worked with {tool}', f\"Total Number of Respondents\"])\n",
        "\n",
        "# Note:\n",
        "# These are shown via Percentage Ratios, so please swap the places of the above line to see results from Have worked with the tool prespective as shown:\n",
        "# language_df.nlargest(5, columns=[f\"Total Number of Respondents\", f\"Percentage Knowing {tool}'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "_n4R94nDtB-8",
        "outputId": "451849fa-997e-41c5-9f67-b53607074c22"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Out of a total of 91 devs in Pakistan, 83 devs use Visual Studio Code\n",
            "############ ⤵️⤵️ PIE CHART ⤵️⤵️ ############\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Below is a filtering System  Below you can change the country name and tool name.\n",
        "# Also, everything should be one at a time!\n",
        "\n",
        "# 🐍 COLUMN LIST 🐍\n",
        "# 1. LanguageHaveWorkedWith\n",
        "# 2. DatabaseHaveWorkedWith\n",
        "# 3. PlatformHaveWorkedWith\n",
        "# 4. WebframeHaveWorkedWith\n",
        "# 5. MiscTechHaveWorkedWith\n",
        "# 6. ToolsTechHaveWorkedWith\n",
        "# 7. NEWCollabToolsHaveWorkedWith\n",
        "\n",
        "specific_countries_input_column = \"NEWCollabToolsHaveWorkedWith\"  # ⬆️⬆️⬆️ ONE OF THE ABOVE ⬆️⬆️⬆️\n",
        "country = \"Pakistan\"\n",
        "tool = \"Visual Studio Code\"\n",
        "\n",
        "def get_programming_language_users(tool, country):\n",
        "  global country_respondents, country_uses_specified_language\n",
        "  country_respondents = survey_df['Country'].value_counts()\n",
        "  country_uses_specified_language = country_grp[specific_countries_input_column].apply(lambda x: x.str.contains(tool).sum())\n",
        "  return f\"Out of a total of {country_respondents[country]} devs in {country}, {country_uses_specified_language[country]} devs use {tool}\"\n",
        "\n",
        "# Print it out\n",
        "print(get_programming_language_users(tool, country))\n",
        "print(\"############ ⤵️⤵️ PIE CHART ⤵️⤵️ ############\")\n",
        "\n",
        "# Chart it out - PIE CHART\n",
        "\n",
        "# Create Needed Variables\n",
        "total_devs = country_respondents[country]\n",
        "devs_knowing = country_uses_specified_language[country]\n",
        "\n",
        "# Styles\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "# Labels\n",
        "labels = [f\"Total Poeple\", f'Poeple using {tool}']\n",
        "\n",
        "# Create DataFrame\n",
        "devs_comparison_df = (pd.DataFrame(\n",
        "    {\"devs_comparison_df\" : [total_devs, devs_knowing]})).reset_index()\n",
        "\n",
        "# Plot DataFrame\n",
        "devs_comparison_df.plot.pie(y=\"devs_comparison_df\", figsize=(10, 10), colors=[\"Salmon\", \"Lightblue\"], autopct='%.0f%%', shadow=True, labels=labels).set(\n",
        "    title=\"Total Devs VS Devs Use\")\n",
        "\n",
        "# Some axis stuff\n",
        "plt.legend(labels=labels, loc=\"best\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BixQshNh1MN9"
      },
      "source": [
        "Thank you for reading. I hope you enjoy it. And Don’t forget to follow me on \n",
        "[Hashnode](https://hashnode.com/@muhammadanas0716) | [GitHub](https://github.com/muhammadanas365) | [Medium](https://medium.com/@muhammadanas0716) | [Twitter](https://twitter.com/Muhamma63227121) | [Kaggle](https://www.kaggle.com/muhammadanas0716) | [LinkedIn](https://www.linkedin.com/in/muhammad-anas-63744b235/)👍\n",
        "\n",
        "***This is Muhammad Anas, and I'll catch you next time***"
      ]
    }
  ],
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      "name": "Stackoverflow Developer Survey 2021 - Analysis.ipynb",
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