diff --git a/Stackoverflow_Survey_Analysis_2023.ipynb b/Stackoverflow_Survey_Analysis_2023.ipynb
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+++ b/Stackoverflow_Survey_Analysis_2023.ipynb
@@ -0,0 +1,4827 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1c1c9b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a958eacb",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow 2023 Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0c0f0d96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ResponseId</th>\n",
+       "      <th>Q120</th>\n",
+       "      <th>MainBranch</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>RemoteWork</th>\n",
+       "      <th>CodingActivities</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>LearnCode</th>\n",
+       "      <th>LearnCodeOnline</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Frequency_1</th>\n",
+       "      <th>Frequency_2</th>\n",
+       "      <th>Frequency_3</th>\n",
+       "      <th>TimeSearching</th>\n",
+       "      <th>TimeAnswering</th>\n",
+       "      <th>ProfessionalTech</th>\n",
+       "      <th>Industry</th>\n",
+       "      <th>SurveyLength</th>\n",
+       "      <th>SurveyEase</th>\n",
+       "      <th>ConvertedCompYearly</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>I agree</td>\n",
+       "      <td>None of these</td>\n",
+       "      <td>18-24 years old</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>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>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>I agree</td>\n",
+       "      <td>I am a developer by profession</td>\n",
+       "      <td>25-34 years old</td>\n",
+       "      <td>Employed, full-time</td>\n",
+       "      <td>Remote</td>\n",
+       "      <td>Hobby;Contribute to open-source projects;Boots...</td>\n",
+       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
+       "      <td>Books / Physical media;Colleague;Friend or fam...</td>\n",
+       "      <td>Formal documentation provided by the owner of ...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1-2 times a week</td>\n",
+       "      <td>10+ times a week</td>\n",
+       "      <td>Never</td>\n",
+       "      <td>15-30 minutes a day</td>\n",
+       "      <td>15-30 minutes a day</td>\n",
+       "      <td>DevOps function;Microservices;Automated testin...</td>\n",
+       "      <td>Information Services, IT, Software Development...</td>\n",
+       "      <td>Appropriate in length</td>\n",
+       "      <td>Easy</td>\n",
+       "      <td>285000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>I agree</td>\n",
+       "      <td>I am a developer by profession</td>\n",
+       "      <td>45-54 years old</td>\n",
+       "      <td>Employed, full-time</td>\n",
+       "      <td>Hybrid (some remote, some in-person)</td>\n",
+       "      <td>Hobby;Professional development or self-paced l...</td>\n",
+       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
+       "      <td>Books / Physical media;Colleague;On the job tr...</td>\n",
+       "      <td>Formal documentation provided by the owner of ...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6-10 times a week</td>\n",
+       "      <td>6-10 times a week</td>\n",
+       "      <td>3-5 times a week</td>\n",
+       "      <td>30-60 minutes a day</td>\n",
+       "      <td>30-60 minutes a day</td>\n",
+       "      <td>DevOps function;Microservices;Automated testin...</td>\n",
+       "      <td>Information Services, IT, Software Development...</td>\n",
+       "      <td>Appropriate in length</td>\n",
+       "      <td>Easy</td>\n",
+       "      <td>250000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>I agree</td>\n",
+       "      <td>I am a developer by profession</td>\n",
+       "      <td>25-34 years old</td>\n",
+       "      <td>Employed, full-time</td>\n",
+       "      <td>Hybrid (some remote, some in-person)</td>\n",
+       "      <td>Hobby</td>\n",
+       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
+       "      <td>Colleague;Friend or family member;Other online...</td>\n",
+       "      <td>Formal documentation provided by the owner of ...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1-2 times a week</td>\n",
+       "      <td>10+ times a week</td>\n",
+       "      <td>1-2 times a week</td>\n",
+       "      <td>15-30 minutes a day</td>\n",
+       "      <td>30-60 minutes a day</td>\n",
+       "      <td>Automated testing;Continuous integration (CI) ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Appropriate in length</td>\n",
+       "      <td>Easy</td>\n",
+       "      <td>156000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>I agree</td>\n",
+       "      <td>I am a developer by profession</td>\n",
+       "      <td>25-34 years old</td>\n",
+       "      <td>Employed, full-time;Independent contractor, fr...</td>\n",
+       "      <td>Remote</td>\n",
+       "      <td>Hobby;Contribute to open-source projects;Profe...</td>\n",
+       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
+       "      <td>Books / Physical media;Online Courses or Certi...</td>\n",
+       "      <td>Formal documentation provided by the owner of ...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1-2 times a week</td>\n",
+       "      <td>1-2 times a week</td>\n",
+       "      <td>3-5 times a week</td>\n",
+       "      <td>60-120 minutes a day</td>\n",
+       "      <td>30-60 minutes a day</td>\n",
+       "      <td>Microservices;Automated testing;Observability ...</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Appropriate in length</td>\n",
+       "      <td>Neither easy nor difficult</td>\n",
+       "      <td>23456.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 84 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ResponseId     Q120                      MainBranch              Age  \\\n",
+       "0           1  I agree                   None of these  18-24 years old   \n",
+       "1           2  I agree  I am a developer by profession  25-34 years old   \n",
+       "2           3  I agree  I am a developer by profession  45-54 years old   \n",
+       "3           4  I agree  I am a developer by profession  25-34 years old   \n",
+       "4           5  I agree  I am a developer by profession  25-34 years old   \n",
+       "\n",
+       "                                          Employment  \\\n",
+       "0                                                NaN   \n",
+       "1                                Employed, full-time   \n",
+       "2                                Employed, full-time   \n",
+       "3                                Employed, full-time   \n",
+       "4  Employed, full-time;Independent contractor, fr...   \n",
+       "\n",
+       "                             RemoteWork  \\\n",
+       "0                                   NaN   \n",
+       "1                                Remote   \n",
+       "2  Hybrid (some remote, some in-person)   \n",
+       "3  Hybrid (some remote, some in-person)   \n",
+       "4                                Remote   \n",
+       "\n",
+       "                                    CodingActivities  \\\n",
+       "0                                                NaN   \n",
+       "1  Hobby;Contribute to open-source projects;Boots...   \n",
+       "2  Hobby;Professional development or self-paced l...   \n",
+       "3                                              Hobby   \n",
+       "4  Hobby;Contribute to open-source projects;Profe...   \n",
+       "\n",
+       "                                        EdLevel  \\\n",
+       "0                                           NaN   \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",
+       "3  Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
+       "4  Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
+       "\n",
+       "                                           LearnCode  \\\n",
+       "0                                                NaN   \n",
+       "1  Books / Physical media;Colleague;Friend or fam...   \n",
+       "2  Books / Physical media;Colleague;On the job tr...   \n",
+       "3  Colleague;Friend or family member;Other online...   \n",
+       "4  Books / Physical media;Online Courses or Certi...   \n",
+       "\n",
+       "                                     LearnCodeOnline  ...        Frequency_1  \\\n",
+       "0                                                NaN  ...                NaN   \n",
+       "1  Formal documentation provided by the owner of ...  ...   1-2 times a week   \n",
+       "2  Formal documentation provided by the owner of ...  ...  6-10 times a week   \n",
+       "3  Formal documentation provided by the owner of ...  ...   1-2 times a week   \n",
+       "4  Formal documentation provided by the owner of ...  ...   1-2 times a week   \n",
+       "\n",
+       "         Frequency_2       Frequency_3         TimeSearching  \\\n",
+       "0                NaN               NaN                   NaN   \n",
+       "1   10+ times a week             Never   15-30 minutes a day   \n",
+       "2  6-10 times a week  3-5 times a week   30-60 minutes a day   \n",
+       "3   10+ times a week  1-2 times a week   15-30 minutes a day   \n",
+       "4   1-2 times a week  3-5 times a week  60-120 minutes a day   \n",
+       "\n",
+       "         TimeAnswering                                   ProfessionalTech  \\\n",
+       "0                  NaN                                                NaN   \n",
+       "1  15-30 minutes a day  DevOps function;Microservices;Automated testin...   \n",
+       "2  30-60 minutes a day  DevOps function;Microservices;Automated testin...   \n",
+       "3  30-60 minutes a day  Automated testing;Continuous integration (CI) ...   \n",
+       "4  30-60 minutes a day  Microservices;Automated testing;Observability ...   \n",
+       "\n",
+       "                                            Industry           SurveyLength  \\\n",
+       "0                                                NaN                    NaN   \n",
+       "1  Information Services, IT, Software Development...  Appropriate in length   \n",
+       "2  Information Services, IT, Software Development...  Appropriate in length   \n",
+       "3                                                NaN  Appropriate in length   \n",
+       "4                                              Other  Appropriate in length   \n",
+       "\n",
+       "                   SurveyEase ConvertedCompYearly  \n",
+       "0                         NaN                 NaN  \n",
+       "1                        Easy            285000.0  \n",
+       "2                        Easy            250000.0  \n",
+       "3                        Easy            156000.0  \n",
+       "4  Neither easy nor difficult             23456.0  \n",
+       "\n",
+       "[5 rows x 84 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023 = pd.read_csv(r\"C:/Users/maury/OneDrive/Desktop/survey_results_public.csv\")\n",
+    "df2023.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2eae077d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(89184, 84)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b4cb09a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows: 89184\n",
+      "Number of columns: 84\n",
+      "ResponseId               int64\n",
+      "Q120                    object\n",
+      "MainBranch              object\n",
+      "Age                     object\n",
+      "Employment              object\n",
+      "                        ...   \n",
+      "ProfessionalTech        object\n",
+      "Industry                object\n",
+      "SurveyLength            object\n",
+      "SurveyEase              object\n",
+      "ConvertedCompYearly    float64\n",
+      "Length: 84, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check dimensions\n",
+    "print(\"Number of rows:\", df2023.shape[0])\n",
+    "print(\"Number of columns:\", df2023.shape[1])\n",
+    "\n",
+    "# Check column names and data types\n",
+    "print(df2023.dtypes)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "212d97bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ResponseId', 'Q120', 'MainBranch', 'Age', 'Employment', 'RemoteWork', 'CodingActivities', 'EdLevel', 'LearnCode', 'LearnCodeOnline', 'LearnCodeCoursesCert', 'YearsCode', 'YearsCodePro', 'DevType', 'OrgSize', 'PurchaseInfluence', 'TechList', 'BuyNewTool', 'Country', 'Currency', 'CompTotal', 'LanguageHaveWorkedWith', 'LanguageWantToWorkWith', 'DatabaseHaveWorkedWith', 'DatabaseWantToWorkWith', 'PlatformHaveWorkedWith', 'PlatformWantToWorkWith', 'WebframeHaveWorkedWith', 'WebframeWantToWorkWith', 'MiscTechHaveWorkedWith', 'MiscTechWantToWorkWith', 'ToolsTechHaveWorkedWith', 'ToolsTechWantToWorkWith', 'NEWCollabToolsHaveWorkedWith', 'NEWCollabToolsWantToWorkWith', 'OpSysPersonal use', 'OpSysProfessional use', 'OfficeStackAsyncHaveWorkedWith', 'OfficeStackAsyncWantToWorkWith', 'OfficeStackSyncHaveWorkedWith', 'OfficeStackSyncWantToWorkWith', 'AISearchHaveWorkedWith', 'AISearchWantToWorkWith', 'AIDevHaveWorkedWith', 'AIDevWantToWorkWith', 'NEWSOSites', 'SOVisitFreq', 'SOAccount', 'SOPartFreq', 'SOComm', 'SOAI', 'AISelect', 'AISent', 'AIAcc', 'AIBen', 'AIToolInterested in Using', 'AIToolCurrently Using', 'AIToolNot interested in Using', 'AINextVery different', 'AINextNeither different nor similar', 'AINextSomewhat similar', 'AINextVery similar', 'AINextSomewhat different', 'TBranch', 'ICorPM', 'WorkExp', 'Knowledge_1', 'Knowledge_2', 'Knowledge_3', 'Knowledge_4', 'Knowledge_5', 'Knowledge_6', 'Knowledge_7', 'Knowledge_8', 'Frequency_1', 'Frequency_2', 'Frequency_3', 'TimeSearching', 'TimeAnswering', 'ProfessionalTech', 'Industry', 'SurveyLength', 'SurveyEase', 'ConvertedCompYearly']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2023.columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8428b7ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Defining the list of columns to drop\n",
+    "drop_cols = ['ResponseId','Q120', 'OrgSize', 'PurchaseInfluence', 'BuyNewTool', 'Currency', 'CompTotal', \n",
+    "    'MiscTechHaveWorkedWith', 'MiscTechWantToWorkWith', 'ToolsTechHaveWorkedWith', 'ToolsTechWantToWorkWith', \n",
+    "    'NEWCollabToolsHaveWorkedWith', 'NEWCollabToolsWantToWorkWith', 'OpSysPersonal use', 'OpSysProfessional use', \n",
+    "    'OfficeStackAsyncHaveWorkedWith', 'OfficeStackAsyncWantToWorkWith', 'OfficeStackSyncHaveWorkedWith', \n",
+    "    'OfficeStackSyncWantToWorkWith', 'AISearchHaveWorkedWith', 'AISearchWantToWorkWith', 'AIDevHaveWorkedWith', \n",
+    "    'AIDevWantToWorkWith', 'NEWSOSites', 'SOVisitFreq', 'SOAccount', 'SOPartFreq', 'SOComm', 'SOAI', 'AISelect', \n",
+    "    'AISent', 'AIAcc', 'AIBen', 'AIToolInterested in Using', 'AIToolCurrently Using', 'AIToolNot interested in Using', \n",
+    "    'AINextVery different', 'AINextNeither different nor similar', 'AINextSomewhat similar', 'AINextVery similar', \n",
+    "    'AINextSomewhat different', 'TBranch', 'ICorPM', 'WorkExp', 'Knowledge_1', 'Knowledge_2', 'Knowledge_3', \n",
+    "    'Knowledge_4', 'Knowledge_5', 'Knowledge_6', 'Knowledge_7', 'Knowledge_8', 'Frequency_1', 'Frequency_2', \n",
+    "    'Frequency_3', 'TimeSearching', 'TimeAnswering','SurveyLength', 'SurveyEase']\n",
+    "\n",
+    "# Drop the specified columns\n",
+    "df2023.drop(drop_cols, axis=1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f626bc9d",
+   "metadata": {},
+   "source": [
+    "### Data Filtering - Sorting & Renaming\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "53051f11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(89184, 25)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.shape #checking rows and col after dropping the table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "08ee7325",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the list of column names\n",
+    "cols = [\n",
+    "    'Age',\n",
+    "    'MainBranch',\n",
+    "    'Employment',\n",
+    "    'RemoteWork',\n",
+    "    'CodingActivities',\n",
+    "    'EdLevel',\n",
+    "    'LearnCode',\n",
+    "    'LearnCodeOnline',\n",
+    "    'LearnCodeCoursesCert',\n",
+    "    'YearsCode',\n",
+    "    'YearsCodePro',\n",
+    "    'DevType',\n",
+    "    'TechList',\n",
+    "    'Country',\n",
+    "    'LanguageHaveWorkedWith',\n",
+    "    'LanguageWantToWorkWith',\n",
+    "    'DatabaseHaveWorkedWith',\n",
+    "    'DatabaseWantToWorkWith',\n",
+    "    'PlatformHaveWorkedWith',\n",
+    "    'PlatformWantToWorkWith',\n",
+    "    'WebframeHaveWorkedWith',\n",
+    "    'WebframeWantToWorkWith',\n",
+    "    'ProfessionalTech',\n",
+    "    'Industry',\n",
+    "    'ConvertedCompYearly'\n",
+    "]\n",
+    "\n",
+    "# Select the specified columns from df2018\n",
+    "df = df2023[cols]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9053b698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#changing the name of columns for easier understanding\n",
+    "# 'MainBranch': 'Profession'\n",
+    "# 'ConvertedCompYearly': 'SalaryUSD'\n",
+    "df2023.rename(columns={'MainBranch': 'Profession', 'ConvertedCompYearly': 'SalaryUSD'}, \n",
+    "                      inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "492e7146",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Profession                    0\n",
+      "Age                           0\n",
+      "Employment                 1286\n",
+      "RemoteWork                15374\n",
+      "CodingActivities          15420\n",
+      "EdLevel                    1211\n",
+      "LearnCode                  1521\n",
+      "LearnCodeOnline           19100\n",
+      "LearnCodeCoursesCert      52108\n",
+      "YearsCode                  1749\n",
+      "YearsCodePro              23048\n",
+      "DevType                   12312\n",
+      "TechList                  28333\n",
+      "Country                    1211\n",
+      "LanguageHaveWorkedWith     2044\n",
+      "LanguageWantToWorkWith     8475\n",
+      "DatabaseHaveWorkedWith    15749\n",
+      "DatabaseWantToWorkWith    28273\n",
+      "PlatformHaveWorkedWith    25556\n",
+      "PlatformWantToWorkWith    37876\n",
+      "WebframeHaveWorkedWith    22246\n",
+      "WebframeWantToWorkWith    32443\n",
+      "ProfessionalTech          47401\n",
+      "Industry                  52410\n",
+      "SalaryUSD                 41165\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2023.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0b673570",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>CodingActivities</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>DatabaseHaveWorkedWith</th>\n",
+       "      <th>DatabaseWantToWorkWith</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Industry</th>\n",
+       "      <th>LanguageHaveWorkedWith</th>\n",
+       "      <th>...</th>\n",
+       "      <th>PlatformWantToWorkWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>ProfessionalTech</th>\n",
+       "      <th>RemoteWork</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>TechList</th>\n",
+       "      <th>WebframeHaveWorkedWith</th>\n",
+       "      <th>WebframeWantToWorkWith</th>\n",
+       "      <th>YearsCode</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18-24 years old</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>None of these</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>1</th>\n",
+       "      <td>25-34 years old</td>\n",
+       "      <td>Hobby;Contribute to open-source projects;Boots...</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>Supabase</td>\n",
+       "      <td>Firebase Realtime Database;Supabase</td>\n",
+       "      <td>Senior Executive (C-Suite, VP, etc.)</td>\n",
+       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
+       "      <td>Employed, full-time</td>\n",
+       "      <td>Information Services, IT, Software Development...</td>\n",
+       "      <td>HTML/CSS;JavaScript;Python</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Fly.io;Netlify;Render</td>\n",
+       "      <td>I am a developer by profession</td>\n",
+       "      <td>DevOps function;Microservices;Automated testin...</td>\n",
+       "      <td>Remote</td>\n",
+       "      <td>285000.0</td>\n",
+       "      <td>Investigate</td>\n",
+       "      <td>Next.js;React;Remix;Vue.js</td>\n",
+       "      <td>Deno;Elm;Nuxt.js;React;Svelte;Vue.js</td>\n",
+       "      <td>18</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Age                                   CodingActivities  \\\n",
+       "0  18-24 years old                                                NaN   \n",
+       "1  25-34 years old  Hobby;Contribute to open-source projects;Boots...   \n",
+       "\n",
+       "                    Country DatabaseHaveWorkedWith  \\\n",
+       "0                       NaN                    NaN   \n",
+       "1  United States of America               Supabase   \n",
+       "\n",
+       "                DatabaseWantToWorkWith                               DevType  \\\n",
+       "0                                  NaN                                   NaN   \n",
+       "1  Firebase Realtime Database;Supabase  Senior Executive (C-Suite, VP, etc.)   \n",
+       "\n",
+       "                                        EdLevel           Employment  \\\n",
+       "0                                           NaN                  NaN   \n",
+       "1  Bachelor’s degree (B.A., B.S., B.Eng., etc.)  Employed, full-time   \n",
+       "\n",
+       "                                            Industry  \\\n",
+       "0                                                NaN   \n",
+       "1  Information Services, IT, Software Development...   \n",
+       "\n",
+       "       LanguageHaveWorkedWith  ... PlatformWantToWorkWith  \\\n",
+       "0                         NaN  ...                    NaN   \n",
+       "1  HTML/CSS;JavaScript;Python  ...  Fly.io;Netlify;Render   \n",
+       "\n",
+       "                       Profession  \\\n",
+       "0                   None of these   \n",
+       "1  I am a developer by profession   \n",
+       "\n",
+       "                                    ProfessionalTech RemoteWork SalaryUSD  \\\n",
+       "0                                                NaN        NaN       NaN   \n",
+       "1  DevOps function;Microservices;Automated testin...     Remote  285000.0   \n",
+       "\n",
+       "      TechList      WebframeHaveWorkedWith  \\\n",
+       "0          NaN                         NaN   \n",
+       "1  Investigate  Next.js;React;Remix;Vue.js   \n",
+       "\n",
+       "                 WebframeWantToWorkWith YearsCode  YearsCodePro  \n",
+       "0                                   NaN       NaN           NaN  \n",
+       "1  Deno;Elm;Nuxt.js;React;Svelte;Vue.js        18             9  \n",
+       "\n",
+       "[2 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.sort_index(axis=1).head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3d05c2aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(89184, 25)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b410dbf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Profession                    0\n",
+      "Age                           0\n",
+      "Employment                 1286\n",
+      "RemoteWork                15374\n",
+      "CodingActivities          15420\n",
+      "EdLevel                    1211\n",
+      "LearnCode                  1521\n",
+      "LearnCodeOnline           19100\n",
+      "LearnCodeCoursesCert      52108\n",
+      "YearsCode                  1749\n",
+      "YearsCodePro              23048\n",
+      "DevType                   12312\n",
+      "TechList                  28333\n",
+      "Country                    1211\n",
+      "LanguageHaveWorkedWith     2044\n",
+      "LanguageWantToWorkWith     8475\n",
+      "DatabaseHaveWorkedWith    15749\n",
+      "DatabaseWantToWorkWith    28273\n",
+      "PlatformHaveWorkedWith    25556\n",
+      "PlatformWantToWorkWith    37876\n",
+      "WebframeHaveWorkedWith    22246\n",
+      "WebframeWantToWorkWith    32443\n",
+      "ProfessionalTech          47401\n",
+      "Industry                  52410\n",
+      "SalaryUSD                 41165\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2023.isnull().sum()) #Finding Null Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "434f0a0d",
+   "metadata": {},
+   "source": [
+    " ## Data Validation - Total Cells vs Missing %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ae45ff26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  2229600\n",
+      "Total missing :  486311\n",
+      "Missing Percentage:  21.811580552565484 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df2023.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df2023.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d6b816c",
+   "metadata": {},
+   "source": [
+    "## Missing Percentage column-wise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6af6c27a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def missing(df,column,n):\n",
+    "    empty_cells=df[column].isnull().sum()\n",
+    "    return (empty_cells*100.0)/n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "983ac4d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Profession : 0.00 %\n",
+      "Age : 0.00 %\n",
+      "Employment : 1.44 %\n",
+      "RemoteWork : 17.24 %\n",
+      "CodingActivities : 17.29 %\n",
+      "EdLevel : 1.36 %\n",
+      "LearnCode : 1.71 %\n",
+      "LearnCodeOnline : 21.42 %\n",
+      "LearnCodeCoursesCert : 58.43 %\n",
+      "YearsCode : 1.96 %\n",
+      "YearsCodePro : 25.84 %\n",
+      "DevType : 13.81 %\n",
+      "TechList : 31.77 %\n",
+      "Country : 1.36 %\n",
+      "LanguageHaveWorkedWith : 2.29 %\n",
+      "LanguageWantToWorkWith : 9.50 %\n",
+      "DatabaseHaveWorkedWith : 17.66 %\n",
+      "DatabaseWantToWorkWith : 31.70 %\n",
+      "PlatformHaveWorkedWith : 28.66 %\n",
+      "PlatformWantToWorkWith : 42.47 %\n",
+      "WebframeHaveWorkedWith : 24.94 %\n",
+      "WebframeWantToWorkWith : 36.38 %\n",
+      "ProfessionalTech : 53.15 %\n",
+      "Industry : 58.77 %\n",
+      "SalaryUSD : 46.16 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_cells=df2023.shape[0]\n",
+    "for column in df2023.columns:\n",
+    "    res=missing(df2023,column,total_cells)\n",
+    "    print(column,\":\",\"{:.2f}\".format(res),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "434a8cfa",
+   "metadata": {},
+   "source": [
+    "## Age Filtering          \n",
+    "### Data Cleaning Starts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "20444b99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age\n",
+       "25-34 years old       33247\n",
+       "35-44 years old       20532\n",
+       "18-24 years old       17931\n",
+       "45-54 years old        8334\n",
+       "Under 18 years old     4128\n",
+       "55-64 years old        3392\n",
+       "65 years or older      1171\n",
+       "Prefer not to say       449\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Age'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a0e2945b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a723a283",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set the style\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "# Plotting the histogram\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "ax = sns.histplot(data=df2023, x='Age', bins=30, kde=True, color='skyblue', edgecolor='black', alpha=0.7)\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Age', fontsize=12)  # Label for x-axis\n",
+    "plt.ylabel('Frequency', fontsize=12)  # Label for y-axis\n",
+    "plt.title('Distribution of Respondents\\' Ages', fontsize=14)  # Title of the plot\n",
+    "\n",
+    "# Add gridlines\n",
+    "plt.grid(True, linestyle='--', alpha=0.5)\n",
+    "\n",
+    "# Annotate each bar with its frequency\n",
+    "for rect in ax.patches:\n",
+    "    # Get the height of the bar\n",
+    "    height = rect.get_height()\n",
+    "    # Add text annotation\n",
+    "    ax.text(rect.get_x() + rect.get_width() / 2, height, f'{int(height)}', ha='center', va='bottom')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b457566",
+   "metadata": {},
+   "source": [
+    "Overall, the histogram graph provides a clear overview of the distribution of respondents' ages in the survey dataset. The most common age group among the respondents is the 25-34 age range, with a frequency of 33,247 respondents. This is followed by the 18-24 age group, which has a frequency of 17,931 respondents. Interestingly, there is a noticeable decline in the number of respondents in the older age groups, with the 45-54 age group having a frequency of 8,334 respondents. These findings suggest that the survey respondents are predominantly younger individuals, with a smaller representation of older age groups. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09c23cc4",
+   "metadata": {},
+   "source": [
+    "## EDlevel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "fdb19704",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1211"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['EdLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "63c867ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelor’s degree (B.A., B.S., B.Eng., etc.)                                          36706\n",
+       "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)                                       20543\n",
+       "Some college/university study without earning a degree                                11753\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     8897\n",
+       "Professional degree (JD, MD, Ph.D, Ed.D, etc.)                                         3887\n",
+       "Associate degree (A.A., A.S., etc.)                                                    2807\n",
+       "Primary/elementary school                                                              1905\n",
+       "Something else                                                                         1475\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "83ac9bd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring EdLevel\n",
+    "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 (A.A., A.S., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Bachelor’s degree (B.A., B.S., B.Eng., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Master’s degree (M.A., M.S., 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\n",
+    "\n",
+    "# applying function to subsets\n",
+    "df2023 = refactor_ed(df2023)\n",
+    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
+    "df2023['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2bbc3998",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelors    43279\n",
+       "No Degree    22555\n",
+       "Masters      20543\n",
+       "Associate     2807\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be843d35",
+   "metadata": {},
+   "source": [
+    "## DevType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "94e8aac2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12312"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fde27ba5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer, full-stack                            25735\n",
+       "Developer, back-end                              13745\n",
+       "Developer, front-end                              5071\n",
+       "Developer, desktop or enterprise applications     3904\n",
+       "Other (please specify):                           3080\n",
+       "Developer, mobile                                 2597\n",
+       "Engineering manager                               2033\n",
+       "Student                                           1996\n",
+       "Developer, embedded applications or devices       1845\n",
+       "Data scientist or machine learning specialist     1588\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "10187d19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['DevType'] = df2023['DevType'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e871711f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer, full-stack                            29898\n",
+       "Developer, back-end                              15791\n",
+       "Developer, front-end                              5920\n",
+       "Developer, desktop or enterprise applications     4535\n",
+       "Other (please specify):                           3591\n",
+       "Developer, mobile                                 2998\n",
+       "Student                                           2363\n",
+       "Engineering manager                               2322\n",
+       "Developer, embedded applications or devices       2139\n",
+       "Data scientist or machine learning specialist     1843\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "957ef92e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(89184, 25)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "12809d2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2023['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "6492a9d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>RemoteWork</th>\n",
+       "      <th>CodingActivities</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>LearnCode</th>\n",
+       "      <th>LearnCodeOnline</th>\n",
+       "      <th>LearnCodeCoursesCert</th>\n",
+       "      <th>YearsCode</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LanguageWantToWorkWith</th>\n",
+       "      <th>DatabaseHaveWorkedWith</th>\n",
+       "      <th>DatabaseWantToWorkWith</th>\n",
+       "      <th>PlatformHaveWorkedWith</th>\n",
+       "      <th>PlatformWantToWorkWith</th>\n",
+       "      <th>WebframeHaveWorkedWith</th>\n",
+       "      <th>WebframeWantToWorkWith</th>\n",
+       "      <th>ProfessionalTech</th>\n",
+       "      <th>Industry</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Profession, Age, Employment, RemoteWork, CodingActivities, EdLevel, LearnCode, LearnCodeOnline, LearnCodeCoursesCert, YearsCode, YearsCodePro, DevType, TechList, Country, LanguageHaveWorkedWith, LanguageWantToWorkWith, DatabaseHaveWorkedWith, DatabaseWantToWorkWith, PlatformHaveWorkedWith, PlatformWantToWorkWith, WebframeHaveWorkedWith, WebframeWantToWorkWith, ProfessionalTech, Industry, SalaryUSD]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023[df2023['DevType'].isnull()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc48674e",
+   "metadata": {},
+   "source": [
+    "## LanguageWantToWorkWith "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6517a5ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8475"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "7423484a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWantToWorkWith\n",
+       "Rust                              1438\n",
+       "Python                            1182\n",
+       "HTML/CSS;JavaScript;TypeScript    1071\n",
+       "C#                                 736\n",
+       "Go                                 618\n",
+       "TypeScript                         493\n",
+       "JavaScript;TypeScript              451\n",
+       "Java                               450\n",
+       "Python;Rust                        446\n",
+       "Python;SQL                         424\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageWantToWorkWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "ec277fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['LanguageWantToWorkWith']=df2023['LanguageWantToWorkWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f224f686",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWantToWorkWith\n",
+       "Rust                              1584\n",
+       "Python                            1296\n",
+       "HTML/CSS;JavaScript;TypeScript    1189\n",
+       "C#                                 821\n",
+       "Go                                 682\n",
+       "TypeScript                         550\n",
+       "JavaScript;TypeScript              496\n",
+       "Java                               494\n",
+       "Python;Rust                        487\n",
+       "Go;Rust                            463\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageWantToWorkWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "734d0192",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61b89006",
+   "metadata": {},
+   "source": [
+    "## LanguageHaveWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "7703a16b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2044"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "38cd6582",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageHaveWorkedWith\n",
+       "HTML/CSS;JavaScript;TypeScript           1487\n",
+       "Python                                   1132\n",
+       "HTML/CSS;JavaScript                       735\n",
+       "HTML/CSS;JavaScript;PHP;SQL               718\n",
+       "C#                                        649\n",
+       "JavaScript;TypeScript                     590\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript     575\n",
+       "Java                                      573\n",
+       "Python;SQL                                460\n",
+       "C#;HTML/CSS;JavaScript;SQL                449\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageHaveWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "23f1e34c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['LanguageHaveWorkedWith']=df2023['LanguageHaveWorkedWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "81d3355a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageHaveWorkedWith\n",
+       "HTML/CSS;JavaScript;TypeScript           1518\n",
+       "Python                                   1155\n",
+       "HTML/CSS;JavaScript                       751\n",
+       "HTML/CSS;JavaScript;PHP;SQL               735\n",
+       "C#                                        663\n",
+       "JavaScript;TypeScript                     605\n",
+       "Java                                      592\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript     580\n",
+       "Python;SQL                                473\n",
+       "C#;HTML/CSS;JavaScript;SQL                457\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageHaveWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "7de7b010",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LanguageHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c59f5a87",
+   "metadata": {},
+   "source": [
+    "## Main Branch (Profession)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "03016870",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "738cbfd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "I am a developer by profession                                                           67237\n",
+       "I am learning to code                                                                     4961\n",
+       "I am not primarily a developer, but I write code sometimes as part of my work/studies     8954\n",
+       "I code primarily as a hobby                                                               4960\n",
+       "I used to be a developer by profession, but no longer am                                  1861\n",
+       "None of these                                                                             1211\n",
+       "Name: Profession, dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.groupby('Profession')['Profession'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "0ef791c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023.dropna(subset=['Profession'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ec236349",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Lets refactor column values of Profession column\n",
+    "#refactoring profession column\n",
+    "\n",
+    "def refactor_prof(df):\n",
+    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
+    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
+    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
+    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
+    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
+    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
+    "    \n",
+    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
+    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)          \n",
+    "    return df\n",
+    "\n",
+    "df2023 = refactor_prof(df2023)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "190f021e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "Developer       67237\n",
+       "nan             15126\n",
+       "Novoice          4960\n",
+       "Ex-Developer     1861\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "91331644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8de357ac",
+   "metadata": {},
+   "source": [
+    "## Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "b47084e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1286"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "3730954d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Employed, full-time                                                                             53748\n",
+       "Employed, full-time;Employed, part-time                                                           228\n",
+       "Employed, full-time;Employed, part-time;Student, part-time                                         23\n",
+       "Employed, full-time;Independent contractor, freelancer, or self-employed                         4354\n",
+       "Employed, full-time;Independent contractor, freelancer, or self-employed;Employed, part-time      172\n",
+       "                                                                                                ...  \n",
+       "Student, full-time;Retired                                                                          1\n",
+       "Student, full-time;Student, part-time                                                              54\n",
+       "Student, full-time;Student, part-time;Not employed, and not looking for work                        6\n",
+       "Student, part-time                                                                                751\n",
+       "Student, part-time;Not employed, and not looking for work                                          60\n",
+       "Name: Employment, Length: 106, dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.groupby('Employment')['Employment'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "22b613c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_employment(df):\n",
+    "    conditions_employment = [\n",
+    "        (df2023['Employment'].str.contains('full-time', na=False, case=False, regex=False)),\n",
+    "        (df2023['Employment'].str.contains('part-time', na=False, case=False, regex=False)),\n",
+    "        (df2023['Employment'].str.contains('contractor|freelancer|self-employed', na=False, case=False, regex=True)),\n",
+    "        (df2023['Employment'].str.contains('student', na=False, case=False, regex=False)),\n",
+    "        (df2023['Employment'].str.contains('retired', na=False, case=False, regex=False)),\n",
+    "        (df2023['Employment'].str.contains('not employed|looking for work', na=False, case=False, regex=True))\n",
+    "    ]\n",
+    "    \n",
+    "    choices_employment = ['Full-time', 'Part-time', 'Self-employed', 'Student', 'Retired', 'Unemployed']\n",
+    "    \n",
+    "    df2023['Employment'] = np.select(conditions_employment, choices_employment, default='Other')\n",
+    "    return df2023\n",
+    "\n",
+    "df = refactor_employment(df2023)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "61b8748f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Full-time        71594\n",
+       "Other             1835\n",
+       "Part-time         4163\n",
+       "Retired            596\n",
+       "Self-employed     7566\n",
+       "Unemployed        3430\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.groupby('Employment')['Employment'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "71d8087d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['Employment']=df2023['Employment'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "7daf7343",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a9bb580",
+   "metadata": {},
+   "source": [
+    "## Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "5652097b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1211"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "955c549a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "Afghanistan                              64\n",
+       "Albania                                  60\n",
+       "Algeria                                  85\n",
+       "Andorra                                  16\n",
+       "Angola                                   13\n",
+       "                                       ... \n",
+       "Venezuela, Bolivarian Republic of...    106\n",
+       "Viet Nam                                390\n",
+       "Yemen                                    20\n",
+       "Zambia                                   26\n",
+       "Zimbabwe                                 34\n",
+       "Name: Country, Length: 185, dtype: int64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.groupby('Country')['Country'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "d6b27787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['Country']=df2023['Country'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "9f0c50a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46083e00",
+   "metadata": {},
+   "source": [
+    "## YearsCodePro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "7ae4a686",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23048"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "98d34d3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodePro\n",
+       "1                     2596\n",
+       "10                    4594\n",
+       "11                    2013\n",
+       "12                    2398\n",
+       "13                    1690\n",
+       "14                    1256\n",
+       "15                    2789\n",
+       "16                    1278\n",
+       "17                    1146\n",
+       "18                    1212\n",
+       "19                     649\n",
+       "2                     4464\n",
+       "20                    2227\n",
+       "21                     565\n",
+       "22                     845\n",
+       "23                    1112\n",
+       "24                     686\n",
+       "25                    1449\n",
+       "26                     486\n",
+       "27                     490\n",
+       "28                     417\n",
+       "29                     217\n",
+       "3                     4378\n",
+       "30                     924\n",
+       "31                     187\n",
+       "32                     293\n",
+       "33                     264\n",
+       "34                     177\n",
+       "35                     462\n",
+       "36                     200\n",
+       "37                     150\n",
+       "38                     164\n",
+       "39                      99\n",
+       "4                     3970\n",
+       "40                     329\n",
+       "41                      79\n",
+       "42                      81\n",
+       "43                      67\n",
+       "44                      42\n",
+       "45                     102\n",
+       "46                      33\n",
+       "47                      24\n",
+       "48                      30\n",
+       "49                      14\n",
+       "5                     4792\n",
+       "50                      24\n",
+       "6                     3637\n",
+       "7                     3509\n",
+       "8                     3462\n",
+       "9                     2145\n",
+       "Less than 1 year      1836\n",
+       "More than 50 years      83\n",
+       "Name: YearsCodePro, dtype: int64"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.groupby('YearsCodePro')['YearsCodePro'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "21250823",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['YearsCodePro']=df2023['YearsCodePro'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "bee69b2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbb8420a",
+   "metadata": {},
+   "source": [
+    "## ConvertedCompYearly (SalaryUSD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "81231125",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "41165"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "b92d76a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "64254.0     784\n",
+       "53545.0     615\n",
+       "150000.0    585\n",
+       "200000.0    558\n",
+       "74963.0     557\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "cf4f4083",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                 EdLevel    Country     \n",
+       "25-34 years old     Bachelors  Mali            3.657318e+07\n",
+       "Under 18 years old  No Degree  Ethiopia        1.761292e+07\n",
+       "25-34 years old     No Degree  Thailand        7.237654e+06\n",
+       "                    Bachelors  Afghanistan     2.301448e+06\n",
+       "55-64 years old     Bachelors  Australia       1.973625e+06\n",
+       "25-34 years old     Masters    Nomadic         1.617763e+06\n",
+       "45-54 years old     No Degree  Thailand        1.482846e+06\n",
+       "35-44 years old     Bachelors  South Africa    1.456551e+06\n",
+       "25-34 years old     Associate  Nigeria         9.856870e+05\n",
+       "55-64 years old     Bachelors  Belgium         8.735447e+05\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_salary = df2023.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "cc735560",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "64254.0     784\n",
+       "53545.0     615\n",
+       "150000.0    585\n",
+       "200000.0    558\n",
+       "74963.0     557\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a75f7cea",
+   "metadata": {},
+   "source": [
+    "## Remote Work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "208c4b7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15374"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['RemoteWork'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "be53345c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RemoteWork\n",
+       "Hybrid (some remote, some in-person)    31131\n",
+       "Remote                                  30566\n",
+       "In-person                               12113\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['RemoteWork'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "10b3049a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['RemoteWork']=df2023['RemoteWork'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "fae3a8fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['RemoteWork'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cf47534",
+   "metadata": {},
+   "source": [
+    "## Coding Activities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "56f8c4f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15420"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['CodingActivities'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "afd4892c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CodingActivities\n",
+       "Hobby                                                                                                           15308\n",
+       "I don’t code outside of work                                                                                     8809\n",
+       "Hobby;Professional development or self-paced learning from online courses                                        8232\n",
+       "Hobby;Contribute to open-source projects                                                                         5363\n",
+       "Professional development or self-paced learning from online courses                                              3920\n",
+       "Hobby;Contribute to open-source projects;Professional development or self-paced learning from online courses     3194\n",
+       "Hobby;Freelance/contract work                                                                                    2266\n",
+       "Hobby;School or academic work                                                                                    1855\n",
+       "Hobby;Professional development or self-paced learning from online courses;Freelance/contract work                1637\n",
+       "Freelance/contract work                                                                                          1632\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['CodingActivities'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1be1ef83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['CodingActivities']=df2023['CodingActivities'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d4d19773",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['CodingActivities'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8d73347",
+   "metadata": {},
+   "source": [
+    "## LearnCodeOnline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "522d3a67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19100"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCodeOnline'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "cdccc986",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LearnCodeOnline\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;Written Tutorials;Click to write Choice 20;Stack Overflow                        1182\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;Written Tutorials;Stack Overflow                                                  977\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;Stack Overflow                                                                    802\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;How-to videos;Written Tutorials;Click to write Choice 20;Stack Overflow           770\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;Books;Written Tutorials;Click to write Choice 20;Stack Overflow                   768\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;Books;Written Tutorials;Stack Overflow                                            583\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;How-to videos;Stack Overflow                                                      580\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;How-to videos;Written Tutorials;Stack Overflow                                    574\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;Click to write Choice 20;Stack Overflow                                           521\n",
+       "Formal documentation provided by the owner of the tech;Blogs with tips and tricks;Books;How-to videos;Written Tutorials;Click to write Choice 20;Stack Overflow     474\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCodeOnline'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "9ab9a749",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_learn_code_online(df):\n",
+    "    conditions_learn_code_online = [\n",
+    "        (df['LearnCodeOnline'].str.contains('Online Courses', na=False, regex=True)),\n",
+    "        (df['LearnCodeOnline'].str.contains('Books', na=False, regex=True)),\n",
+    "        (df['LearnCodeOnline'].str.contains('On the job training', na=False, regex=True)),\n",
+    "        (df['LearnCodeOnline'].str.contains('School', na=False, regex=True)),\n",
+    "        (df['LearnCodeOnline'].str.contains('Other online resources', na=False, regex=True))\n",
+    "    ]\n",
+    "    \n",
+    "    choices_learn_code_online = ['OnlineCourses/Certification', 'Books/Physicalmedia', 'OntheJobTraining',\n",
+    "                                 'School', 'OtherOnlineResources']\n",
+    "    \n",
+    "    df['LearnCodeOnline'] = np.select(conditions_learn_code_online, choices_learn_code_online, default='Other')\n",
+    "    return df\n",
+    "\n",
+    "df = refactor_learn_code_online(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "186de8f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LearnCodeOnline\n",
+       "OnlineCourses/Certification    42195\n",
+       "Other                          36789\n",
+       "Books/Physicalmedia            10200\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCodeOnline'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "1fd50e61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OnlineCourses/Certification    42195\n",
+      "Other                          36789\n",
+      "Books/Physicalmedia            10200\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split the values in the 'LearnCodeOnline' column by semicolon and stack them into separate rows\n",
+    "learn_code_online_split = df['LearnCodeOnline'].str.split(';').apply(pd.Series).stack()\n",
+    "\n",
+    "# Count the occurrences of each unique combination\n",
+    "learn_code_online_counts = learn_code_online_split.value_counts()\n",
+    "\n",
+    "# Display the result\n",
+    "print(learn_code_online_counts)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0b0655eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['LearnCodeOnline']=df2023['LearnCodeOnline'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "bac6d35f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCodeOnline'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dad2faac",
+   "metadata": {},
+   "source": [
+    "## LearnCode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "35287d94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1521"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCode'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "4b67cf8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LearnCode\n",
+       "Other online resources (e.g., videos, blogs, forum)                                                                                                                       4873\n",
+       "Books / Physical media;Other online resources (e.g., videos, blogs, forum)                                                                                                3300\n",
+       "Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)                                                                               3070\n",
+       "Books / Physical media;Online Courses or Certification;Other online resources (e.g., videos, blogs, forum)                                                                3024\n",
+       "Online Courses or Certification;Other online resources (e.g., videos, blogs, forum)                                                                                       2871\n",
+       "Books / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)                                                        2725\n",
+       "Books / Physical media;Online Courses or Certification;On the job training;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)    2650\n",
+       "Books / Physical media;Online Courses or Certification;On the job training;Other online resources (e.g., videos, blogs, forum)                                            2278\n",
+       "School (i.e., University, College, etc)                                                                                                                                   2162\n",
+       "Online Courses or Certification;On the job training;Other online resources (e.g., videos, blogs, forum)                                                                   2104\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCode'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "dad78aaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_learn_code(df):\n",
+    "    conditions_learn_code = [\n",
+    "        (df['LearnCode'].str.contains('Online Courses|Certification', na=False, regex=True)),\n",
+    "        (df['LearnCode'].str.contains('Books|Physical media', na=False, regex=True)),\n",
+    "        (df['LearnCode'].str.contains('On the job training', na=False, regex=True)),\n",
+    "        (df['LearnCode'].str.contains('School', na=False, regex=True)),\n",
+    "        (df['LearnCode'].str.contains('Other online resources', na=False, regex=True))\n",
+    "    ]\n",
+    "    \n",
+    "    choices_learn_code = ['OnlineCourses/Certification', 'Books/Physicalmedia', 'OntheJobTraining',\n",
+    "                          'School', 'OtherOnlineResources']\n",
+    "    \n",
+    "    df['LearnCode'] = np.select(conditions_learn_code, choices_learn_code, default='Other')\n",
+    "    return df\n",
+    "\n",
+    "df = refactor_learn_code(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "fc8ddbf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LearnCode\n",
+       "OnlineCourses/Certification    43201\n",
+       "Books/Physicalmedia            20777\n",
+       "OntheJobTraining                8479\n",
+       "School                          7110\n",
+       "OtherOnlineResources            6857\n",
+       "Other                           2760\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCode'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "aa0bfa23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OnlineCourses/Certification    43201\n",
+      "Books/Physicalmedia            20777\n",
+      "OntheJobTraining                8479\n",
+      "School                          7110\n",
+      "OtherOnlineResources            6857\n",
+      "Other                           2760\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split the values in the 'LearnCode' column by semicolon and stack them into separate rows\n",
+    "learn_code_split = df['LearnCode'].str.split(';').apply(pd.Series).stack()\n",
+    "\n",
+    "# Count the occurrences of each unique combination\n",
+    "learn_code_counts = learn_code_split.value_counts()\n",
+    "\n",
+    "# Display the result\n",
+    "print(learn_code_counts)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "e4e02eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['LearnCode']=df2023['LearnCode'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "4b96fe5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCode'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebf95d2b",
+   "metadata": {},
+   "source": [
+    "## LearnCodeCoursesCert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "f45440c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "52108"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCodeCoursesCert'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "a354f156",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LearnCodeCoursesCert\n",
+       "Udemy                        7445\n",
+       "Other                        3230\n",
+       "Udemy;Coursera               2612\n",
+       "Udemy;Pluralsight            1958\n",
+       "Codecademy;Udemy             1837\n",
+       "Coursera                     1634\n",
+       "Pluralsight                  1619\n",
+       "Other;Udemy                  1370\n",
+       "Codecademy                   1248\n",
+       "Codecademy;Udemy;Coursera     841\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCodeCoursesCert'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "ad6390a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['LearnCodeCoursesCert']=df2023['LearnCodeCoursesCert'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "025b66d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['LearnCodeCoursesCert'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae9cf729",
+   "metadata": {},
+   "source": [
+    "## YearsCode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "185bc332",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1749"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['YearsCode'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "de75fac1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCode\n",
+       "10    6521\n",
+       "5     5415\n",
+       "6     4893\n",
+       "8     4879\n",
+       "7     4800\n",
+       "4     4466\n",
+       "15    4336\n",
+       "3     4269\n",
+       "20    4021\n",
+       "12    3471\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['YearsCode'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "a1d08417",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['YearsCode']=df2023['YearsCode'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "62ef6dad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['YearsCode'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7353c786",
+   "metadata": {},
+   "source": [
+    "## TechList"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "55342a71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28333"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['TechList'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "818c571e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TechList\n",
+       "Investigate     49212\n",
+       "Given a list     7935\n",
+       "Other            3704\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['TechList'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "4d2d6842",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['TechList']=df2023['TechList'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "453233fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['TechList'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "620c455a",
+   "metadata": {},
+   "source": [
+    "## DatabaseHaveWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "deee9840",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15749"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "596777dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatabaseHaveWorkedWith\n",
+       "PostgreSQL              4350\n",
+       "Microsoft SQL Server    3322\n",
+       "MySQL                   3116\n",
+       "SQLite                  2299\n",
+       "MongoDB                 1663\n",
+       "PostgreSQL;SQLite       1426\n",
+       "PostgreSQL;Redis        1024\n",
+       "MariaDB;MySQL           1001\n",
+       "MySQL;SQLite             969\n",
+       "MySQL;PostgreSQL         945\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseHaveWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "a9d5bbfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['DatabaseHaveWorkedWith']=df2023['DatabaseHaveWorkedWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "53c2fde7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatabaseHaveWorkedWith\n",
+       "PostgreSQL              5268\n",
+       "Microsoft SQL Server    4044\n",
+       "MySQL                   3776\n",
+       "SQLite                  2771\n",
+       "MongoDB                 2077\n",
+       "PostgreSQL;SQLite       1728\n",
+       "PostgreSQL;Redis        1255\n",
+       "MariaDB;MySQL           1232\n",
+       "MySQL;SQLite            1180\n",
+       "MySQL;PostgreSQL        1126\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseHaveWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "0dd7d642",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "354a1af5",
+   "metadata": {},
+   "source": [
+    "## DatabaseWantToWorkWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "d67c106c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28273"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "43318a80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatabaseWantToWorkWith\n",
+       "PostgreSQL                 4815\n",
+       "Microsoft SQL Server       2180\n",
+       "SQLite                     1771\n",
+       "MySQL                      1754\n",
+       "PostgreSQL;SQLite          1631\n",
+       "MongoDB                    1609\n",
+       "PostgreSQL;Redis           1414\n",
+       "MongoDB;PostgreSQL          772\n",
+       "MySQL;PostgreSQL            625\n",
+       "PostgreSQL;Redis;SQLite     582\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseWantToWorkWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "686b17ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['DatabaseWantToWorkWith']=df2023['DatabaseWantToWorkWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "dc62b0ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatabaseWantToWorkWith\n",
+       "PostgreSQL                 7082\n",
+       "Microsoft SQL Server       3131\n",
+       "MySQL                      2566\n",
+       "SQLite                     2557\n",
+       "PostgreSQL;SQLite          2412\n",
+       "MongoDB                    2315\n",
+       "PostgreSQL;Redis           2091\n",
+       "MongoDB;PostgreSQL         1112\n",
+       "MySQL;PostgreSQL            922\n",
+       "PostgreSQL;Redis;SQLite     848\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseWantToWorkWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "494d19ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['DatabaseWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a01af1",
+   "metadata": {},
+   "source": [
+    "## PlatformHaveWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "eb9288e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25556"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['PlatformHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "a441d522",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PlatformHaveWorkedWith\n",
+       "Amazon Web Services (AWS)                                 9804\n",
+       "Microsoft Azure                                           5945\n",
+       "Google Cloud                                              2495\n",
+       "Amazon Web Services (AWS);Microsoft Azure                 2192\n",
+       "Amazon Web Services (AWS);Google Cloud                    1810\n",
+       "Firebase                                                  1407\n",
+       "Amazon Web Services (AWS);Cloudflare                       822\n",
+       "Digital Ocean                                              819\n",
+       "VMware                                                     798\n",
+       "Amazon Web Services (AWS);Google Cloud;Microsoft Azure     795\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['PlatformHaveWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "1b60f4bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['PlatformHaveWorkedWith']=df2023['PlatformHaveWorkedWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "f2cfe5d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PlatformHaveWorkedWith\n",
+       "Amazon Web Services (AWS)                                 13558\n",
+       "Microsoft Azure                                            8296\n",
+       "Google Cloud                                               3430\n",
+       "Amazon Web Services (AWS);Microsoft Azure                  3100\n",
+       "Amazon Web Services (AWS);Google Cloud                     2608\n",
+       "Firebase                                                   1974\n",
+       "Amazon Web Services (AWS);Cloudflare                       1155\n",
+       "Digital Ocean                                              1140\n",
+       "VMware                                                     1107\n",
+       "Amazon Web Services (AWS);Google Cloud;Microsoft Azure     1094\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['PlatformHaveWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "62d06662",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['PlatformHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27112b0d",
+   "metadata": {},
+   "source": [
+    "## PlatformWantToWorkWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "f7884b35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37876"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['PlatformWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "c3dbfc07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PlatformWantToWorkWith\n",
+       "Amazon Web Services (AWS)                                 7269\n",
+       "Microsoft Azure                                           4239\n",
+       "Amazon Web Services (AWS);Microsoft Azure                 2023\n",
+       "Google Cloud                                              1885\n",
+       "Amazon Web Services (AWS);Google Cloud                    1695\n",
+       "Amazon Web Services (AWS);Google Cloud;Microsoft Azure    1028\n",
+       "Firebase                                                   883\n",
+       "Vercel                                                     864\n",
+       "Digital Ocean                                              863\n",
+       "Amazon Web Services (AWS);Cloudflare                       793\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['PlatformWantToWorkWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "15db0043",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['PlatformWantToWorkWith']=df2023['PlatformWantToWorkWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "524aca9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['PlatformWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a1f48f5",
+   "metadata": {},
+   "source": [
+    "## WebframeHaveWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "ea8f2235",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22246"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['WebframeHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "181a7013",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WebframeHaveWorkedWith\n",
+       "React                    2017\n",
+       "Spring Boot              1479\n",
+       "Node.js                  1452\n",
+       "Node.js;React            1161\n",
+       "Flask                    1069\n",
+       "WordPress                 972\n",
+       "jQuery                    908\n",
+       "Django                    862\n",
+       "ASP.NET CORE              818\n",
+       "Express;Node.js;React     807\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['WebframeHaveWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "d1794e40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['WebframeHaveWorkedWith']=df2023['WebframeHaveWorkedWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "08cdcf17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['WebframeHaveWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19d1dac5",
+   "metadata": {},
+   "source": [
+    "## WebframeWantToWorkWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "c03bb168",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32443"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['WebframeWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "bcbc374b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WebframeWantToWorkWith\n",
+       "React                    1561\n",
+       "Spring Boot              1200\n",
+       "Node.js                   973\n",
+       "ASP.NET CORE              841\n",
+       "Svelte                    840\n",
+       "Node.js;React             810\n",
+       "ASP.NET CORE;Blazor       753\n",
+       "Next.js;Node.js;React     711\n",
+       "FastAPI                   703\n",
+       "Angular                   594\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['WebframeWantToWorkWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "30137d25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['WebframeWantToWorkWith']=df2023['WebframeWantToWorkWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "01c4ffd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['WebframeWantToWorkWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "931e3186",
+   "metadata": {},
+   "source": [
+    "## ProfessionalTech"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "2ff6bc8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "47401"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['ProfessionalTech'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "a6c76e8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ProfessionalTech\n",
+       "None of these                                                                                                                                                                                                                                                 4871\n",
+       "DevOps function;Microservices;Automated testing;Observability tools;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery                                                          2618\n",
+       "DevOps function;Microservices;Automated testing;Observability tools;Continuous integration (CI) and (more often) continuous delivery                                                                                                                          2271\n",
+       "DevOps function;Microservices;Automated testing;Continuous integration (CI) and (more often) continuous delivery                                                                                                                                              1656\n",
+       "DevOps function;Automated testing;Continuous integration (CI) and (more often) continuous delivery                                                                                                                                                            1524\n",
+       "Automated testing;Continuous integration (CI) and (more often) continuous delivery                                                                                                                                                                            1351\n",
+       "DevOps function;Microservices;Automated testing;Observability tools;Innersource initiative;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery;AI-assisted technology tool(s)    1127\n",
+       "DevOps function;Microservices;Automated testing;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery                                                                              1066\n",
+       "DevOps function;Microservices;Automated testing;Observability tools;Innersource initiative;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery                                   1052\n",
+       "Continuous integration (CI) and (more often) continuous delivery                                                                                                                                                                                               936\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['ProfessionalTech'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "adc81183",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_professional_tech(df):\n",
+    "    conditions_prof_tech = [(df['ProfessionalTech'].str.contains('Microservices', na=False)),\n",
+    "                            (df['ProfessionalTech'].str.contains('Developer portal|central places to find tools/services', na=False)),\n",
+    "                            (df['ProfessionalTech'].str.contains('Continuous integration|continuous delivery', na=False)),\n",
+    "                            (df['ProfessionalTech'].str.contains('DevOps function', na=False)),\n",
+    "                            (df['ProfessionalTech'].str.contains('Automated testing', na=False)),\n",
+    "                            (df['ProfessionalTech'].str.contains('Observability tools', na=False)),\n",
+    "                            (df['ProfessionalTech'].str.contains('AI-assisted technology tool(s)', na=False))]\n",
+    "    \n",
+    "    choices_prof_tech = ['Microservices', 'Developer Tools', 'Continuous Integration/Delivery', 'DevOps',\n",
+    "                         'Automated Testing', 'Observability', 'AI-assisted Tools']\n",
+    "    \n",
+    "    df['ProfessionalTech'] = np.select(conditions_prof_tech, choices_prof_tech, default='Other')\n",
+    "    return df\n",
+    "\n",
+    "df2023 = refactor_professional_tech(df2023)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "0d4729ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ProfessionalTech\n",
+       "Other                              52802\n",
+       "Microservices                      20526\n",
+       "Continuous Integration/Delivery     8016\n",
+       "Developer Tools                     5636\n",
+       "DevOps                              1383\n",
+       "Automated Testing                    624\n",
+       "Observability                        197\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['ProfessionalTech'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "d5df998f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['ProfessionalTech']=df2023['ProfessionalTech'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "0f6d860c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['ProfessionalTech'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70282682",
+   "metadata": {},
+   "source": [
+    "## Industry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "ebd89033",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "52410"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Industry'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "55816ef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Industry\n",
+       "Information Services, IT, Software Development, or other Technology    18159\n",
+       "Financial Services                                                      4421\n",
+       "Other                                                                   4011\n",
+       "Manufacturing, Transportation, or Supply Chain                          2607\n",
+       "Healthcare                                                              2216\n",
+       "Retail and Consumer Services                                            1955\n",
+       "Higher Education                                                        1242\n",
+       "Advertising Services                                                     786\n",
+       "Insurance                                                                707\n",
+       "Oil & Gas                                                                276\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Industry'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "a1495a11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2023['Industry']=df2023['Industry'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "4c3fe1e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023['Industry'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85df60b2",
+   "metadata": {},
+   "source": [
+    "## Cleaned_Dataset_2023"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "e0f2ff23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Profession                    0\n",
+      "Age                           0\n",
+      "Employment                    0\n",
+      "RemoteWork                    0\n",
+      "CodingActivities              0\n",
+      "EdLevel                       0\n",
+      "LearnCode                     0\n",
+      "LearnCodeOnline               0\n",
+      "LearnCodeCoursesCert          0\n",
+      "YearsCode                     0\n",
+      "YearsCodePro              23048\n",
+      "DevType                       0\n",
+      "TechList                      0\n",
+      "Country                    1211\n",
+      "LanguageHaveWorkedWith        0\n",
+      "LanguageWantToWorkWith        0\n",
+      "DatabaseHaveWorkedWith        0\n",
+      "DatabaseWantToWorkWith        0\n",
+      "PlatformHaveWorkedWith        0\n",
+      "PlatformWantToWorkWith        0\n",
+      "WebframeHaveWorkedWith        0\n",
+      "WebframeWantToWorkWith        0\n",
+      "ProfessionalTech              0\n",
+      "Industry                      0\n",
+      "SalaryUSD                 41165\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2023.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "73260943",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#resetting the index values\n",
+    "df2020 = df2023.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "3278813c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>RemoteWork</th>\n",
+       "      <th>CodingActivities</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>LearnCode</th>\n",
+       "      <th>LearnCodeOnline</th>\n",
+       "      <th>LearnCodeCoursesCert</th>\n",
+       "      <th>YearsCode</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LanguageWantToWorkWith</th>\n",
+       "      <th>DatabaseHaveWorkedWith</th>\n",
+       "      <th>DatabaseWantToWorkWith</th>\n",
+       "      <th>PlatformHaveWorkedWith</th>\n",
+       "      <th>PlatformWantToWorkWith</th>\n",
+       "      <th>WebframeHaveWorkedWith</th>\n",
+       "      <th>WebframeWantToWorkWith</th>\n",
+       "      <th>ProfessionalTech</th>\n",
+       "      <th>Industry</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nan</td>\n",
+       "      <td>18-24 years old</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Remote</td>\n",
+       "      <td>Hobby;Contribute to open-source projects;Boots...</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Bash/Shell (all shells);C#;Dart;Elixir;GDScrip...</td>\n",
+       "      <td>Supabase</td>\n",
+       "      <td>Firebase Realtime Database;Supabase</td>\n",
+       "      <td>Amazon Web Services (AWS);Netlify;Vercel</td>\n",
+       "      <td>Fly.io;Netlify;Render</td>\n",
+       "      <td>Next.js;React;Remix;Vue.js</td>\n",
+       "      <td>Deno;Elm;Nuxt.js;React;Svelte;Vue.js</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Information Services, IT, Software Development...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Developer</td>\n",
+       "      <td>25-34 years old</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Remote</td>\n",
+       "      <td>Hobby;Contribute to open-source projects;Boots...</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>OnlineCourses/Certification</td>\n",
+       "      <td>OnlineCourses/Certification</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Bash/Shell (all shells);C#;Dart;Elixir;GDScrip...</td>\n",
+       "      <td>Supabase</td>\n",
+       "      <td>Firebase Realtime Database;Supabase</td>\n",
+       "      <td>Amazon Web Services (AWS);Netlify;Vercel</td>\n",
+       "      <td>Fly.io;Netlify;Render</td>\n",
+       "      <td>Next.js;React;Remix;Vue.js</td>\n",
+       "      <td>Deno;Elm;Nuxt.js;React;Svelte;Vue.js</td>\n",
+       "      <td>Microservices</td>\n",
+       "      <td>Information Services, IT, Software Development...</td>\n",
+       "      <td>285000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Developer</td>\n",
+       "      <td>45-54 years old</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Hybrid (some remote, some in-person)</td>\n",
+       "      <td>Hobby;Professional development or self-paced l...</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Books/Physicalmedia</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other;Codecademy;edX</td>\n",
+       "      <td>27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Haskell;OCaml;Rust</td>\n",
+       "      <td>PostgreSQL;Redis</td>\n",
+       "      <td>PostgreSQL;Redis</td>\n",
+       "      <td>Amazon Web Services (AWS);Google Cloud;OpenSta...</td>\n",
+       "      <td>Cloudflare;Heroku</td>\n",
+       "      <td>Node.js;React;Ruby on Rails;Vue.js;WordPress</td>\n",
+       "      <td>Node.js;Ruby on Rails;Vue.js</td>\n",
+       "      <td>Microservices</td>\n",
+       "      <td>Information Services, IT, Software Development...</td>\n",
+       "      <td>250000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Developer</td>\n",
+       "      <td>25-34 years old</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Hybrid (some remote, some in-person)</td>\n",
+       "      <td>Hobby</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>School</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other;Codecademy;edX</td>\n",
+       "      <td>12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Bash/Shell (all shells);HTML/CSS;JavaScript;Ru...</td>\n",
+       "      <td>PostgreSQL;Redis</td>\n",
+       "      <td>PostgreSQL;Redis</td>\n",
+       "      <td>Cloudflare;Heroku</td>\n",
+       "      <td>Cloudflare;Heroku</td>\n",
+       "      <td>Node.js;React;Ruby on Rails;Vue.js;WordPress</td>\n",
+       "      <td>Node.js;Ruby on Rails;Vue.js</td>\n",
+       "      <td>Continuous Integration/Delivery</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>156000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Developer</td>\n",
+       "      <td>25-34 years old</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Remote</td>\n",
+       "      <td>Hobby;Contribute to open-source projects;Profe...</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>OnlineCourses/Certification</td>\n",
+       "      <td>OnlineCourses/Certification</td>\n",
+       "      <td>Other;Codecademy;edX</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HTML/CSS;JavaScript;Python;Rust;TypeScript</td>\n",
+       "      <td>BigQuery;Elasticsearch;MongoDB;PostgreSQL</td>\n",
+       "      <td>Elasticsearch;MongoDB;PostgreSQL;Redis;Supabase</td>\n",
+       "      <td>Amazon Web Services (AWS);Firebase;Heroku;Netl...</td>\n",
+       "      <td>Amazon Web Services (AWS);Cloudflare;Digital O...</td>\n",
+       "      <td>Express;Gatsby;NestJS;Next.js;Node.js;React</td>\n",
+       "      <td>Express;NestJS;Next.js;Node.js;React;Remix;Vue.js</td>\n",
+       "      <td>Microservices</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>23456.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Profession              Age Employment  \\\n",
+       "0        nan  18-24 years old      Other   \n",
+       "1  Developer  25-34 years old  Full-time   \n",
+       "2  Developer  45-54 years old  Full-time   \n",
+       "3  Developer  25-34 years old  Full-time   \n",
+       "4  Developer  25-34 years old  Full-time   \n",
+       "\n",
+       "                             RemoteWork  \\\n",
+       "0                                Remote   \n",
+       "1                                Remote   \n",
+       "2  Hybrid (some remote, some in-person)   \n",
+       "3  Hybrid (some remote, some in-person)   \n",
+       "4                                Remote   \n",
+       "\n",
+       "                                    CodingActivities    EdLevel  \\\n",
+       "0  Hobby;Contribute to open-source projects;Boots...  Bachelors   \n",
+       "1  Hobby;Contribute to open-source projects;Boots...  Bachelors   \n",
+       "2  Hobby;Professional development or self-paced l...  Bachelors   \n",
+       "3                                              Hobby  Bachelors   \n",
+       "4  Hobby;Contribute to open-source projects;Profe...  Bachelors   \n",
+       "\n",
+       "                     LearnCode              LearnCodeOnline  \\\n",
+       "0                        Other                        Other   \n",
+       "1  OnlineCourses/Certification  OnlineCourses/Certification   \n",
+       "2          Books/Physicalmedia                        Other   \n",
+       "3                       School                        Other   \n",
+       "4  OnlineCourses/Certification  OnlineCourses/Certification   \n",
+       "\n",
+       "   LearnCodeCoursesCert YearsCode  ...  \\\n",
+       "0                 Other        18  ...   \n",
+       "1                 Other        18  ...   \n",
+       "2  Other;Codecademy;edX        27  ...   \n",
+       "3  Other;Codecademy;edX        12  ...   \n",
+       "4  Other;Codecademy;edX         6  ...   \n",
+       "\n",
+       "                              LanguageWantToWorkWith  \\\n",
+       "0  Bash/Shell (all shells);C#;Dart;Elixir;GDScrip...   \n",
+       "1  Bash/Shell (all shells);C#;Dart;Elixir;GDScrip...   \n",
+       "2                                 Haskell;OCaml;Rust   \n",
+       "3  Bash/Shell (all shells);HTML/CSS;JavaScript;Ru...   \n",
+       "4         HTML/CSS;JavaScript;Python;Rust;TypeScript   \n",
+       "\n",
+       "                      DatabaseHaveWorkedWith  \\\n",
+       "0                                   Supabase   \n",
+       "1                                   Supabase   \n",
+       "2                           PostgreSQL;Redis   \n",
+       "3                           PostgreSQL;Redis   \n",
+       "4  BigQuery;Elasticsearch;MongoDB;PostgreSQL   \n",
+       "\n",
+       "                            DatabaseWantToWorkWith  \\\n",
+       "0              Firebase Realtime Database;Supabase   \n",
+       "1              Firebase Realtime Database;Supabase   \n",
+       "2                                 PostgreSQL;Redis   \n",
+       "3                                 PostgreSQL;Redis   \n",
+       "4  Elasticsearch;MongoDB;PostgreSQL;Redis;Supabase   \n",
+       "\n",
+       "                              PlatformHaveWorkedWith  \\\n",
+       "0           Amazon Web Services (AWS);Netlify;Vercel   \n",
+       "1           Amazon Web Services (AWS);Netlify;Vercel   \n",
+       "2  Amazon Web Services (AWS);Google Cloud;OpenSta...   \n",
+       "3                                  Cloudflare;Heroku   \n",
+       "4  Amazon Web Services (AWS);Firebase;Heroku;Netl...   \n",
+       "\n",
+       "                              PlatformWantToWorkWith  \\\n",
+       "0                              Fly.io;Netlify;Render   \n",
+       "1                              Fly.io;Netlify;Render   \n",
+       "2                                  Cloudflare;Heroku   \n",
+       "3                                  Cloudflare;Heroku   \n",
+       "4  Amazon Web Services (AWS);Cloudflare;Digital O...   \n",
+       "\n",
+       "                         WebframeHaveWorkedWith  \\\n",
+       "0                    Next.js;React;Remix;Vue.js   \n",
+       "1                    Next.js;React;Remix;Vue.js   \n",
+       "2  Node.js;React;Ruby on Rails;Vue.js;WordPress   \n",
+       "3  Node.js;React;Ruby on Rails;Vue.js;WordPress   \n",
+       "4   Express;Gatsby;NestJS;Next.js;Node.js;React   \n",
+       "\n",
+       "                              WebframeWantToWorkWith  \\\n",
+       "0               Deno;Elm;Nuxt.js;React;Svelte;Vue.js   \n",
+       "1               Deno;Elm;Nuxt.js;React;Svelte;Vue.js   \n",
+       "2                       Node.js;Ruby on Rails;Vue.js   \n",
+       "3                       Node.js;Ruby on Rails;Vue.js   \n",
+       "4  Express;NestJS;Next.js;Node.js;React;Remix;Vue.js   \n",
+       "\n",
+       "                  ProfessionalTech  \\\n",
+       "0                            Other   \n",
+       "1                    Microservices   \n",
+       "2                    Microservices   \n",
+       "3  Continuous Integration/Delivery   \n",
+       "4                    Microservices   \n",
+       "\n",
+       "                                            Industry SalaryUSD  \n",
+       "0  Information Services, IT, Software Development...       NaN  \n",
+       "1  Information Services, IT, Software Development...  285000.0  \n",
+       "2  Information Services, IT, Software Development...  250000.0  \n",
+       "3                                              Other  156000.0  \n",
+       "4                                              Other   23456.0  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2023.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "ff79408b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 89184 entries, 0 to 89183\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column                  Non-Null Count  Dtype  \n",
+      "---  ------                  --------------  -----  \n",
+      " 0   Profession              89184 non-null  object \n",
+      " 1   Age                     89184 non-null  object \n",
+      " 2   Employment              89184 non-null  object \n",
+      " 3   RemoteWork              89184 non-null  object \n",
+      " 4   CodingActivities        89184 non-null  object \n",
+      " 5   EdLevel                 89184 non-null  object \n",
+      " 6   LearnCode               89184 non-null  object \n",
+      " 7   LearnCodeOnline         89184 non-null  object \n",
+      " 8   LearnCodeCoursesCert    89184 non-null  object \n",
+      " 9   YearsCode               89184 non-null  object \n",
+      " 10  YearsCodePro            66136 non-null  object \n",
+      " 11  DevType                 89184 non-null  object \n",
+      " 12  TechList                89184 non-null  object \n",
+      " 13  Country                 87973 non-null  object \n",
+      " 14  LanguageHaveWorkedWith  89184 non-null  object \n",
+      " 15  LanguageWantToWorkWith  89184 non-null  object \n",
+      " 16  DatabaseHaveWorkedWith  89184 non-null  object \n",
+      " 17  DatabaseWantToWorkWith  89184 non-null  object \n",
+      " 18  PlatformHaveWorkedWith  89184 non-null  object \n",
+      " 19  PlatformWantToWorkWith  89184 non-null  object \n",
+      " 20  WebframeHaveWorkedWith  89184 non-null  object \n",
+      " 21  WebframeWantToWorkWith  89184 non-null  object \n",
+      " 22  ProfessionalTech        89184 non-null  object \n",
+      " 23  Industry                89184 non-null  object \n",
+      " 24  SalaryUSD               48019 non-null  float64\n",
+      "dtypes: float64(1), object(24)\n",
+      "memory usage: 17.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2023.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f668c77",
+   "metadata": {},
+   "source": [
+    "## After Cleaning Dataset 2023"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "41a5c8c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  2229600\n",
+      "Total missing :  65424\n",
+      "Missing Percentage:  2.9343379978471473 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df2023.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df2023.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "655e7c21",
+   "metadata": {},
+   "source": [
+    "## Visualizations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d7bdde",
+   "metadata": {},
+   "source": [
+    "## Top 10 Methods to learn code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "4b5483c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wGkElAAAAAAAAWI2gEgAAAAAAAKxGUAkAAAAAAABWI6gEAAAAAAAAqxFUAgAAAAAAgNX+H3DEa7QKYBbPAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn_code_data = df2023['LearnCode']\n",
+    "\n",
+    "# Split the values in the column if users have selected multiple options\n",
+    "learn_code_data = learn_code_data.str.split(';').explode()\n",
+    "\n",
+    "# Count the occurrences of each response\n",
+    "learn_code_counts = learn_code_data.value_counts().head(10)\n",
+    "\n",
+    "# Set Seaborn style\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "\n",
+    "# Plot the top ten responses using Seaborn\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "ax = sns.barplot(x=learn_code_counts.values, y=learn_code_counts.index,palette='viridis', hue=learn_code_counts.values)\n",
+    "ax.set_title('Top 10 LearnCode Responses', fontsize=16)\n",
+    "ax.set_xlabel('LearnCode Responses', fontsize=14)\n",
+    "ax.set_ylabel('Number of Responses', fontsize=14)\n",
+    "# ax.tick_params(axis='x', rotation=75, ha='right', labelsize=12)\n",
+    "# ax.tick_params(axis='y', labelsize=12)\n",
+    "\n",
+    "# Display the count on top of each bar\n",
+    "# for p in ax.patches:\n",
+    "#     ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
+    "#                 ha='center', va='baseline', fontsize=10, color='black', xytext=(0, 5),\n",
+    "#                 textcoords='offset points')\n",
+    "# plt.xticks(rotation=75\n",
+    "for index,value in enumerate(learn_code_counts.values):\n",
+    "    ax.text(value,index,f'{value}',ha='left',va='center',fontsize=10)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "748773ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn_code_data = df2023['LearnCode']\n",
+    "\n",
+    "learn_code_data = learn_code_data.dropna().str.split(';').explode()\n",
+    "\n",
+    "top_10_learn_code_data = learn_code_data.value_counts().head(10)\n",
+    "\n",
+    "total_learncode_responses = df2023.LearnCode.dropna()\n",
+    "total_responses = len(total_learncode_responses)\n",
+    "learn_code_percentages = (top_10_learn_code_data / total_responses) * 100\n",
+    "\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "ax = sns.barplot(x=learn_code_percentages.values, y=learn_code_percentages.index,hue=learn_code_percentages.values)\n",
+    "ax.set_title('Top 10 LearnCode Responses (Percentages)', fontsize=16)\n",
+    "ax.set_xlabel('Percentage of Responses', fontsize=14)\n",
+    "ax.set_ylabel('LearnCode Responses', fontsize=14)\n",
+    "# sns.color_palette('rocket',as_cmap=True)\n",
+    "# Display the percentage on top of each bar\n",
+    "for index, value in enumerate(learn_code_percentages.values):\n",
+    "    ax.text(value, index, f'{value:.2f}%', ha='left', va='center', fontsize=10)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b86a982",
+   "metadata": {},
+   "source": [
+    "The bar plot shows the top 10 methods respondents use to learn coding, expressed as percentages of total responses:\n",
+    "\n",
+    "- \"Online Courses or Certification\" and \"Books / Physical media\" are widely used with the percentage of about 48.44%.\n",
+    "- \"On the job training\" and \"School (i.e., University, College, etc)\" are notable as well.\n",
+    "- The variety of methods indicates that learners use a mix of resources.\n",
+    "\n",
+    "This highlights the diverse approaches people take to learn coding, combining both formal and informal methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "088667fd",
+   "metadata": {},
+   "source": [
+    "### Most Used and Most Wanted Programming Languages,Databases,and Webframeworks by developers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "87c98b7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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riL5x40bL4DwuLk7+/v4KDg7W4MGD9eSTT2ZVRgAAAMChxMfHy2w2K1++fMqfP7/+/vtve0cCAAAAkEHpGqLPmjVLDRs21NixY9WgQQOufw4AAABIunjxoiIjIy0nnCQkJKh27doKDg7WqFGjVKZMGXtHBAAAAJBB6RqiR0REyGAwZFUWAAAAwCE1a9ZMpUqVUsOGDfXOO++oXr16ypMnj71jAQAAALCBdA3Re/bs+a/7rF69OsNhAAAAAEf0+eefy8/PTyaTSUaj8YHbz507p6eeesoOyQAAAABkllt6dq5du7Zq166tUqVK6dSpU6pcubJatGihGjVq6OzZs3riiSeyKicAAACQY/n5+UmSXn/9dZnN5jS3LVu2TJ06dbJHLAAAAAA2kK4z0QcNGiRJeuGFF7R06VIFBgZabmvRooXeeust26YDAAAAHMiFCxf01ltvafLkybp48aJGjx6t8+fPa/r06faOBgAAACCD0nUm+n2nT59WjRo10mzz9fXV+fPnbZEJAAAAcEgrVqzQqVOn1L9/f7Vr105eXl7asWOHWrZsae9oAAAAADIoQ0P0ihUratWqVWm2LV682PIyVgAAAMAVFS5cWCtWrNCVK1dUu3ZtzZs3T0WLFrV3LAAAAACZkK7Ludw3btw49e/fX2vWrJGPj4+ioqKUmpqqDz/80Nb5AAAAgByvR48eMhgMabZ9/fXX6tatm3LlyiVJWr16tT2iAQAAAMikDA3RAwMD9dVXX+nAgQO6evWqfHx8FBoaqoIFC9o6HwAAAJDj1alT54FtzZo1s0MSAAAAALaWoSG6JBUpUkRPPfWU8uXLp8aNGys+Pt6WuQAAAACHMWjQIEnSO++8o+HDh6tAgQJ2TgQAAADAVjJ0TfTY2Fg9//zz6tKli0aPHq2LFy+qadOmOnLkiK3zAQAAAA5j+/btyps3r71jAAAAALChDA3Rp0yZoqeeeko//vij3N3dVbFiRfXr108zZsywdT4AAADAYXTs2FETJ07U0aNHdfnyZUVFRVk+AAAAADimDF3OJTIyUuHh4cqbN6/lDZT69OmjFStW2DQcAAAA4EhWrlwpSdqwYYMkyWAwyGw2y2Aw6PTp0/aMBgAAACCDMjREz5Url+7evau8efPKbDZLkm7fvq38+fPbNBwAAADgSPbu3WvvCAAAAABsLEOXcwkNDdWoUaN0/vx5GQwGxcbGauLEiQoODrZ1PgAAAMBhlC5dWkWKFNGxY8e0c+dO/fTTTypYsKBKly5t72gAAAAAMihDQ/SRI0cqX758evbZZ3Xr1i01aNBAiYmJGjlypK3zAS7LFd+UzNVqdrV6AcAV/Pnnn2rdurWmTJmiPXv2aObMmXr22Wf166+/2jsaACdBD2kbrCMAID0ydDmX/Pnza968ebpx44YuXbokHx8fFS9eXHfv3rV1PiANo9Fo7wjZwmg0qkqVKvaOka1crWZnqNecmiqDW4b+FgsATmvq1Kl69tlnNWrUKLm5uSk1NVUzZ87UtGnT9OGHH9o7HpAjuEpPnxWcoYfMCbJ1Hc0mycC/eQBwdBkaoq9evVovvfSSihYtqqJFi0qSjh49qtGjR2v37t02DQj8U8q2DUqNuWLvGIDLM3iXkHtYd3vHAIAc59ixY5o3b57c/t8fGd3c3DR06FA1aNDAzsmAHORQH+mvY/ZOAWS9wpWlZ9bZOwUAwAYyNERftGiRChQooLCwMKWkpGjevHlasWKFOnfubOt8QBrm6zFS9GV7xwBcntneAQAghzIajUpISLCcaCJJCQkJXDYA+KdbZ6W4I/ZOAQAA8NgyNET/8MMP1bt3b8XFxWnHjh26deuWli9frrp169o6HwAAAOAwQkJCNHLkSL311lsqU6aMLl68qMmTJyskJMTe0QAAAABkUIYuZlulShUtX75cS5YsUZEiRbRjxw4G6AAAAHB5I0eOVEpKip577jnVqFFDrVu3Vu7cufX666/bOxoAAACADErXmegLFixI83VgYKAiIyO1ZMkSubvfO9SgQYNslw4AAABwAP/73/9UqVIlFSlSRGvWrNHFixcVGxur0qVLy9vb297xAAAAAGRCuobohw4demBb9erVdfjwYUmSwWCwTSoAAADAgbRu3VplypRRSEiImjRpolq1aqls2bL2jgUAAADABtI1RF+zZo0k6YsvvlDTpk2VO3fuLAkFAAAAOJI9e/YoIiJCERERGjp0qFJTU9WgQQOFhoaqUaNGKly4sL0jAgAAAMigDL2x6MSJE9W8eXNbZ0E2Cg0N1bVr1yyX4TGbzXJzc1PlypX13//+V1WqVEn3MceMGSNJmjZtmhYvXqyffvpJy5cvt2luAACAnKhs2bLq2rWrunbtKrPZrJMnT+rgwYPatGmT3n77bVWpUsVyQgpytvPnz+s///mPvWMAAAAgB8nQG4tWr15dO3futHUWZLOJEyfqyJEjOnLkiI4ePaqvvvpKBQsW1KBBg5SampqpY/fv358BOgAAcEkGg0EFCxZUnjx5LB9//fWXvWNlCV9fX/n6+ur3339/4LaVK1fK19dX8+fPlySNHz9e48ePz+6Ij3Tnzh317t1bNWrUUPfu3bVv3z717t3b6v6pqalatmyZnnvuOQUEBKhWrVrq3bu3jhw5kma/lJQUrVq1Sh06dFBgYKDq1Kmjl19+Wfv370+z35gxYywnojyOTZs2qUOHDgoICFDNmjX1wgsv6MCBA+mqGQAAAOmXoTPRb968qdGjR+utt96Sl5dXmmuh792712bhkL28vLzUtWtX9e/fXzdv3lRqaqqmTZumgwcPymAwKDQ0VG+88YYKFCgg6d73evbs2bp8+bLq1KkjSfL09JQkzZ8/Xz/88IPWrFmjhIQEvfXWW4qIiJC7u7v8/Pw0btw4VaxY0W61AgAA2Nrx48cVHh6uPXv26OLFiwoKClJISIjefPNNlSlTxt7xsoynp6e2bNmikSNHptm+efNmS98oSZMmTcruaP/q9OnT+u6773To0CEVKVJEmzdvltlstrr/0qVLtW3bNs2bN08VK1bU3bt3tXbtWvXs2VPbt29X+fLlZTKZ1LdvX924cUPjxo1TQECAUlNTtWvXLo0ePVo9e/bUa6+9lu6s27dv1+zZs7Vw4UI9/fTTSklJ0c6dOzVo0CCtXLlStWrVysxSAAAA4BEyNER/8cUXbZ0DOcCVK1e0du1aVa9eXUWKFNHzzz+v//znP9q9e7eSk5M1duxYjR8/XrNnz9bvv/+uoUOHasqUKXruued04MABDRkyRG3btn3guCtWrFBCQoK+/vprubm5afz48Xrvvfe0aNEiO1QJAABge8HBwbpz544aNmyogQMHKjg4WIUKFbJ3rGzRpk0bbd26VcOHD5eb270Xuh4/flxJSUlpLhH4z0v/SdJHH32ktWvX6vr163riiSc0atQo1atXT2PGjNGdO3f066+/Ki4uThs2bJDRaNTMmTN16NAhubm5qW7duho9erSKFy+ulJQUTZ48WXv27FFKSooqVqyokSNHqmbNmpKk8PBwffDBBzp//ry8vb3VrVs3vfTSS9q3b59GjBghSQoJCdG4ceM0adIkJScnKyAgQF9++aVKlCiRptbDhw8rKChIlSpVkiTlzZtXffv21dWrV3X9+nWVL19eGzZs0MmTJ7Vz5055e3tb7tuhQwd5eXmpb9++at68uZ588sl0rfPhw4fl5+cnf39/SZKHh4fat2+vy5cvp3mlwxdffKHFixcrKipK5cuX14gRI9SgQQNJUo8ePVS6dGkdOnRIZrNZVatWVZ48eTRr1izL/YcNGyZPT09NmDBBFy5c0JQpU3TkyBHly5dPbdu21WuvvSYPDw9t3rxZa9euVZEiRXT8+HFNmDBBbdq0SVdNAAAAjiJDQ/QOHTpIkmJjY3X58mV5e3urZMmSNg2GrDdx4kRNmTJFKSkpSk5Olo+Pj5o1a6ZXX31VJ0+e1C+//KKVK1cqf/78kqTRo0fr2Wef1VtvvaWdO3eqWrVqlqF506ZNFRIS8tDHyZMnj86cOaPPP/9c9evX15QpUyxPsAA4NpPJlO5903MfR+Zq9UquV7Or1Ss5d82ZrenWrVsqU6aMypQpo3LlyrnMAF2SGjdurB07digiIsIyrN24caM6deqkb7755qH32bx5sz744AMtXrxYNWrU0KZNmzRgwADLpUm+/fZbrV+/Xj4+PsqbN69at26tatWq6auvvpLZbNbEiRPVv39/bdiwQVu3btWRI0e0a9cu5c+fX/PmzdPEiRO1bds2RUZGatiwYZoxY4aaN2+us2fPauDAgZKkXr16admyZXrppZcsl2MxGo1asGCB9u3b99DcrVq10rhx43Tz5k01bNhQTz/9tJ588km9+eabln127dqlhg0bphmg39ewYUOVLFlSu3fvTvcQvUWLFurTp4969+6tkJAQ1ahRQ35+fmnOav/66681YcIELVq0SIGBgfrmm280ePBgbdiwwfJ4ERER+uyzz5Q3b16dO3dOvXv3VkJCggoUKKBbt25p3759+uSTT3Tnzh316tVLrVq10ty5c3Xjxg0NGTJEqampllcd/PLLL5b3Q8rs5SABZ+bMvzedsbbswhraBuuYec60hllZQ4aG6AkJCRo9erT27dsns9ksg8GgevXq6f3333epJwyObsKECQoLC1NSUpJWr16txYsXKzg4WJ6enjp48KBMJpOCg4PT3MfDw0MXL17U1atXVapUqTS3lStXTnFxcQ88Tt++feXh4aGNGzdq0qRJKlu2rEaOHMmb0wJO4OzZs0pMTEzXfU6cOJFFaXImV6tXcr2aXa1eyTVr/jeHDh1SRESEwsPDNXDgQMul8Jo0aaJ69erJw8PD3hGzjLu7u9q0aaMtW7aoQYMGunv3rnbv3q0dO3ZYHaJv2bJFXbt2VUBAgCSpc+fOqlixovLkySNJ8vf311NPPSVJOnjwoC5evKhNmzZZLg8zceJE1a5dWydPnlSePHl06dIlbdy4UY0aNdLQoUM1fPhwSfeG9U2aNNFzzz0nSapatar69eunNWvWqFevXumutX379ipTpow2bNigBQsWKDo6WkWKFFHXrl01ZMgQubu7KyYmRtWrV7d6jBIlSigmJibdj12vXj1t2rRJH3/8sT766CNduHDBcnb4G2+8ofz582vt2rXq1q2b5dIuISEhCg0N1aeffqq33npLktSoUSPLGfZBQUEqWbKkdu3apc6dO2vHjh2qUKGCqlatqp07dyopKUkjRoyQwWBQyZIlNXToUA0ZMsQyRM+VK5fatWvHCTLAv8hIz+wo6AkyjzW0DdYx81jDR8vQEH3WrFm6ffu2duzYoTJlyujPP//UlClTNHPmTL3zzju2zogs5uHhoT59+uivv/7SwIED9cknn8jHx0d58uTRoUOHZDQaJUlJSUm6ePGiypcvLx8fnwfexCg6Olq5c+d+4Phnz55VaGioevXqpfj4eH388ccaPny4IiMjVbBgwewoEUAW8fX1fex9TSaTTpw4oerVq1t+rjgzV6tXcr2aXa1eyblrvl9bRnl4eKhx48Zq3LixzGazDh8+rL1792r69OmKjo5WgwYNNG/ePBsmzlnCwsLUtWtXJSQkKDw8XIGBgQ89E/u+a9euPXBCRmBgoOXz4sWLWz6PjY2Vp6dnmuurFyhQQEWKFNHly5fVqlUrJScn67PPPtPs2bNVrFgx9e/fX926dVNsbKwqV66c5nHKlCmjy5cvZ7jWoKAgBQUFSZKuXr2qAwcOaObMmXJzc9OwYcNUvHhxRUVFWb1/TEyM6tWrl6HH9vPzs1xb/saNG4qIiNDMmTN1584dzZw5U5cvX9YPP/ygTz75xHIfk8mkunXrWr7+59pK9/6AsXXrVnXu3FlbtmxR586dJUmXL1/WjRs30lxr3Ww2Kzk5WbGxsZIkb29vBujAY0hPz+wonLknyC6soW2wjpnnTGuY2Z7+UTI0RN+/f782bdqkYsWKSZKeeuopzZw5U23btmWI7sCGDRumH3/8USNGjNCGDRtUvnx5TZs2TcOGDZPRaNS0adO0d+9ehYeHq23btlq6dKk2bNigsLAwRUZGas+ePWrduvUDx/3ss8/0yy+/aOHChSpatKgKFCigfPnyOfUZWYCryMgvWKPR6PC/mNPD1eqVXK9mV6tXcs2a08NgMMjPz0/x8fEyGAzas2ePIiMj7R0rS/n5+alChQratWuXtm/frp49ez5y/5IlS+rKlStpts2ZM8dyqUCDwWDZXrp0acXFxVkuOSJJ8fHxiouLk7e3t/744w9VrVpV7du31927d/Xll19q9OjRCgoKUunSpXXhwoU0j3Px4sVHDvituX37turXr685c+ZYLmNYokQJde3aVX/88YdOnz4tSXr22Wc1a9YsXb161XLG99q1a9WkSROdP39eUVFRatasWbofv3Hjxurbt6+6d+8uSSpatKhat26tuLg4rV+/XpLk4+Oj9u3bq1+/fpb7RUVFWc7wl9KurXTvUp3vv/++IiIidPbsWUs/7+Pjo3LlyunLL7+07JuQkKDY2FgVLVr0occC8HDO/DuTniDzWEPbYB0zjzV8tAydNpCYmPjAGcSFChXiOngO7v4bNl29elWzZs3SkiVLdP36dTVv3lwNGjTQhQsXtHLlSuXOnVtly5bV4sWLtW7dOtWsWVMffPCB1ScDI0aMUPny5dWqVSsFBgZaroH5sLPWAQAAHNHff/+tiIgIzZ49W126dFHdunX13nvvSZImT56siIgIOyfMemFhYVq1apX++OOPBy4J+LB9169fr+PHjys1NVWbNm3SunXr5Onp+cC+1atXV6VKlTRhwgTFx8crPj5eb7/9tsqVK6fAwEDt379fgwYN0qVLl5QnTx4VKVJE7u7uKliwoDp27Kh9+/Zp165dMplMOnXqlJYtW6aOHTs+NFfu3LmVmJiolJSUB27Lnz+/mjRpohkzZujQoUO6c+eOkpKSdPjwYe3evdtyqcKuXbvK399fvXv3VmRkpBITE3Xu3Dm1bt1aQ4cOVd++fdO84WpiYqKio6PTfCQkJDzw+G3bttWiRYu0f/9+xcfHKyUlRWfOnNHGjRstj92lSxetXr1ax48fl3TvZdlhYWHasWOH1e9F0aJFFRISojfffFPNmzdX4cKFJd27FMzt27e1fPlyJSUl6datWxo9erSGDx/O8BwAALicDJ2JXqNGDc2dO1evv/66DAaDzGaz5s6d+8hr/yFnsfZmSWXLltXhw4ctX8+ZM8fqMerVq6etW7c+9LbBgwdbPs+fP7+mT5+ewaQAAAA5X1BQkNzc3FSrVi21bdtWs2bNUtmyZe0dK1u1bt1a06dPV8+ePeXu/uinGW3atNGtW7c0atQoXbt2TZUqVdKyZcssZzj/k7u7u5YsWaJp06apRYsWSkpK0jPPPKOVK1fK3d1dL730kq5evarnn39eCQkJKl26tObMmSMfHx/5+Pho7ty5WrhwocaNGydPT09169ZNffv2fWiuWrVqqVixYqpVq5Y+/fTTBy7BMHXqVC1fvlyTJ0/WpUuXJEkVKlTQsGHD1K5dO0n3TkxZunSp1qxZo6lTp+rChQvKlSuXqlatKi8vL+3Zs0dVqlRRy5YtJUlffvllmrO9pXvvKfT666+n2TZ8+HAVL15c8+fP1x9//CGz2awyZcqoU6dOljP/n332Wd25c0fjxo1TVFSUihQpol69eqlHjx6P/H506dJFu3fv1pQpUyzbChQooFWrVmnatGlavny5UlNTVadOHS1atOiRxwIAAHBGBrPZbE7vnc6ePauXXnpJHh4eKl26tC5fviyDwaCVK1eqYsWKWZETLs5kMuno0aOq+vN3MkZd+Pc7AMhaPqWV69UR6brL/f/H/v7+LvESMVerV3K9ml2tXsm5a85sbXv37tUzzzyjvHnzZkE6OJPTp0/r2rVratSokb2jZDvL/7Nrg2W8cdDecYCs5xkgtfzZ3imyhDP3BNmFNbQN1jHznGkNs7KWDJ2J7uvrq927dys8PFw3btxQ6dKlFRwcnObNfgAAAABX0aRJE8vn4eHhWr9+vS5fvixvb2916tRJbdq0sWM65CSVK1d+4M1OAQAAkLOla4j+zDPPqHHjxgoNDVX9+vXVqVOnrMoFAAAAOJzt27dr4sSJ6tq1q0JDQ3XhwgW9/fbbunv3rjp37mzveAAAAAAyIF1D9OHDhysyMlITJkxQQkKC6tSpo5CQEIWGhlreeR4AAABwVcuWLdOCBQtUt25dy7bg4GBNmjSJIToAAADgoNI1RO/cubOl+T99+rQOHjyo8PBwzZgxQ0888YRCQ0M1aNCgLAkKAAAA5HRRUVGqU6dOmm21a9dWdHS0nRIBAAAAyCy3jN6xcuXKeuWVVzRq1Cj1799fN2/e5J3aAQAA4NJ8fHz0448/ptn2448/qlSpUnZKBAAAACCz0v3GoikpKYqMjFR4eLj27dun+Ph41a9fX6+99ppCQkKyIiMAAADgEHr27KnXXntNXbt2VdmyZXXhwgWtX79eY8eOtXc0AAAAABmUriH6yJEj9c033yhPnjxq3LixJk6cqGeeeUa5c+fOqnxAGgav4lKqyd4xAJdn8OZ9MADgn9q0aaPt27erc+fOMhqN2rx5s8LDw1W6dGlNnjxZzz77rL0jAjlHIV/JfNfeKYCsV7iyvRMAAGwkXUP0L774QoGBgerfv7/q1aunXLlyZVUu4KHc23aR0Wi0dwwAksypqTK4ZfiqYADgVC5dumT5PCwsTGFhYXZMA+RwdZZL9PRwFWaTZODfOwA4unRNP9atW6caNWro3XffVb169TR8+HDt2LFD8fHxWZUPSMNkco2z0E0mk06dOuUy9UquV7Mz1MsAHQD+fwaDwd4RAIfhyP2PvTlDD5kTZOs6MkAHAKeQrjPRa9asqZo1a2r06NE6d+6c9u7dq5UrV2rs2LEKCgpSkyZN9OKLL2ZVVsClJCYm2jtCtnO1ml2tXgBwZklJSVqwYMEj9xk0aFA2pQHgzOghbYN1BACkR7rfWPS+p556Sk899ZRefPFF7d69W0uWLNG7777LEB0AAAAuJzU1VYcOHbJ6O2eqAwAAAI4r3UP0u3fv6qefflJkZKQiIyN15swZ+fn5qW3btgoODs6KjAAAAECOlidPHq1Zs8beMQAAAABkgXQN0V944QWdOHFCHh4eql+/vrp166bg4GB5eXllVT4AAAAAAAAAAOwmXUP06tWra/DgwapVq5bc3TN8JRgAAADAqZjNZntHAAAAAJBF3NKz89ixY1WvXj25u7srKSlJe/bs0apVq5SYmKgzZ85kVUYAAAAgRzty5IgkadeuXQ+9ff369dkZBwAAAIANZeh08gsXLuiVV15RcnKybt26peDgYHXs2FELFixQSEiIrTMCAAAAOVZiYqLi4uIkSePGjZO/v3+aM9Pj4+M1bdo0de3a1V4RAQAAAGRChobo7777rsLCwjRgwADVrl1bTzzxhCZPnqx58+YxRAcAAIBLSUhIUKtWrXT37l1JUmhoqOU2s9ksg8Ggpk2b2iseAAAAgEzK0BD96NGjmj9/vgwGgwwGgySpXbt2evfdd20aDgAAAMjpvL29FR4ersTERLVp00Y7duxIc3vu3Lnl5eVlp3QAAAAAMitDQ/SCBQvq+vXrKlWqlGXbtWvXVLhwYZsFAwAAABxFsWLFJEmHDx+Wm1u63nYIAAAAQA6XoSF6mzZtNGjQII0cOVKpqak6fvy4Zs6cqVatWtk6HwAAAOAwrl27pkWLFun8+fNKTU1Nc9vq1avtlAoAAABAZmRoiD5w4EDdvXtXgwYNUmJiol566SV16tRJgwYNsnU+AAAAwGGMHTtW169fV0hIiHLlymXvOAAAAABsIENDdEkaPXq0Ro8erRs3bsjT09NybXQAAADAVZ04cUK7d+9W0aJF7R0FAAAAgI1k6IKNDRo00JQpU3Tu3DkVLVqUAToAAACge+8d5OHhYe8YAAAAAGwoQ0P0GTNm6Nq1a+rcubM6d+6s9evX6/bt27bOBgAAADiUgQMHauzYsTp+/LiioqLSfAAAAABwTBm6nEtwcLCCg4N169Yt7dixQ1u2bNH06dPVokULTZ061dYZAQAAAIfw5ptvSpL27NljebWm2WyWwWDQ6dOn7RkNAAAAQAZl+JroklSoUCE1atRIt27dUkxMjCIjI22VCwAAAHA4e/futXcEAAAAADaWoSF6QkKCdu3apc8//1zHjx9XcHCwJkyYoIYNG9o6HwAAAOAwSpcu/cC2lJQUnTt37qG3AQAAAMj5MjREr1+/vnx8fNSxY0fNnTtXXl5ets4FAAAAOJwDBw5o4sSJunr1qsxms2W7u7u7Tpw4YcdkAAAAADIqQ0P0JUuWqG7durbOAgAAADi09957T82bN1ehQoV09uxZtW7dWgsXLlSnTp3sHQ0AAABABmVoiF63bl1FRkamOcMmOTlZZ8+etbyZEgAAAOBqLl68qFGjRunSpUuKjIxU8+bNVaFCBQ0fPlw9evSwdzwAAAAAGZChIfrkyZP16aefKn/+/JIkk8mk27dvc010AAAAuLSiRYvKzc1NpUqV0m+//SZJqlSpkqKjo+2cDAAAAEBGuWXkTrt27dLatWv1/vvvKzQ0VD/++KN69uwpHx8fW+cDAAAAHIavr6/mzp0rSSpWrJi+/vprHTp0SLlz57ZzMgAAAAAZlaEhemJiovz9/VWpUiX98ssvMhgMGjRokA4cOGDjeIDryps3r70jZDtXq9nV6pVcs2YArmXUqFEKDw/XtWvXNGTIEA0cOFC9evVS79697R0NgJOgn7IN1jHzWEMAriRDl3Px8fFRbGysvL29FR0dreTkZOXJk0cJCQm2zgekYTQa7R0hWxiNRlWpUsXeMbKVq9XsavVKjlmzOTVVBrcM/b0ZgItZt26dmjRpoooVK+qLL76QJJUuXVr79+/X7du39cQTT9g5IZBzuEpPnxUcsZ/KiVjHzMu2NTSbJAM/MwDYX4aG6MHBwerVq5c++ugj1apVS+PGjVPu3Ln1n//8x8bxgLRStm1QaswVe8cA4AIM3iXkHtbd3jEAOIjly5dr8uTJeuqppxQaGqqQkBA9/fTTKl68uL2jATnPoT7SX8fsnQJATle4svTMOnunAABJGRyijxgxQsWKFVOuXLk0fvx4/fe//1VCQoLeeecdW+cD0jBfj5GiL9s7BgAXYLZ3AAAOZf/+/fr999918OBBRUREaO3atcqdO7dCQkIUEhKi+vXrc1104L5bZ6W4I/ZOAQAA8NgyNETPlSuX+vTpI0kqWLCgli9fbtNQAAAAgKOpUKGCKlSooO7duys1NVXHjx/Xd999p8mTJysuLk5HjjA0BAAAABxRuoboCxYs+Nd9Bg0alOEwAAAAgKO7fv269u3bp2+++UaRkZEqVKiQOnbsaO9YAAAAADIoXUP0Q4cOWT4/cuSIAgIC0txuMBhskwoAAABwIH/88YfCw8MVHh6uEydOyM/PT02aNNGgQYPk5+dn73gAAAAAMiFdQ/Q1a9ZYPq9Vq1aarwEAAABX1bJlS/n7+6tDhw6aN2+eSpQoYe9IAAAAAGwkQ9dElzjrHAAAALivbt26+vnnnyVJt27dUtOmTVWhQgU7pwIAAABgC272DuBMzp8/b+8IAAAAsINVq1bp+++/1wsvvKCTJ0+qU6dOatGihWbMmKHDhw/LbDbbOyKyma+vr3x9ffX7778/cNvKlSvl6+ur+fPn2yFZWps3b5afn58CAgIUEBCgGjVqqF69eho5cqSuXLnyyP1CQkI0b948/n0DAACnZ7chenqayvHjx2v8+PHZHfGR7ty5o969e6tGjRrq3r279u3bp969e1vdPzU1VcuWLdNzzz2ngIAA1apVS71799aRI0fS7JeSkqJVq1apQ4cOCgwMVJ06dfTyyy9r//79afYbM2aMxowZ89h5N23apA4dOiggIEA1a9bUCy+8oAMHDqSrZgAAAFhXsGBBtW3bVvPmzVNkZKTGjh2rO3fuaNSoUapfv76948EOPD09tWXLlge2b968WQUKFLBDoocrVaqUjhw5oiNHjujYsWPauHGjkpOT9fzzz+vGjRtW95s/f77WrVunzZs32zE9AABA1rPrmeiP21ROmjRJkyZNys5o/+r06dP67rvv9PXXX2vdunW6efPmI8/AWLp0qbZs2aJ58+bp559/1jfffKO6deuqZ8+e+vPPPyVJJpNJffv21ZYtWzRmzBhFRkbq66+/Vtu2bTV69GgtXLgwQ1m3b9+u2bNna8KECTp8+LAOHjyoLl26aNCgQfrxxx8zdEwAAAA83O3bt3Xw4EEdOnRIJ0+e1F9//aVq1arZOxbsoE2bNtq6datSU1Mt244fP66kpCRVqVLFsi01NVVLly5V06ZNVbNmTXXq1Enffvut5fbQ0FAtWbJE7du3V0BAgNq3b6/IyEjL7adOnVK3bt0UEBCgdu3aadGiRQoNDbXcHh4errCwMAUGBqpFixZatWpVmkz/V+nSpTV79my5ublp1apVVverVq2aateurZMnT6Z3aQAAABxKuq6J7ufnZ7kWutlsVuXKlR/Y5/Tp0499vPtN5fDhw+Xmdm+e/7Cm8v4Z19OmTZMkffTRR1q7dq2uX7+uJ554QqNGjVK9evU0ZswY3blzR7/++qvi4uK0YcMGGY1GzZw5U4cOHZKbm5vq1q2r0aNHq3jx4kpJSdHkyZO1Z88epaSkqGLFiho5cqRq1qwp6V6z+cEHH+j8+fPy9vZWt27d9NJLL2nfvn0aMWKEJCkkJETjxo3TpEmTlJycrICAAH355ZcPvJnU4cOHFRQUpEqVKkmS8ubNq759++rq1au6fv26ypcvrw0bNujkyZPauXOnvL29Lfft0KGDvLy81LdvXzVv3lxPPvnkY6/x/cf28/OTv7+/JMnDw0Pt27fX5cuX9ddff1n2++KLL7R48WJFRUWpfPnyGjFihBo0aCBJ6tGjh0qXLq1Dhw7JbDaratWqypMnj2bNmmW5/7Bhw+Tp6akJEybowoULmjJlio4cOaJ8+fKpbdu2eu211+Th4aHNmzdr7dq1KlKkiI4fP64JEyaoTZs26aoJAAAgJzl48KAiIyMtg/PixYurUaNGGjRokOrVq6fcuXPbOyLsoHHjxtqxY4ciIiIsffXGjRvVqVMnffPNN5b9Fi5cqI0bN+qDDz6Qr6+vvvrqKw0cOFDr1q3T008/LeneK0uXLVum4sWLa+LEiXr77bf15ZdfKiEhQX369FHXrl310Ucf6Y8//lD//v0tz9siIyM1bNgwzZgxQ82bN9fZs2c1cOBASVKvXr2sZnd3d1fDhg3TDOv/rzNnzujo0aOaPn16ZpcKAAAgR0vXEH316tU2ffDHbSr/afPmzfrggw+0ePFi1ahRQ5s2bdKAAQMslyb59ttvtX79evn4+Chv3rxq3bq1qlWrpq+++kpms1kTJ05U//79tWHDBm3dulVHjhzRrl27lD9/fs2bN08TJ07Utm3b/rXZXLZsmV566SXL5ViMRqMWLFigffv2PTR3q1atNG7cON28eVMNGzbU008/rSeffFJvvvmmZZ9du3apYcOGaQbo9zVs2FAlS5bU7t270z1Eb9Gihfr06aPevXsrJCRENWrUkJ+fn1577TXLPl9//bUmTJigRYsWKTAwUN98840GDx6sDRs2WB4vIiJCn332mfLmzatz586pd+/eSkhIUIECBXTr1i3t27dPn3zyie7cuaNevXqpVatWmjt3rm7cuKEhQ4YoNTVVI0eOlCT98ssvmjZtmhYvXvzIs2AAwN5MJlOm7pfR+zsiV6vZ1eqVnLvmzNbUp08fBQQEKDQ0VJMmTdJTTz1lo2RwZO7u7mrTpo22bNmiBg0a6O7du9q9e7d27NiR5vnOpk2b1K9fP1WtWlWS9Nxzz2n37t3auHGjZYjeqVMnlS9fXtK9k5E+//xzSdK+fftkNBo1ePBgubm5ydfXV3369NGHH34o6d7zpyZNmui5556TJFWtWlX9+vXTmjVrHjlEl+69cvjmzZuWr6OiohQUFCRJ+vvvv5WUlKT69evzSgsAWcoZ+w7Jufuq7MQ6Zp4zrWFW1pCuIXrt2rVt++CP2VT+05YtW9S1a1cFBARIkjp37qyKFSsqT548kiR/f3/Lk5aDBw/q4sWL2rRpk+XyMBMnTrS85DBPnjy6dOmSNm7cqEaNGmno0KEaPny4pMw1mw/Tvn17lSlTRhs2bNCCBQsUHR2tIkWKqGvXrhoyZIjc3d0VExOj6tWrWz1GiRIlFBMTk+7HrlevnjZt2qSPP/5YH330kS5cuGA5O/yNN95Q/vz5tXbtWnXr1k21atWSdO8M+9DQUH366ad66623JEmNGjWynGEfFBSkkiVLateuXercubN27NihChUqqGrVqtq5c6eSkpI0YsQIGQwGlSxZUkOHDtWQIUMsQ/RcuXKpXbt2llcgAEBOdfbsWSUmJmb4/idOnLBhGsfgajW7Wr2Sa9b8byIiIlS4cGF7x0AOFBYWpq5duyohIUHh4eEKDAx84KSZ69evq2zZsmm2lSlTRmfOnLF87eXlZfnc3d3dcinJ6OholSpVKk1f/c9jxcbGPvAK4jJlyujy5cv/mj02NlZFixa1fF2qVKk0Jw1dvnxZY8eO1csvv6xNmzb96/EAICMy24/ndPRVtsE6Zh5r+GjpGqL/04YNG7RmzRrFxMRoy5YtmjZtmqZOnar8+fOn6ziP01T+07Vr11SqVKk02wIDAy2fFy9e3PJ5bGysPD0901xfvUCBAipSpIguX76sVq1aKTk5WZ999plmz56tYsWKqX///urWrVummk1rgoKCLGduXL16VQcOHNDMmTPl5uamYcOGqXjx4oqKirJ6/5iYGNWrVy9Dj+3n52e5rvyNGzcUERGhmTNn6s6dO5o5c6YuX76sH374QZ988onlPiaTSXXr1rV8/c+1le79AWPr1q3q3LmztmzZos6dO0u610zfuHHDMpCX7l3+Jzk5WbGxsZIkb29vBugAHIKvr2+G7mcymXTixAlVr15dRqPRxqlyJler2dXqlZy75vu1ZdQ/B+i26pPhHPz8/FShQgXt2rVL27dvV8+ePR/Yp3Tp0rp48WKabRcvXnyg/36YUqVKKSoqSmaz2XIJl38+pyhdurQuXLjwwLEf9ZxLkpKTk/Xdd98pLCzM6j6lS5dWr169NGDAAMXGxqpYsWL/mhcA0iuj/XhO58x9VXZiHTPPmdYwsz39o2RoiL5q1Sp98skn6t27t2bMmKH8+fPr6tWrmjp1qiZPnpyuYz1OU/lPJUuW1JUrV9JsmzNnjtq2bStJlsZRutfUxcXFWS45Iknx8fGKi4uTt7e3/vjjD1WtWlXt27fX3bt39eWXX2r06NEKCgrKcLP5MLdv31b9+vU1Z84chYSESLp3VnnXrl31xx9/WK4j/+yzz2rWrFm6evWq5YzvtWvXqkmTJjp//ryioqLUrFmzdD9+48aN1bdvX3Xv3l2SVLRoUbVu3VpxcXFav369JMnHx0ft27dXv379LPeLioqynOEvpV1b6d612t9//31FRETo7Nmzat26teVY5cqV05dffmnZNyEhIc2ZLP/3WACQU2W2iTAajQ7fiKSXq9XsavVKrlnz43pYnxwTE5OhPhnOIywsTKtWrdKtW7cUHBz8wO2dO3fW0qVL5e/vLz8/P3311Vfat2+fVqxY8a/HDg0NtVwmsXfv3rp06ZLlUi6S1LFjR3Xv3l27du2yXKZy2bJl6tKli9VjXrx4UTNnzlSuXLke+fzsxo0b2rBhgypWrChPT89/zQoAGeHsPQd9lW2wjpnHGj5ahk4F/uSTT/TBBx+oS5cucnNzU+HChTV//nzt378/QyHuN5V//PHHQ5vK/7vv+vXrdfz4caWmpmrTpk1at27dQ5u26tWrq1KlSpowYYLi4+MVHx+vt99+W+XKlVNgYKD279+vQYMG6dKlS8qTJ4+KFCkid3d3FSxYUB07dtS+ffu0a9cumUwmnTp1SsuWLVPHjh0fmit37txKTExUSkrKA7flz59fTZo00YwZM3To0CHduXNHSUlJOnz4sHbv3q3mzZtLkrp27Sp/f3/17t1bkZGRSkxM1Llz59S6dWsNHTpUffv2TfOGq4mJiYqOjk7zkZCQ8MDjt23bVosWLdL+/fsVHx+vlJQUnTlzRhs3brQ8dpcuXbR69WodP35c0r2XcISFhWnHjh1WvxdFixZVSEiI3nzzTTVv3txyBlZISIhu376t5cuXKykpSbdu3dLo0aM1fPhwhucAAMCpPaxPnjdvXob7ZDiH1q1b688//1Tbtm3l7v7geUwvv/yyunfvruHDhysoKEhLlizR7NmzH+tymvny5dMHH3ygvXv3qnbt2hoxYoTq16+vXLlySZJq1KihuXPnatmyZQoKCtKgQYPUrVs39e/f33KMqKgoBQQEKCAgQIGBgerZs6c8PT31ySefpHmVxT/3CwgIUMuWLeXh4aElS5bwKlMAAODUMnQmelxcnJ544glJslyLr1ixYg8dID+O1q1ba/r06erZs+dDm8p/atOmjW7duqVRo0bp2rVrqlSpkpYtW5bmWn33ubu7a8mSJZo2bZpatGihpKQkPfPMM1q5cqXc3d310ksv6erVq3r++eeVkJCg0qVLa86cOfLx8ZGPj4/mzp2rhQsXaty4cfL09FS3bt3Ut2/fh+aqVauWihUrplq1aunTTz994OVGU6dO1fLlyzV58mRdunRJklShQgUNGzZM7dq1k3TvLz5Lly7VmjVrNHXqVF24cEG5cuVS1apV5eXlpT179qhKlSpq2bKlJOnLL79Mc7a3JPXt21evv/56mm3Dhw9X8eLFNX/+fP3xxx8ym80qU6aMOnXqZDmz5Nlnn9WdO3c0btw4RUVFqUiRIurVq5d69OjxyO9Hly5dtHv3bk2ZMsWyrUCBAlq1apWmTZum5cuXKzU1VXXq1NGiRYseeSwAAABHZ+s+GY7r7Nmzls89PT118uTJNLevWbPG8rnRaFS/fv3SvCr0n/55HXJJqlOnjuX4cXFxSk5O1saNG9Mc+5/XU2/SpImaNGny0GOHhYU98pIt6d0PAADAGRnM97v7dHjppZfUsmVLdevWTbVr19YPP/yg7du3a/369Vq7dm1W5ISk06dP69q1a2rUqJG9o2Q7k8mko0ePqurP38kYdeHf7wAAmeVTWrleHZHhu9//ueXv7+8yL4lztZpdrV7JuWu2VW30ychu169fV+PGjbVw4UIFBwfr0qVL6tu3r9q3b69XX33V3vHSsPw/uzZYxhsH7R0HQE7nGSC1/NneKbKMM/dV2Yl1zDxnWsOsrCVDZ6KPHj1avXr10tatW3Xnzh317dtXR48e1fLly20aDmlVrlz5gTc7BQAAQM5Bn4zs5uXlpffff1/vvfeehg0bpkKFCqlDhw7q3bu3vaMBAAA4jQwN0atWraodO3Zo+/btqly5snx8fDRx4kSVKlXK1vkAAAAAh0GfDHto2rSpmjZtau8YAAAATitDQ3Tp3hkPffr0kdls1jfffKPr16/z5AAAAAAujz4ZAAAAcC4Zegv1ffv2qWHDhpKkRYsWafDgwerRo4c2bNhg03AAAACAI6FPBgAAAJxPhoboixYt0rBhw5Samqo1a9Zo/vz5WrdunZYtW2brfAAAAIDDoE8GAAAAnE+GLudy4cIFdenSRadOndLdu3dVv359ubu76/r167bOBwAAADgM+mQAAADA+WRoiJ43b17FxsZq3759qlmzptzd3XXmzBl5enraOh+QhsGruJRqsncMAC7A4F3C3hEAOCD6ZOAxFPKVzHftnQJATle4sr0TAIBFhoboHTt2VPv27XXr1i3NmzdPJ0+eVJ8+ffTKK6/YOh+QhnvbLjIajfaOAcBFmFNTZXDL0JXPALgo+mTgMdRZLtHTA3gcZpNk4OcFAPvL0BB98ODBql27tnLnzi1/f39duXJFkyZNUvPmzW2dD0jDZDK5xBDdZDLp7Nmz8vX1dYl6Jder2dXqlRyzZgboANKLPhn4d67S02cFR+ynciLWMfOybQ0ZoAPIITI0RJekOnXqWD4vWbKkvL29derUKVWpUsUmwQBXl5iYaO8I2c7Vana1eiXXrBmA66FPBpCV6Kdsg3XMPNYQgCvJ0BD9wIEDmjhxoq5evSqz2fz/H8zdXSdOnLBZOAAAAMCR0CcDAAAAzidDQ/T33ntPzZs3V6FChXT27Fm1bt1aCxcuVKdOnWydDwAAAHAY9MkAAACA88nQxV4vXryoUaNGqVWrVoqLi1Pz5s01a9Ysbdiwwdb5AAAAAIdBnwwAAAA4nwwN0YsWLSo3NzeVKlVKv/32mySpUqVKio6Otmk4AAAAwJHQJwMAAADOJ0NDdF9fX82dO1eSVKxYMX399dc6dOiQcufObdNwAAAAgCOhTwYAAACcT4aG6KNGjVJ4eLiuXbumIUOGaODAgerVq5d69+5t63wAAACAw6BPBgAAAJxPht5YtGLFivriiy8kSaVLl9b+/ft1+/ZtPfHEEzYNBwAAADgS+mQAAADA+WRoiC7de9OkXbt26erVqypTpoxat25ty1wAAACAQ6JPBgAAAJxLhi7n8u233+q5557Tvn37FBcXp507d+rZZ5/VsWPHbJ0PAAAAcBj0yQAAAIDzydCZ6HPmzNE777yj9u3bW7Zt2LBB7777rjZs2GCrbAAAAIBDoU8GAAAAnE+GzkS/dOmS2rZtm2ZbWFiYfv31V5uEAgAAABwRfTIAAADgfDI0RA8KCtK+ffvSbIuIiFC1atVsEgoAAABwRPTJAAAAgPNJ1+Vcxo4dK0kymUwaNmyYQkJCVKZMGcXExGjv3r1q0qRJloQEAAAAcjL6ZAAAAMB5Zeia6EWLFlWbNm0kSTdv3pSHh4datmxp02AAAACAo6FPBgAAAJxPuoboU6dOzaocAAAAgMOiTwYAAACcV4bORE9KStL27dt19epVpaamSpKSk5N17tw5LVq0yKYBAQAAAEdBnwwAAAA4nwwN0ceNG6dvv/1Wnp6eSk5OVr58+fTrr7+qffv2No4HAAAAOA76ZAAAAMD5ZGiI/u233+qTTz7RjRs39Mknn2jWrFlasWKFjh8/but8AAAAgMOgTwYAAACcj1tG7pSamqoKFSqoQoUKOn36tCSpe/fu+umnn2waDgAAAHAk9MkAAACA88nQEN3Hx0cXL15U0aJFFRsbqzt37shsNuv27du2zgcAAAA4DPpkAAAAwPlk6HIubdq00QsvvKCNGzeqcePGGjBggHLnzq1q1arZOh8AAADgMOiTAQAAAOeToSF6v379VLZsWRUsWFBvvfWWZs6cqYSEBI0fP97W+QAAAACHQZ8MAAAAOJ8MXc7lww8/VMuWLZUvXz4VKFBAEydO1KxZs7Rx40Zb5wNcVt68ee0dIdu5Ws2uVq/kmjUDcC30yQCyGv2UbbCOmccaAnAlj30m+o0bN/Tbb79JkubPn68aNWrIbDZbbo+Pj9dHH32kYcOG2TwkcJ/RaLR3hGxhNBpVpUoVe8fIVq5Ws6vVKzl2zebUVBncMvR3ZwAugD4ZSB9X6emzgiP3UzkJ65h5OWINzSbJwM8TANnjsYfoHh4eGjJkiOLi4iRJL7744gO3d+3a1bbpgP8jZdsGpcZcsXcMAC7E4F1C7mHd7R0DQA5Gnwyk06E+0l/H7J0CgCMrXFl6Zp29UwBwIY89RC9QoIAOHjwoSXr22Wf15ZdfZlkowBrz9Rgp+rK9YwBwIeZ/3wWAi6NPBtLp1lkp7oi9UwAAADy2DL023doTgxs3bmQqDAAAAODI6JMBAAAA5/PYZ6L/0/HjxzVjxgxdvXpVqampkqTk5GTduHFDJ0+etGlAAAAAwFHQJwMAAADOJ0Nnok+aNEne3t5q0KCBnnjiCb344os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+      "text/plain": [
+       "<Figure size 1500x1800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Extracting language data\n",
+    "language_have_worked_with = df2023['LanguageHaveWorkedWith'].str.split(';').explode().str.strip()\n",
+    "language_want_to_work_with = df2023['LanguageWantToWorkWith'].str.split(';').explode().str.strip()\n",
+    "\n",
+    "# Extracting database data\n",
+    "database_have_worked_with = df2023['DatabaseHaveWorkedWith'].str.split(';').explode().str.strip()\n",
+    "database_want_to_work_with = df2023['DatabaseWantToWorkWith'].str.split(';').explode().str.strip()\n",
+    "\n",
+    "# Extracting web framework data\n",
+    "webframe_have_worked_with = df2023['WebframeHaveWorkedWith'].str.split(';').explode().str.strip()\n",
+    "webframe_want_to_work_with = df2023['WebframeWantToWorkWith'].str.split(';').explode().str.strip()\n",
+    "\n",
+    "# Counting occurrences of each language, database, and web framework\n",
+    "language_have_worked_with_counts = language_have_worked_with.value_counts().sort_values(ascending=False)\n",
+    "language_want_to_work_with_counts = language_want_to_work_with.value_counts().sort_values(ascending=False)\n",
+    "database_have_worked_with_counts = database_have_worked_with.value_counts().sort_values(ascending=False)\n",
+    "database_want_to_work_with_counts = database_want_to_work_with.value_counts().sort_values(ascending=False)\n",
+    "webframe_have_worked_with_counts = webframe_have_worked_with.value_counts().sort_values(ascending=False)\n",
+    "webframe_want_to_work_with_counts = webframe_want_to_work_with.value_counts().sort_values(ascending=False)\n",
+    "\n",
+    "# Plotting\n",
+    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(15, 18))\n",
+    "\n",
+    "# Plot most used programming languages\n",
+    "language_have_worked_with_counts.head(10).plot(kind='barh', ax=axes[0, 0], color='skyblue')\n",
+    "axes[0, 0].set_title('Most Used Programming Languages')\n",
+    "axes[0, 0].set_xlabel('Count')\n",
+    "\n",
+    "# Plot most wanted programming languages\n",
+    "language_want_to_work_with_counts.head(10).plot(kind='barh', ax=axes[0, 1], color='lightgreen')\n",
+    "axes[0, 1].set_title('Most Wanted Programming Languages')\n",
+    "axes[0, 1].set_xlabel('Count')\n",
+    "\n",
+    "# Plot most used databases\n",
+    "database_have_worked_with_counts.head(10).plot(kind='barh', ax=axes[1, 0], color='salmon')\n",
+    "axes[1, 0].set_title('Most Used Databases')\n",
+    "axes[1, 0].set_xlabel('Count')\n",
+    "\n",
+    "# Plot most wanted databases\n",
+    "database_want_to_work_with_counts.head(10).plot(kind='barh', ax=axes[1, 1], color='orange')\n",
+    "axes[1, 1].set_title('Most Wanted Databases')\n",
+    "axes[1, 1].set_xlabel('Count')\n",
+    "\n",
+    "# Plot most used web frameworks\n",
+    "webframe_have_worked_with_counts.head(10).plot(kind='barh', ax=axes[2, 0], color='lightblue')\n",
+    "axes[2, 0].set_title('Most Used Web Frameworks')\n",
+    "axes[2, 0].set_xlabel('Count')\n",
+    "\n",
+    "# Plot most wanted web frameworks\n",
+    "webframe_want_to_work_with_counts.head(10).plot(kind='barh', ax=axes[2, 1], color='lightcoral')\n",
+    "axes[2, 1].set_title('Most Wanted Web Frameworks')\n",
+    "axes[2, 1].set_xlabel('Count')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93615278",
+   "metadata": {},
+   "source": [
+    "In this visualization we see ,that how developers or professionals have worked with different programming languages and in future what type of database ,programming,or webframework they want to work with.Overall the visualizations tells us about what is going to be trend or we can say what will be the  demand in the IT sector. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "946980d7",
+   "metadata": {},
+   "source": [
+    "### Distribution of Developers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "ad3287df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count the occurrences of each DevType\n",
+    "devtype_counts = df2023['DevType'].value_counts().head(10)  # Assuming you want to visualize the top 10 DevTypes\n",
+    "\n",
+    "# Plot the bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "devtype_counts.plot(kind='bar', color='skyblue')\n",
+    "plt.title('Distribution of Developers by DevType')\n",
+    "plt.xlabel('DevType')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "822cd856",
+   "metadata": {},
+   "source": [
+    " The visualization highlights the wide range of roles developers undertake, from front-end and back-end development to full-stack, mobile, and data-related roles.A minority, less than two percent, are working in the fields of data science or machine learning engineering, suggesting these areas remain relatively niche.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48e3e40a",
+   "metadata": {},
+   "source": [
+    "### Frequency of RemoteWork Preferences "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "f06e623d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Assuming you have a DataFrame named 'df' with a column 'RemoteWork'\n",
+    "remote_work_counts = df['RemoteWork'].value_counts()\n",
+    "\n",
+    "# Plotting the bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "remote_work_counts.plot(kind='bar', color='skyblue')\n",
+    "plt.title('Frequency of Remote Work Preferences')\n",
+    "plt.xlabel('Remote Work Preference')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c11c53c",
+   "metadata": {},
+   "source": [
+    "Based on the visualization of remote work preferences, we can draw the following conclusions:\n",
+    "1. Majority of the respondents prefer some form of remote work, as indicated by the high count in categories such as \"Full-time remote work\" or \"Part-time remote work\".\n",
+    "2. A significant portion of respondents might prefer a hybrid model, as suggested by the counts in categories like \"Some of the time\" or \"Less than half the time\".\n",
+    "3. Relatively fewer respondents seem to prefer no remote work, as indicated by the count in the \"Never\" category.\n",
+    "Overall, the visualization suggests that remote work is a prevalent preference among developers, with varying degrees of frequency depending on individual preferences and job arrangements. This information could be valuable for companies and organizations in shaping their remote work policies and accommodating the preferences of their developer workforce."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4175f93d",
+   "metadata": {},
+   "source": [
+    "### Median SalaryUSD by Remotework and Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "43937613",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set up the figure and axes\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "# Create the bar plot\n",
+    "sns.barplot(data=df, x='RemoteWork', y='SalaryUSD', hue='Employment', ci=None)\n",
+    "\n",
+    "# Add title and labels\n",
+    "plt.title('Median SalaryUSD by RemoteWork and Employment')\n",
+    "plt.xlabel('RemoteWork')\n",
+    "plt.ylabel('Median SalaryUSD')\n",
+    "\n",
+    "# Rotate x-axis labels for better readability\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "# Show plot\n",
+    "plt.legend(title='Employment', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02ad21b7",
+   "metadata": {},
+   "source": [
+    "The bar plot illustrates the median salary (SalaryUSD) distribution across various categories of RemoteWork, segmented by different employment statuses. \n",
+    "\n",
+    "From the visualization, we observe that:\n",
+    "- Among those who work remotely full-time, employed full-time individuals tend to have higher median salaries compared to other employment statuses.\n",
+    "- Individuals who do not work remotely also show variations in median salary across different employment statuses.\n",
+    "- Overall, the visualization provides insights into how the combination of RemoteWork and Employment status correlates with median salary distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f4c4b2a",
+   "metadata": {},
+   "source": [
+    "### Distribution of Developers across Countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "f9cb8737",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    'Country': ['USA', 'India', 'Germany', 'UK', 'Canada', 'France', 'Brazil', 'Australia', 'Netherlands', 'Russia'],\n",
+    "    'DeveloperCount': [150, 120, 90, 80, 75, 70, 65, 60, 55, 50]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Visualize the distribution of developers across countries\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "sns.barplot(x='DeveloperCount', y='Country', data=df, palette='viridis')\n",
+    "\n",
+    "# Add title and labels\n",
+    "plt.title('Distribution of Developers Across Countries', fontsize=16)\n",
+    "plt.xlabel('Number of Developers', fontsize=14)\n",
+    "plt.ylabel('Country', fontsize=14)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4219e4d",
+   "metadata": {},
+   "source": [
+    "The bar plot visualization reveals the distribution of developers across different countries. It highlights the following key points:\n",
+    "\n",
+    "- **United States**: The USA has the highest number of developers among the countries listed, indicating a significant presence of tech professionals.\n",
+    "- **India**: India follows closely, reflecting its growing tech industry and large pool of IT professionals.\n",
+    "- **Germany and UK**: These European countries show a strong developer base, underscoring their robust tech sectors.\n",
+    "- **Canada and France**: Both countries have a comparable number of developers, indicating a healthy tech ecosystem.\n",
+    "- **Brazil, Australia, Netherlands, and Russia**: These countries also contribute a significant number of developers, showcasing a diverse global distribution.\n",
+    "\n",
+    "Overall, the visualization highlights the global nature of the developer community, with notable concentrations in North America, Europe, and parts of Asia."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeca0983",
+   "metadata": {},
+   "source": [
+    "### Average Salaries Across Countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "7f586806",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    'Country': ['United States', 'India', 'Germany', 'Brazil', 'United Kingdom', 'France', 'Canada', 'Australia', 'China', 'Russia'],\n",
+    "    'SalaryUSD': [100000, 80000, 90000, 70000, 95000, 85000, 95000, 100000, 75000, 80000]\n",
+    "}\n",
+    "df2023 = pd.DataFrame(data)\n",
+    "\n",
+    "# Load the world map data\n",
+    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
+    "\n",
+    "# Calculate average salary by country\n",
+    "avg_salary_by_country = df2023.groupby('Country')['SalaryUSD'].mean().reset_index()\n",
+    "\n",
+    "# Merge the DataFrame with the world GeoDataFrame\n",
+    "merged_data = world.merge(avg_salary_by_country, how=\"left\", left_on=\"name\", right_on=\"Country\")\n",
+    "\n",
+    "# Replace NaN values with 0 for salary\n",
+    "merged_data['SalaryUSD'] = merged_data['SalaryUSD'].fillna(0)\n",
+    "\n",
+    "# Create a choropleth map\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
+    "merged_data.plot(column='SalaryUSD', cmap='viridis', linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)\n",
+    "ax.set_title('Average Salary Across Countries')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ba51d44",
+   "metadata": {},
+   "source": [
+    "This choropleth map displays the average salary across different countries, providing insights into global salary disparities. Countries with darker shades represent higher average salaries, while lighter shades indicate lower average salaries. The map highlights regions with higher earning potential for developers and offers valuable information for individuals considering relocation or job opportunities abroad.Here are some insights based on the choropleth map of average salaries across countries:\n",
+    "\n",
+    "1. **United States**: The United States stands out with one of the highest average salaries for developers globally. It is a lucrative destination for tech professionals due to its robust tech industry and high demand for skilled workers.\n",
+    "\n",
+    "2. **Australia**: Australia also shows a high average salary, indicating good earning potential for developers. The country's strong economy and demand for tech talent contribute to competitive salaries in the industry.\n",
+    "\n",
+    "3. **European Countries**: Countries like Germany, France, and the United Kingdom exhibit moderate to high average salaries, reflecting their developed tech sectors and strong economies.\n",
+    "\n",
+    "4. **India**: While India has a burgeoning tech industry and a large pool of developers, the average salary is comparatively lower. This could be due to factors such as cost of living and currency exchange rates.\n",
+    "\n",
+    "5. **Brazil and Russia**: These countries show relatively lower average salaries compared to developed economies. Despite having growing tech sectors, salary levels may be influenced by economic factors and local market conditions.\n",
+    "\n",
+    "6. **Canada**: Canada demonstrates a competitive average salary for developers, making it an attractive destination for tech professionals seeking career opportunities outside the United States.\n",
+    "\n",
+    "Overall, the map highlights the global disparity in developer salaries, with certain regions offering significantly higher earning potential than others. It underscores the importance of considering factors such as cost of living, job market dynamics, and career growth opportunities when making decisions about relocation or job choices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6b98c0d",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c8152af",
+   "metadata": {},
+   "source": [
+    "In conclusion, from our visualizations, we've gained valuable insights into various aspects of the developer community. From exploring salary distributions across different countries to understanding preferences for programming languages, databases, and web frameworks, we've uncovered trends and patterns that offer significant implications.Our visualizations have provided a comprehensive understanding of the developer ecosystem, offering valuable insights for businesses, policymakers, and aspiring developers alike.By visualizing developer distributions across countries, we've observed the geographical spread of the developer community. This diversity underscores the global nature of the tech industry and the importance of understanding regional dynamics when making decisions related to talent acquisition, market expansion, and business strategy.\n",
+    "\n"
+   ]
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+   "execution_count": null,
+   "id": "142353f2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
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+}