new_analysis-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import streamlit as st\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import pycountry\n",
    "from scipy.stats import norm\n",
    "import random\n",
    "from scipy.stats import norm\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ResponseId</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>LearnCode</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>...</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>NEWStuck</th>\n",
       "      <th>NEWOtherComms</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Sexuality</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Accessibility</th>\n",
       "      <th>MentalHealth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>developer</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>18 - 24 years</td>\n",
       "      <td>bootcampother</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>MacOS</td>\n",
       "      <td>overflowgo</td>\n",
       "      <td>No</td>\n",
       "      <td>25-34 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>None of the above</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>student</td>\n",
       "      <td>Student, full-time</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>11 - 17 years</td>\n",
       "      <td>etcschool</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>Windows</td>\n",
       "      <td>stack</td>\n",
       "      <td>No</td>\n",
       "      <td>18-24 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>None of the above</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>developer</td>\n",
       "      <td>Student, full-time</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>11 - 17 years</td>\n",
       "      <td>forum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>MacOS</td>\n",
       "      <td>itwatch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18-24 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>None of the above</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>developer</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>11 - 17 years</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Windows</td>\n",
       "      <td>overflowgo</td>\n",
       "      <td>No</td>\n",
       "      <td>35-44 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>I am deaf / hard of hearing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>developer</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>5 - 10 years</td>\n",
       "      <td>friend</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>Linux-based</td>\n",
       "      <td>itwatch</td>\n",
       "      <td>No</td>\n",
       "      <td>25-34 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0.1  Unnamed: 0  ResponseId MainBranch  \\\n",
       "0             0           0           1  developer   \n",
       "1             1           1           2    student   \n",
       "2             2           2           3  developer   \n",
       "3             3           3           4  developer   \n",
       "4             4           4           5  developer   \n",
       "\n",
       "                                          Employment  \\\n",
       "0  Independent contractor, freelancer, or self-em...   \n",
       "1                                 Student, full-time   \n",
       "2                                 Student, full-time   \n",
       "3                                 Employed full-time   \n",
       "4  Independent contractor, freelancer, or self-em...   \n",
       "\n",
       "                                             Country  \\\n",
       "0                                           Slovakia   \n",
       "1                                        Netherlands   \n",
       "2                                 Russian Federation   \n",
       "3                                            Austria   \n",
       "4  United Kingdom of Great Britain and Northern I...   \n",
       "\n",
       "                                             EdLevel     Age1stCode  \\\n",
       "0  Secondary school (e.g. American high school, G...  18 - 24 years   \n",
       "1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)  11 - 17 years   \n",
       "2       Bachelor’s degree (B.A., B.S., B.Eng., etc.)  11 - 17 years   \n",
       "3    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)  11 - 17 years   \n",
       "4    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   5 - 10 years   \n",
       "\n",
       "       LearnCode YearsCode  ...        OpSys    NEWStuck NEWOtherComms  \\\n",
       "0  bootcampother       NaN  ...        MacOS  overflowgo            No   \n",
       "1      etcschool         7  ...      Windows       stack            No   \n",
       "2          forum       NaN  ...        MacOS     itwatch           Yes   \n",
       "3          other       NaN  ...      Windows  overflowgo            No   \n",
       "4         friend        17  ...  Linux-based     itwatch            No   \n",
       "\n",
       "               Age Gender Trans                Sexuality  \\\n",
       "0  25-34 years old    Man    No  Straight / Heterosexual   \n",
       "1  18-24 years old    Man    No  Straight / Heterosexual   \n",
       "2  18-24 years old    Man    No        Prefer not to say   \n",
       "3  35-44 years old    Man    No  Straight / Heterosexual   \n",
       "4  25-34 years old    Man    No                      NaN   \n",
       "\n",
       "                      Ethnicity                Accessibility  \\\n",
       "0  White or of European descent            None of the above   \n",
       "1  White or of European descent            None of the above   \n",
       "2             Prefer not to say            None of the above   \n",
       "3  White or of European descent  I am deaf / hard of hearing   \n",
       "4  White or of European descent            None of the above   \n",
       "\n",
       "        MentalHealth  \n",
       "0  None of the above  \n",
       "1  None of the above  \n",
       "2  None of the above  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021=pd.read_csv('./Data/nlp_df2021.csv')\n",
    "df2021.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 83439 entries, 0 to 83438\n",
      "Data columns (total 39 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Unnamed: 0.1                  83439 non-null  int64  \n",
      " 1   Unnamed: 0                    83439 non-null  int64  \n",
      " 2   ResponseId                    83439 non-null  int64  \n",
      " 3   MainBranch                    83439 non-null  object \n",
      " 4   Employment                    83323 non-null  object \n",
      " 5   Country                       83439 non-null  object \n",
      " 6   EdLevel                       83126 non-null  object \n",
      " 7   Age1stCode                    83243 non-null  object \n",
      " 8   LearnCode                     83439 non-null  object \n",
      " 9   YearsCode                     81641 non-null  object \n",
      " 10  YearsCodePro                  61216 non-null  object \n",
      " 11  DevType                       66484 non-null  object \n",
      " 12  OrgSize                       60726 non-null  object \n",
      " 13  CompTotal                     47183 non-null  float64\n",
      " 14  CompFreq                      52150 non-null  object \n",
      " 15  LanguageHaveWorkedWith        82357 non-null  object \n",
      " 16  LanguageWantToWorkWith        76821 non-null  object \n",
      " 17  DatabaseHaveWorkedWith        69546 non-null  object \n",
      " 18  DatabaseWantToWorkWith        58299 non-null  object \n",
      " 19  PlatformHaveWorkedWith        52135 non-null  object \n",
      " 20  PlatformWantToWorkWith        41619 non-null  object \n",
      " 21  WebframeHaveWorkedWith        61707 non-null  object \n",
      " 22  WebframeWantToWorkWith        52095 non-null  object \n",
      " 23  MiscTechHaveWorkedWith        47055 non-null  object \n",
      " 24  MiscTechWantToWorkWith        38021 non-null  object \n",
      " 25  ToolsTechHaveWorkedWith       72537 non-null  object \n",
      " 26  ToolsTechWantToWorkWith       65480 non-null  object \n",
      " 27  NEWCollabToolsHaveWorkedWith  81234 non-null  object \n",
      " 28  NEWCollabToolsWantToWorkWith  73022 non-null  object \n",
      " 29  OpSys                         83294 non-null  object \n",
      " 30  NEWStuck                      83439 non-null  object \n",
      " 31  NEWOtherComms                 82828 non-null  object \n",
      " 32  Age                           82407 non-null  object \n",
      " 33  Gender                        82286 non-null  object \n",
      " 34  Trans                         80678 non-null  object \n",
      " 35  Sexuality                     73366 non-null  object \n",
      " 36  Ethnicity                     79464 non-null  object \n",
      " 37  Accessibility                 77603 non-null  object \n",
      " 38  MentalHealth                  76920 non-null  object \n",
      "dtypes: float64(1), int64(3), object(35)\n",
      "memory usage: 24.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df2021.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0.1                        0\n",
       "Unnamed: 0                          0\n",
       "ResponseId                          0\n",
       "MainBranch                          0\n",
       "Employment                        116\n",
       "Country                             0\n",
       "EdLevel                           313\n",
       "Age1stCode                        196\n",
       "LearnCode                           0\n",
       "YearsCode                        1798\n",
       "YearsCodePro                    22223\n",
       "DevType                         16955\n",
       "OrgSize                         22713\n",
       "CompTotal                       36256\n",
       "CompFreq                        31289\n",
       "LanguageHaveWorkedWith           1082\n",
       "LanguageWantToWorkWith           6618\n",
       "DatabaseHaveWorkedWith          13893\n",
       "DatabaseWantToWorkWith          25140\n",
       "PlatformHaveWorkedWith          31304\n",
       "PlatformWantToWorkWith          41820\n",
       "WebframeHaveWorkedWith          21732\n",
       "WebframeWantToWorkWith          31344\n",
       "MiscTechHaveWorkedWith          36384\n",
       "MiscTechWantToWorkWith          45418\n",
       "ToolsTechHaveWorkedWith         10902\n",
       "ToolsTechWantToWorkWith         17959\n",
       "NEWCollabToolsHaveWorkedWith     2205\n",
       "NEWCollabToolsWantToWorkWith    10417\n",
       "OpSys                             145\n",
       "NEWStuck                            0\n",
       "NEWOtherComms                     611\n",
       "Age                              1032\n",
       "Gender                           1153\n",
       "Trans                            2761\n",
       "Sexuality                       10073\n",
       "Ethnicity                        3975\n",
       "Accessibility                    5836\n",
       "MentalHealth                     6519\n",
       "dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2021=df2021.drop(['Unnamed: 0'], axis=1)#drop the unnecessary columns\n",
    "df2021=df2021.drop(['Unnamed: 0.1'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sorted_value_counts(df, column):\n",
    "    # Get the value counts for the specified column\n",
    "    value_counts = df[column].value_counts()\n",
    "    \n",
    "    # Ensure the index is of type string to handle alphabetical sorting correctly\n",
    "    value_counts.index = value_counts.index.astype(str)\n",
    "    \n",
    "    # Sort the value counts by the index alphabetically\n",
    "    value_counts = value_counts.sort_index()\n",
    "    \n",
    "    # Return the sorted value counts\n",
    "    return value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "def substr_replace(df, column,substr, replacement):\n",
    "    df[column] = np.where(\n",
    "    df[column].str.contains(substr, case=False, na=False, regex=False), \n",
    "    replacement, \n",
    "    df[column]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_midpoint_age1stcode(age_col):\n",
    "    if isinstance(age_col, str):\n",
    "        if 'Younger than' in age_col:\n",
    "            return math.ceil((0 + 5) / 2)  # Assume 0-5 years for 'Younger than 5 years'\n",
    "        elif 'Older than' in age_col:\n",
    "            return math.ceil(64 + (69 - 64) / 2)  # Assume 64-69 years for 'Older than 64 years'\n",
    "        else:\n",
    "            start, end = age_col.replace(' years', '').split(' - ')\n",
    "            start = int(start)\n",
    "            end = int(end)\n",
    "            return  math.ceil((start + end) / 2)\n",
    "    return None  # Handle non-string cases\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_age_midpoint(age_col):\n",
    "    if isinstance(age_col, str):\n",
    "        if 'Under' in age_col:\n",
    "            return math.ceil((0 + 18) / 2)  # Assume 0-18 years for 'Under 18 years'\n",
    "        elif 'older' in age_col:\n",
    "            return math.ceil(65 + (69 - 65) / 2)  # Assume 65-69 years for 'Older than 65 years'\n",
    "        elif '-' in age_col:  # Check if the string contains a hyphen\n",
    "            start, end = age_col.replace(' years old', '').split('-')\n",
    "            start = int(start)\n",
    "            end = int(end)\n",
    "            return math.ceil((start + end) / 2)\n",
    "        else:\n",
    "            # If the string doesn't contain a hyphen, return None or handle accordingly\n",
    "            return None\n",
    "    return None  # Handle non-string cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Employment\n",
       "Employed full-time                                      53584\n",
       "Student, full-time                                      11781\n",
       "Independent contractor, freelancer, or self-employed     8041\n",
       "Not employed, but looking for work                       2961\n",
       "Employed part-time                                       2461\n",
       "Student, part-time                                       2051\n",
       "Not employed, and not looking for work                   1228\n",
       "I prefer not to say                                       890\n",
       "Retired                                                   326\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['Employment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\190981787.py:1: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2021['Employment'] = df2021['Employment'].fillna(method='ffill') #fill the missing values with the previous value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdLevel\n",
       "Bachelor’s degree (B.A., B.S., B.Eng., etc.)                                          35357\n",
       "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)                                       17512\n",
       "Some college/university study without earning a degree                                10589\n",
       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     9534\n",
       "Other doctoral degree (Ph.D., Ed.D., etc.)                                             2567\n",
       "Primary/elementary school                                                              2479\n",
       "Associate degree (A.A., A.S., etc.)                                                    2231\n",
       "Something else                                                                         1914\n",
       "Professional degree (JD, MD, etc.)                                                     1256\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['EdLevel']=df2021['EdLevel'].fillna(\"Something else\")#fill the missing values with \"Something else\"\n",
    "df2021['EdLevel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age1stCode_avg\n",
       "14.0    44170\n",
       "21.0    20065\n",
       "8.0     11720\n",
       "30.0     4199\n",
       "40.0     1312\n",
       "3.0       905\n",
       "50.0      544\n",
       "60.0      245\n",
       "17.0      196\n",
       "67.0       83\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['Age1stCode_avg'] = df2021['Age1stCode'].apply(calculate_midpoint_age1stcode)\n",
    "df2021['Age1stCode_avg']=df2021['Age1stCode_avg'].fillna(math.ceil(df2021['Age1stCode_avg'].mean()))#fill the missing values with the mean\n",
    "df2021['Age1stCode_avg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_avg\n",
       "30.0    32568\n",
       "21.0    20993\n",
       "40.0    15183\n",
       "50.0     5472\n",
       "9.0      5376\n",
       "60.0     1819\n",
       "31.0     1607\n",
       "67.0      421\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['Age_avg'] = df2021['Age'].apply(calculate_age_midpoint)\n",
    "df2021['Age_avg']=df2021['Age_avg'].fillna(math.ceil(df2021['Age_avg'].mean()))#fill the missing values with the mean\n",
    "df2021['Age_avg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LearnCode\n",
       "online                    21614\n",
       "certification              8926\n",
       "other                      8076\n",
       "school                     7242\n",
       "bootcampother              5835\n",
       "forumonline                5198\n",
       "family                     4044\n",
       "forum                      3352\n",
       "please                     2292\n",
       "etcschool                  2292\n",
       "friend                     2063\n",
       "schoolonline               1807\n",
       "etcschoolfriend            1764\n",
       "certificationcolleague     1592\n",
       "etcfriend                  1416\n",
       "certificationother         1135\n",
       "colleague                   683\n",
       "schoolother                 673\n",
       "forumother                  619\n",
       "etcother                    599\n",
       "bootcamp                    497\n",
       "forumcolleague              383\n",
       "bootcampschoolonline        327\n",
       "etcschoolother              296\n",
       "schoolcolleague             263\n",
       "bootcamponline              215\n",
       "forumfriend                 151\n",
       "specifycolleague             85\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['LearnCode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\81534222.py:1: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\81534222.py:2: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2021['YearsCode']=df2021['YearsCode'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['YearsCodePro']=df2021['YearsCodePro'].fillna(method='ffill')#fill the missing values with the previous value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1991386150.py:1: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OpSys\n",
       "Windows                              37821\n",
       "Linux-based                          21121\n",
       "MacOS                                21025\n",
       "Windows Subsystem for Linux (WSL)     2749\n",
       "Other (please specify):                576\n",
       "BSD                                    147\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['OpSys']=df2021['OpSys'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021[\"OpSys\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sexuality\n",
       "Straight / Heterosexual                                                           61094\n",
       "Prefer not to say                                                                  4783\n",
       "Bisexual                                                                           2879\n",
       "Gay or Lesbian                                                                     1367\n",
       "Prefer to self-describe:                                                           1258\n",
       "Straight / Heterosexual;Bisexual                                                    492\n",
       "Queer                                                                               376\n",
       "Bisexual;Queer                                                                      273\n",
       "Straight / Heterosexual;Prefer to self-describe:                                    225\n",
       "Gay or Lesbian;Queer                                                                149\n",
       "Straight / Heterosexual;Queer                                                        78\n",
       "Bisexual;Gay or Lesbian                                                              71\n",
       "Prefer to self-describe:;Queer                                                       61\n",
       "Bisexual;Gay or Lesbian;Queer                                                        49\n",
       "Bisexual;Prefer to self-describe:                                                    36\n",
       "Bisexual;Prefer to self-describe:;Queer                                              26\n",
       "Straight / Heterosexual;Bisexual;Prefer to self-describe:;Gay or Lesbian;Queer       22\n",
       "Straight / Heterosexual;Gay or Lesbian                                               21\n",
       "Straight / Heterosexual;Bisexual;Gay or Lesbian;Queer                                21\n",
       "Straight / Heterosexual;Bisexual;Gay or Lesbian                                      18\n",
       "Straight / Heterosexual;Bisexual;Queer                                               15\n",
       "Prefer to self-describe:;Gay or Lesbian;Queer                                        14\n",
       "Straight / Heterosexual;Bisexual;Prefer to self-describe:                            11\n",
       "Bisexual;Prefer to self-describe:;Gay or Lesbian;Queer                               10\n",
       "Straight / Heterosexual;Prefer to self-describe:;Queer                                5\n",
       "Prefer to self-describe:;Gay or Lesbian                                               5\n",
       "Bisexual;Prefer to self-describe:;Gay or Lesbian                                      3\n",
       "Straight / Heterosexual;Bisexual;Prefer to self-describe:;Gay or Lesbian              2\n",
       "Straight / Heterosexual;Gay or Lesbian;Queer                                          1\n",
       "Straight / Heterosexual;Bisexual;Prefer to self-describe:;Queer                       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['Sexuality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1383141237.py:1: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DevType\n",
       "Developer, full-stack                                                                                                                                                                                                                  10781\n",
       "Developer, back-end                                                                                                                                                                                                                     6870\n",
       "Developer, front-end                                                                                                                                                                                                                    2989\n",
       "Developer, front-end;Developer, full-stack;Developer, back-end                                                                                                                                                                          2628\n",
       "Developer, full-stack;Developer, back-end                                                                                                                                                                                               2224\n",
       "                                                                                                                                                                                                                                       ...  \n",
       "Developer, desktop or enterprise applications;Developer, full-stack;Engineer, data;Data scientist or machine learning specialist;Database administrator;Developer, QA or test;Data or business analyst;Designer                            1\n",
       "Developer, front-end;Developer, desktop or enterprise applications;Developer, full-stack;Engineer, data;Other (please specify):;Data scientist or machine learning specialist;Developer, back-end;Data or business analyst;Designer        1\n",
       "Data scientist or machine learning specialist;Scientist;Student;Data or business analyst;Educator                                                                                                                                          1\n",
       "Developer, mobile;Developer, full-stack;Academic researcher;Database administrator;DevOps specialist                                                                                                                                       1\n",
       "Developer, mobile;Developer, desktop or enterprise applications;Data scientist or machine learning specialist;Developer, back-end;Engineering manager                                                                                      1\n",
       "Name: count, Length: 8626, dtype: int64"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['DevType']=df2021['DevType'].fillna(method='ffill')#fill the missing values with \"Something else\"\n",
    "df2021['DevType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['CompFreq']=df2021['CompFreq'].fillna(df2021['CompFreq'].mode()[0])#fill the missing values with the previous value\n",
    "df2021['CompFreq'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['CompTotal']=df2021['CompTotal'].fillna(df2021['CompTotal'].mean())#fill the missing values with the mean\n",
    "df2021['CompTotal'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2021 = df2021.drop(columns=['MiscTechHaveWorkedWith'])\n",
    "df2021 = df2021.drop(columns=['MiscTechWantToWorkWith'])\n",
    "df2021 = df2021.drop(columns=['PlatformWantToWorkWith'])\n",
    "df2021 = df2021.drop(columns=['PlatformHaveWorkedWith'])\n",
    "df2021=df2021.drop(columns=['Age'])\n",
    "df2021=df2021.drop(columns=['Age1stCode'])\n",
    "df2021=df2021.drop(columns=['Trans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LanguageHaveWorkedWith\n",
       "Python                                    1337\n",
       "HTML/CSS;JavaScript;Node.js;TypeScript     811\n",
       "Java                                       715\n",
       "HTML/CSS;JavaScript;PHP;SQL                674\n",
       "C#                                         597\n",
       "C#;HTML/CSS;JavaScript;SQL                 558\n",
       "HTML/CSS;JavaScript                        531\n",
       "HTML/CSS;JavaScript;Node.js                494\n",
       "HTML/CSS;JavaScript;TypeScript             491\n",
       "HTML/CSS;JavaScript;Python                 432\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['LanguageHaveWorkedWith'].value_counts()[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LanguageWantToWorkWith\n",
       "Python                                                                             1389\n",
       "Rust                                                                                821\n",
       "HTML/CSS;JavaScript;Node.js;TypeScript                                              812\n",
       "C#                                                                                  700\n",
       "Java                                                                                512\n",
       "                                                                                   ... \n",
       "C#;HTML/CSS;Java;JavaScript;Node.js;Objective-C;PHP;Python;SQL;Swift;TypeScript       1\n",
       "C#;Elixir;Erlang;HTML/CSS;JavaScript;Python;Ruby;Rust;SQL                             1\n",
       "C#;Go;JavaScript;Kotlin;Node.js;Python;SQL;Swift;TypeScript                           1\n",
       "Bash/Shell;C;C++;HTML/CSS;Java;JavaScript;Objective-C;PHP;PowerShell;SQL              1\n",
       "Elixir;HTML/CSS;Java;JavaScript;Node.js;PHP;SQL;TypeScript                            1\n",
       "Name: count, Length: 27545, dtype: int64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['LanguageWantToWorkWith'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MentalHealth\n",
       "None of the above                                                                                                                                                                                                                                    56459\n",
       "Prefer not to say                                                                                                                                                                                                                                     3932\n",
       "I have a concentration and/or memory disorder (e.g. ADHD)                                                                                                                                                                                             3059\n",
       "I have an anxiety disorder                                                                                                                                                                                                                            2840\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder)                                                                                                                                                                               2324\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder                                                                                                                                                    1811\n",
       "I have autism / an autism spectrum disorder (e.g. Asperger's)                                                                                                                                                                                         1224\n",
       "Or, in your own words:                                                                                                                                                                                                                                1050\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder                                                                                          1009\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder)                                                                                                                      664\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have an anxiety disorder                                                                                                                                                                   660\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's)                             386\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have autism / an autism spectrum disorder (e.g. Asperger's)                                                                                                                                377\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have autism / an autism spectrum disorder (e.g. Asperger's)                                                                                                                  197\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's)                                                                                       191\n",
       "I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's)                                                                                                                                                               166\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have autism / an autism spectrum disorder (e.g. Asperger's)                                                        115\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's)                                                                                                     114\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);Or, in your own words:                                                                                                                                                                        69\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder);Or, in your own words:                                                                                                                                                          56\n",
       "I have an anxiety disorder;Or, in your own words:                                                                                                                                                                                                       52\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder;Or, in your own words:                                                                                                                               36\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:       26\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder;Or, in your own words:                                                                     24\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder);Or, in your own words:                                                                                                21\n",
       "I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:                                                                                                                                                                    20\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have an anxiety disorder;Or, in your own words:                                                                                                                                             11\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:                                                                                                           9\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:                                                                                             7\n",
       "I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:                                                                  3\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have a mood or emotional disorder (e.g. depression, bipolar disorder);I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:                                   3\n",
       "I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:                                                                                                                                          3\n",
       "I have a concentration and/or memory disorder (e.g. ADHD);I have an anxiety disorder;I have autism / an autism spectrum disorder (e.g. Asperger's);Or, in your own words:                                                                                2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['MentalHealth'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Man                                                                                   74817\n",
       "Woman                                                                                  4120\n",
       "Prefer not to say                                                                      1442\n",
       "Non-binary, genderqueer, or gender non-conforming                                       690\n",
       "Or, in your own words:                                                                  413\n",
       "Man;Or, in your own words:                                                              268\n",
       "Man;Non-binary, genderqueer, or gender non-conforming                                   252\n",
       "Woman;Non-binary, genderqueer, or gender non-conforming                                 147\n",
       "Man;Woman                                                                                41\n",
       "Non-binary, genderqueer, or gender non-conforming;Or, in your own words:                 21\n",
       "Man;Woman;Non-binary, genderqueer, or gender non-conforming                              21\n",
       "Man;Woman;Non-binary, genderqueer, or gender non-conforming;Or, in your own words:       17\n",
       "Woman;Or, in your own words:                                                             16\n",
       "Man;Non-binary, genderqueer, or gender non-conforming;Or, in your own words:             11\n",
       "Woman;Non-binary, genderqueer, or gender non-conforming;Or, in your own words:            9\n",
       "Man;Woman;Or, in your own words:                                                          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:1: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:2: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:3: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:4: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:5: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:6: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:7: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:8: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:9: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:10: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:11: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:12: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:13: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:14: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4242856068.py:15: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2021['LanguageHaveWorkedWith']=df2021['LanguageHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['LanguageWantToWorkWith']=df2021['LanguageWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['DatabaseHaveWorkedWith']=df2021['DatabaseHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['DatabaseWantToWorkWith']=df2021['DatabaseWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['WebframeHaveWorkedWith']=df2021['WebframeHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['WebframeWantToWorkWith']=df2021['WebframeWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['ToolsTechHaveWorkedWith']=df2021['ToolsTechHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['ToolsTechWantToWorkWith']=df2021['ToolsTechWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['NEWCollabToolsHaveWorkedWith']=df2021['NEWCollabToolsHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['NEWCollabToolsWantToWorkWith']=df2021['NEWCollabToolsWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2021['OrgSize']=df2021['OrgSize'].fillna(method='ffill')\n",
    "df2021['Accessibility']=df2021['Accessibility'].fillna(method='ffill')\n",
    "df2021['MentalHealth']=df2021['MentalHealth'].fillna(method='ffill')\n",
    "df2021['NEWOtherComms']=df2021['NEWOtherComms'].fillna(method='ffill')\n",
    "df2021['Ethnicity']=df2021['Ethnicity'].fillna(method='ffill')\n",
    "df2021['Sexuality']=df2021['Sexuality'].fillna('Prefer not to say')\n",
    "df2021['Gender']=df2021['Gender'].fillna(\"Prefer not to say\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      83439.000000\n",
      "mean      102961.667833\n",
      "std       112840.529772\n",
      "min            0.000000\n",
      "25%        55000.000000\n",
      "50%       102961.667833\n",
      "75%       102961.667833\n",
      "max      1000000.000000\n",
      "Name: CompTotal, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mean_comp_total = df2021.loc[df2021['CompTotal'] <= 1000000, 'CompTotal'].mean()\n",
    "\n",
    "df2021.loc[df2021['CompTotal'] > 1000000, 'CompTotal'] = mean_comp_total\n",
    "\n",
    "df2021.reset_index(drop=True, inplace=True)  # Reset index if needed\n",
    "\n",
    "print(df2021['CompTotal'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrgSize\n",
       "20 to 99 employees                                    17054\n",
       "100 to 499 employees                                  14365\n",
       "10,000 or more employees                              11100\n",
       "2 to 9 employees                                       9067\n",
       "1,000 to 4,999 employees                               8590\n",
       "10 to 19 employees                                     7465\n",
       "Just me - I am a freelancer, sole proprietor, etc.     5679\n",
       "500 to 999 employees                                   5184\n",
       "5,000 to 9,999 employees                               3116\n",
       "I don’t know                                           1819\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill the missing values with the previous value\n",
    "df2021['OrgSize'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2021.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResponseId                      0\n",
       "MainBranch                      0\n",
       "Employment                      0\n",
       "Country                         0\n",
       "EdLevel                         0\n",
       "LearnCode                       0\n",
       "YearsCode                       0\n",
       "YearsCodePro                    0\n",
       "DevType                         0\n",
       "OrgSize                         0\n",
       "CompTotal                       0\n",
       "CompFreq                        0\n",
       "LanguageHaveWorkedWith          0\n",
       "LanguageWantToWorkWith          0\n",
       "DatabaseHaveWorkedWith          0\n",
       "DatabaseWantToWorkWith          0\n",
       "WebframeHaveWorkedWith          0\n",
       "WebframeWantToWorkWith          0\n",
       "ToolsTechHaveWorkedWith         0\n",
       "ToolsTechWantToWorkWith         0\n",
       "NEWCollabToolsHaveWorkedWith    0\n",
       "NEWCollabToolsWantToWorkWith    0\n",
       "OpSys                           0\n",
       "NEWStuck                        0\n",
       "NEWOtherComms                   0\n",
       "Gender                          0\n",
       "Sexuality                       0\n",
       "Ethnicity                       0\n",
       "Accessibility                   0\n",
       "MentalHealth                    0\n",
       "Age1stCode_avg                  0\n",
       "Age_avg                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2021.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_boxplot(data, x, y, title):\n",
    "    fig = go.Figure()\n",
    "    for group_name, group_data in data.groupby(x):\n",
    "        fig.add_trace(go.Box(y=group_data[y], name=group_name))\n",
    "    fig.update_layout(title=title, xaxis_title=x, yaxis_title=y)\n",
    "    st.plotly_chart(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bar(data, x, y, title):\n",
    "    fig = px.bar(data, x=x, y=y, title=title)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bar_plotly(df, column_name, top_n=10, height=450, width=700):\n",
    "    df_counts = df[column_name].value_counts().head(top_n).reset_index()\n",
    "    df_counts.columns = [column_name, 'Count']\n",
    "    \n",
    "    fig = px.bar(df_counts, x=column_name, y='Count', \n",
    "                 labels={column_name: column_name, 'Count': 'Number of Developers'},\n",
    "                 color=column_name, color_discrete_sequence=px.colors.qualitative.Pastel)\n",
    "    \n",
    "    fig.update_layout(xaxis_title=column_name, yaxis_title='Number of Developers')\n",
    "    fig.update_layout(height=height, width=width)\n",
    "\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_comparison_plotly(df, column1_name, column2_name, top_n=10, height=450, width=700):\n",
    "    # Get the top values of column1_name\n",
    "    df_top = df[column1_name].value_counts().head(top_n).index.tolist()\n",
    "    \n",
    "    # Filter the dataframe to include only rows where column1_name is in the top values\n",
    "    df_filtered = df[df[column1_name].isin(df_top)]\n",
    "    \n",
    "    # Group by column1_name and get the mean (or any other statistic) of column2_name\n",
    "    df_grouped = df_filtered.groupby(column1_name)[column2_name].mean().reset_index()\n",
    "    \n",
    "    # Sort by column1_name to maintain order in the plot\n",
    "    df_grouped = df_grouped.sort_values(by=column1_name)\n",
    "    \n",
    "    # Plotting using Plotly Express\n",
    "    fig = px.bar(df_grouped, x=column1_name, y=column2_name,\n",
    "                 labels={column1_name: column1_name, column2_name: f'Average {column2_name}'},\n",
    "                 color_discrete_sequence=px.colors.qualitative.Pastel)\n",
    "    \n",
    "    fig.update_layout(xaxis_title=column1_name, yaxis_title=f'Average {column2_name}')\n",
    "    fig.update_layout(height=height, width=width)\n",
    "\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "def plot_pie_plotly(df, column_name, top_n=10, height=600, width=600):\n",
    "    participation_rate = df[column_name].value_counts().keys().tolist()[:top_n]\n",
    "    count = df[column_name].value_counts().tolist()[:top_n]\n",
    "\n",
    "    fig_pie = go.Figure(data=[go.Pie(labels=participation_rate, values=count, hole=.3)])\n",
    "\n",
    "    fig_pie.update_layout(\n",
    "        title='Top {} Distribution'.format(column_name),\n",
    "        title_x=0.5,  # Center the title\n",
    "        height=height,\n",
    "        width=width,\n",
    "        showlegend=True,\n",
    "        legend=dict(\n",
    "            x=1,\n",
    "            y=0.5,\n",
    "            traceorder='normal',\n",
    "            font=dict(\n",
    "                size=12,\n",
    "            ),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    # color differentiation and spacing of labels\n",
    "    fig_pie.update_traces(\n",
    "        marker=dict(\n",
    "            colors=px.colors.qualitative.Plotly,\n",
    "            line=dict(color='#000000', width=2)\n",
    "        ),\n",
    "        textinfo='percent+label',\n",
    "        textfont_size=14,\n",
    "        textposition='inside',\n",
    "        hoverinfo='label+percent+name',\n",
    "    )\n",
    "\n",
    "    return fig_pie\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_value_counts_plotly(df, column_name):\n",
    "    colors = ['lightseagreen', 'lightgreen', 'lightyellow', 'lightcoral', 'lightsalmon', 'lavender']\n",
    "\n",
    "    counts = df[column_name].value_counts()\n",
    "    fig = go.Figure(go.Bar(x=counts.index, y=counts.values, marker_color=random.choice(colors)))\n",
    "    fig.update_layout(title=f'Value Counts for {column_name}', xaxis_title='Response', yaxis_title='Count')\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gender_vs_top5countries(df):\n",
    "    all_data = df.groupby(['Country', 'Gender']).size().reset_index(name='Count')\n",
    "    all_data['Total'] = all_data.groupby('Country')['Count'].transform('sum')\n",
    "    all_data['Percentage'] = all_data['Count'] / all_data['Total'] * 100\n",
    "\n",
    "\n",
    "    top_countries = all_data.groupby('Country')['Total'].max().nlargest(5).index\n",
    "    top_data = all_data[all_data['Country'].isin(top_countries)]\n",
    "\n",
    "    # men and women data\n",
    "    men_data = top_data[top_data['Gender'] == 'Man']\n",
    "    women_data = top_data[top_data['Gender'] == 'Woman']\n",
    "\n",
    "    fig = go.Figure()\n",
    "\n",
    "    #bars for 'Men'\n",
    "    fig.add_trace(go.Bar(x=men_data['Country'], y=men_data['Percentage'], name='Men', marker_color='darkblue'))\n",
    "\n",
    "    #bars for 'Women'\n",
    "    fig.add_trace(go.Bar(x=women_data['Country'], y=women_data['Percentage'], name='Women', marker_color='#5E96E9'))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title='Gender vs Top 5 Countries in 2019',\n",
    "        xaxis_title='Top 5 Countries',\n",
    "        yaxis_title='Percentage',\n",
    "        barmode='group'\n",
    "    )\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def highest_paying_countries(df, salary_cap=1000000, top_n=10, name='Median'):\n",
    "    # Filter data for Data Scientists\n",
    "    ds = df[df['DevType'].str.contains('Data scientist', na=False)]\n",
    "    \n",
    "    # Remove rows with unrealistic salaries\n",
    "    ds = ds[ds['CompTotal'] <= salary_cap]\n",
    "    \n",
    "    # Group by country and calculate median salary\n",
    "    ds_median_salary = ds.groupby('Country')['CompTotal'].median().reset_index(name=name)\n",
    "    \n",
    "    # Sort values by median salary in descending order\n",
    "    ds_median_salary.sort_values(by=[name], ascending=False, inplace=True)\n",
    "    \n",
    "    # Select top n countries with highest median salaries\n",
    "    top_median_salary = ds_median_salary.head(top_n)\n",
    "    \n",
    "    # Create a horizontal bar plot with multicolor bars\n",
    "    fig = px.bar(top_median_salary, x=name, y='Country', orientation='h',\n",
    "                 labels={name: f'Average Salary in US$ ({name})', 'Country': 'Country'},\n",
    "                 title=f'Top {top_n} highest paying data scientist countries',\n",
    "                 color='Country',  # Color bars by Country for multicolor effect\n",
    "                 color_discrete_sequence=px.colors.qualitative.Set3)  # Use qualitative color palette\n",
    "    \n",
    "    # Update layout for better readability\n",
    "    fig.update_layout(yaxis={'categoryorder':'total ascending'}, \n",
    "                      title={'x':0.5, 'xanchor': 'center', 'yanchor': 'top'})\n",
    "    \n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def clustered_graph(df, col1, col2):   \n",
    "    top_10 = df[col2].value_counts().nlargest(10).index\n",
    "\n",
    "    filtered_df = df[df[col2].isin(top_10)]\n",
    "\n",
    "    sns.catplot(data=filtered_df, x=col2, hue=col1, kind=\"count\", height=6, aspect=2)\n",
    "    plt.title(f'Clustered Bar Chart of {col1} and {col2}')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stacked_graph_for_correlation(df, col1, col2, height=600, width=800):\n",
    "    # Create a frequency table\n",
    "    freq_table = df.groupby([col1, col2]).size().reset_index(name='counts')\n",
    "    \n",
    "    fig = px.bar(freq_table, x=col2, y='counts', color=col1, \n",
    "                 title=f'Distribution of {col1} by {col2}',\n",
    "                 labels={col1: col1, col2: col2, 'counts': 'Counts'},\n",
    "                 height=height, width=width)\n",
    "    \n",
    "    fig.update_layout(\n",
    "        title={'text': f'{col1} Distribution by {col2}', 'x': 0.5},\n",
    "        xaxis_title=col2,\n",
    "        yaxis_title='Counts',\n",
    "        legend_title=col1,\n",
    "        legend=dict(x=1, y=1)\n",
    "    )\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_mental_health(value):\n",
    "    if pd.isna(value):\n",
    "        return \"Other/Prefer not to say/Or, in your own words\"\n",
    "    if \"concentration and/or memory disorder\" in value:\n",
    "        return \"Concentration/Memory Disorder\"\n",
    "    elif \"anxiety disorder\" in value:\n",
    "        return \"Anxiety Disorder\"\n",
    "    elif \"mood or emotional disorder\" in value:\n",
    "        return \"Mood/Emotional Disorder\"\n",
    "    elif \"autism / an autism spectrum disorder\" in value:\n",
    "        return \"Autism Spectrum Disorder\"\n",
    "    elif \";\" in value:  # Multiple disorders\n",
    "        return \"Multiple Disorders\"\n",
    "    elif \"Prefer not to say\" in value or \"Or, in your own words:\" in value or \"None of the above\" in value:\n",
    "        return \"Other/Prefer not to say/Or, in your own words\"\n",
    "    else:\n",
    "        return \"Other/Prefer not to say/Or, in your own words\"\n",
    "\n",
    "df2021['MentalHealth'] = df2021['MentalHealth'].apply(map_mental_health)\n",
    "\n",
    "# print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_ethnicity(value):\n",
    "    if pd.isna(value):\n",
    "        return \"Other/Prefer not to say\"\n",
    "    \n",
    "    categories = {\n",
    "        \"White or of European descent\": [\"White or of European descent\"],\n",
    "        \"South Asian\": [\"South Asian\"],\n",
    "        \"Hispanic or Latino/a/x\": [\"Hispanic or Latino/a/x\", \"Hispanic or Latino/a/x;Or, in your own words:\"],\n",
    "        \"Southeast Asian\": [\"Southeast Asian\"],\n",
    "        \"East Asian\": [\"East Asian\"],\n",
    "        \"Black or of African descent\": [\"Black or of African descent\"],\n",
    "        \"Middle Eastern\": [\"Middle Eastern\"],\n",
    "        \"Indigenous\": [\"Indigenous\", \"Native American\", \"Pacific Islander\", \"Indigenous Australian\"],\n",
    "        \"Multiracial/Biracial\": [\"Multiracial\", \"Biracial\"],\n",
    "        \"Other/Prefer not to say\": [\"Prefer not to say\", \"Or, in your own words:\", \"I don't know\"]\n",
    "    }\n",
    "    \n",
    "    for category, keywords in categories.items():\n",
    "        for keyword in keywords:\n",
    "            if keyword in value:\n",
    "                return category\n",
    "    return \"Other/Prefer not to say\"\n",
    "\n",
    "df2021['EthnicityCategory'] = df2021['Ethnicity'].apply(map_ethnicity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "def counts(df, column_name, year):\n",
    "    language_counts = df[column_name].str.split(';', expand=True).stack().value_counts().to_frame(name=year)\n",
    "    language_counts[column_name] = language_counts.index\n",
    "    language_counts.reset_index(drop=True, inplace=True)\n",
    "    language_counts = language_counts[[column_name, year]]\n",
    "    return language_counts\n",
    "\n",
    "def compare_column_and_plot(column):\n",
    "    languagedesire_2021 = counts(df2021, column, '2021')\n",
    "    languagedesire_2022 = counts(df2022, column, '2022')\n",
    "\n",
    "    print(\"2021 Data:\")\n",
    "    print(languagedesire_2021.head())\n",
    "    print(\"2022 Data:\")\n",
    "    print(languagedesire_2022.head())\n",
    "\n",
    "    # Merge language counts for both years\n",
    "    languagedesire_all = pd.merge(languagedesire_2021, languagedesire_2022, on=column, how='outer')\n",
    "    \n",
    "    print(\"Merged Data:\")\n",
    "    print(languagedesire_all.head())\n",
    "\n",
    "    # Fill NaN values with 0 and convert counts to integers\n",
    "    languagedesire_all.fillna(0, inplace=True)\n",
    "    languagedesire_all['2021'] = languagedesire_all['2021'].astype(int)\n",
    "    languagedesire_all['2022'] = languagedesire_all['2022'].astype(int)\n",
    "    \n",
    "    languagedesire_all.set_index(column, inplace=True)\n",
    "    \n",
    "    print(\"Filled and Converted Data:\")\n",
    "    print(languagedesire_all.head())\n",
    "\n",
    "    languagedesire19_20 = languagedesire_all.div(languagedesire_all.sum())\n",
    "    \n",
    "    print(\"Percentage Data:\")\n",
    "    print(languagedesire19_20.head())\n",
    "\n",
    "    fig = go.Figure()\n",
    "\n",
    "    colors = ['#636EFA', '#EF553B']  # Define custom colors for the bars\n",
    "\n",
    "    for i, col in enumerate(languagedesire19_20.columns):\n",
    "        fig.add_trace(go.Bar(\n",
    "            x=languagedesire19_20.index, \n",
    "            y=languagedesire19_20[col], \n",
    "            name=col,\n",
    "            marker=dict(color=colors[i % len(colors)])  # Apply custom colors\n",
    "        ))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title='Comparison of Language Desires by Year',\n",
    "        xaxis_title=column,\n",
    "        yaxis_title='Percentages',\n",
    "        font=dict(size=14),\n",
    "        barmode='group',\n",
    "        height=600,\n",
    "        width=800\n",
    "    )\n",
    "\n",
    "    return fig "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "def counts(df, column_name):\n",
    "    # Split the entries in the column, stack them, and count occurrences\n",
    "    language_counts = df[column_name].str.split(';', expand=True).stack().value_counts().to_frame(name='Count')\n",
    "    language_counts.reset_index(inplace=True)\n",
    "    language_counts.columns = ['Language', 'Count']\n",
    "    return language_counts\n",
    "\n",
    "def compare_language_columns_and_plot(df, column1, column2):\n",
    "    # Get counts for both columns\n",
    "    worked_with_counts = counts(df, column1)\n",
    "    want_to_work_with_counts = counts(df, column2)\n",
    "\n",
    "    # Merge the counts on the language column\n",
    "    all_languages = pd.merge(worked_with_counts, want_to_work_with_counts, on='Language', how='outer')\n",
    "    \n",
    "    # Fill NaN values with 0 and convert counts to integers\n",
    "    all_languages.fillna(0, inplace=True)\n",
    "    all_languages['Count_x'] = all_languages['Count_x'].astype(int)\n",
    "    all_languages['Count_y'] = all_languages['Count_y'].astype(int)\n",
    "    \n",
    "    all_languages.set_index('Language', inplace=True)\n",
    "    \n",
    "    fig = go.Figure()\n",
    "\n",
    "    colors = ['#636EFA', '#EF553B']  # Define custom colors for the bars\n",
    "\n",
    "    for i, col in enumerate(all_languages.columns):\n",
    "        fig.add_trace(go.Bar(\n",
    "            x=all_languages.index, \n",
    "            y=all_languages[col], \n",
    "            name=col.split('_')[1],  # Use column names to label the traces\n",
    "            marker=dict(color=colors[i % len(colors)])  # Apply custom colors\n",
    "        ))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title='Comparison of Languages Worked With and Want to Work With',\n",
    "        xaxis_title='Programming Languages',\n",
    "        yaxis_title='Count',\n",
    "        font=dict(size=14),\n",
    "        barmode='stack',\n",
    "        height=600,\n",
    "        width=800\n",
    "    )\n",
    "\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphs"
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          "text": "Response"
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   "source": [
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    "fig.show()"
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      "2021 Data:\n",
      "  LanguageWantToWorkWith   2021\n",
      "0             JavaScript  40208\n",
      "1                 Python  37970\n",
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      "3             TypeScript  29173\n",
      "4                    SQL  28958\n",
      "2022 Data:\n",
      "  LanguageWantToWorkWith   2022\n",
      "0             JavaScript  33404\n",
      "1                 Python  31021\n",
      "2             TypeScript  27642\n",
      "3               HTML/CSS  26914\n",
      "4                    SQL  26304\n",
      "Merged Data:\n",
      "  LanguageWantToWorkWith     2021     2022\n",
      "0                    APL    602.0    655.0\n",
      "1               Assembly   3894.0   3219.0\n",
      "2             Bash/Shell  15258.0  14019.0\n",
      "3                      C  10522.0   8291.0\n",
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      "Filled and Converted Data:\n",
      "                         2021   2022\n",
      "LanguageWantToWorkWith              \n",
      "APL                       602    655\n",
      "Assembly                 3894   3219\n",
      "Bash/Shell              15258  14019\n",
      "C                       10522   8291\n",
      "C#                      19529  16597\n",
      "Percentage Data:\n",
      "                            2021      2022\n",
      "LanguageWantToWorkWith                    \n",
      "APL                     0.001522  0.002021\n",
      "Assembly                0.009846  0.009932\n",
      "Bash/Shell              0.038580  0.043256\n",
      "C                       0.026605  0.025582\n",
      "C#                      0.049379  0.051210\n"
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           ],
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          "colorway": [
           "#000001",
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           "#000006",
           "#000007",
           "#000008",
           "#000009",
           "#000010"
          ]
         }
        },
        "title": {
         "text": "Comparison of Languages Worked With and Want to Work With"
        },
        "width": 800,
        "xaxis": {
         "title": {
          "text": "Programming Languages"
         }
        },
        "yaxis": {
         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = compare_language_columns_and_plot(df2021, 'WebframeHaveWorkedWith', 'WebframeWantToWorkWith')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\4068930218.py:9: UserWarning:\n",
      "\n",
      "FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clustered_graph(df2021,'MentalHealth','Sexuality')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2022 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Unnamed: 0.1  Unnamed: 0  ResponseId  MainBranch           Employment  \\\n",
       "0             0           0           1       other                  NaN   \n",
       "1             1           1           2  profession  Employed, full-time   \n",
       "2             2           2           3        code  Employed, full-time   \n",
       "3             3           3           4  profession  Employed, full-time   \n",
       "4             4           4           5  profession  Employed, full-time   \n",
       "\n",
       "  RemoteWork                          CodingActivities  \\\n",
       "0      other                                       NaN   \n",
       "1     remote  Hobby;Contribute to open-source projects   \n",
       "2     remote                                     Hobby   \n",
       "3     remote              I don’t code outside of work   \n",
       "4     remote                                     Hobby   \n",
       "\n",
       "                                           EdLevel    LearnCode  \\\n",
       "0                                              NaN        other   \n",
       "1                                              NaN        other   \n",
       "2  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)      college   \n",
       "3     Bachelor’s degree (B.A., B.S., B.Eng., etc.)  mediaschool   \n",
       "4     Bachelor’s degree (B.A., B.S., B.Eng., etc.)      college   \n",
       "\n",
       "                                     LearnCodeOnline  ... Knowledge_5  \\\n",
       "0                                                NaN  ...         NaN   \n",
       "1                                                NaN  ...         NaN   \n",
       "2  Technical documentation;Blogs;Programming Game...  ...         NaN   \n",
       "3                                                NaN  ...         NaN   \n",
       "4  Technical documentation;Blogs;Stack Overflow;O...  ...         NaN   \n",
       "\n",
       "  Knowledge_6 Knowledge_7 Frequency_1 Frequency_2 Frequency_3 TimeSearching  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN           NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN           NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN           NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN           NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN           NaN   \n",
       "\n",
       "   TimeAnswering Onboarding ProfessionalTech  \n",
       "0            NaN        NaN              NaN  \n",
       "1            NaN        NaN              NaN  \n",
       "2            NaN        NaN              NaN  \n",
       "3            NaN        NaN              NaN  \n",
       "4            NaN        NaN              NaN  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022=pd.read_csv('./Data/nlp_df2022.csv')\n",
    "df2022.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73268 entries, 0 to 73267\n",
      "Data columns (total 68 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   Unnamed: 0.1                    73268 non-null  int64  \n",
      " 1   Unnamed: 0                      73268 non-null  int64  \n",
      " 2   ResponseId                      73268 non-null  int64  \n",
      " 3   MainBranch                      73268 non-null  object \n",
      " 4   Employment                      71709 non-null  object \n",
      " 5   RemoteWork                      73268 non-null  object \n",
      " 6   CodingActivities                58899 non-null  object \n",
      " 7   EdLevel                         71571 non-null  object \n",
      " 8   LearnCode                       73268 non-null  object \n",
      " 9   LearnCodeOnline                 50685 non-null  object \n",
      " 10  LearnCodeCoursesCert            29389 non-null  object \n",
      " 11  YearsCode                       71331 non-null  object \n",
      " 12  YearsCodePro                    51833 non-null  object \n",
      " 13  DevType                         61302 non-null  object \n",
      " 14  OrgSize                         51039 non-null  object \n",
      " 15  PurchaseInfluence               50969 non-null  object \n",
      " 16  Country                         71771 non-null  object \n",
      " 17  CompTotal                       38422 non-null  float64\n",
      " 18  CompFreq                        44425 non-null  object \n",
      " 19  LanguageHaveWorkedWith          70975 non-null  object \n",
      " 20  LanguageWantToWorkWith          67027 non-null  object \n",
      " 21  DatabaseHaveWorkedWith          60121 non-null  object \n",
      " 22  DatabaseWantToWorkWith          51014 non-null  object \n",
      " 23  PlatformHaveWorkedWith          49924 non-null  object \n",
      " 24  PlatformWantToWorkWith          40415 non-null  object \n",
      " 25  WebframeHaveWorkedWith          53544 non-null  object \n",
      " 26  WebframeWantToWorkWith          46122 non-null  object \n",
      " 27  MiscTechHaveWorkedWith          44992 non-null  object \n",
      " 28  MiscTechWantToWorkWith          36810 non-null  object \n",
      " 29  ToolsTechHaveWorkedWith         54171 non-null  object \n",
      " 30  ToolsTechWantToWorkWith         46566 non-null  object \n",
      " 31  NEWCollabToolsHaveWorkedWith    70347 non-null  object \n",
      " 32  NEWCollabToolsWantToWorkWith    64108 non-null  object \n",
      " 33  OpSysProfessional use           65503 non-null  object \n",
      " 34  OpSysPersonal use               70963 non-null  object \n",
      " 35  VersionControlSystem            71379 non-null  object \n",
      " 36  VCInteraction                   68156 non-null  object \n",
      " 37  VCHostingPersonal use           0 non-null      float64\n",
      " 38  VCHostingProfessional use       0 non-null      float64\n",
      " 39  OfficeStackAsyncHaveWorkedWith  46223 non-null  object \n",
      " 40  OfficeStackAsyncWantToWorkWith  32072 non-null  object \n",
      " 41  OfficeStackSyncHaveWorkedWith   62128 non-null  object \n",
      " 42  OfficeStackSyncWantToWorkWith   47688 non-null  object \n",
      " 43  Blockchain                      71071 non-null  object \n",
      " 44  Age                             70946 non-null  object \n",
      " 45  Gender                          70853 non-null  object \n",
      " 46  Trans                           70315 non-null  object \n",
      " 47  Sexuality                       66565 non-null  object \n",
      " 48  Ethnicity                       69474 non-null  object \n",
      " 49  Accessibility                   67244 non-null  object \n",
      " 50  MentalHealth                    66447 non-null  object \n",
      " 51  TBranch                         52670 non-null  object \n",
      " 52  ICorPM                          36283 non-null  object \n",
      " 53  WorkExp                         36769 non-null  float64\n",
      " 54  Knowledge_1                     35804 non-null  object \n",
      " 55  Knowledge_2                     34973 non-null  object \n",
      " 56  Knowledge_3                     35133 non-null  object \n",
      " 57  Knowledge_4                     35097 non-null  object \n",
      " 58  Knowledge_5                     35014 non-null  object \n",
      " 59  Knowledge_6                     34991 non-null  object \n",
      " 60  Knowledge_7                     34977 non-null  object \n",
      " 61  Frequency_1                     35371 non-null  object \n",
      " 62  Frequency_2                     35344 non-null  object \n",
      " 63  Frequency_3                     34515 non-null  object \n",
      " 64  TimeSearching                   36198 non-null  object \n",
      " 65  TimeAnswering                   36022 non-null  object \n",
      " 66  Onboarding                      35679 non-null  object \n",
      " 67  ProfessionalTech                34906 non-null  object \n",
      "dtypes: float64(4), int64(3), object(61)\n",
      "memory usage: 38.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df2022.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2022 = df2022.drop(df2022.columns[51:67], axis=1)\n",
    "df2022=df2022.drop(['Unnamed: 0'], axis=1)\n",
    "df2022=df2022.drop(['Unnamed: 0.1'], axis=1)\n",
    "df2022 = df2022.drop(columns=['MiscTechHaveWorkedWith'])\n",
    "df2022 = df2022.drop(columns=['MiscTechWantToWorkWith'])\n",
    "df2022 = df2022.drop(columns=['PlatformWantToWorkWith'])\n",
    "df2022 = df2022.drop(columns=['PlatformHaveWorkedWith'])\n",
    "df2022=df2022.drop(columns=['Trans'])\n",
    "df2022=df2022.drop(columns=['VCHostingPersonal use'])\n",
    "df2022=df2022.drop(columns=['VCHostingProfessional use'])\n",
    "df2022=df2022.drop(columns=['LearnCodeCoursesCert'])\n",
    "df2022=df2022.drop(columns=['OfficeStackAsyncHaveWorkedWith'])\n",
    "df2022=df2022.drop(columns=['OfficeStackAsyncWantToWorkWith'])\n",
    "df2022=df2022.drop(columns=['OfficeStackSyncHaveWorkedWith'])\n",
    "df2022=df2022.drop(columns=['OfficeStackSyncWantToWorkWith'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResponseId                          0\n",
       "MainBranch                          0\n",
       "Employment                       1559\n",
       "RemoteWork                          0\n",
       "CodingActivities                14369\n",
       "EdLevel                          1697\n",
       "LearnCode                           0\n",
       "LearnCodeOnline                 22583\n",
       "YearsCode                        1937\n",
       "YearsCodePro                    21435\n",
       "DevType                         11966\n",
       "OrgSize                         22229\n",
       "PurchaseInfluence               22299\n",
       "Country                          1497\n",
       "CompTotal                       34846\n",
       "CompFreq                        28843\n",
       "LanguageHaveWorkedWith           2293\n",
       "LanguageWantToWorkWith           6241\n",
       "DatabaseHaveWorkedWith          13147\n",
       "DatabaseWantToWorkWith          22254\n",
       "WebframeHaveWorkedWith          19724\n",
       "WebframeWantToWorkWith          27146\n",
       "ToolsTechHaveWorkedWith         19097\n",
       "ToolsTechWantToWorkWith         26702\n",
       "NEWCollabToolsHaveWorkedWith     2921\n",
       "NEWCollabToolsWantToWorkWith     9160\n",
       "OpSysProfessional use            7765\n",
       "OpSysPersonal use                2305\n",
       "VersionControlSystem             1889\n",
       "VCInteraction                    5112\n",
       "Blockchain                       2197\n",
       "Age                              2322\n",
       "Gender                           2415\n",
       "Sexuality                        6703\n",
       "Ethnicity                        3794\n",
       "Accessibility                    6024\n",
       "MentalHealth                     6821\n",
       "ProfessionalTech                38362\n",
       "dtype: int64"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:1: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:3: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:4: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:5: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:6: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:7: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:8: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:9: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:10: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:11: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:12: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:13: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:18: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:19: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:20: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:21: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:22: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:23: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:24: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:25: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:26: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:27: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:28: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:29: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:30: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:31: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:32: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "C:\\Users\\Nikita\\AppData\\Local\\Temp\\ipykernel_17576\\1557613256.py:33: FutureWarning:\n",
      "\n",
      "Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2022['Employment'] = df2022['Employment'].fillna(method='ffill') #fill the missing values with the previous value\n",
    "df2022['EdLevel']=df2022['EdLevel'].fillna(\"Something else\")#fill the missing values with \"Something else\"\n",
    "df2022['CodingActivities']=df2022['CodingActivities'].fillna(method=\"ffill\")#fill the missing values with \"Something else\"\n",
    "df2022['YearsCode']=df2022['YearsCode'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['YearsCodePro']=df2022['YearsCodePro'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['LearnCodeOnline']=df2022['LearnCodeOnline'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['Blockchain']=df2022['Blockchain'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['DevType']=df2022['DevType'].fillna(method='ffill')#fill the missing values with \"Something else\"\n",
    "df2022['OrgSize']=df2022['OrgSize'].fillna(method='ffill')\n",
    "df2022['Accessibility']=df2022['Accessibility'].fillna(method='ffill')\n",
    "df2022['MentalHealth']=df2022['MentalHealth'].fillna(method='ffill')\n",
    "df2022['Ethnicity']=df2022['Ethnicity'].fillna(method='ffill')\n",
    "df2022[\"PurchaseInfluence\"]=df2022[\"PurchaseInfluence\"].fillna(method='ffill')\n",
    "df2022['Sexuality']=df2022['Sexuality'].fillna('Prefer not to say')\n",
    "df2022['Gender']=df2022['Gender'].fillna(\"Prefer not to say\")\n",
    "df2022['CompFreq']=df2022['CompFreq'].fillna(df2022['CompFreq'].mode()[0])#fill the missing values with the previous value\n",
    "df2022['CompTotal']=df2022['CompTotal'].fillna(df2022['CompTotal'].mean())#fill the missing values with the mean\n",
    "df2022['LanguageHaveWorkedWith']=df2022['LanguageHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['LanguageWantToWorkWith']=df2022['LanguageWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['DatabaseHaveWorkedWith']=df2022['DatabaseHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['DatabaseWantToWorkWith']=df2022['DatabaseWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['WebframeHaveWorkedWith']=df2022['WebframeHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['WebframeWantToWorkWith']=df2022['WebframeWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['NEWCollabToolsHaveWorkedWith']=df2022['NEWCollabToolsHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['NEWCollabToolsWantToWorkWith']=df2022['NEWCollabToolsWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['OpSysPersonal use']=df2022['OpSysPersonal use'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['OpSysProfessional use']=df2022['OpSysProfessional use'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['ToolsTechHaveWorkedWith']=df2022['ToolsTechHaveWorkedWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['ToolsTechWantToWorkWith']=df2022['ToolsTechWantToWorkWith'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['Country']=df2022['Country'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['ProfessionalTech']=df2022['ProfessionalTech'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['VersionControlSystem']=df2022['VersionControlSystem'].fillna(method='ffill')#fill the missing values with the previous value\n",
    "df2022['VCInteraction']=df2022['VCInteraction'].fillna(method='ffill')#fill the missing values with the previous value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2022.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResponseId                      0\n",
       "MainBranch                      0\n",
       "Employment                      0\n",
       "RemoteWork                      0\n",
       "CodingActivities                0\n",
       "EdLevel                         0\n",
       "LearnCode                       0\n",
       "LearnCodeOnline                 0\n",
       "YearsCode                       0\n",
       "YearsCodePro                    0\n",
       "DevType                         0\n",
       "OrgSize                         0\n",
       "PurchaseInfluence               0\n",
       "Country                         0\n",
       "CompTotal                       0\n",
       "CompFreq                        0\n",
       "LanguageHaveWorkedWith          0\n",
       "LanguageWantToWorkWith          0\n",
       "DatabaseHaveWorkedWith          0\n",
       "DatabaseWantToWorkWith          0\n",
       "WebframeHaveWorkedWith          0\n",
       "WebframeWantToWorkWith          0\n",
       "ToolsTechHaveWorkedWith         0\n",
       "ToolsTechWantToWorkWith         0\n",
       "NEWCollabToolsHaveWorkedWith    0\n",
       "NEWCollabToolsWantToWorkWith    0\n",
       "OpSysProfessional use           0\n",
       "OpSysPersonal use               0\n",
       "VersionControlSystem            0\n",
       "VCInteraction                   0\n",
       "Blockchain                      0\n",
       "Age                             0\n",
       "Gender                          0\n",
       "Sexuality                       0\n",
       "Ethnicity                       0\n",
       "Accessibility                   0\n",
       "MentalHealth                    0\n",
       "ProfessionalTech                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United States of America                                13314\n",
       "India                                                    6507\n",
       "Germany                                                  5364\n",
       "United Kingdom of Great Britain and Northern Ireland     4142\n",
       "Canada                                                   2455\n",
       "                                                        ...  \n",
       "Djibouti                                                    1\n",
       "Seychelles                                                  1\n",
       "Solomon Islands                                             1\n",
       "Saint Kitts and Nevis                                       1\n",
       "Gambia                                                      1\n",
       "Name: count, Length: 180, dtype: int64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LearnCodeOnline\n",
       "Technical documentation;Blogs;Written Tutorials;Stack Overflow                                                                                                                                                        1025\n",
       "Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online forum                                                                                                                                            995\n",
       "Technical documentation;Blogs;Stack Overflow                                                                                                                                                                           826\n",
       "Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online books;Online forum                                                                                                                               744\n",
       "Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online books                                                                                                                                            627\n",
       "                                                                                                                                                                                                                      ... \n",
       "Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online challenges (e.g., daily or weekly coding challenges);How-to videos;Coding sessions (live or recorded);Certification videos                         1\n",
       "Technical documentation;Blogs;Online challenges (e.g., daily or weekly coding challenges);Interactive tutorial;Coding sessions (live or recorded)                                                                        1\n",
       "Technical documentation;Blogs;Programming Games;Written Tutorials;Stack Overflow;Online challenges (e.g., daily or weekly coding challenges);How-to videos;Coding sessions (live or recorded);Certification videos       1\n",
       "Blogs;Stack Overflow;Online books;Video-based Online Courses;Online forum;How-to videos;Written-based Online Courses;Interactive tutorial;Coding sessions (live or recorded);Certification videos                        1\n",
       "Stack Overflow;Video-based Online Courses;How-to videos;Auditory material (e.g., podcasts);Coding sessions (live or recorded);Certification videos                                                                       1\n",
       "Name: count, Length: 7160, dtype: int64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022['LearnCodeOnline'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_avg\n",
       "30.0    28109\n",
       "21.0    16645\n",
       "40.0    13987\n",
       "50.0     5281\n",
       "9.0      3865\n",
       "60.0     1978\n",
       "67.0      554\n",
       "32.0      521\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022['Age_avg'] = df2022['Age'].apply(calculate_age_midpoint)\n",
    "df2022['Age_avg']=df2022['Age_avg'].fillna(math.ceil(df2022['Age_avg'].mean()))#fill the missing values with the mean\n",
    "df2022['Age_avg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CodingActivities\n",
       "Hobby                                                                                                                                  21761\n",
       "I don’t code outside of work                                                                                                            8690\n",
       "Hobby;Contribute to open-source projects                                                                                                7993\n",
       "Hobby;Freelance/contract work                                                                                                           4293\n",
       "Hobby;School or academic work                                                                                                           3642\n",
       "                                                                                                                                       ...  \n",
       "Contribute to open-source projects;Freelance/contract work;Other (please specify):;School or academic work                                 4\n",
       "Contribute to open-source projects;Bootstrapping a business;Freelance/contract work;Other (please specify):                                2\n",
       "Hobby;Contribute to open-source projects;Bootstrapping a business;Other (please specify):;School or academic work                          2\n",
       "Contribute to open-source projects;Bootstrapping a business;Freelance/contract work;Other (please specify):;School or academic work        1\n",
       "Bootstrapping a business;Other (please specify):;School or academic work                                                                   1\n",
       "Name: count, Length: 63, dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022['CodingActivities'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LanguageHaveWorkedWith\n",
       "HTML/CSS;JavaScript;TypeScript                                                                 1254\n",
       "Python                                                                                          950\n",
       "HTML/CSS;JavaScript                                                                             915\n",
       "HTML/CSS;JavaScript;PHP;SQL                                                                     747\n",
       "C#;HTML/CSS;JavaScript;SQL;TypeScript                                                           575\n",
       "                                                                                               ... \n",
       "JavaScript;Kotlin;Rust                                                                            1\n",
       "Bash/Shell;C#;Dart;Groovy;HTML/CSS;JavaScript;Kotlin;Objective-C;PHP;Python;Ruby;Rust;Swift       1\n",
       "Bash/Shell;C#;Java;PowerShell;SQL;TypeScript                                                      1\n",
       "Bash/Shell;Groovy;HTML/CSS;Java;JavaScript;Perl;Python;SQL                                        1\n",
       "C#;JavaScript;Lua;PowerShell;SQL;TypeScript                                                       1\n",
       "Name: count, Length: 24854, dtype: int64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2022['LanguageHaveWorkedWith'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      70940.000000\n",
      "mean      110641.766488\n",
      "std       106062.230734\n",
      "min            0.000000\n",
      "25%        70000.000000\n",
      "50%       110641.766488\n",
      "75%       110641.766488\n",
      "max      1000000.000000\n",
      "Name: CompTotal, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mean_comp_total = df2022.loc[df2022['CompTotal'] <= 1000000, 'CompTotal'].mean()\n",
    "\n",
    "df2022.loc[df2022['CompTotal'] > 1000000, 'CompTotal'] = mean_comp_total\n",
    "\n",
    "df2022.reset_index(drop=True, inplace=True)  # Reset index if needed\n",
    "\n",
    "print(df2022['CompTotal'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphs"
   ]
  },
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   "source": [
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  },
  {
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   "metadata": {},
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        },
        "title": {
         "text": "MentalHealth Distribution by Sexuality",
         "x": 0.5
        },
        "width": 1000,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
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         "title": {
          "text": "Sexuality"
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        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
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         "title": {
          "text": "Counts"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "\n",
    "df2022['MentalHealth'] = df2022['MentalHealth'].apply(map_mental_health)\n",
    "df2022['EthnicityCategory'] = df2022['Ethnicity'].apply(map_ethnicity)\n",
    "fig = stacked_graph_for_correlation(df2022, 'MentalHealth', 'Sexuality', 1400, 1000)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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