Task 2 -Stage 8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab34caa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0030a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data from the Excel file\n",
    "file_path = 'data.xlsx'\n",
    "\n",
    "doctors_df = pd.read_excel(file_path, sheet_name='doctorsinGermany')\n",
    "retirement_age_df = pd.read_excel(file_path, sheet_name='MeanRetirementAge')\n",
    "labour_force_df = pd.read_excel(file_path, sheet_name='LabourForce')\n",
    "population_df = pd.read_excel(file_path, sheet_name='Population')\n",
    "population_projection_df = pd.read_excel(file_path, sheet_name='populatioprojection_Agegroup')\n",
    "age_distribution_df = pd.read_excel(file_path, sheet_name='AgeDistribution')\n",
    "medical_graduates_df = pd.read_excel(file_path, sheet_name='medicalgraduates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a67ff76a",
   "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>Year</th>\n",
       "      <th>Practicing Doctors</th>\n",
       "      <th>Doctors Without medical activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>428500</td>\n",
       "      <td>140300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>421300</td>\n",
       "      <td>136200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>416100</td>\n",
       "      <td>132200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>409100</td>\n",
       "      <td>127800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>402100</td>\n",
       "      <td>123600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Practicing Doctors  Doctors Without medical activity\n",
       "0  2023              428500                            140300\n",
       "1  2022              421300                            136200\n",
       "2  2021              416100                            132200\n",
       "3  2020              409100                            127800\n",
       "4  2019              402100                            123600"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doctors_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8aea1aa4",
   "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>Year</th>\n",
       "      <th>Standard Age</th>\n",
       "      <th>Insured for 35 years</th>\n",
       "      <th>Insured for 45 years</th>\n",
       "      <th>Severely Disabled</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Women</th>\n",
       "      <th>Mine Workers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1992</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1996</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1997</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.292</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1998</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.792</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1999</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>61.292</td>\n",
       "      <td>60.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.292</td>\n",
       "      <td>63.292</td>\n",
       "      <td>60.292</td>\n",
       "      <td>61.792</td>\n",
       "      <td>60.292</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2001</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.792</td>\n",
       "      <td>63.792</td>\n",
       "      <td>60.792</td>\n",
       "      <td>62.292</td>\n",
       "      <td>60.792</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2002</td>\n",
       "      <td>65.000</td>\n",
       "      <td>64.292</td>\n",
       "      <td>64.292</td>\n",
       "      <td>61.292</td>\n",
       "      <td>62.792</td>\n",
       "      <td>61.292</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2003</td>\n",
       "      <td>65.000</td>\n",
       "      <td>64.792</td>\n",
       "      <td>64.792</td>\n",
       "      <td>61.792</td>\n",
       "      <td>63.292</td>\n",
       "      <td>61.792</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2004</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>62.292</td>\n",
       "      <td>63.792</td>\n",
       "      <td>62.292</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2005</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>62.792</td>\n",
       "      <td>64.292</td>\n",
       "      <td>62.792</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2006</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>64.792</td>\n",
       "      <td>63.292</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2007</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.792</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2008</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>64.292</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2009</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>64.792</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2010</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2011</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2012</td>\n",
       "      <td>65.083</td>\n",
       "      <td>65.083</td>\n",
       "      <td>65.083</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.083</td>\n",
       "      <td>65.083</td>\n",
       "      <td>60.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>65.159</td>\n",
       "      <td>65.159</td>\n",
       "      <td>65.159</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.159</td>\n",
       "      <td>65.159</td>\n",
       "      <td>60.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014</td>\n",
       "      <td>65.235</td>\n",
       "      <td>65.235</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.000</td>\n",
       "      <td>65.235</td>\n",
       "      <td>65.235</td>\n",
       "      <td>60.614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015</td>\n",
       "      <td>65.311</td>\n",
       "      <td>65.311</td>\n",
       "      <td>63.000</td>\n",
       "      <td>63.292</td>\n",
       "      <td>65.311</td>\n",
       "      <td>65.311</td>\n",
       "      <td>60.689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016</td>\n",
       "      <td>65.386</td>\n",
       "      <td>65.386</td>\n",
       "      <td>63.167</td>\n",
       "      <td>63.538</td>\n",
       "      <td>65.386</td>\n",
       "      <td>65.386</td>\n",
       "      <td>60.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017</td>\n",
       "      <td>65.462</td>\n",
       "      <td>65.462</td>\n",
       "      <td>63.333</td>\n",
       "      <td>63.614</td>\n",
       "      <td>65.462</td>\n",
       "      <td>65.462</td>\n",
       "      <td>60.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018</td>\n",
       "      <td>65.538</td>\n",
       "      <td>65.538</td>\n",
       "      <td>63.500</td>\n",
       "      <td>63.689</td>\n",
       "      <td>65.538</td>\n",
       "      <td>65.538</td>\n",
       "      <td>60.917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019</td>\n",
       "      <td>65.614</td>\n",
       "      <td>65.614</td>\n",
       "      <td>63.667</td>\n",
       "      <td>63.765</td>\n",
       "      <td>65.614</td>\n",
       "      <td>65.614</td>\n",
       "      <td>61.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020</td>\n",
       "      <td>65.689</td>\n",
       "      <td>65.689</td>\n",
       "      <td>63.833</td>\n",
       "      <td>63.841</td>\n",
       "      <td>65.689</td>\n",
       "      <td>65.689</td>\n",
       "      <td>61.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021</td>\n",
       "      <td>65.765</td>\n",
       "      <td>65.765</td>\n",
       "      <td>64.000</td>\n",
       "      <td>63.917</td>\n",
       "      <td>65.765</td>\n",
       "      <td>65.765</td>\n",
       "      <td>61.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2022</td>\n",
       "      <td>65.841</td>\n",
       "      <td>65.841</td>\n",
       "      <td>64.167</td>\n",
       "      <td>64.000</td>\n",
       "      <td>65.841</td>\n",
       "      <td>65.841</td>\n",
       "      <td>61.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2023</td>\n",
       "      <td>65.917</td>\n",
       "      <td>65.917</td>\n",
       "      <td>64.333</td>\n",
       "      <td>64.167</td>\n",
       "      <td>65.917</td>\n",
       "      <td>65.917</td>\n",
       "      <td>61.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2024</td>\n",
       "      <td>66.000</td>\n",
       "      <td>66.000</td>\n",
       "      <td>64.500</td>\n",
       "      <td>64.300</td>\n",
       "      <td>66.000</td>\n",
       "      <td>66.000</td>\n",
       "      <td>61.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2025</td>\n",
       "      <td>66.167</td>\n",
       "      <td>66.167</td>\n",
       "      <td>64.667</td>\n",
       "      <td>64.433</td>\n",
       "      <td>66.167</td>\n",
       "      <td>66.167</td>\n",
       "      <td>61.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2026</td>\n",
       "      <td>66.300</td>\n",
       "      <td>66.300</td>\n",
       "      <td>64.833</td>\n",
       "      <td>64.567</td>\n",
       "      <td>66.300</td>\n",
       "      <td>66.300</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2027</td>\n",
       "      <td>66.433</td>\n",
       "      <td>66.433</td>\n",
       "      <td>65.000</td>\n",
       "      <td>64.700</td>\n",
       "      <td>66.433</td>\n",
       "      <td>66.433</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2028</td>\n",
       "      <td>66.567</td>\n",
       "      <td>66.567</td>\n",
       "      <td>65.000</td>\n",
       "      <td>64.833</td>\n",
       "      <td>66.567</td>\n",
       "      <td>66.567</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2029</td>\n",
       "      <td>66.700</td>\n",
       "      <td>66.700</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>66.700</td>\n",
       "      <td>66.700</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2030</td>\n",
       "      <td>66.833</td>\n",
       "      <td>66.833</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>66.833</td>\n",
       "      <td>66.833</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2031</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2032</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2033</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2034</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2035</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2036</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2037</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2038</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2039</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2040</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>67.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Standard Age  Insured for 35 years  Insured for 45 years  \\\n",
       "0   1992        65.000                63.000                63.000   \n",
       "1   1993        65.000                63.000                63.000   \n",
       "2   1994        65.000                63.000                63.000   \n",
       "3   1995        65.000                63.000                63.000   \n",
       "4   1996        65.000                63.000                63.000   \n",
       "5   1997        65.000                63.000                63.000   \n",
       "6   1998        65.000                63.000                63.000   \n",
       "7   1999        65.000                63.000                63.000   \n",
       "8   2000        65.000                63.292                63.292   \n",
       "9   2001        65.000                63.792                63.792   \n",
       "10  2002        65.000                64.292                64.292   \n",
       "11  2003        65.000                64.792                64.792   \n",
       "12  2004        65.000                65.000                65.000   \n",
       "13  2005        65.000                65.000                65.000   \n",
       "14  2006        65.000                65.000                65.000   \n",
       "15  2007        65.000                65.000                65.000   \n",
       "16  2008        65.000                65.000                65.000   \n",
       "17  2009        65.000                65.000                65.000   \n",
       "18  2010        65.000                65.000                65.000   \n",
       "19  2011        65.000                65.000                65.000   \n",
       "20  2012        65.083                65.083                65.083   \n",
       "21  2013        65.159                65.159                65.159   \n",
       "22  2014        65.235                65.235                63.000   \n",
       "23  2015        65.311                65.311                63.000   \n",
       "24  2016        65.386                65.386                63.167   \n",
       "25  2017        65.462                65.462                63.333   \n",
       "26  2018        65.538                65.538                63.500   \n",
       "27  2019        65.614                65.614                63.667   \n",
       "28  2020        65.689                65.689                63.833   \n",
       "29  2021        65.765                65.765                64.000   \n",
       "30  2022        65.841                65.841                64.167   \n",
       "31  2023        65.917                65.917                64.333   \n",
       "32  2024        66.000                66.000                64.500   \n",
       "33  2025        66.167                66.167                64.667   \n",
       "34  2026        66.300                66.300                64.833   \n",
       "35  2027        66.433                66.433                65.000   \n",
       "36  2028        66.567                66.567                65.000   \n",
       "37  2029        66.700                66.700                65.000   \n",
       "38  2030        66.833                66.833                65.000   \n",
       "39  2031        67.000                67.000                65.000   \n",
       "40  2032        67.000                67.000                65.000   \n",
       "41  2033        67.000                67.000                65.000   \n",
       "42  2034        67.000                67.000                65.000   \n",
       "43  2035        67.000                67.000                65.000   \n",
       "44  2036        67.000                67.000                65.000   \n",
       "45  2037        67.000                67.000                65.000   \n",
       "46  2038        67.000                67.000                65.000   \n",
       "47  2039        67.000                67.000                65.000   \n",
       "48  2040        67.000                67.000                65.000   \n",
       "\n",
       "    Severely Disabled  Unemployed   Women  Mine Workers  \n",
       "0              60.000      60.000  60.000        60.000  \n",
       "1              60.000      60.000  60.000        60.000  \n",
       "2              60.000      60.000  60.000        60.000  \n",
       "3              60.000      60.000  60.000        60.000  \n",
       "4              60.000      60.000  60.000        60.000  \n",
       "5              60.000      60.292  60.000        60.000  \n",
       "6              60.000      60.792  60.000        60.000  \n",
       "7              60.000      61.292  60.000        60.000  \n",
       "8              60.292      61.792  60.292        60.000  \n",
       "9              60.792      62.292  60.792        60.000  \n",
       "10             61.292      62.792  61.292        60.000  \n",
       "11             61.792      63.292  61.792        60.000  \n",
       "12             62.292      63.792  62.292        60.000  \n",
       "13             62.792      64.292  62.792        60.000  \n",
       "14             63.000      64.792  63.292        60.000  \n",
       "15             63.000      65.000  63.792        60.000  \n",
       "16             63.000      65.000  64.292        60.000  \n",
       "17             63.000      65.000  64.792        60.000  \n",
       "18             63.000      65.000  65.000        60.000  \n",
       "19             63.000      65.000  65.000        60.000  \n",
       "20             63.000      65.083  65.083        60.292  \n",
       "21             63.000      65.159  65.159        60.538  \n",
       "22             63.000      65.235  65.235        60.614  \n",
       "23             63.292      65.311  65.311        60.689  \n",
       "24             63.538      65.386  65.386        60.765  \n",
       "25             63.614      65.462  65.462        60.841  \n",
       "26             63.689      65.538  65.538        60.917  \n",
       "27             63.765      65.614  65.614        61.000  \n",
       "28             63.841      65.689  65.689        61.167  \n",
       "29             63.917      65.765  65.765        61.300  \n",
       "30             64.000      65.841  65.841        61.433  \n",
       "31             64.167      65.917  65.917        61.567  \n",
       "32             64.300      66.000  66.000        61.700  \n",
       "33             64.433      66.167  66.167        61.833  \n",
       "34             64.567      66.300  66.300        62.000  \n",
       "35             64.700      66.433  66.433        62.000  \n",
       "36             64.833      66.567  66.567        62.000  \n",
       "37             65.000      66.700  66.700        62.000  \n",
       "38             65.000      66.833  66.833        62.000  \n",
       "39             65.000      67.000  67.000        62.000  \n",
       "40             65.000      67.000  67.000        62.000  \n",
       "41             65.000      67.000  67.000        62.000  \n",
       "42             65.000      67.000  67.000        62.000  \n",
       "43             65.000      67.000  67.000        62.000  \n",
       "44             65.000      67.000  67.000        62.000  \n",
       "45             65.000      67.000  67.000        62.000  \n",
       "46             65.000      67.000  67.000        62.000  \n",
       "47             65.000      67.000  67.000        62.000  \n",
       "48             65.000      67.000  67.000        62.000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retirement_age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74d49ca4",
   "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>Year</th>\n",
       "      <th>Labour force</th>\n",
       "      <th>Persons in employment (national concept)</th>\n",
       "      <th>Persons in employment (domestic concept)</th>\n",
       "      <th>Self-employed</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Agriculture, forestry and fishing</th>\n",
       "      <th>Production industry (incl. construction)</th>\n",
       "      <th>Services</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment rate (%)</th>\n",
       "      <th>Employees subject to social insurance contributions</th>\n",
       "      <th>Persons exclusively in marginal employment</th>\n",
       "      <th>Registered unemployed</th>\n",
       "      <th>Rate of registered unemployment (%)</th>\n",
       "      <th>Reported job vacancies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>44309000</td>\n",
       "      <td>42285000</td>\n",
       "      <td>42350000</td>\n",
       "      <td>4495000</td>\n",
       "      <td>37855000</td>\n",
       "      <td>641000</td>\n",
       "      <td>10434000</td>\n",
       "      <td>31275000</td>\n",
       "      <td>2024000</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29616000</td>\n",
       "      <td>5066000</td>\n",
       "      <td>2950000</td>\n",
       "      <td>0.069</td>\n",
       "      <td>457000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>46800000</td>\n",
       "      <td>45457000</td>\n",
       "      <td>45596000</td>\n",
       "      <td>3909000</td>\n",
       "      <td>41687000</td>\n",
       "      <td>557000</td>\n",
       "      <td>10738000</td>\n",
       "      <td>34301000</td>\n",
       "      <td>1343000</td>\n",
       "      <td>0.029</td>\n",
       "      <td>34445000</td>\n",
       "      <td>4164000</td>\n",
       "      <td>2418000</td>\n",
       "      <td>0.053</td>\n",
       "      <td>845000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>47119000</td>\n",
       "      <td>45785000</td>\n",
       "      <td>45933000</td>\n",
       "      <td>3879000</td>\n",
       "      <td>42054000</td>\n",
       "      <td>554000</td>\n",
       "      <td>10780000</td>\n",
       "      <td>34599000</td>\n",
       "      <td>1334000</td>\n",
       "      <td>0.028</td>\n",
       "      <td>34709000</td>\n",
       "      <td>4266000</td>\n",
       "      <td>2609000</td>\n",
       "      <td>0.057</td>\n",
       "      <td>761000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Labour force  Persons in employment (national concept)  \\\n",
       "0  2013      44309000                                  42285000   \n",
       "1  2022      46800000                                  45457000   \n",
       "2  2023      47119000                                  45785000   \n",
       "\n",
       "   Persons in employment (domestic concept)  Self-employed  Employees  \\\n",
       "0                                  42350000        4495000   37855000   \n",
       "1                                  45596000        3909000   41687000   \n",
       "2                                  45933000        3879000   42054000   \n",
       "\n",
       "   Agriculture, forestry and fishing  \\\n",
       "0                             641000   \n",
       "1                             557000   \n",
       "2                             554000   \n",
       "\n",
       "   Production industry (incl. construction)  Services  Unemployed  \\\n",
       "0                                  10434000  31275000     2024000   \n",
       "1                                  10738000  34301000     1343000   \n",
       "2                                  10780000  34599000     1334000   \n",
       "\n",
       "   Unemployment rate (%)  Employees subject to social insurance contributions  \\\n",
       "0                  0.046                                           29616000     \n",
       "1                  0.029                                           34445000     \n",
       "2                  0.028                                           34709000     \n",
       "\n",
       "   Persons exclusively in marginal employment  Registered unemployed  \\\n",
       "0                                     5066000                2950000   \n",
       "1                                     4164000                2418000   \n",
       "2                                     4266000                2609000   \n",
       "\n",
       "   Rate of registered unemployment (%)  Reported job vacancies  \n",
       "0                                0.069                  457000  \n",
       "1                                0.053                  845000  \n",
       "2                                0.057                  761000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labour_force_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d274067",
   "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>Year</th>\n",
       "      <th>Total Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>72814900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>73377632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>74025784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>74714353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>75318337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2020</td>\n",
       "      <td>83160871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2021</td>\n",
       "      <td>83196078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2022</td>\n",
       "      <td>83797985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2023</td>\n",
       "      <td>84482267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2024</td>\n",
       "      <td>84552242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Total Population\n",
       "0   1960          72814900\n",
       "1   1961          73377632\n",
       "2   1962          74025784\n",
       "3   1963          74714353\n",
       "4   1964          75318337\n",
       "..   ...               ...\n",
       "60  2020          83160871\n",
       "61  2021          83196078\n",
       "62  2022          83797985\n",
       "63  2023          84482267\n",
       "64  2024          84552242\n",
       "\n",
       "[65 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "458a7122",
   "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>Year</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Population (Under 20yrs)</th>\n",
       "      <th>Population (20-66yrs)</th>\n",
       "      <th>Population (67 yrs+)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>83200000</td>\n",
       "      <td>15400000</td>\n",
       "      <td>51400000</td>\n",
       "      <td>16400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2030</td>\n",
       "      <td>85140000</td>\n",
       "      <td>16440000</td>\n",
       "      <td>49960000</td>\n",
       "      <td>18720000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2040</td>\n",
       "      <td>84780000</td>\n",
       "      <td>15980000</td>\n",
       "      <td>48000000</td>\n",
       "      <td>20800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2050</td>\n",
       "      <td>83840000</td>\n",
       "      <td>15560000</td>\n",
       "      <td>47860000</td>\n",
       "      <td>20400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2060</td>\n",
       "      <td>82700000</td>\n",
       "      <td>15560000</td>\n",
       "      <td>46440000</td>\n",
       "      <td>20740000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2070</td>\n",
       "      <td>82260000</td>\n",
       "      <td>15400000</td>\n",
       "      <td>45740000</td>\n",
       "      <td>21140000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Total Population  Population (Under 20yrs)  Population (20-66yrs)  \\\n",
       "0  2021          83200000                  15400000               51400000   \n",
       "1  2030          85140000                  16440000               49960000   \n",
       "2  2040          84780000                  15980000               48000000   \n",
       "3  2050          83840000                  15560000               47860000   \n",
       "4  2060          82700000                  15560000               46440000   \n",
       "5  2070          82260000                  15400000               45740000   \n",
       "\n",
       "   Population (67 yrs+)  \n",
       "0            16400000.0  \n",
       "1            18720000.0  \n",
       "2            20800000.0  \n",
       "3            20400000.0  \n",
       "4            20740000.0  \n",
       "5            21140000.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_projection_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a736bee",
   "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>Year</th>\n",
       "      <th>Total number</th>\n",
       "      <th>under 20(%)</th>\n",
       "      <th>20 to 40(%)</th>\n",
       "      <th>40 to 60(%)</th>\n",
       "      <th>60 to 80(%)</th>\n",
       "      <th>80 to 100(%)</th>\n",
       "      <th>100 and more(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>84669326</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>84358845</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>83237124</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>83155031</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>83166711</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>83019213</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>82792351</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>82521653</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>82175684</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014</td>\n",
       "      <td>81197537</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>80767463</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012</td>\n",
       "      <td>80523746</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2011</td>\n",
       "      <td>80327900</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Total number  under 20(%)  20 to 40(%)  40 to 60(%)  60 to 80(%)  \\\n",
       "0   2023      84669326        0.188        0.245        0.268        0.226   \n",
       "1   2022      84358845        0.188        0.245        0.273        0.222   \n",
       "2   2021      83237124        0.185        0.244        0.277        0.220   \n",
       "3   2020      83155031        0.184        0.245        0.281        0.218   \n",
       "4   2019      83166711        0.184        0.246        0.284        0.217   \n",
       "5   2018      83019213        0.184        0.246        0.288        0.217   \n",
       "6   2017      82792351        0.184        0.246        0.291        0.217   \n",
       "7   2016      82521653        0.184        0.245        0.294        0.216   \n",
       "8   2016      82175684        0.183        0.245        0.298        0.216   \n",
       "9   2014      81197537        0.182        0.241        0.303        0.218   \n",
       "10  2013      80767463        0.182        0.240        0.307        0.218   \n",
       "11  2012      80523746        0.183        0.239        0.309        0.216   \n",
       "12  2011      80327900        0.184        0.238        0.311        0.214   \n",
       "\n",
       "    80 to 100(%)  100 and more(%)  \n",
       "0          0.072                0  \n",
       "1          0.072                0  \n",
       "2          0.073                0  \n",
       "3          0.071                0  \n",
       "4          0.068                0  \n",
       "5          0.065                0  \n",
       "6          0.062                0  \n",
       "7          0.060                0  \n",
       "8          0.057                0  \n",
       "9          0.056                0  \n",
       "10         0.054                0  \n",
       "11         0.054                0  \n",
       "12         0.053                0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_distribution_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41abae6c",
   "metadata": {
    "scrolled": true
   },
   "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>Year</th>\n",
       "      <th>Number of employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>10320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>10007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>9563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>9928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>9647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>9215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>9599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>9801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>9587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>9572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>9894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>10069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2008</td>\n",
       "      <td>9857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>9574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006</td>\n",
       "      <td>8724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2005</td>\n",
       "      <td>8870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2004</td>\n",
       "      <td>8896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2003</td>\n",
       "      <td>8947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2002</td>\n",
       "      <td>8852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Number of employees\n",
       "0   2021                10320\n",
       "1   2020                10007\n",
       "2   2019                10234\n",
       "3   2018                 9563\n",
       "4   2017                 9928\n",
       "5   2016                 9647\n",
       "6   2015                 9215\n",
       "7   2014                 9599\n",
       "8   2013                 9801\n",
       "9   2012                 9587\n",
       "10  2011                 9572\n",
       "11  2010                 9894\n",
       "12  2009                10069\n",
       "13  2008                 9857\n",
       "14  2007                 9574\n",
       "15  2006                 8724\n",
       "16  2005                 8870\n",
       "17  2004                 8896\n",
       "18  2003                 8947\n",
       "19  2002                 8852"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medical_graduates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f7a07b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seeing that the Year column is an integer, lets convert it to a date field\n",
    "medical_graduates_df['Year'] = pd.to_datetime(medical_graduates_df['Year'], format='%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dfa93f6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot for medical graduates\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(medical_graduates_df['Year'], medical_graduates_df['Number of employees'], marker='o', color='blue')\n",
    "\n",
    "# Adding titles and labels\n",
    "plt.title('Number of Medical Graduates Over Time')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Employees')\n",
    "plt.grid(True)\n",
    "\n",
    "# Reverse the x-axis to show the earliest year on the left\n",
    "plt.gca().invert_xaxis()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c81b1161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Mean Retirement Age data\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Plot each column\n",
    "plt.plot(retirement_age_df['Year'], retirement_age_df['Standard Age'], label='Standard Age')\n",
    "plt.plot(retirement_age_df['Year'], retirement_age_df['Insured for 35 years'], label='Insured for 35 years')\n",
    "plt.plot(retirement_age_df['Year'], retirement_age_df['Insured for 45 years'], label='Insured for 45 years')\n",
    "\n",
    "# Adding titles and labels\n",
    "plt.title('Mean Retirement Age Over Time')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Retirement Age')\n",
    "plt.legend()\n",
    "\n",
    "# Show grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c499dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the total Population of Germany\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(population_df['Year'], population_df['Total Population'], marker='o', linestyle='-', color='b')\n",
    "plt.title('Historical Population of Germany (1960 - 2024)')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Population')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f2f08601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Population Projection of Germany\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(population_projection_df['Year'], population_projection_df['Population (Under 20yrs)'], marker='o', label='Population (Under 20yrs)')\n",
    "plt.plot(population_projection_df['Year'], population_projection_df['Population (20-66yrs)'], marker='o', label='Population (20-66yrs)')\n",
    "plt.plot(population_projection_df['Year'], population_projection_df['Population (67 yrs+)'], marker='o', label='Population (67 yrs+)')\n",
    "\n",
    "plt.title('Population Projection by Age Group (2021-2070)')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Population')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c85f79f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Growth and Aging Trends:\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Project the population growth and aging trends\n",
    "\n",
    "# Calculate the growth rates for each age group\n",
    "population_projection_df['Growth Rate (Under 20yrs)'] = population_projection_df['Population (Under 20yrs)'].pct_change()\n",
    "population_projection_df['Growth Rate (20-66yrs)'] = population_projection_df['Population (20-66yrs)'].pct_change()\n",
    "population_projection_df['Growth Rate (67 yrs+)'] = population_projection_df['Population (67 yrs+)'].pct_change()\n",
    "\n",
    "print(\"Population Growth and Aging Trends:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e894d497",
   "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>Year</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Population (Under 20yrs)</th>\n",
       "      <th>Population (20-66yrs)</th>\n",
       "      <th>Population (67 yrs+)</th>\n",
       "      <th>Growth Rate (Under 20yrs)</th>\n",
       "      <th>Growth Rate (20-66yrs)</th>\n",
       "      <th>Growth Rate (67 yrs+)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>83200000</td>\n",
       "      <td>15400000</td>\n",
       "      <td>51400000</td>\n",
       "      <td>16400000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2030</td>\n",
       "      <td>85140000</td>\n",
       "      <td>16440000</td>\n",
       "      <td>49960000</td>\n",
       "      <td>18720000.0</td>\n",
       "      <td>0.067532</td>\n",
       "      <td>-0.028016</td>\n",
       "      <td>0.141463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2040</td>\n",
       "      <td>84780000</td>\n",
       "      <td>15980000</td>\n",
       "      <td>48000000</td>\n",
       "      <td>20800000.0</td>\n",
       "      <td>-0.027981</td>\n",
       "      <td>-0.039231</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2050</td>\n",
       "      <td>83840000</td>\n",
       "      <td>15560000</td>\n",
       "      <td>47860000</td>\n",
       "      <td>20400000.0</td>\n",
       "      <td>-0.026283</td>\n",
       "      <td>-0.002917</td>\n",
       "      <td>-0.019231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2060</td>\n",
       "      <td>82700000</td>\n",
       "      <td>15560000</td>\n",
       "      <td>46440000</td>\n",
       "      <td>20740000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.029670</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2070</td>\n",
       "      <td>82260000</td>\n",
       "      <td>15400000</td>\n",
       "      <td>45740000</td>\n",
       "      <td>21140000.0</td>\n",
       "      <td>-0.010283</td>\n",
       "      <td>-0.015073</td>\n",
       "      <td>0.019286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Total Population  Population (Under 20yrs)  Population (20-66yrs)  \\\n",
       "0  2021          83200000                  15400000               51400000   \n",
       "1  2030          85140000                  16440000               49960000   \n",
       "2  2040          84780000                  15980000               48000000   \n",
       "3  2050          83840000                  15560000               47860000   \n",
       "4  2060          82700000                  15560000               46440000   \n",
       "5  2070          82260000                  15400000               45740000   \n",
       "\n",
       "   Population (67 yrs+)  Growth Rate (Under 20yrs)  Growth Rate (20-66yrs)  \\\n",
       "0            16400000.0                        NaN                     NaN   \n",
       "1            18720000.0                   0.067532               -0.028016   \n",
       "2            20800000.0                  -0.027981               -0.039231   \n",
       "3            20400000.0                  -0.026283               -0.002917   \n",
       "4            20740000.0                   0.000000               -0.029670   \n",
       "5            21140000.0                  -0.010283               -0.015073   \n",
       "\n",
       "   Growth Rate (67 yrs+)  \n",
       "0                    NaN  \n",
       "1               0.141463  \n",
       "2               0.111111  \n",
       "3              -0.019231  \n",
       "4               0.016667  \n",
       "5               0.019286  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_projection_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "000932b1",
   "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>Year</th>\n",
       "      <th>Projected Practicing Doctors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024</td>\n",
       "      <td>423705.866145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025</td>\n",
       "      <td>429231.051462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2026</td>\n",
       "      <td>434756.236779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2027</td>\n",
       "      <td>440281.422096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2028</td>\n",
       "      <td>445806.607413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2029</td>\n",
       "      <td>451331.792730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2030</td>\n",
       "      <td>456856.978046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2031</td>\n",
       "      <td>462382.163363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2032</td>\n",
       "      <td>467907.348680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2033</td>\n",
       "      <td>473432.533997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2034</td>\n",
       "      <td>478957.719314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2035</td>\n",
       "      <td>484482.904631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2036</td>\n",
       "      <td>490008.089948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2037</td>\n",
       "      <td>495533.275264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2038</td>\n",
       "      <td>501058.460581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2039</td>\n",
       "      <td>506583.645898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2040</td>\n",
       "      <td>512108.831215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2041</td>\n",
       "      <td>517634.016532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2042</td>\n",
       "      <td>523159.201849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2043</td>\n",
       "      <td>528684.387166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Projected Practicing Doctors\n",
       "0   2024                 423705.866145\n",
       "1   2025                 429231.051462\n",
       "2   2026                 434756.236779\n",
       "3   2027                 440281.422096\n",
       "4   2028                 445806.607413\n",
       "5   2029                 451331.792730\n",
       "6   2030                 456856.978046\n",
       "7   2031                 462382.163363\n",
       "8   2032                 467907.348680\n",
       "9   2033                 473432.533997\n",
       "10  2034                 478957.719314\n",
       "11  2035                 484482.904631\n",
       "12  2036                 490008.089948\n",
       "13  2037                 495533.275264\n",
       "14  2038                 501058.460581\n",
       "15  2039                 506583.645898\n",
       "16  2040                 512108.831215\n",
       "17  2041                 517634.016532\n",
       "18  2042                 523159.201849\n",
       "19  2043                 528684.387166"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2: Estimate the supply of doctors\n",
    "\n",
    "# Prepare the data for linear regression\n",
    "X = doctors_df['Year'].values.reshape(-1, 1)\n",
    "y = doctors_df['Practicing Doctors'].values\n",
    "\n",
    "# Fit the linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Project the number of doctors for the next 20 years\n",
    "future_years = np.arange(2024, 2044).reshape(-1, 1)\n",
    "projected_doctors = model.predict(future_years)\n",
    "\n",
    "# Display the projected number of doctors\n",
    "projected_doctors_df = pd.DataFrame({'Year': future_years.flatten(), 'Projected Practicing Doctors': projected_doctors})\n",
    "projected_doctors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a13b99aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the projection\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(doctors_df['Year'], doctors_df['Practicing Doctors'], label='Historical Data', marker='o')\n",
    "plt.plot(projected_doctors_df['Year'], projected_doctors_df['Projected Practicing Doctors'], label='Projected Data', linestyle='--', marker='x')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Practicing Doctors')\n",
    "plt.title('Projection of Practicing Doctors in Germany (2024-2043)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd0f4eb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projected demand for doctors:\n",
      "    Year  Projected Demand\n",
      "0   2021     528684.387166\n",
      "1   2022     536994.331462\n",
      "2   2023     545304.275759\n",
      "3   2024     553614.220056\n",
      "4   2025     561924.164353\n",
      "5   2026     570234.108650\n",
      "6   2027     578544.052947\n",
      "7   2028     586853.997244\n",
      "8   2029     595163.941541\n",
      "9   2030     603473.885838\n",
      "10  2031     610179.151236\n",
      "11  2032     616884.416634\n",
      "12  2033     623589.682032\n",
      "13  2034     630294.947431\n",
      "14  2035     637000.212829\n",
      "15  2036     643705.478227\n",
      "16  2037     650410.743625\n",
      "17  2038     657116.009023\n",
      "18  2039     663821.274422\n",
      "19  2040     670526.539820\n",
      "20  2041     669237.065705\n",
      "21  2042     667947.591590\n",
      "22  2043     666658.117475\n",
      "23  2044     665368.643360\n",
      "24  2045     664079.169245\n",
      "25  2046     662789.695130\n",
      "26  2047     661500.221015\n",
      "27  2048     660210.746899\n",
      "28  2049     658921.272784\n",
      "29  2050     657631.798669\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Forecast the demand for doctors based on population aging\n",
    "\n",
    "# Interpolate population projections for each year\n",
    "years = range(2021, 2051)\n",
    "total_population = np.interp(years, population_projection_df['Year'], population_projection_df['Total Population'])\n",
    "population_67_plus = np.interp(years, population_projection_df['Year'], population_projection_df['Population (67 yrs+)'])\n",
    "\n",
    "# Calculate the ratio of doctors to population, focusing on the aging population\n",
    "current_doctor_ratio = projected_doctors_df['Projected Practicing Doctors'].iloc[-1] / population_projection_df['Total Population'].iloc[0]\n",
    "current_elderly_ratio = population_projection_df['Population (67 yrs+)'].iloc[0] / population_projection_df['Total Population'].iloc[0]\n",
    "\n",
    "# Assume that the demand for doctors increases proportionally with the increase in the elderly population ratio\n",
    "demand_projection = []\n",
    "for i, year in enumerate(years):\n",
    "    elderly_ratio = population_67_plus[i] / total_population[i]\n",
    "    demand_factor = elderly_ratio / current_elderly_ratio\n",
    "    required_doctors = total_population[i] * current_doctor_ratio * demand_factor\n",
    "    demand_projection.append(required_doctors)\n",
    "\n",
    "demand_projection_df = pd.DataFrame({\n",
    "    'Year': years,\n",
    "    'Projected Demand': demand_projection\n",
    "})\n",
    "\n",
    "print(\"\\\n",
    "Projected demand for doctors:\")\n",
    "print(demand_projection_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8fa6b197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doctor deficit projection:\n",
      "    Year  Projected Practicing Doctors  Projected Demand  Doctor Deficit\n",
      "0   2024                 423705.866145     553614.220056   129908.353911\n",
      "1   2025                 429231.051462     561924.164353   132693.112891\n",
      "2   2026                 434756.236779     570234.108650   135477.871871\n",
      "3   2027                 440281.422096     578544.052947   138262.630851\n",
      "4   2028                 445806.607413     586853.997244   141047.389831\n",
      "5   2029                 451331.792730     595163.941541   143832.148811\n",
      "6   2030                 456856.978046     603473.885838   146616.907791\n",
      "7   2031                 462382.163363     610179.151236   147796.987873\n",
      "8   2032                 467907.348680     616884.416634   148977.067954\n",
      "9   2033                 473432.533997     623589.682032   150157.148035\n",
      "10  2034                 478957.719314     630294.947431   151337.228117\n",
      "11  2035                 484482.904631     637000.212829   152517.308198\n",
      "12  2036                 490008.089948     643705.478227   153697.388279\n",
      "13  2037                 495533.275264     650410.743625   154877.468361\n",
      "14  2038                 501058.460581     657116.009023   156057.548442\n",
      "15  2039                 506583.645898     663821.274422   157237.628523\n",
      "16  2040                 512108.831215     670526.539820   158417.708605\n",
      "17  2041                 517634.016532     669237.065705   151603.049173\n",
      "18  2042                 523159.201849     667947.591590   144788.389741\n",
      "19  2043                 528684.387166     666658.117475   137973.730309\n",
      "20  2021                           NaN     528684.387166             NaN\n",
      "21  2022                           NaN     536994.331462             NaN\n",
      "22  2023                           NaN     545304.275759             NaN\n",
      "23  2044                           NaN     665368.643360             NaN\n",
      "24  2045                           NaN     664079.169245             NaN\n",
      "25  2046                           NaN     662789.695130             NaN\n",
      "26  2047                           NaN     661500.221015             NaN\n",
      "27  2048                           NaN     660210.746899             NaN\n",
      "28  2049                           NaN     658921.272784             NaN\n",
      "29  2050                           NaN     657631.798669             NaN\n"
     ]
    }
   ],
   "source": [
    "# Step 4: Calculate and visualize the doctor deficit over the next 20 years\n",
    "\n",
    "# Merge supply and demand projections\n",
    "deficit_df = pd.merge(projected_doctors_df, demand_projection_df, on='Year', how='outer')\n",
    "deficit_df['Doctor Deficit'] = deficit_df['Projected Demand'] - deficit_df['Projected Practicing Doctors']\n",
    "\n",
    "print(\"\\\n",
    "Doctor deficit projection:\")\n",
    "print(deficit_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "526e53c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the doctor deficit\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(deficit_df['Year'], deficit_df['Projected Practicing Doctors'], label='Projected Supply')\n",
    "plt.plot(deficit_df['Year'], deficit_df['Projected Demand'], label='Projected Demand')\n",
    "plt.fill_between(deficit_df['Year'], deficit_df['Projected Practicing Doctors'], deficit_df['Projected Demand'], \n",
    "                 where=(deficit_df['Projected Demand'] > deficit_df['Projected Practicing Doctors']),\n",
    "                 alpha=0.3, color='red', label='Doctor Deficit')\n",
    "plt.title('Projected Doctor Supply, Demand, and Deficit in Germany')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Doctors')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}