M004.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b847273-be0d-4ced-b1ec-78440b845d5d",
   "metadata": {},
   "source": [
    "# Fehlerbehandlung mit Pandas\n",
    "\n",
    "Daten innerhalb von Sets sind oftmals fehlerhaft\n",
    "\n",
    "Diese Fehler müssen für die Analyse korrigiert werden\n",
    "\n",
    "Beispiele: Prozentzeichen, NaN, N.A., ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28c221d5-d917-485a-b419-70c72f1123b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bb31822-61d9-4e3b-9ff9-584432e010ae",
   "metadata": {},
   "source": [
    "pd.isnull, pd.notnull: Prüfen, ob ein Wert NaN oder None ist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5ccb4d0-9f4e-4e00-9b19-827153f5f3be",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd5a9af9-609a-4db8-80b9-43538d2eacbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62385c74-7e29-4b6e-9808-d0f3a92db95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c694b81e-cf4a-4450-8ce8-41a1130068c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "beb8888b-80af-4688-b826-91fa6690c5bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c0de0e4-82f9-44ce-8dbb-a83af5188d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce4a2157-f8e6-4568-a02c-58d20ab662bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b3829cdd-5f88-4c68-8042-7864033ceecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Data/PopulationDataFastFertig.csv\", thousands=\",\", decimal=\".\", index_col=\"Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "46d2ee4d-96c3-4cf1-b5a7-846fa0434c2e",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             NaN      N.A.    N.A.     10 %     0.00 %  \n",
       "Falkland Islands       NaN      N.A.    N.A.     66 %     0.00 %  \n",
       "Niue                   NaN      N.A.    N.A.     46 %     0.00 %  \n",
       "Tokelau                NaN      N.A.    N.A.      0 %     0.00 %  \n",
       "Vatican State          NaN      N.A.    N.A.     N.A.     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac904f10-3db8-4a70-9be1-b429f6460c9d",
   "metadata": {},
   "source": [
    "## Probleme\n",
    "\n",
    "- NaN\n",
    "- N.A.\n",
    "- Prozentzeichen\n",
    "- Datentypen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5a079d-3f8b-4c90-bd4d-daa69bdc8e71",
   "metadata": {},
   "source": [
    "### NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ebf81812-2d45-47a9-ae7e-442975092cf9",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pop  YearlyChange  NetChange  Density  LandArea  Migrants  \\\n",
       "Country                                                                         \n",
       "China             False         False      False    False     False     False   \n",
       "India             False         False      False    False     False     False   \n",
       "United States     False         False      False    False     False     False   \n",
       "Indonesia         False         False      False    False     False     False   \n",
       "Pakistan          False         False      False    False     False     False   \n",
       "...                 ...           ...        ...      ...       ...       ...   \n",
       "Montserrat        False         False      False    False     False      True   \n",
       "Falkland Islands  False         False      False    False     False      True   \n",
       "Niue              False         False      False    False     False      True   \n",
       "Tokelau           False         False      False    False     False      True   \n",
       "Vatican State     False         False      False    False     False      True   \n",
       "\n",
       "                  Fert.Rate  Med.Age  UrbanPop  WorldShare  \n",
       "Country                                                     \n",
       "China                 False    False     False       False  \n",
       "India                 False    False     False       False  \n",
       "United States         False    False     False       False  \n",
       "Indonesia             False    False     False       False  \n",
       "Pakistan              False    False     False       False  \n",
       "...                     ...      ...       ...         ...  \n",
       "Montserrat            False    False     False       False  \n",
       "Falkland Islands      False    False     False       False  \n",
       "Niue                  False    False     False       False  \n",
       "Tokelau               False    False     False       False  \n",
       "Vatican State         False    False     False       False  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(data)  # Alle Zellen finden, welche NaN sind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1a1178b-793c-4255-b6be-5da39ced41af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pop    YearlyChange  NetChange  Density  LandArea  Migrants  Fert.Rate  Med.Age  UrbanPop  WorldShare\n",
       "False  False         False      False    False     False     False      False    False     False         201\n",
       "                                                   True      False      False    False     False          34\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "79fa2ada-a83e-45b1-a64c-06e1c91220ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China               False\n",
       "India               False\n",
       "United States       False\n",
       "Indonesia           False\n",
       "Pakistan            False\n",
       "                    ...  \n",
       "Montserrat           True\n",
       "Falkland Islands     True\n",
       "Niue                 True\n",
       "Tokelau              True\n",
       "Vatican State        True\n",
       "Name: Migrants, Length: 235, dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Migrants\"].isnull()  # Filterung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d569671-207f-428f-8ffb-b4aa833ea332",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Isle of Man</th>\n",
       "      <td>85033</td>\n",
       "      <td>0.53 %</td>\n",
       "      <td>449</td>\n",
       "      <td>149</td>\n",
       "      <td>570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>53 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>77265</td>\n",
       "      <td>0.16 %</td>\n",
       "      <td>123</td>\n",
       "      <td>164</td>\n",
       "      <td>470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>88 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominica</th>\n",
       "      <td>71986</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>178</td>\n",
       "      <td>96</td>\n",
       "      <td>750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>74 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cayman Islands</th>\n",
       "      <td>65722</td>\n",
       "      <td>1.19 %</td>\n",
       "      <td>774</td>\n",
       "      <td>274</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>97 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda</th>\n",
       "      <td>62278</td>\n",
       "      <td>-0.36 %</td>\n",
       "      <td>-228</td>\n",
       "      <td>1246</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>97 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marshall Islands</th>\n",
       "      <td>59190</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>399</td>\n",
       "      <td>329</td>\n",
       "      <td>180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>70 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern Mariana Islands</th>\n",
       "      <td>57559</td>\n",
       "      <td>0.60 %</td>\n",
       "      <td>343</td>\n",
       "      <td>125</td>\n",
       "      <td>460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>88 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenland</th>\n",
       "      <td>56770</td>\n",
       "      <td>0.17 %</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>410450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>87 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>55191</td>\n",
       "      <td>-0.22 %</td>\n",
       "      <td>-121</td>\n",
       "      <td>276</td>\n",
       "      <td>200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>88 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Kitts &amp; Nevis</th>\n",
       "      <td>53199</td>\n",
       "      <td>0.71 %</td>\n",
       "      <td>376</td>\n",
       "      <td>205</td>\n",
       "      <td>260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>33 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Faeroe Islands</th>\n",
       "      <td>48863</td>\n",
       "      <td>0.38 %</td>\n",
       "      <td>185</td>\n",
       "      <td>35</td>\n",
       "      <td>1396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>43 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sint Maarten</th>\n",
       "      <td>42876</td>\n",
       "      <td>1.15 %</td>\n",
       "      <td>488</td>\n",
       "      <td>1261</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>96 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monaco</th>\n",
       "      <td>39242</td>\n",
       "      <td>0.71 %</td>\n",
       "      <td>278</td>\n",
       "      <td>26337</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turks and Caicos</th>\n",
       "      <td>38717</td>\n",
       "      <td>1.38 %</td>\n",
       "      <td>526</td>\n",
       "      <td>41</td>\n",
       "      <td>950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>89 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Martin</th>\n",
       "      <td>38666</td>\n",
       "      <td>1.75 %</td>\n",
       "      <td>664</td>\n",
       "      <td>730</td>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liechtenstein</th>\n",
       "      <td>38128</td>\n",
       "      <td>0.29 %</td>\n",
       "      <td>109</td>\n",
       "      <td>238</td>\n",
       "      <td>160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>15 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Marino</th>\n",
       "      <td>33931</td>\n",
       "      <td>0.21 %</td>\n",
       "      <td>71</td>\n",
       "      <td>566</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>97 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gibraltar</th>\n",
       "      <td>33691</td>\n",
       "      <td>-0.03 %</td>\n",
       "      <td>-10</td>\n",
       "      <td>3369</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British Virgin Islands</th>\n",
       "      <td>30231</td>\n",
       "      <td>0.67 %</td>\n",
       "      <td>201</td>\n",
       "      <td>202</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>52 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caribbean Netherlands</th>\n",
       "      <td>26223</td>\n",
       "      <td>0.94 %</td>\n",
       "      <td>244</td>\n",
       "      <td>80</td>\n",
       "      <td>328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>75 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palau</th>\n",
       "      <td>18094</td>\n",
       "      <td>0.48 %</td>\n",
       "      <td>86</td>\n",
       "      <td>39</td>\n",
       "      <td>460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cook Islands</th>\n",
       "      <td>17564</td>\n",
       "      <td>0.09 %</td>\n",
       "      <td>16</td>\n",
       "      <td>73</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>75 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>15003</td>\n",
       "      <td>0.90 %</td>\n",
       "      <td>134</td>\n",
       "      <td>167</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>11792</td>\n",
       "      <td>1.25 %</td>\n",
       "      <td>146</td>\n",
       "      <td>393</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>62 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallis &amp; Futuna</th>\n",
       "      <td>11239</td>\n",
       "      <td>-1.69 %</td>\n",
       "      <td>-193</td>\n",
       "      <td>80</td>\n",
       "      <td>140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nauru</th>\n",
       "      <td>10824</td>\n",
       "      <td>0.63 %</td>\n",
       "      <td>68</td>\n",
       "      <td>541</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Barthelemy</th>\n",
       "      <td>9877</td>\n",
       "      <td>0.30 %</td>\n",
       "      <td>30</td>\n",
       "      <td>470</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Helena</th>\n",
       "      <td>6077</td>\n",
       "      <td>0.30 %</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>27 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Pierre &amp; Miquelon</th>\n",
       "      <td>5794</td>\n",
       "      <td>-0.48 %</td>\n",
       "      <td>-28</td>\n",
       "      <td>25</td>\n",
       "      <td>230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>100 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                      \n",
       "Isle of Man               85033       0.53 %        449      149       570   \n",
       "Andorra                   77265       0.16 %        123      164       470   \n",
       "Dominica                  71986       0.25 %        178       96       750   \n",
       "Cayman Islands            65722       1.19 %        774      274       240   \n",
       "Bermuda                   62278      -0.36 %       -228     1246        50   \n",
       "Marshall Islands          59190       0.68 %        399      329       180   \n",
       "Northern Mariana Islands  57559       0.60 %        343      125       460   \n",
       "Greenland                 56770       0.17 %         98        0    410450   \n",
       "American Samoa            55191      -0.22 %       -121      276       200   \n",
       "Saint Kitts & Nevis       53199       0.71 %        376      205       260   \n",
       "Faeroe Islands            48863       0.38 %        185       35      1396   \n",
       "Sint Maarten              42876       1.15 %        488     1261        34   \n",
       "Monaco                    39242       0.71 %        278    26337         1   \n",
       "Turks and Caicos          38717       1.38 %        526       41       950   \n",
       "Saint Martin              38666       1.75 %        664      730        53   \n",
       "Liechtenstein             38128       0.29 %        109      238       160   \n",
       "San Marino                33931       0.21 %         71      566        60   \n",
       "Gibraltar                 33691      -0.03 %        -10     3369        10   \n",
       "British Virgin Islands    30231       0.67 %        201      202       150   \n",
       "Caribbean Netherlands     26223       0.94 %        244       80       328   \n",
       "Palau                     18094       0.48 %         86       39       460   \n",
       "Cook Islands              17564       0.09 %         16       73       240   \n",
       "Anguilla                  15003       0.90 %        134      167        90   \n",
       "Tuvalu                    11792       1.25 %        146      393        30   \n",
       "Wallis & Futuna           11239      -1.69 %       -193       80       140   \n",
       "Nauru                     10824       0.63 %         68      541        20   \n",
       "Saint Barthelemy           9877       0.30 %         30      470        21   \n",
       "Saint Helena               6077       0.30 %         18       16       390   \n",
       "Saint Pierre & Miquelon    5794      -0.48 %        -28       25       230   \n",
       "Montserrat                 4992       0.06 %          3       50       100   \n",
       "Falkland Islands           3480       3.05 %        103        0     12170   \n",
       "Niue                       1626       0.68 %         11        6       260   \n",
       "Tokelau                    1357       1.27 %         17      136        10   \n",
       "Vatican State               801       0.25 %          2     2003         0   \n",
       "\n",
       "                          Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                                   \n",
       "Isle of Man                    NaN      N.A.    N.A.     53 %     0.00 %  \n",
       "Andorra                        NaN      N.A.    N.A.     88 %     0.00 %  \n",
       "Dominica                       NaN      N.A.    N.A.     74 %     0.00 %  \n",
       "Cayman Islands                 NaN      N.A.    N.A.     97 %     0.00 %  \n",
       "Bermuda                        NaN      N.A.    N.A.     97 %     0.00 %  \n",
       "Marshall Islands               NaN      N.A.    N.A.     70 %     0.00 %  \n",
       "Northern Mariana Islands       NaN      N.A.    N.A.     88 %     0.00 %  \n",
       "Greenland                      NaN      N.A.    N.A.     87 %     0.00 %  \n",
       "American Samoa                 NaN      N.A.    N.A.     88 %     0.00 %  \n",
       "Saint Kitts & Nevis            NaN      N.A.    N.A.     33 %     0.00 %  \n",
       "Faeroe Islands                 NaN      N.A.    N.A.     43 %     0.00 %  \n",
       "Sint Maarten                   NaN      N.A.    N.A.     96 %     0.00 %  \n",
       "Monaco                         NaN      N.A.    N.A.     N.A.     0.00 %  \n",
       "Turks and Caicos               NaN      N.A.    N.A.     89 %     0.00 %  \n",
       "Saint Martin                   NaN      N.A.    N.A.      0 %     0.00 %  \n",
       "Liechtenstein                  NaN      N.A.    N.A.     15 %     0.00 %  \n",
       "San Marino                     NaN      N.A.    N.A.     97 %     0.00 %  \n",
       "Gibraltar                      NaN      N.A.    N.A.     N.A.     0.00 %  \n",
       "British Virgin Islands         NaN      N.A.    N.A.     52 %     0.00 %  \n",
       "Caribbean Netherlands          NaN      N.A.    N.A.     75 %     0.00 %  \n",
       "Palau                          NaN      N.A.    N.A.     N.A.     0.00 %  \n",
       "Cook Islands                   NaN      N.A.    N.A.     75 %     0.00 %  \n",
       "Anguilla                       NaN      N.A.    N.A.     N.A.     0.00 %  \n",
       "Tuvalu                         NaN      N.A.    N.A.     62 %     0.00 %  \n",
       "Wallis & Futuna                NaN      N.A.    N.A.      0 %     0.00 %  \n",
       "Nauru                          NaN      N.A.    N.A.     N.A.     0.00 %  \n",
       "Saint Barthelemy               NaN      N.A.    N.A.      0 %     0.00 %  \n",
       "Saint Helena                   NaN      N.A.    N.A.     27 %     0.00 %  \n",
       "Saint Pierre & Miquelon        NaN      N.A.    N.A.    100 %     0.00 %  \n",
       "Montserrat                     NaN      N.A.    N.A.     10 %     0.00 %  \n",
       "Falkland Islands               NaN      N.A.    N.A.     66 %     0.00 %  \n",
       "Niue                           NaN      N.A.    N.A.     46 %     0.00 %  \n",
       "Tokelau                        NaN      N.A.    N.A.      0 %     0.00 %  \n",
       "Vatican State                  NaN      N.A.    N.A.     N.A.     0.00 %  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[\"Migrants\"].isnull()]  # Filterung anwenden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1f82f8e0-b6e7-4fb1-ab74-08fbe29d0be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lk3\\AppData\\Local\\Temp\\ipykernel_9664\\693164160.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[data[\"Migrants\"].isnull()][\"Migrants\"] = 0  # Hier wird eine Kopie der NaN-Datensätze erzeugt, Änderungen auf diese Datensätze werden nicht auf das Datenset angewandt\n"
     ]
    }
   ],
   "source": [
    "data[data[\"Migrants\"].isnull()][\"Migrants\"] = 0  # Hier wird eine Kopie der NaN-Datensätze erzeugt, Änderungen auf diese Datensätze werden nicht auf das Datenset angewandt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5677c84f-d098-4fcf-a4a9-bb4948a98797",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": 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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             NaN      N.A.    N.A.     10 %     0.00 %  \n",
       "Falkland Islands       NaN      N.A.    N.A.     66 %     0.00 %  \n",
       "Niue                   NaN      N.A.    N.A.     46 %     0.00 %  \n",
       "Tokelau                NaN      N.A.    N.A.      0 %     0.00 %  \n",
       "Vatican State          NaN      N.A.    N.A.     N.A.     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6c397c78-6fa8-4250-bf0d-6c64ffeb80b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "nan = data[data[\"Migrants\"].isnull()][\"Migrants\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b25523ca-22ec-4bee-b5b7-fc32ed9ed847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Isle of Man', 'Andorra', 'Dominica', 'Cayman Islands', 'Bermuda',\n",
       "       'Marshall Islands', 'Northern Mariana Islands', 'Greenland',\n",
       "       'American Samoa', 'Saint Kitts & Nevis', 'Faeroe Islands',\n",
       "       'Sint Maarten', 'Monaco', 'Turks and Caicos', 'Saint Martin',\n",
       "       'Liechtenstein', 'San Marino', 'Gibraltar', 'British Virgin Islands',\n",
       "       'Caribbean Netherlands', 'Palau', 'Cook Islands', 'Anguilla', 'Tuvalu',\n",
       "       'Wallis & Futuna', 'Nauru', 'Saint Barthelemy', 'Saint Helena',\n",
       "       'Saint Pierre & Miquelon', 'Montserrat', 'Falkland Islands', 'Niue',\n",
       "       'Tokelau', 'Vatican State'],\n",
       "      dtype='object', name='Country')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f7e6ba96-3ac1-4d73-98d6-e03312ca6e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[nan.index, \"Migrants\"] = 0  # Suche alle Zeilen, welche in dem Index enthalten sind, und überschreibe in der \"Migrants\" Spalte die Werte mit 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4e28677e-220b-4a98-b955-3227dbf9d651",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0      N.A.    N.A.     10 %     0.00 %  \n",
       "Falkland Islands       0.0      N.A.    N.A.     66 %     0.00 %  \n",
       "Niue                   0.0      N.A.    N.A.     46 %     0.00 %  \n",
       "Tokelau                0.0      N.A.    N.A.      0 %     0.00 %  \n",
       "Vatican State          0.0      N.A.    N.A.     N.A.     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "17d19cc9-9fba-4268-8a9c-b91a6bddcad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pop    YearlyChange  NetChange  Density  LandArea  Migrants  Fert.Rate  Med.Age  UrbanPop  WorldShare\n",
       "False  False         False      False    False     False     False      False    False     False         235\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f91129e-2340-4217-bd44-64b270e9108a",
   "metadata": {},
   "source": [
    "#### Automatisches Auffüllen von NaN\n",
    "\n",
    "fillna: Füllt Löcher mit Werten die in den Daten vorher/nachher kommen\n",
    "\n",
    "- ffill: forwards-fill (von oben)\n",
    "- bfill: backwards-fill (von unten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c48be521-6744-4dbc-b56a-0f7eed72cc48",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lk3\\AppData\\Local\\Temp\\ipykernel_9664\\2671324543.py:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  data.fillna(method=\"ffill\")  # Nur eine Vorschau, übernehmen mit inplace=True\n"
     ]
    },
    {
     "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0      N.A.    N.A.     10 %     0.00 %  \n",
       "Falkland Islands       0.0      N.A.    N.A.     66 %     0.00 %  \n",
       "Niue                   0.0      N.A.    N.A.     46 %     0.00 %  \n",
       "Tokelau                0.0      N.A.    N.A.      0 %     0.00 %  \n",
       "Vatican State          0.0      N.A.    N.A.     N.A.     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(method=\"ffill\")  # Nur eine Vorschau, übernehmen mit inplace=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c6df8118-1f0c-499a-828b-9a2976dfd84c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lk3\\AppData\\Local\\Temp\\ipykernel_9664\\3219607963.py:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  data.fillna(method=\"ffill\", inplace=True)\n"
     ]
    }
   ],
   "source": [
    "data.fillna(method=\"ffill\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4904c90e-5e81-4407-9947-59764945d73f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": 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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0      N.A.    N.A.     10 %     0.00 %  \n",
       "Falkland Islands       0.0      N.A.    N.A.     66 %     0.00 %  \n",
       "Niue                   0.0      N.A.    N.A.     46 %     0.00 %  \n",
       "Tokelau                0.0      N.A.    N.A.      0 %     0.00 %  \n",
       "Vatican State          0.0      N.A.    N.A.     N.A.     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ffill()  # Siehe Warnung darüber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc7ba07-153a-4827-90dd-5606b7476d9f",
   "metadata": {},
   "source": [
    "### N.A.\n",
    "\n",
    "Aufgabe: Suche alle Zellen die N.A. enthalten, und ersetze den Inhalt dieser Zellen mit 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4459a949-2e28-4e44-927e-f0a830aadd1e",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pop  YearlyChange  NetChange  Density  LandArea  Migrants  \\\n",
       "Country                                                                         \n",
       "China             False         False      False    False     False     False   \n",
       "India             False         False      False    False     False     False   \n",
       "United States     False         False      False    False     False     False   \n",
       "Indonesia         False         False      False    False     False     False   \n",
       "Pakistan          False         False      False    False     False     False   \n",
       "...                 ...           ...        ...      ...       ...       ...   \n",
       "Montserrat        False         False      False    False     False     False   \n",
       "Falkland Islands  False         False      False    False     False     False   \n",
       "Niue              False         False      False    False     False     False   \n",
       "Tokelau           False         False      False    False     False     False   \n",
       "Vatican State     False         False      False    False     False     False   \n",
       "\n",
       "                  Fert.Rate  Med.Age  UrbanPop  WorldShare  \n",
       "Country                                                     \n",
       "China                 False    False     False       False  \n",
       "India                 False    False     False       False  \n",
       "United States         False    False     False       False  \n",
       "Indonesia             False    False     False       False  \n",
       "Pakistan              False    False     False       False  \n",
       "...                     ...      ...       ...         ...  \n",
       "Montserrat             True     True     False       False  \n",
       "Falkland Islands       True     True     False       False  \n",
       "Niue                   True     True     False       False  \n",
       "Tokelau                True     True     False       False  \n",
       "Vatican State          True     True      True       False  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data == \"N.A.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "80100898-647c-4483-9ae5-1c6e706ea551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pop    YearlyChange  NetChange  Density  LandArea  Migrants  Fert.Rate  Med.Age  UrbanPop  WorldShare\n",
       "False  False         False      False    False     False     False      False    False     False         194\n",
       "                                                             True       True     False     False          28\n",
       "                                                             False      False    True      False           7\n",
       "                                                             True       True     True      False           6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data == \"N.A.\").value_counts()  # Fehlerhafte Spalten: Fert.Rate, Med.Age, UrbanPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "820f3360-5356-4aa3-a71a-a3c8d6985391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China               False\n",
       "India               False\n",
       "United States       False\n",
       "Indonesia           False\n",
       "Pakistan            False\n",
       "                    ...  \n",
       "Montserrat           True\n",
       "Falkland Islands     True\n",
       "Niue                 True\n",
       "Tokelau              True\n",
       "Vatican State        True\n",
       "Name: Fert.Rate, Length: 235, dtype: bool"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Fert.Rate\"] == \"N.A.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1ab33b5f-d6b8-4d0a-80b0-d57202a835cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "na = data[data[\"Fert.Rate\"] == \"N.A.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fb343481-9a23-4385-9142-cb81d42ecea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Isle of Man', 'Andorra', 'Dominica', 'Cayman Islands', 'Bermuda',\n",
       "       'Marshall Islands', 'Northern Mariana Islands', 'Greenland',\n",
       "       'American Samoa', 'Saint Kitts & Nevis', 'Faeroe Islands',\n",
       "       'Sint Maarten', 'Monaco', 'Turks and Caicos', 'Saint Martin',\n",
       "       'Liechtenstein', 'San Marino', 'Gibraltar', 'British Virgin Islands',\n",
       "       'Caribbean Netherlands', 'Palau', 'Cook Islands', 'Anguilla', 'Tuvalu',\n",
       "       'Wallis & Futuna', 'Nauru', 'Saint Barthelemy', 'Saint Helena',\n",
       "       'Saint Pierre & Miquelon', 'Montserrat', 'Falkland Islands', 'Niue',\n",
       "       'Tokelau', 'Vatican State'],\n",
       "      dtype='object', name='Country')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "03afcdcd-3ae9-4904-8b0a-15389d6bdf4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[na.index, \"Fert.Rate\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fd3be001-e3c4-4c03-b396-93e331e02fed",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0         0    N.A.     10 %     0.00 %  \n",
       "Falkland Islands       0.0         0    N.A.     66 %     0.00 %  \n",
       "Niue                   0.0         0    N.A.     46 %     0.00 %  \n",
       "Tokelau                0.0         0    N.A.      0 %     0.00 %  \n",
       "Vatican State          0.0         0    N.A.     N.A.     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3571a7-6f64-45b5-99d4-e2144e3d88e2",
   "metadata": {},
   "source": [
    "Nachdem wir jetzt diese Routine bei mehreren Spalten anwenden müssen, können wir eine Funktion anlegen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "befebf93-8c1d-4370-8e49-78091a1f8e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixNA(columnName: str):\n",
    "    na = data[data[columnName] == \"N.A.\"]  # Suche die N.A. Werte in der gegebenen Spalte\n",
    "    index = na.index  # Entnehme alle Zeilennamen (Index) der gefundenen fehlerhaften Werte\n",
    "    data.loc[index, columnName] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d91bbe62-eecc-4544-90db-3b66c5bcb4b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixNA(\"Med.Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4399a63e-e8a7-481a-8072-3195477cf3b0",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N.A.</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0         0       0     10 %     0.00 %  \n",
       "Falkland Islands       0.0         0       0     66 %     0.00 %  \n",
       "Niue                   0.0         0       0     46 %     0.00 %  \n",
       "Tokelau                0.0         0       0      0 %     0.00 %  \n",
       "Vatican State          0.0         0       0     N.A.     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "220e36eb-f31a-4445-a6dd-bec7e93a4a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pop', 'YearlyChange', 'NetChange', 'Density', 'LandArea', 'Migrants',\n",
       "       'Fert.Rate', 'Med.Age', 'UrbanPop', 'WorldShare'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns  # Liste von den Spaltennamen im DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2e2a87fc-974d-486b-ae07-94bee3cb69cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in data.columns:\n",
    "    fixNA(col)  # Hier werden alle N.A. Werte aus allen Spalten entfernt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cc34f77e-95ad-4841-90ef-67e526348293",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39 %</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99 %</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59 %</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07 %</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00 %</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06 %</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05 %</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68 %</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27 %</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25 %</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776       0.39 %    5540090      153   9388211   \n",
       "India             1380004385       0.99 %   13586631      464   2973190   \n",
       "United States      331002651       0.59 %    1937734       36   9147420   \n",
       "Indonesia          273523615       1.07 %    2898047      151   1811570   \n",
       "Pakistan           220892340       2.00 %    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992       0.06 %          3       50       100   \n",
       "Falkland Islands        3480       3.05 %        103        0     12170   \n",
       "Niue                    1626       0.68 %         11        6       260   \n",
       "Tokelau                 1357       1.27 %         17      136        10   \n",
       "Vatican State            801       0.25 %          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0         0       0     10 %     0.00 %  \n",
       "Falkland Islands       0.0         0       0     66 %     0.00 %  \n",
       "Niue                   0.0         0       0     46 %     0.00 %  \n",
       "Tokelau                0.0         0       0      0 %     0.00 %  \n",
       "Vatican State          0.0         0       0        0     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977f39bb-bf30-4889-8b2a-749cc4a5f2de",
   "metadata": {},
   "source": [
    "### Prozentzeichen\n",
    "\n",
    "Hier müssen jetzt die Prozente nicht explizit gesucht werden, sondern wir können einfach aus ALLEN Zellen Prozente entfernen\n",
    "\n",
    "Hierfür können wir die replace Funktion benutzen\n",
    "\n",
    "Die replace Funktion ersetzt alle Vorkommnisse eines Textes mit einem anderen Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a67a959d-2a04-429f-b825-e75a5d785c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China               0.39\n",
       "India               0.99\n",
       "United States       0.59\n",
       "Indonesia           1.07\n",
       "Pakistan            2.00\n",
       "                    ... \n",
       "Montserrat          0.06\n",
       "Falkland Islands    3.05\n",
       "Niue                0.68\n",
       "Tokelau             1.27\n",
       "Vatican State       0.25\n",
       "Name: YearlyChange, Length: 235, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"YearlyChange\"].replace(\" %\", \"\", regex=True)  # regex=True ist hier notwendig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e9a95b44-3df4-4c45-b9bb-90d83e7bdbc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"YearlyChange\"] = data[\"YearlyChange\"].replace(\" %\", \"\", regex=True)  # Mache eine Kopie von der Spalte, ersetze in dieser Kopie alle Prozentzeichen, überschreibe die originale Spalte mit der Kopie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "06dea01b-7f21-4518-b4a3-02de7786d4da",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61 %</td>\n",
       "      <td>18.47 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35 %</td>\n",
       "      <td>17.70 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83 %</td>\n",
       "      <td>4.25 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56 %</td>\n",
       "      <td>3.51 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35 %</td>\n",
       "      <td>2.83 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 %</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00 %</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776         0.39    5540090      153   9388211   \n",
       "India             1380004385         0.99   13586631      464   2973190   \n",
       "United States      331002651         0.59    1937734       36   9147420   \n",
       "Indonesia          273523615         1.07    2898047      151   1811570   \n",
       "Pakistan           220892340         2.00    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992         0.06          3       50       100   \n",
       "Falkland Islands        3480         3.05        103        0     12170   \n",
       "Niue                    1626         0.68         11        6       260   \n",
       "Tokelau                 1357         1.27         17      136        10   \n",
       "Vatican State            801         0.25          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38     61 %    18.47 %  \n",
       "India            -532687.0       2.2      28     35 %    17.70 %  \n",
       "United States     954806.0       1.8      38     83 %     4.25 %  \n",
       "Indonesia         -98955.0       2.3      30     56 %     3.51 %  \n",
       "Pakistan         -233379.0       3.6      23     35 %     2.83 %  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0         0       0     10 %     0.00 %  \n",
       "Falkland Islands       0.0         0       0     66 %     0.00 %  \n",
       "Niue                   0.0         0       0     46 %     0.00 %  \n",
       "Tokelau                0.0         0       0      0 %     0.00 %  \n",
       "Vatican State          0.0         0       0        0     0.00 %  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "de9e8f02-fd5a-49a0-a427-b16f995be46d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixPct(columnName: str):\n",
    "    data[columnName] = data[columnName].replace(\" %\", \"\", regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f514c54b-204d-443e-b56e-7c67cf898c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in data.columns:\n",
    "    fixPct(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6f05d456-f9a1-4ec9-afe5-5a18fd5fa0e8",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61</td>\n",
       "      <td>18.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>17.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                   \n",
       "China             1439323776         0.39    5540090      153   9388211   \n",
       "India             1380004385         0.99   13586631      464   2973190   \n",
       "United States      331002651         0.59    1937734       36   9147420   \n",
       "Indonesia          273523615         1.07    2898047      151   1811570   \n",
       "Pakistan           220892340         2.00    4327022      287    770880   \n",
       "...                      ...          ...        ...      ...       ...   \n",
       "Montserrat              4992         0.06          3       50       100   \n",
       "Falkland Islands        3480         3.05        103        0     12170   \n",
       "Niue                    1626         0.68         11        6       260   \n",
       "Tokelau                 1357         1.27         17      136        10   \n",
       "Vatican State            801         0.25          2     2003         0   \n",
       "\n",
       "                  Migrants Fert.Rate Med.Age UrbanPop WorldShare  \n",
       "Country                                                           \n",
       "China            -348399.0       1.7      38       61      18.47  \n",
       "India            -532687.0       2.2      28       35      17.70  \n",
       "United States     954806.0       1.8      38       83       4.25  \n",
       "Indonesia         -98955.0       2.3      30       56       3.51  \n",
       "Pakistan         -233379.0       3.6      23       35       2.83  \n",
       "...                    ...       ...     ...      ...        ...  \n",
       "Montserrat             0.0         0       0       10       0.00  \n",
       "Falkland Islands       0.0         0       0       66       0.00  \n",
       "Niue                   0.0         0       0       46       0.00  \n",
       "Tokelau                0.0         0       0        0       0.00  \n",
       "Vatican State          0.0         0       0        0       0.00  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984049be-58cb-40d8-ad98-7117bec39663",
   "metadata": {},
   "source": [
    "### Datentypen\n",
    "\n",
    "Nach dem Anpassen der Prozentzeichen sind die entsprechenden Spalten weiterhin Strings (object)\n",
    "\n",
    "Zwei Möglichkeiten:\n",
    "\n",
    "- convert_dtypes(): Versucht alle Spalten automatisch anzupassen\n",
    "- astype(Typ): Bei einer Spalte Zeile für Zeile ausprobieren, ob die Konvertierung funktioniert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1e93fe77-d626-4472-8bea-da4c3d216a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 235 entries, China to Vatican State\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Pop           235 non-null    int64  \n",
      " 1   YearlyChange  235 non-null    object \n",
      " 2   NetChange     235 non-null    int64  \n",
      " 3   Density       235 non-null    int64  \n",
      " 4   LandArea      235 non-null    int64  \n",
      " 5   Migrants      235 non-null    float64\n",
      " 6   Fert.Rate     235 non-null    object \n",
      " 7   Med.Age       235 non-null    object \n",
      " 8   UrbanPop      235 non-null    object \n",
      " 9   WorldShare    235 non-null    object \n",
      "dtypes: float64(1), int64(4), object(5)\n",
      "memory usage: 28.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fa485e36-60dd-493d-8bbb-6175949d1276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 235 entries, China to Vatican State\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Pop           235 non-null    Int64 \n",
      " 1   YearlyChange  235 non-null    string\n",
      " 2   NetChange     235 non-null    Int64 \n",
      " 3   Density       235 non-null    Int64 \n",
      " 4   LandArea      235 non-null    Int64 \n",
      " 5   Migrants      235 non-null    Int64 \n",
      " 6   Fert.Rate     235 non-null    object\n",
      " 7   Med.Age       235 non-null    object\n",
      " 8   UrbanPop      235 non-null    object\n",
      " 9   WorldShare    235 non-null    string\n",
      "dtypes: Int64(5), object(3), string(2)\n",
      "memory usage: 29.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.convert_dtypes().info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "48466a65-55e8-4d2e-9e62-f419ab61807d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"YearlyChange\"] = data[\"YearlyChange\"].astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d9364a2e-f615-4b6a-a888-c25a2f1a552a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 235 entries, China to Vatican State\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Pop           235 non-null    int64  \n",
      " 1   YearlyChange  235 non-null    float32\n",
      " 2   NetChange     235 non-null    int64  \n",
      " 3   Density       235 non-null    int64  \n",
      " 4   LandArea      235 non-null    int64  \n",
      " 5   Migrants      235 non-null    float64\n",
      " 6   Fert.Rate     235 non-null    object \n",
      " 7   Med.Age       235 non-null    object \n",
      " 8   UrbanPop      235 non-null    object \n",
      " 9   WorldShare    235 non-null    object \n",
      "dtypes: float32(1), float64(1), int64(4), object(4)\n",
      "memory usage: 27.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "25b2e286-5c66-4aef-b77d-f9a046a700e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixType(columnName: str, targetType):  # Hier wird ein Parameter benötigt, welcher den Zieltypen definiert (float32, int32, int16, ...)\n",
    "    data[columnName] = data[columnName].astype(targetType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f07408d5-3ff2-4374-958d-6475888aa3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"Pop\", np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2aa6bc2d-5e9c-419b-baa6-fca767e8ac5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"NetChange\", np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1b0a8195-be4d-4ad9-a5cd-f902eb9dec51",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"Density\", np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7e23e138-d3f2-44fa-a0e7-651a98bf9f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"LandArea\", np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "587103b2-8178-4226-a988-7f79d285a0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Migrants\n",
       "True    235\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data[\"Migrants\"] % 1 == 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "74f0dddc-8c70-4e83-8fb2-d0bca4b857c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"Migrants\", np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "9a3e1160-3e06-4e9e-9c93-4c316278966a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"Fert.Rate\", np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "49318243-87da-4f7d-943a-96e989c2e158",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"Med.Age\", np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7b32cc2d-8e3c-4ab4-91ea-d1784172f470",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"UrbanPop\", np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "29df5753-c018-4928-9b5a-c733e05fa469",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType(\"WorldShare\", np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "0b92c14a-120b-4b5f-beac-978e3b19ffe9",
   "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>Pop</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>NetChange</th>\n",
       "      <th>Density</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>Migrants</th>\n",
       "      <th>Fert.Rate</th>\n",
       "      <th>Med.Age</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>WorldShare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1439323776</td>\n",
       "      <td>0.39</td>\n",
       "      <td>5540090</td>\n",
       "      <td>153</td>\n",
       "      <td>9388211</td>\n",
       "      <td>-348399</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>61</td>\n",
       "      <td>18.469999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1380004385</td>\n",
       "      <td>0.99</td>\n",
       "      <td>13586631</td>\n",
       "      <td>464</td>\n",
       "      <td>2973190</td>\n",
       "      <td>-532687</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>17.700001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>331002651</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1937734</td>\n",
       "      <td>36</td>\n",
       "      <td>9147420</td>\n",
       "      <td>954806</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>4.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>273523615</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2898047</td>\n",
       "      <td>151</td>\n",
       "      <td>1811570</td>\n",
       "      <td>-98955</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>3.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>220892340</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4327022</td>\n",
       "      <td>287</td>\n",
       "      <td>770880</td>\n",
       "      <td>-233379</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>2.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montserrat</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falkland Islands</th>\n",
       "      <td>3480</td>\n",
       "      <td>3.05</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>12170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1626</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau</th>\n",
       "      <td>1357</td>\n",
       "      <td>1.27</td>\n",
       "      <td>17</td>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican State</th>\n",
       "      <td>801</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>235 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pop  YearlyChange  NetChange  Density  LandArea  \\\n",
       "Country                                                                    \n",
       "China             1439323776          0.39    5540090      153   9388211   \n",
       "India             1380004385          0.99   13586631      464   2973190   \n",
       "United States      331002651          0.59    1937734       36   9147420   \n",
       "Indonesia          273523615          1.07    2898047      151   1811570   \n",
       "Pakistan           220892340          2.00    4327022      287    770880   \n",
       "...                      ...           ...        ...      ...       ...   \n",
       "Montserrat              4992          0.06          3       50       100   \n",
       "Falkland Islands        3480          3.05        103        0     12170   \n",
       "Niue                    1626          0.68         11        6       260   \n",
       "Tokelau                 1357          1.27         17      136        10   \n",
       "Vatican State            801          0.25          2     2003         0   \n",
       "\n",
       "                  Migrants  Fert.Rate  Med.Age  UrbanPop  WorldShare  \n",
       "Country                                                               \n",
       "China              -348399        1.7       38        61   18.469999  \n",
       "India              -532687        2.2       28        35   17.700001  \n",
       "United States       954806        1.8       38        83    4.250000  \n",
       "Indonesia           -98955        2.3       30        56    3.510000  \n",
       "Pakistan           -233379        3.6       23        35    2.830000  \n",
       "...                    ...        ...      ...       ...         ...  \n",
       "Montserrat               0        0.0        0        10    0.000000  \n",
       "Falkland Islands         0        0.0        0        66    0.000000  \n",
       "Niue                     0        0.0        0        46    0.000000  \n",
       "Tokelau                  0        0.0        0         0    0.000000  \n",
       "Vatican State            0        0.0        0         0    0.000000  \n",
       "\n",
       "[235 rows x 10 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f0fdfe9e-2a16-438c-a3dd-f20b3e1366e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 235 entries, China to Vatican State\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Pop           235 non-null    int32  \n",
      " 1   YearlyChange  235 non-null    float32\n",
      " 2   NetChange     235 non-null    int32  \n",
      " 3   Density       235 non-null    int16  \n",
      " 4   LandArea      235 non-null    int32  \n",
      " 5   Migrants      235 non-null    int32  \n",
      " 6   Fert.Rate     235 non-null    float32\n",
      " 7   Med.Age       235 non-null    int8   \n",
      " 8   UrbanPop      235 non-null    int8   \n",
      " 9   WorldShare    235 non-null    float32\n",
      "dtypes: float32(3), int16(1), int32(4), int8(2)\n",
      "memory usage: 17.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "47ce1555-e631-4895-82cd-a42798425d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38869257950530034"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-(17.3/28.3)  # 38% Speicherersparnis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "022220ae-cd44-4508-b92e-af57a9232369",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixType(columnNames: list[str], targetType):\n",
    "    for columnName in columnNames:\n",
    "        data[columnName] = data[columnName].astype(targetType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "25304e13-c70c-45b4-a35f-91f47f844cea",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixType([\"Med.Age\", \"UrbanPop\"], np.int8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7e5caf-255a-4821-a36a-5ae3f4bd1310",
   "metadata": {},
   "source": [
    "Beispiel: Konvertierung von Datumswerten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "07898f81-e279-4ffa-b694-fa36983d11b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertDate(column):\n",
    "    dateCol = data[column]\n",
    "    fixedDates = []\n",
    "    for x in dateCol:\n",
    "        date = x.split(\"-\")\n",
    "        localDate = f\"{date[2]}.{date[1]}.{date[0]}\"\n",
    "        fixedDates.append(localDate)\n",
    "    data[column] = pd.Series(fixedDates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "51aab243-5adb-4f90-a3e2-1c560f395fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"Data/PopulationDataFertig.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64dc2d49-1113-440d-a228-b05f7609a046",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}