matchmaker.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "318408dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aec11f3",
   "metadata": {},
   "source": [
    "Datansatz laden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca80f3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"data/df_singles_manipulated.csv\"\n",
    "\n",
    "df = pd.read_csv(path, index_col=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2beda742",
   "metadata": {},
   "source": [
    "Die Namen, Ortskoordinaten und alle anderen persönlichen Angaben wurden anonymisiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef63d668",
   "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>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>FoodPreference</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Kitchen</th>\n",
       "      <th>Kitchen_Story</th>\n",
       "      <th>Kitchen_Longitude</th>\n",
       "      <th>Kitchen_Latitude</th>\n",
       "      <th>agerange</th>\n",
       "      <th>count_wg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0025cbb5-f0db-4a77-b768-65baec7cef5e</td>\n",
       "      <td>Person1</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.679344</td>\n",
       "      <td>50.594411</td>\n",
       "      <td>18-23</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02c6697f-db9b-467d-8ba6-613cf41a7c01</td>\n",
       "      <td>Person2</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24-27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03f1c860-2ffa-4a68-9f92-ac652725e0b9</td>\n",
       "      <td>Person3</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.669295</td>\n",
       "      <td>50.587220</td>\n",
       "      <td>18-23</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04002b30-8726-483c-8faf-4507738f621c</td>\n",
       "      <td>Person4</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28-30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09b2825d-fcf8-4151-911c-50e216742ec1</td>\n",
       "      <td>Person5</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.717014</td>\n",
       "      <td>50.589000</td>\n",
       "      <td>24-27</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0b511e62-6cc0-4b32-be5c-56e020ae89dc</td>\n",
       "      <td>Person6</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.685272</td>\n",
       "      <td>50.584559</td>\n",
       "      <td>18-23</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0f24f366-32da-48ad-bf00-1ffe5bfc7b7c</td>\n",
       "      <td>Person7</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0f494170-fac6-477a-aa46-11ab304c81bf</td>\n",
       "      <td>Person8</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24-27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0f55fc24-1e80-4ebf-9de4-0b149f804cd6</td>\n",
       "      <td>Person9</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28-30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0fbcd559-457a-435a-8745-1b3db9e5f9d4</td>\n",
       "      <td>Person10</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     ID      Name  FoodPreference  Age  \\\n",
       "0  0025cbb5-f0db-4a77-b768-65baec7cef5e   Person1               3   21   \n",
       "1  02c6697f-db9b-467d-8ba6-613cf41a7c01   Person2               3   24   \n",
       "2  03f1c860-2ffa-4a68-9f92-ac652725e0b9   Person3               3   22   \n",
       "3  04002b30-8726-483c-8faf-4507738f621c   Person4               3   29   \n",
       "4  09b2825d-fcf8-4151-911c-50e216742ec1   Person5               1   26   \n",
       "5  0b511e62-6cc0-4b32-be5c-56e020ae89dc   Person6               3   23   \n",
       "6  0f24f366-32da-48ad-bf00-1ffe5bfc7b7c   Person7               3   23   \n",
       "7  0f494170-fac6-477a-aa46-11ab304c81bf   Person8               1   26   \n",
       "8  0f55fc24-1e80-4ebf-9de4-0b149f804cd6   Person9               1   29   \n",
       "9  0fbcd559-457a-435a-8745-1b3db9e5f9d4  Person10               1   21   \n",
       "\n",
       "      Sex  Kitchen  Kitchen_Story  Kitchen_Longitude  Kitchen_Latitude  \\\n",
       "0  female        1            1.0           8.679344         50.594411   \n",
       "1  female        0            2.0                NaN               NaN   \n",
       "2  female        2            1.0           8.669295         50.587220   \n",
       "3  female        0            2.0                NaN               NaN   \n",
       "4    male        1            1.0           8.717014         50.589000   \n",
       "5  female        1            0.0           8.685272         50.584559   \n",
       "6  female        0            0.0                NaN               NaN   \n",
       "7    male        0            0.0                NaN               NaN   \n",
       "8    male        0            0.0                NaN               NaN   \n",
       "9  female        0            1.0                NaN               NaN   \n",
       "\n",
       "  agerange  count_wg  \n",
       "0    18-23       2.0  \n",
       "1    24-27       NaN  \n",
       "2    18-23       1.0  \n",
       "3    28-30       NaN  \n",
       "4    24-27       1.0  \n",
       "5    18-23       1.0  \n",
       "6    18-23       NaN  \n",
       "7    24-27       NaN  \n",
       "8    28-30       NaN  \n",
       "9    18-23       NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fabf393",
   "metadata": {},
   "source": [
    "Eine Übersicht der Dateitypen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff149fbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 205 entries, 0 to 204\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   ID                 205 non-null    object \n",
      " 1   Name               205 non-null    object \n",
      " 2   FoodPreference     205 non-null    int64  \n",
      " 3   Age                205 non-null    int64  \n",
      " 4   Sex                205 non-null    object \n",
      " 5   Kitchen            205 non-null    int64  \n",
      " 6   Kitchen_Story      205 non-null    float64\n",
      " 7   Kitchen_Longitude  104 non-null    float64\n",
      " 8   Kitchen_Latitude   104 non-null    float64\n",
      " 9   agerange           203 non-null    object \n",
      " 10  count_wg           104 non-null    float64\n",
      "dtypes: float64(4), int64(3), object(4)\n",
      "memory usage: 19.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7790bf2c",
   "metadata": {},
   "source": [
    "Durch die Prächenbildung wird jedem Teilnehmenden ein möglichst passender Kochpartner*in zugewiesen. Das finale Dataframe hat folgende Spalten (hier ein Beispiel mit für die ersten beiden Zeilen):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "efcad25f",
   "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>Einzelanmeldung</th>\n",
       "      <th>Person_1</th>\n",
       "      <th>Person_1_Age</th>\n",
       "      <th>Person_1_AgeRange</th>\n",
       "      <th>Person_1_FoodPreference</th>\n",
       "      <th>Person_1_Kitchen</th>\n",
       "      <th>Person_1_KitchenLatitude</th>\n",
       "      <th>Person_1_KitchenLongitude</th>\n",
       "      <th>Person_1_Name</th>\n",
       "      <th>Person_1_Sex</th>\n",
       "      <th>...</th>\n",
       "      <th>Person_2</th>\n",
       "      <th>Person_2_Age</th>\n",
       "      <th>Person_2_AgeRange</th>\n",
       "      <th>Person_2_FoodPreference</th>\n",
       "      <th>Person_2_Kitchen</th>\n",
       "      <th>Person_2_KitchenLatitude</th>\n",
       "      <th>Person_2_KitchenLongitude</th>\n",
       "      <th>Person_2_Name</th>\n",
       "      <th>Person_2_Sex</th>\n",
       "      <th>Person_2_Story</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0025cbb5-f0db-4a77-b768-65baec7cef5e</td>\n",
       "      <td>21</td>\n",
       "      <td>18-23</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>50.593928</td>\n",
       "      <td>8.679703</td>\n",
       "      <td>Person1</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>04002b30-8726-483c-8faf-4507738f621c</td>\n",
       "      <td>29</td>\n",
       "      <td>28-30</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person4</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>03f1c860-2ffa-4a68-9f92-ac652725e0b9</td>\n",
       "      <td>22</td>\n",
       "      <td>18-23</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>50.587402</td>\n",
       "      <td>8.672367</td>\n",
       "      <td>Person3</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>02c6697f-db9b-467d-8ba6-613cf41a7c01</td>\n",
       "      <td>24</td>\n",
       "      <td>24-27</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person2</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Einzelanmeldung                              Person_1  Person_1_Age  \\\n",
       "0                1  0025cbb5-f0db-4a77-b768-65baec7cef5e            21   \n",
       "1                1  03f1c860-2ffa-4a68-9f92-ac652725e0b9            22   \n",
       "\n",
       "  Person_1_AgeRange  Person_1_FoodPreference  Person_1_Kitchen  \\\n",
       "0             18-23                        3                 1   \n",
       "1             18-23                        3                 2   \n",
       "\n",
       "   Person_1_KitchenLatitude  Person_1_KitchenLongitude Person_1_Name  \\\n",
       "0                 50.593928                   8.679703       Person1   \n",
       "1                 50.587402                   8.672367       Person3   \n",
       "\n",
       "  Person_1_Sex  ...                              Person_2 Person_2_Age  \\\n",
       "0       female  ...  04002b30-8726-483c-8faf-4507738f621c           29   \n",
       "1       female  ...  02c6697f-db9b-467d-8ba6-613cf41a7c01           24   \n",
       "\n",
       "   Person_2_AgeRange Person_2_FoodPreference  Person_2_Kitchen  \\\n",
       "0              28-30                       3                 0   \n",
       "1              24-27                       3                 0   \n",
       "\n",
       "   Person_2_KitchenLatitude  Person_2_KitchenLongitude  Person_2_Name  \\\n",
       "0                       NaN                        NaN        Person4   \n",
       "1                       NaN                        NaN        Person2   \n",
       "\n",
       "  Person_2_Sex Person_2_Story  \n",
       "0       female            2.0  \n",
       "1       female            2.0  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\n",
    "     'Einzelanmeldung': [1,1],\n",
    "     'Person_1': ['0025cbb5-f0db-4a77-b768-65baec7cef5e', '03f1c860-2ffa-4a68-9f92-ac652725e0b9'], \n",
    "     'Person_1_Age': [21, 22], \n",
    "     'Person_1_AgeRange': ['18-23', '18-23'],\n",
    "     'Person_1_FoodPreference': [3, 3], \n",
    "     'Person_1_Kitchen': [1,2],\n",
    "     'Person_1_KitchenLatitude': [50.593928, 50.587402], \n",
    "     'Person_1_KitchenLongitude': [8.679703, 8.672367],\n",
    "     'Person_1_Name': ['Person1', 'Person3'], \n",
    "     'Person_1_Sex': ['female', 'female'], \n",
    "     'Person_1_Story': [1.0, 1.0], \n",
    "     'Person_2': ['04002b30-8726-483c-8faf-4507738f621c', '02c6697f-db9b-467d-8ba6-613cf41a7c01'],\n",
    "     'Person_2_Age' : [29, 24], \n",
    "     'Person_2_AgeRange': ['28-30', '24-27'], \n",
    "     'Person_2_FoodPreference': [3,3],\n",
    "     'Person_2_Kitchen': [0, 0], \n",
    "     'Person_2_KitchenLatitude': [np.nan, np.nan],\n",
    "     'Person_2_KitchenLongitude': [np.nan, np.nan], \n",
    "     'Person_2_Name': ['Person4', 'Person2'], \n",
    "     'Person_2_Sex': ['female', 'female'],\n",
    "     'Person_2_Story': [2.0, 2.0],\n",
    "     }\n",
    "couples = pd.DataFrame(data=d)\n",
    "\n",
    "couples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a19935",
   "metadata": {},
   "source": [
    "Eine Auflistung der Spaltennamen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45c58ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Einzelanmeldung',\n",
       " 'Person_1',\n",
       " 'Person_1_Age',\n",
       " 'Person_1_AgeRange',\n",
       " 'Person_1_FoodPreference',\n",
       " 'Person_1_Kitchen',\n",
       " 'Person_1_KitchenLatitude',\n",
       " 'Person_1_KitchenLongitude',\n",
       " 'Person_1_Name',\n",
       " 'Person_1_Sex',\n",
       " 'Person_1_Story',\n",
       " 'Person_2',\n",
       " 'Person_2_Age',\n",
       " 'Person_2_AgeRange',\n",
       " 'Person_2_FoodPreference',\n",
       " 'Person_2_Kitchen',\n",
       " 'Person_2_KitchenLatitude',\n",
       " 'Person_2_KitchenLongitude',\n",
       " 'Person_2_Name',\n",
       " 'Person_2_Sex',\n",
       " 'Person_2_Story']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(couples.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34fe9e34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 21 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Einzelanmeldung            2 non-null      int64  \n",
      " 1   Person_1                   2 non-null      object \n",
      " 2   Person_1_Age               2 non-null      int64  \n",
      " 3   Person_1_AgeRange          2 non-null      object \n",
      " 4   Person_1_FoodPreference    2 non-null      int64  \n",
      " 5   Person_1_Kitchen           2 non-null      int64  \n",
      " 6   Person_1_KitchenLatitude   2 non-null      float64\n",
      " 7   Person_1_KitchenLongitude  2 non-null      float64\n",
      " 8   Person_1_Name              2 non-null      object \n",
      " 9   Person_1_Sex               2 non-null      object \n",
      " 10  Person_1_Story             2 non-null      float64\n",
      " 11  Person_2                   2 non-null      object \n",
      " 12  Person_2_Age               2 non-null      int64  \n",
      " 13  Person_2_AgeRange          2 non-null      object \n",
      " 14  Person_2_FoodPreference    2 non-null      int64  \n",
      " 15  Person_2_Kitchen           2 non-null      int64  \n",
      " 16  Person_2_KitchenLatitude   0 non-null      float64\n",
      " 17  Person_2_KitchenLongitude  0 non-null      float64\n",
      " 18  Person_2_Name              2 non-null      object \n",
      " 19  Person_2_Sex               2 non-null      object \n",
      " 20  Person_2_Story             2 non-null      float64\n",
      "dtypes: float64(6), int64(7), object(8)\n",
      "memory usage: 464.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "couples.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "130c0677",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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