MBS_C4_preproc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f1e7c1b",
   "metadata": {},
   "source": [
    "# Einführung Multi-Variate Datenanalyse (MVDA)\n",
    "\n",
    "## Übung 4: Datenvorverarbeitung (data preprocessing)\n",
    "\n",
    "für Kurse Mathematische Berechnungssoftware (MBS)\n",
    "\n",
    "_Simeon Sauer, SS2020_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6cf2c8",
   "metadata": {},
   "source": [
    "### 0 - benötigte python-Module laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d2b05ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # Bibliothek zum Einlesen / Verwalten / Verarbeiten von tabellarischen Daten\n",
    "import matplotlib.pyplot as plt # Bibliothek zum Plotten / Visualisieren"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "969f0bce",
   "metadata": {},
   "source": [
    "### 1 - csv-Datei mit Trainingdaten einlesen\n",
    "\n",
    "Quelle des Datensatzes:\n",
    "https://www.kaggle.com/c/bioresponse\n",
    "\n",
    "(Datensatz hier leicht verändert, z.B. fehldende und ungültige Werte hinzugefügt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2da87fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Activity</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "      <th>D4</th>\n",
       "      <th>D5</th>\n",
       "      <th>D6</th>\n",
       "      <th>D7</th>\n",
       "      <th>D8</th>\n",
       "      <th>D9</th>\n",
       "      <th>...</th>\n",
       "      <th>D1767</th>\n",
       "      <th>D1768</th>\n",
       "      <th>D1769</th>\n",
       "      <th>D1770</th>\n",
       "      <th>D1771</th>\n",
       "      <th>D1772</th>\n",
       "      <th>D1773</th>\n",
       "      <th>D1774</th>\n",
       "      <th>D1775</th>\n",
       "      <th>D1776</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.497009</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.678031</td>\n",
       "      <td>0.273166</td>\n",
       "      <td>0.585445</td>\n",
       "      <td>0.743663</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.606291</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111209</td>\n",
       "      <td>0.803455</td>\n",
       "      <td>0.106105</td>\n",
       "      <td>0.411754</td>\n",
       "      <td>0.836582</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>0.480124</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.209791</td>\n",
       "      <td>0.610350</td>\n",
       "      <td>0.356453</td>\n",
       "      <td>0.517720</td>\n",
       "      <td>0.679051</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538825</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.196344</td>\n",
       "      <td>0.724230</td>\n",
       "      <td>0.235606</td>\n",
       "      <td>0.288764</td>\n",
       "      <td>0.805110</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.517794</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.494734</td>\n",
       "      <td>0.781422</td>\n",
       "      <td>0.154361</td>\n",
       "      <td>0.303809</td>\n",
       "      <td>0.812646</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1777 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Activity        D1        D2    D3   D4        D5        D6        D7  \\\n",
       "0       1.0  0.000000  0.497009  0.10  0.0  0.132956  0.678031  0.273166   \n",
       "1       1.0  0.366667  0.606291  0.05  0.0  0.111209  0.803455  0.106105   \n",
       "2       1.0  0.033300  0.480124  0.00  0.0  0.209791  0.610350  0.356453   \n",
       "3       1.0  0.000000  0.538825  0.00  0.5  0.196344  0.724230  0.235606   \n",
       "4       0.0  0.100000  0.517794  0.00  0.0  0.494734  0.781422  0.154361   \n",
       "\n",
       "         D8        D9  ...  D1767  D1768  D1769  D1770  D1771  D1772  D1773  \\\n",
       "0  0.585445  0.743663  ...      0      0      0      0      0      0      0   \n",
       "1  0.411754  0.836582  ...      1      1      1      1      0      1      0   \n",
       "2  0.517720  0.679051  ...      0      0      0      0      0      0      0   \n",
       "3  0.288764  0.805110  ...      0      0      0      0      0      0      0   \n",
       "4  0.303809  0.812646  ...      0      0      0      0      0      0      0   \n",
       "\n",
       "   D1774  D1775  D1776  \n",
       "0      0      0      0  \n",
       "1      0      1      0  \n",
       "2      0      0      0  \n",
       "3      0      0      0  \n",
       "4      0      0      0  \n",
       "\n",
       "[5 rows x 1777 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_raw = pd.read_csv('MBS_C4_dataset.csv')\n",
    "dat_raw.head() # die ersten 5 Zeilen ausgeben, um zu schauen ob csv-import Funtioniert hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b7c6f42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anzahl Zeilen: 3751 ; Anzahl Spalten: 1777\n"
     ]
    }
   ],
   "source": [
    "def print_colrow_no(df):\n",
    "    print('Anzahl Zeilen:', df.shape[0], '; Anzahl Spalten:', df.shape[1])\n",
    "    \n",
    "print_colrow_no(dat_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4d0733",
   "metadata": {},
   "source": [
    "### 2 - deskriptive Statistik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9fc1288",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Activity</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "      <th>D4</th>\n",
       "      <th>D5</th>\n",
       "      <th>D6</th>\n",
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       "      <th>D1772</th>\n",
       "      <th>D1773</th>\n",
       "      <th>D1774</th>\n",
       "      <th>D1775</th>\n",
       "      <th>D1776</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3749.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
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       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "      <td>3751.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.568152</td>\n",
       "      <td>0.076948</td>\n",
       "      <td>0.592436</td>\n",
       "      <td>0.068142</td>\n",
       "      <td>0.038990</td>\n",
       "      <td>0.212112</td>\n",
       "      <td>0.686653</td>\n",
       "      <td>0.274713</td>\n",
       "      <td>0.455133</td>\n",
       "      <td>0.749517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026926</td>\n",
       "      <td>0.014663</td>\n",
       "      <td>0.013863</td>\n",
       "      <td>0.021861</td>\n",
       "      <td>0.015196</td>\n",
       "      <td>0.016796</td>\n",
       "      <td>0.012263</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.020261</td>\n",
       "      <td>0.011197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.683449</td>\n",
       "      <td>0.079989</td>\n",
       "      <td>0.105860</td>\n",
       "      <td>0.078414</td>\n",
       "      <td>0.115885</td>\n",
       "      <td>0.102592</td>\n",
       "      <td>0.078702</td>\n",
       "      <td>0.090017</td>\n",
       "      <td>0.162731</td>\n",
       "      <td>0.071702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161889</td>\n",
       "      <td>0.120215</td>\n",
       "      <td>0.116938</td>\n",
       "      <td>0.146249</td>\n",
       "      <td>0.122348</td>\n",
       "      <td>0.128522</td>\n",
       "      <td>0.110074</td>\n",
       "      <td>0.107683</td>\n",
       "      <td>0.140911</td>\n",
       "      <td>0.105236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.282128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002630</td>\n",
       "      <td>0.137873</td>\n",
       "      <td>0.006130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.275590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>0.517811</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138118</td>\n",
       "      <td>0.625627</td>\n",
       "      <td>0.207374</td>\n",
       "      <td>0.378062</td>\n",
       "      <td>0.707339</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066700</td>\n",
       "      <td>0.585989</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.190926</td>\n",
       "      <td>0.674037</td>\n",
       "      <td>0.277845</td>\n",
       "      <td>0.499942</td>\n",
       "      <td>0.738961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.668395</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.261726</td>\n",
       "      <td>0.740663</td>\n",
       "      <td>0.335816</td>\n",
       "      <td>0.569962</td>\n",
       "      <td>0.788177</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964381</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994735</td>\n",
       "      <td>0.790831</td>\n",
       "      <td>0.989870</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1777 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Activity           D1           D2           D3           D4  \\\n",
       "count  3749.000000  3751.000000  3751.000000  3751.000000  3751.000000   \n",
       "mean      0.568152     0.076948     0.592436     0.068142     0.038990   \n",
       "std       1.683449     0.079989     0.105860     0.078414     0.115885   \n",
       "min       0.000000     0.000000     0.282128     0.000000     0.000000   \n",
       "25%       0.000000     0.033300     0.517811     0.000000     0.000000   \n",
       "50%       1.000000     0.066700     0.585989     0.050000     0.000000   \n",
       "75%       1.000000     0.100000     0.668395     0.100000     0.000000   \n",
       "max      99.000000     1.000000     0.964381     0.950000     1.000000   \n",
       "\n",
       "                D5           D6           D7           D8           D9  ...  \\\n",
       "count  3751.000000  3751.000000  3751.000000  3751.000000  3751.000000  ...   \n",
       "mean      0.212112     0.686653     0.274713     0.455133     0.749517  ...   \n",
       "std       0.102592     0.078702     0.090017     0.162731     0.071702  ...   \n",
       "min       0.002630     0.137873     0.006130     0.000000     0.275590  ...   \n",
       "25%       0.138118     0.625627     0.207374     0.378062     0.707339  ...   \n",
       "50%       0.190926     0.674037     0.277845     0.499942     0.738961  ...   \n",
       "75%       0.261726     0.740663     0.335816     0.569962     0.788177  ...   \n",
       "max       1.000000     0.994735     0.790831     0.989870     1.000000  ...   \n",
       "\n",
       "             D1767        D1768        D1769        D1770        D1771  \\\n",
       "count  3751.000000  3751.000000  3751.000000  3751.000000  3751.000000   \n",
       "mean      0.026926     0.014663     0.013863     0.021861     0.015196   \n",
       "std       0.161889     0.120215     0.116938     0.146249     0.122348   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "             D1772        D1773        D1774        D1775        D1776  \n",
       "count  3751.000000  3751.000000  3751.000000  3751.000000  3751.000000  \n",
       "mean      0.016796     0.012263     0.011730     0.020261     0.011197  \n",
       "std       0.128522     0.110074     0.107683     0.140911     0.105236  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 1777 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_raw.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554e0fe4",
   "metadata": {},
   "source": [
    "### 3 - ungültigen Wert `99` für Variable `Activity` streichen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5e231fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anzahl Zeilen: 3750 ; Anzahl Spalten: 1777\n"
     ]
    }
   ],
   "source": [
    "## Alternative 1:\n",
    "dat_clean = dat_raw[dat_raw['Activity'] != 99]\n",
    "\n",
    "## Alterative 2:\n",
    "# dat_clean = dat_raw[dat_raw['Activity'].isin([0, 1])]\n",
    "\n",
    "## Alternative 3:\n",
    "#dat_clean = dat_raw\n",
    "#dat_clean['Activity'].replace(99, pd.NA, inplace=True)\n",
    "\n",
    "print_colrow_no(dat_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94be6c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0., nan])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check: Welche Werte nimmt 'Activity' jetzt noch an?\n",
    "dat_clean['Activity'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db67b77",
   "metadata": {},
   "source": [
    "### 4 - missing values behandeln\n",
    "\n",
    "welche Variablen haben überhaupt fehlende Werte (_missing values_ bzw. _not-a-number_, kurz `NaN`)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fdaa3123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Activity': 2, 'D1044': 1, 'D1589': 1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dazu können wir z.B. eine Variable vom Typ 'Dictionary' erzeugen, die diese Information enthält\n",
    "# Um die Analyse später wiederholen zu können, legen wir sie gleich als Funktion an:\n",
    "def analyze_NaN(df: pd.DataFrame, drop_zero_NaNs = True) -> dict:\n",
    "    ''' calculates no of missing values per column of pandas dataframe\n",
    "    \n",
    "    Input:\n",
    "        df ... DataFrame to be analyzed\n",
    "        drop_zero_NaNs ... drop columns with zero NaN from result\n",
    "    \n",
    "    Output:\n",
    "        NaN_dict ... dictionary with columns of `df` as keys and no of missing values as values\n",
    "    '''\n",
    "    \n",
    "    NaN_dict = {var: df[var].isna().sum()\n",
    "                for var in dat_raw\n",
    "                if df[var].isna().sum() > 0 or (not drop_zero_NaNs)}\n",
    "    \n",
    "    return NaN_dict # Rückgabewert der Funktion, damit auch Ende der Funktion\n",
    "\n",
    "analyze_NaN(dat_clean) # Aufruf der Funktion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99250a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 1: Fehlende Daten durch Mittelwert der Spalte ersetzen:\n",
    "dat = dat_clean.fillna(dat_clean.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e79c8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 2: Zeilen mit fehlenden Daten löschen:\n",
    "dat = dat_clean.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53a2ef4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anzahl Zeilen: 3746 ; Anzahl Spalten: 1777\n"
     ]
    }
   ],
   "source": [
    "# überprüfen, ob jetzt wirklich alle NaNs weg sind\n",
    "# (doppelts 'sum()', um über Zeilen *und* Spalten zu summieren)\n",
    "assert dat.isna().sum().sum() == 0, 'dat_2 enthält immer noch NaNs!'\n",
    "\n",
    "print_colrow_no(dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae15471",
   "metadata": {},
   "source": [
    "### 5 - Daten visualisieren\n",
    "\n",
    "z.B. als Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fbf26ec5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dat, aspect='auto')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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