exploration.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration\n",
    "\n",
    "This notebook is a step-by-step guide to the data enginering research projekt of Carsten Schmotz\n",
    "\n",
    "Content:\n",
    "\n",
    "- Installed dependencies\n",
    "- Datasources\n",
    "- Load data\n",
    "- Data exploration\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\besitzer\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (2.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\besitzer\\appdata\\roaming\\python\\python311\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\besitzer\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\besitzer\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\besitzer\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas) (1.24.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\besitzer\\appdata\\roaming\\python\\python311\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Invalid requirement: \"'SQLAlchemy==1.4.46'\"\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas\n",
    "%pip install 'SQLAlchemy==1.4.46'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasources\n",
    "### Datasource1: Car registration for cars with alternative engines in Germany\n",
    "* Metadata URL: https://mobilithek.info/offers/573358160767496192\n",
    "* Data URL: https://www.kba.de/SharedDocs/Downloads/DE/Statistik/Fahrzeuge/FZ28/fz28_2022_09.xlsx?__blob=publicationFile&v=4 \n",
    "* Data type: xlsx\n",
    "\n",
    "Short description.\n",
    "New car registrations in 2022 with electric, H2M and hybrid drive. \n",
    "The car owner, the car type and the model are listed in segments of Germany. \n",
    "\n",
    "\n",
    "### Data source2: Energy price and household use\n",
    "* Metadata URL: https://www.govdata.de/web/guest/suchen/-/details/strompreise-fur-haushalte-deutschland-jahrejahresverbrauchsklassen-preisbestandteile\n",
    "* Data URL: https://www-genesis.destatis.de/genesis/downloads/00/tables/61243-0002_00.csv \n",
    "* Data type: CSV\n",
    "\n",
    "Short description.\n",
    "Electricity prices for households: Germany, years, annual consumption classes, price components"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_sql_table('carregistration', 'sqlite:///./CarRegistration.sqlite')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at the first rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monat</th>\n",
       "      <th>Insgesamt</th>\n",
       "      <th>Alternative Antrieb</th>\n",
       "      <th>Alternative in Prozent</th>\n",
       "      <th>Elektroantriebe Ingesamt</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jahr 2022 insgesamt</td>\n",
       "      <td>2651357.0</td>\n",
       "      <td>1315567.0</td>\n",
       "      <td>49.6</td>\n",
       "      <td>833487.0</td>\n",
       "      <td>63.4</td>\n",
       "      <td>470559.0</td>\n",
       "      <td>835</td>\n",
       "      <td>362093.0</td>\n",
       "      <td>465228.0</td>\n",
       "      <td>72598.0</td>\n",
       "      <td>312090.0</td>\n",
       "      <td>72598.0</td>\n",
       "      <td>153135.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dezember</td>\n",
       "      <td>314318.0</td>\n",
       "      <td>215862.0</td>\n",
       "      <td>68.7</td>\n",
       "      <td>174200.0</td>\n",
       "      <td>80.7</td>\n",
       "      <td>104325.0</td>\n",
       "      <td>74</td>\n",
       "      <td>69801.0</td>\n",
       "      <td>40359.0</td>\n",
       "      <td>6349.0</td>\n",
       "      <td>26705.0</td>\n",
       "      <td>6349.0</td>\n",
       "      <td>13654.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>November</td>\n",
       "      <td>260512.0</td>\n",
       "      <td>147962.0</td>\n",
       "      <td>56.8</td>\n",
       "      <td>102656.0</td>\n",
       "      <td>69.4</td>\n",
       "      <td>57980.0</td>\n",
       "      <td>95</td>\n",
       "      <td>44581.0</td>\n",
       "      <td>43697.0</td>\n",
       "      <td>6824.0</td>\n",
       "      <td>29852.0</td>\n",
       "      <td>6824.0</td>\n",
       "      <td>13845.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oktober</td>\n",
       "      <td>208642.0</td>\n",
       "      <td>105405.0</td>\n",
       "      <td>50.5</td>\n",
       "      <td>67883.0</td>\n",
       "      <td>64.4</td>\n",
       "      <td>35781.0</td>\n",
       "      <td>38</td>\n",
       "      <td>32064.0</td>\n",
       "      <td>36469.0</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>25496.0</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>10972.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>September</td>\n",
       "      <td>224816.0</td>\n",
       "      <td>114276.0</td>\n",
       "      <td>50.8</td>\n",
       "      <td>72746.0</td>\n",
       "      <td>63.7</td>\n",
       "      <td>44389.0</td>\n",
       "      <td>21</td>\n",
       "      <td>28336.0</td>\n",
       "      <td>40097.0</td>\n",
       "      <td>6371.0</td>\n",
       "      <td>27990.0</td>\n",
       "      <td>6371.0</td>\n",
       "      <td>12107.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>August</td>\n",
       "      <td>199183.0</td>\n",
       "      <td>93404.0</td>\n",
       "      <td>46.9</td>\n",
       "      <td>57040.0</td>\n",
       "      <td>61.1</td>\n",
       "      <td>32006.0</td>\n",
       "      <td>315</td>\n",
       "      <td>24719.0</td>\n",
       "      <td>35116.0</td>\n",
       "      <td>4791.0</td>\n",
       "      <td>22926.0</td>\n",
       "      <td>4791.0</td>\n",
       "      <td>12190.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Juli</td>\n",
       "      <td>205911.0</td>\n",
       "      <td>90425.0</td>\n",
       "      <td>43.9</td>\n",
       "      <td>52579.0</td>\n",
       "      <td>58.1</td>\n",
       "      <td>28815.0</td>\n",
       "      <td>52</td>\n",
       "      <td>23712.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>24384.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>11866.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Juni</td>\n",
       "      <td>224558.0</td>\n",
       "      <td>98888.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>58492.0</td>\n",
       "      <td>59.1</td>\n",
       "      <td>32234.0</td>\n",
       "      <td>55</td>\n",
       "      <td>26203.0</td>\n",
       "      <td>39160.0</td>\n",
       "      <td>5756.0</td>\n",
       "      <td>25817.0</td>\n",
       "      <td>5756.0</td>\n",
       "      <td>13341.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mai</td>\n",
       "      <td>207199.0</td>\n",
       "      <td>90619.0</td>\n",
       "      <td>43.7</td>\n",
       "      <td>52427.0</td>\n",
       "      <td>57.9</td>\n",
       "      <td>29182.0</td>\n",
       "      <td>36</td>\n",
       "      <td>23209.0</td>\n",
       "      <td>37450.0</td>\n",
       "      <td>5970.0</td>\n",
       "      <td>25420.0</td>\n",
       "      <td>5970.0</td>\n",
       "      <td>12030.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>April</td>\n",
       "      <td>180264.0</td>\n",
       "      <td>79940.0</td>\n",
       "      <td>44.3</td>\n",
       "      <td>43919.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>22175.0</td>\n",
       "      <td>47</td>\n",
       "      <td>21697.0</td>\n",
       "      <td>35089.0</td>\n",
       "      <td>6266.0</td>\n",
       "      <td>23983.0</td>\n",
       "      <td>6266.0</td>\n",
       "      <td>11106.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Monat  Insgesamt  Alternative Antrieb   \n",
       "0  Jahr 2022 insgesamt  2651357.0            1315567.0  \\\n",
       "1             Dezember   314318.0             215862.0   \n",
       "2             November   260512.0             147962.0   \n",
       "3              Oktober   208642.0             105405.0   \n",
       "4            September   224816.0             114276.0   \n",
       "5               August   199183.0              93404.0   \n",
       "6                 Juli   205911.0              90425.0   \n",
       "7                 Juni   224558.0              98888.0   \n",
       "8                  Mai   207199.0              90619.0   \n",
       "9                April   180264.0              79940.0   \n",
       "\n",
       "   Alternative in Prozent  Elektroantriebe Ingesamt  Unnamed: 6  Unnamed: 7   \n",
       "0                    49.6                  833487.0        63.4    470559.0  \\\n",
       "1                    68.7                  174200.0        80.7    104325.0   \n",
       "2                    56.8                  102656.0        69.4     57980.0   \n",
       "3                    50.5                   67883.0        64.4     35781.0   \n",
       "4                    50.8                   72746.0        63.7     44389.0   \n",
       "5                    46.9                   57040.0        61.1     32006.0   \n",
       "6                    43.9                   52579.0        58.1     28815.0   \n",
       "7                    44.0                   58492.0        59.1     32234.0   \n",
       "8                    43.7                   52427.0        57.9     29182.0   \n",
       "9                    44.3                   43919.0        54.9     22175.0   \n",
       "\n",
       "  Unnamed: 8  Unnamed: 9  Unnamed: 10  Unnamed: 11  Unnamed: 12  Unnamed: 13   \n",
       "0        835    362093.0     465228.0      72598.0     312090.0      72598.0  \\\n",
       "1         74     69801.0      40359.0       6349.0      26705.0       6349.0   \n",
       "2         95     44581.0      43697.0       6824.0      29852.0       6824.0   \n",
       "3         38     32064.0      36469.0       6091.0      25496.0       6091.0   \n",
       "4         21     28336.0      40097.0       6371.0      27990.0       6371.0   \n",
       "5        315     24719.0      35116.0       4791.0      22926.0       4791.0   \n",
       "6         52     23712.0      36250.0       5628.0      24384.0       5628.0   \n",
       "7         55     26203.0      39160.0       5756.0      25817.0       5756.0   \n",
       "8         36     23209.0      37450.0       5970.0      25420.0       5970.0   \n",
       "9         47     21697.0      35089.0       6266.0      23983.0       6266.0   \n",
       "\n",
       "   Unnamed: 14 Unnamed: 15  \n",
       "0     153135.0           -  \n",
       "1      13654.0           -  \n",
       "2      13845.0           -  \n",
       "3      10972.0           -  \n",
       "4      12107.0           -  \n",
       "5      12190.0           -  \n",
       "6      11866.0           -  \n",
       "7      13341.0           -  \n",
       "8      12030.0           -  \n",
       "9      11106.0           -  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration\n",
    "Print some basic information about the data. Your data exploration would continue here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28 entries, 0 to 27\n",
      "Data columns (total 5 columns):\n",
      " #   Column                        Non-Null Count  Dtype \n",
      "---  ------                        --------------  ----- \n",
      " 0   Jahr                          27 non-null     object\n",
      " 1   Haushalte                     24 non-null     object\n",
      " 2   Insgesamt                     25 non-null     object\n",
      " 3   Netzkosten                    25 non-null     object\n",
      " 4   Steuern, Abgaben und Umlagen  25 non-null     object\n",
      "dtypes: object(5)\n",
      "memory usage: 1.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([None, '2019', '2020', '2021', '2022', '__________',\n",
       "       '© Statistisches Bundesamt (Destatis), 2023',\n",
       "       'Stand: 31.03.2023 / 20:06:14'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "\n",
    "df['Jahr'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_sql_table('prize', 'sqlite:///./Energyprize.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jahr</th>\n",
       "      <th>Haushalte</th>\n",
       "      <th>Insgesamt</th>\n",
       "      <th>Netzkosten</th>\n",
       "      <th>Steuern, Abgaben und Umlagen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>EUR/KWh</td>\n",
       "      <td>EUR/KWh</td>\n",
       "      <td>EUR/KWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>unter 1 000 KWh</td>\n",
       "      <td>0,0925</td>\n",
       "      <td>0,1808</td>\n",
       "      <td>0,1837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>1 000 bis unter 2 500 KWh</td>\n",
       "      <td>0,0683</td>\n",
       "      <td>0,0938</td>\n",
       "      <td>0,1622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2 500 bis unter 5 000 KWh</td>\n",
       "      <td>0,0581</td>\n",
       "      <td>0,0740</td>\n",
       "      <td>0,1556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>5 000 bis unter 15 000 KWh</td>\n",
       "      <td>0,0547</td>\n",
       "      <td>0,0592</td>\n",
       "      <td>0,1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>15 000 KWh und mehr</td>\n",
       "      <td>0,0492</td>\n",
       "      <td>0,0444</td>\n",
       "      <td>0,1432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>Insgesamt</td>\n",
       "      <td>0,0617</td>\n",
       "      <td>0,0804</td>\n",
       "      <td>0,1571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>unter 1 000 KWh</td>\n",
       "      <td>0,1213</td>\n",
       "      <td>0,1546</td>\n",
       "      <td>0,1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>1 000 bis unter 2 500 KWh</td>\n",
       "      <td>0,0742</td>\n",
       "      <td>0,0992</td>\n",
       "      <td>0,1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>2 500 bis unter 5 000 KWh</td>\n",
       "      <td>0,0574</td>\n",
       "      <td>0,0864</td>\n",
       "      <td>0,1589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Jahr                   Haushalte Insgesamt Netzkosten   \n",
       "0  None                        None   EUR/KWh    EUR/KWh  \\\n",
       "1  2019             unter 1 000 KWh    0,0925     0,1808   \n",
       "2  2019   1 000 bis unter 2 500 KWh    0,0683     0,0938   \n",
       "3  2019   2 500 bis unter 5 000 KWh    0,0581     0,0740   \n",
       "4  2019  5 000 bis unter 15 000 KWh    0,0547     0,0592   \n",
       "5  2019         15 000 KWh und mehr    0,0492     0,0444   \n",
       "6  2019                   Insgesamt    0,0617     0,0804   \n",
       "7  2020             unter 1 000 KWh    0,1213     0,1546   \n",
       "8  2020   1 000 bis unter 2 500 KWh    0,0742     0,0992   \n",
       "9  2020   2 500 bis unter 5 000 KWh    0,0574     0,0864   \n",
       "\n",
       "  Steuern, Abgaben und Umlagen  \n",
       "0                      EUR/KWh  \n",
       "1                       0,1837  \n",
       "2                       0,1622  \n",
       "3                       0,1556  \n",
       "4                       0,1504  \n",
       "5                       0,1432  \n",
       "6                       0,1571  \n",
       "7                       0,1849  \n",
       "8                       0,1659  \n",
       "9                       0,1589  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28 entries, 0 to 27\n",
      "Data columns (total 5 columns):\n",
      " #   Column                        Non-Null Count  Dtype \n",
      "---  ------                        --------------  ----- \n",
      " 0   Jahr                          27 non-null     object\n",
      " 1   Haushalte                     24 non-null     object\n",
      " 2   Insgesamt                     25 non-null     object\n",
      " 3   Netzkosten                    25 non-null     object\n",
      " 4   Steuern, Abgaben und Umlagen  25 non-null     object\n",
      "dtypes: object(5)\n",
      "memory usage: 1.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([None, '2019', '2020', '2021', '2022', '__________',\n",
       "       '© Statistisches Bundesamt (Destatis), 2023',\n",
       "       'Stand: 31.03.2023 / 20:06:14'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "\n",
    "df['Jahr'].unique()"
   ]
  }
 ],
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