Wasser_Kraftwerke_Bayern.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18dc9bd9",
   "metadata": {},
   "source": [
    "needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7dbb99c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "#import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import pandas as pd\n",
    "from urllib.parse import urlparse\n",
    "#import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f9c229",
   "metadata": {},
   "source": [
    "Variable initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d23793e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Variable initialisation\n",
    "\n",
    "## webscrapping variables\n",
    "links = []\n",
    "url = 'https://www.landeskraftwerke.bayern/standorte.htm'\n",
    "domain_url = urlparse(url).netloc\n",
    "\n",
    "## data variables\n",
    "location = []\n",
    "inst_power = []\n",
    "yearly_prod_power = []\n",
    "construction_year = []\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d85ed9c",
   "metadata": {},
   "source": [
    "SCRAPE DATA FROM THE WEBPAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ec9b603",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open and prepare the web page for scrapping \n",
    "page = requests.get(url)\n",
    "soup = BeautifulSoup(page.text, 'html.parser')\n",
    "\n",
    "#extract the links from the drop menu\n",
    "content = soup.findAll('ul',attrs={\"class\":\"submenu konventionelle-wasserkrafwerke\"})\n",
    "for item in content:\n",
    "    for link in item.findAll('a'):\n",
    "        href = link.get('href')\n",
    "        links.append('https://' + domain_url + '/' + href)\n",
    "        location.append(href.replace('.htm',''))\n",
    "\n",
    "# get data from the submenu url links        \n",
    "for link in links:\n",
    "    soup_1 = BeautifulSoup(requests.get(link).text, 'html.parser')\n",
    "    content_1 = soup_1.findAll('table', attrs={\"class\":\"tablenormal\"})\n",
    "\n",
    "    #temp values\n",
    "    i = 0\n",
    "\n",
    "    for data in content_1[0].findAll('td', attrs={\"class\":\"col0\"}):\n",
    "        \n",
    "        if data.find('strong') is None:\n",
    "            pass\n",
    "        else:\n",
    "                \n",
    "        # construction year: \n",
    "            if 'Inbetriebnahme' in (data.find('strong').text):\n",
    "                    construction_year.append(content_1[0].findAll('td', attrs={\"class\":\"col1\"})[i].text)\n",
    "        # installed power: \n",
    "            elif 'Installierte Leistung' in (data.find('strong').text):\n",
    "                inst_power.append(content_1[0].findAll('td', attrs={\"class\":\"col1\"})[i].text)\n",
    "        \n",
    "        # yearly produced power:\n",
    "            elif 'Mittlere Jahresarbeit' in (data.find('strong').text):\n",
    "                yearly_prod_power.append(content_1[0].findAll('td', attrs={\"class\":\"col1\"})[i].text)\n",
    "        \n",
    "        i += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4674541b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1,2 Mio. kWh', '0,5 Mio. kWh', '2,9 Mio. kWh', '0,3 Mio. kWh', '3,6 Mio. kWh', '8,1 Mio. kWh', '3,3 Mio. kWh', '0,4 Mio. kWh', '1,1 Mio. kWh', '0,9 Mio. kWh', '2,9 Mio. kWh', '2,3 Mio. kWh', '1,4 Mio. kWh', '0,3 Mio. kWh', '0,8 Mio. kWh', '8,7 Mio. kWh', '16,4 Mio. kWh', '0,1 Mio. kWh']\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "print(yearly_prod_power)\n",
    "print(len(yearly_prod_power))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9a2fdf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1996', '2008', '1976', '1991', '1982', '1993', '2013', '1967', '1973', '1972', '1994', '2013', '1990', '1967', '1968', '1959, Sanierung 2003', '2000', '1964']\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "print(construction_year)\n",
    "print(len(construction_year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5979200b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['615 kW', '130 kW', '1.300 KW', '150 kW', '600 kW', '3.300 kW', '1.093 kW', '160 KW', '420 kW', '300 kW', '765 kW', '848 kW', '450 kW', '90 kW', '250 kW', '2.508 kW', '3.780 kW', '40 kW']\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "print (inst_power)\n",
    "print(len(inst_power))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc0b1b08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['brombachsee', 'drachensee', 'eixendorf', 'foermitz', 'frauenau', 'hilpoltstein', 'leerstetten', 'liebenstein', 'mauthaus', 'postmuenster', 'rothsee1', 'rothsee2', 'rottachsee', 'silbersee', 'surspeicher', 'sylvenstein1', 'sylvenstein2', 'windach']\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "print(location)\n",
    "\n",
    "print(len(location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbf8cfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'installed power': inst_power,\n",
    "        'construction year': construction_year,\n",
    "        'yearly produced power': yearly_prod_power}\n",
    "\n",
    "df = pd.DataFrame(data, index=location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef921276",
   "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>installed power</th>\n",
       "      <th>construction year</th>\n",
       "      <th>yearly produced power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>brombachsee</th>\n",
       "      <td>615 kW</td>\n",
       "      <td>1996</td>\n",
       "      <td>1,2 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drachensee</th>\n",
       "      <td>130 kW</td>\n",
       "      <td>2008</td>\n",
       "      <td>0,5 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eixendorf</th>\n",
       "      <td>1.300 KW</td>\n",
       "      <td>1976</td>\n",
       "      <td>2,9 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foermitz</th>\n",
       "      <td>150 kW</td>\n",
       "      <td>1991</td>\n",
       "      <td>0,3 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frauenau</th>\n",
       "      <td>600 kW</td>\n",
       "      <td>1982</td>\n",
       "      <td>3,6 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hilpoltstein</th>\n",
       "      <td>3.300 kW</td>\n",
       "      <td>1993</td>\n",
       "      <td>8,1 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leerstetten</th>\n",
       "      <td>1.093 kW</td>\n",
       "      <td>2013</td>\n",
       "      <td>3,3 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liebenstein</th>\n",
       "      <td>160 KW</td>\n",
       "      <td>1967</td>\n",
       "      <td>0,4 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mauthaus</th>\n",
       "      <td>420 kW</td>\n",
       "      <td>1973</td>\n",
       "      <td>1,1 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postmuenster</th>\n",
       "      <td>300 kW</td>\n",
       "      <td>1972</td>\n",
       "      <td>0,9 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rothsee1</th>\n",
       "      <td>765 kW</td>\n",
       "      <td>1994</td>\n",
       "      <td>2,9 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rothsee2</th>\n",
       "      <td>848 kW</td>\n",
       "      <td>2013</td>\n",
       "      <td>2,3 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rottachsee</th>\n",
       "      <td>450 kW</td>\n",
       "      <td>1990</td>\n",
       "      <td>1,4 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>silbersee</th>\n",
       "      <td>90 kW</td>\n",
       "      <td>1967</td>\n",
       "      <td>0,3 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surspeicher</th>\n",
       "      <td>250 kW</td>\n",
       "      <td>1968</td>\n",
       "      <td>0,8 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sylvenstein1</th>\n",
       "      <td>2.508 kW</td>\n",
       "      <td>1959, Sanierung 2003</td>\n",
       "      <td>8,7 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sylvenstein2</th>\n",
       "      <td>3.780 kW</td>\n",
       "      <td>2000</td>\n",
       "      <td>16,4 Mio. kWh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windach</th>\n",
       "      <td>40 kW</td>\n",
       "      <td>1964</td>\n",
       "      <td>0,1 Mio. kWh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             installed power     construction year yearly produced power\n",
       "brombachsee           615 kW                  1996          1,2 Mio. kWh\n",
       "drachensee            130 kW                  2008          0,5 Mio. kWh\n",
       "eixendorf           1.300 KW                  1976          2,9 Mio. kWh\n",
       "foermitz              150 kW                  1991          0,3 Mio. kWh\n",
       "frauenau              600 kW                  1982          3,6 Mio. kWh\n",
       "hilpoltstein        3.300 kW                  1993          8,1 Mio. kWh\n",
       "leerstetten         1.093 kW                  2013          3,3 Mio. kWh\n",
       "liebenstein           160 KW                  1967          0,4 Mio. kWh\n",
       "mauthaus              420 kW                  1973          1,1 Mio. kWh\n",
       "postmuenster          300 kW                  1972          0,9 Mio. kWh\n",
       "rothsee1              765 kW                  1994          2,9 Mio. kWh\n",
       "rothsee2              848 kW                  2013          2,3 Mio. kWh\n",
       "rottachsee            450 kW                  1990          1,4 Mio. kWh\n",
       "silbersee              90 kW                  1967          0,3 Mio. kWh\n",
       "surspeicher           250 kW                  1968          0,8 Mio. kWh\n",
       "sylvenstein1        2.508 kW  1959, Sanierung 2003          8,7 Mio. kWh\n",
       "sylvenstein2        3.780 kW                  2000         16,4 Mio. kWh\n",
       "windach                40 kW                  1964          0,1 Mio. kWh"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8eaf5ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'160 kw'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[7,0].lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1a015b80",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'lower'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-23-57d60ea96087>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'installed power'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'installed power'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'.'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m''\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 4\u001B[0;31m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlower\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m' mio. kwh'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m''\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      5\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mfloat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m','\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m'.'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0;36m10E6\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001B[0m in \u001B[0;36mapply\u001B[0;34m(self, func, convert_dtype, args, **kwds)\u001B[0m\n\u001B[1;32m   4136\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   4137\u001B[0m                 \u001B[0mvalues\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mastype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mobject\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_values\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 4138\u001B[0;31m                 \u001B[0mmapped\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlib\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmap_infer\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mconvert\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mconvert_dtype\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   4139\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   4140\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmapped\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0misinstance\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmapped\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mSeries\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32mpandas/_libs/lib.pyx\u001B[0m in \u001B[0;36mpandas._libs.lib.map_infer\u001B[0;34m()\u001B[0m\n",
      "\u001B[0;32m<ipython-input-23-57d60ea96087>\u001B[0m in \u001B[0;36m<lambda>\u001B[0;34m(x)\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'installed power'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'installed power'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'.'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m''\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 4\u001B[0;31m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlower\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m' mio. kwh'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m''\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      5\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'yearly produced power'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mfloat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m','\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m'.'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0;36m10E6\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mAttributeError\u001B[0m: 'float' object has no attribute 'lower'"
     ]
    }
   ],
   "source": [
    "df['installed power'] = df['installed power'].apply(lambda x : x.lower().replace(' kw', ''))\n",
    "df['installed power'] = df['installed power'].apply(lambda x : int(x.replace('.', '')))\n",
    "\n",
    "df['yearly produced power'] = df['yearly produced power'].apply(lambda x : x.lower().replace(' mio. kwh', ''))\n",
    "df['yearly produced power'] = df['yearly produced power'].apply(lambda x : float(x.replace(',','.'))*10E6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35e95b9b",
   "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>installed power</th>\n",
       "      <th>construction year</th>\n",
       "      <th>yearly produced power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>brombachsee</th>\n",
       "      <td>615 kW</td>\n",
       "      <td>1996</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drachensee</th>\n",
       "      <td>130 kW</td>\n",
       "      <td>2008</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eixendorf</th>\n",
       "      <td>1.300 KW</td>\n",
       "      <td>1976</td>\n",
       "      <td>29000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foermitz</th>\n",
       "      <td>150 kW</td>\n",
       "      <td>1991</td>\n",
       "      <td>3000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frauenau</th>\n",
       "      <td>600 kW</td>\n",
       "      <td>1982</td>\n",
       "      <td>36000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hilpoltstein</th>\n",
       "      <td>3.300 kW</td>\n",
       "      <td>1993</td>\n",
       "      <td>81000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leerstetten</th>\n",
       "      <td>1.093 kW</td>\n",
       "      <td>2013</td>\n",
       "      <td>33000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liebenstein</th>\n",
       "      <td>160 KW</td>\n",
       "      <td>1967</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mauthaus</th>\n",
       "      <td>420 kW</td>\n",
       "      <td>1973</td>\n",
       "      <td>11000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postmuenster</th>\n",
       "      <td>300 kW</td>\n",
       "      <td>1972</td>\n",
       "      <td>9000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rothsee1</th>\n",
       "      <td>765 kW</td>\n",
       "      <td>1994</td>\n",
       "      <td>29000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rothsee2</th>\n",
       "      <td>848 kW</td>\n",
       "      <td>2013</td>\n",
       "      <td>23000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rottachsee</th>\n",
       "      <td>450 kW</td>\n",
       "      <td>1990</td>\n",
       "      <td>14000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>silbersee</th>\n",
       "      <td>90 kW</td>\n",
       "      <td>1967</td>\n",
       "      <td>3000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surspeicher</th>\n",
       "      <td>250 kW</td>\n",
       "      <td>1968</td>\n",
       "      <td>8000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sylvenstein1</th>\n",
       "      <td>2.508 kW</td>\n",
       "      <td>1959, Sanierung 2003</td>\n",
       "      <td>87000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sylvenstein2</th>\n",
       "      <td>3.780 kW</td>\n",
       "      <td>2000</td>\n",
       "      <td>164000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windach</th>\n",
       "      <td>40 kW</td>\n",
       "      <td>1964</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             installed power     construction year  yearly produced power\n",
       "brombachsee           615 kW                  1996             12000000.0\n",
       "drachensee            130 kW                  2008              5000000.0\n",
       "eixendorf           1.300 KW                  1976             29000000.0\n",
       "foermitz              150 kW                  1991              3000000.0\n",
       "frauenau              600 kW                  1982             36000000.0\n",
       "hilpoltstein        3.300 kW                  1993             81000000.0\n",
       "leerstetten         1.093 kW                  2013             33000000.0\n",
       "liebenstein           160 KW                  1967              4000000.0\n",
       "mauthaus              420 kW                  1973             11000000.0\n",
       "postmuenster          300 kW                  1972              9000000.0\n",
       "rothsee1              765 kW                  1994             29000000.0\n",
       "rothsee2              848 kW                  2013             23000000.0\n",
       "rottachsee            450 kW                  1990             14000000.0\n",
       "silbersee              90 kW                  1967              3000000.0\n",
       "surspeicher           250 kW                  1968              8000000.0\n",
       "sylvenstein1        2.508 kW  1959, Sanierung 2003             87000000.0\n",
       "sylvenstein2        3.780 kW                  2000            164000000.0\n",
       "windach                40 kW                  1964              1000000.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4bb2958f",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'int' object has no attribute 'lower'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-20-f3c5aac79057>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlower\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m', sanierung 2003'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m''\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001B[0m in \u001B[0;36mapply\u001B[0;34m(self, func, convert_dtype, args, **kwds)\u001B[0m\n\u001B[1;32m   4136\u001B[0m             \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   4137\u001B[0m                 \u001B[0mvalues\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mastype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mobject\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_values\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 4138\u001B[0;31m                 \u001B[0mmapped\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlib\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmap_infer\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mconvert\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mconvert_dtype\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   4139\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   4140\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmapped\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0misinstance\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmapped\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mSeries\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32mpandas/_libs/lib.pyx\u001B[0m in \u001B[0;36mpandas._libs.lib.map_infer\u001B[0;34m()\u001B[0m\n",
      "\u001B[0;32m<ipython-input-20-f3c5aac79057>\u001B[0m in \u001B[0;36m<lambda>\u001B[0;34m(x)\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlower\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m', sanierung 2003'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m''\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdf\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'construction year'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;32mlambda\u001B[0m \u001B[0mx\u001B[0m \u001B[0;34m:\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mAttributeError\u001B[0m: 'int' object has no attribute 'lower'"
     ]
    }
   ],
   "source": [
    "df['construction year'] = df['construction year'].apply(lambda x : x.lower().replace(', sanierung 2003', ''))\n",
    "df['construction year'] = df['construction year'].apply(lambda x : int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6b0bcaa",
   "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>installed power</th>\n",
       "      <th>construction year</th>\n",
       "      <th>yearly produced power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>brombachsee</th>\n",
       "      <td>615</td>\n",
       "      <td>1996</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drachensee</th>\n",
       "      <td>130</td>\n",
       "      <td>2008</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eixendorf</th>\n",
       "      <td>1300</td>\n",
       "      <td>1976</td>\n",
       "      <td>29000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foermitz</th>\n",
       "      <td>150</td>\n",
       "      <td>1991</td>\n",
       "      <td>3000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frauenau</th>\n",
       "      <td>600</td>\n",
       "      <td>1982</td>\n",
       "      <td>36000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hilpoltstein</th>\n",
       "      <td>3300</td>\n",
       "      <td>1993</td>\n",
       "      <td>81000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leerstetten</th>\n",
       "      <td>1093</td>\n",
       "      <td>2013</td>\n",
       "      <td>33000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liebenstein</th>\n",
       "      <td>160</td>\n",
       "      <td>1967</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mauthaus</th>\n",
       "      <td>420</td>\n",
       "      <td>1973</td>\n",
       "      <td>11000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postmuenster</th>\n",
       "      <td>300</td>\n",
       "      <td>1972</td>\n",
       "      <td>9000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rothsee1</th>\n",
       "      <td>765</td>\n",
       "      <td>1994</td>\n",
       "      <td>29000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rothsee2</th>\n",
       "      <td>848</td>\n",
       "      <td>2013</td>\n",
       "      <td>23000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rottachsee</th>\n",
       "      <td>450</td>\n",
       "      <td>1990</td>\n",
       "      <td>14000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>silbersee</th>\n",
       "      <td>90</td>\n",
       "      <td>1967</td>\n",
       "      <td>3000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surspeicher</th>\n",
       "      <td>250</td>\n",
       "      <td>1968</td>\n",
       "      <td>8000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sylvenstein1</th>\n",
       "      <td>2508</td>\n",
       "      <td>1959</td>\n",
       "      <td>87000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sylvenstein2</th>\n",
       "      <td>3780</td>\n",
       "      <td>2000</td>\n",
       "      <td>164000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windach</th>\n",
       "      <td>40</td>\n",
       "      <td>1964</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              installed power  construction year  yearly produced power\n",
       "brombachsee               615               1996             12000000.0\n",
       "drachensee                130               2008              5000000.0\n",
       "eixendorf                1300               1976             29000000.0\n",
       "foermitz                  150               1991              3000000.0\n",
       "frauenau                  600               1982             36000000.0\n",
       "hilpoltstein             3300               1993             81000000.0\n",
       "leerstetten              1093               2013             33000000.0\n",
       "liebenstein               160               1967              4000000.0\n",
       "mauthaus                  420               1973             11000000.0\n",
       "postmuenster              300               1972              9000000.0\n",
       "rothsee1                  765               1994             29000000.0\n",
       "rothsee2                  848               2013             23000000.0\n",
       "rottachsee                450               1990             14000000.0\n",
       "silbersee                  90               1967              3000000.0\n",
       "surspeicher               250               1968              8000000.0\n",
       "sylvenstein1             2508               1959             87000000.0\n",
       "sylvenstein2             3780               2000            164000000.0\n",
       "windach                    40               1964              1000000.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "85c9dd84",
   "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>installed power</th>\n",
       "      <th>construction year</th>\n",
       "      <th>yearly produced power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>installed power</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.193167</td>\n",
       "      <td>0.950854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>construction year</th>\n",
       "      <td>0.193167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearly produced power</th>\n",
       "      <td>0.950854</td>\n",
       "      <td>0.182284</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       installed power  construction year  \\\n",
       "installed power               1.000000           0.193167   \n",
       "construction year             0.193167           1.000000   \n",
       "yearly produced power         0.950854           0.182284   \n",
       "\n",
       "                       yearly produced power  \n",
       "installed power                     0.950854  \n",
       "construction year                   0.182284  \n",
       "yearly produced power               1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7486250f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "212f3228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f97316d18e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "952dc829",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}