GitHubIntegration.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GitHubIntegration.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMSpAwXSFQpZQf4Fz1LOR09",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mtomcanyi/colab-public/blob/main/GitHubIntegration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6UlWyjRdz75"
      },
      "source": [
        "# GitHub Integration Essentials"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loJsu3O3d8Kq"
      },
      "source": [
        "## Code and data split\n",
        "Code can be stored on GitHub and data can be stored on Google Drive. One time token authorization is needed for the duration of the session.\n",
        "\n",
        "Root of the drive is mounted at `/content/drive/MyDrive`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rODTMWkjdbgb",
        "outputId": "5f1468de-1885-495b-aef3-12edaaff8485"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive');\n",
        "drivePfx = '/content/drive/MyDrive'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCV9qbD-js3C"
      },
      "source": [
        "## Package versions\n",
        "Quick dump of Python and package versions available in Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OF4o0ytirby",
        "outputId": "13b75df5-e93b-4330-dae4-ff94b206b164"
      },
      "source": [
        "! python -V\n",
        "\n",
        "for package in ['numpy','pandas','matplotlib','sklearn','bs4','requests','seaborn']:\n",
        "  ! echo \"-----------------------\"\n",
        "  ! pip show $package"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.7.12\n",
            "-----------------------\n",
            "Name: numpy\n",
            "Version: 1.19.5\n",
            "Summary: NumPy is the fundamental package for array computing with Python.\n",
            "Home-page: https://www.numpy.org\n",
            "Author: Travis E. Oliphant et al.\n",
            "Author-email: None\n",
            "License: BSD\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: \n",
            "Required-by: yellowbrick, xgboost, xarray, wordcloud, torchvision, torchtext, tifffile, thinc, Theano-PyMC, tensorflow, tensorflow-probability, tensorflow-hub, tensorflow-datasets, tensorboard, tables, statsmodels, spacy, sklearn-pandas, seaborn, scs, scipy, scikit-learn, scikit-image, resampy, qdldl, PyWavelets, python-louvain, pystan, pysndfile, pymc3, pyerfa, pyemd, pyarrow, plotnine, patsy, pandas, osqp, opt-einsum, opencv-python, opencv-contrib-python, numexpr, numba, nibabel, netCDF4, moviepy, mlxtend, mizani, missingno, matplotlib, matplotlib-venn, lightgbm, librosa, Keras-Preprocessing, kapre, jpeg4py, jaxlib, jax, imgaug, imbalanced-learn, imageio, hyperopt, holoviews, h5py, gym, gensim, folium, fix-yahoo-finance, fbprophet, fastprogress, fastdtw, fastai, fa2, ecos, daft, cvxpy, cufflinks, cmdstanpy, cftime, Bottleneck, bokeh, blis, autograd, atari-py, astropy, arviz, altair, albumentations\n",
            "-----------------------\n",
            "Name: pandas\n",
            "Version: 1.1.5\n",
            "Summary: Powerful data structures for data analysis, time series, and statistics\n",
            "Home-page: https://pandas.pydata.org\n",
            "Author: None\n",
            "Author-email: None\n",
            "License: BSD\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: python-dateutil, numpy, pytz\n",
            "Required-by: xarray, vega-datasets, statsmodels, sklearn-pandas, seaborn, pymc3, plotnine, pandas-profiling, pandas-gbq, pandas-datareader, mlxtend, mizani, holoviews, gspread-dataframe, google-colab, fix-yahoo-finance, fbprophet, fastai, cufflinks, cmdstanpy, arviz, altair\n",
            "-----------------------\n",
            "Name: matplotlib\n",
            "Version: 3.2.2\n",
            "Summary: Python plotting package\n",
            "Home-page: https://matplotlib.org\n",
            "Author: John D. Hunter, Michael Droettboom\n",
            "Author-email: matplotlib-users@python.org\n",
            "License: PSF\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: python-dateutil, kiwisolver, pyparsing, numpy, cycler\n",
            "Required-by: yellowbrick, seaborn, scikit-image, pycocotools, plotnine, pandas-profiling, mlxtend, mizani, missingno, matplotlib-venn, keras-vis, imgaug, fbprophet, fastai, descartes, daft, arviz\n",
            "-----------------------\n",
            "Name: sklearn\n",
            "Version: 0.0\n",
            "Summary: A set of python modules for machine learning and data mining\n",
            "Home-page: https://pypi.python.org/pypi/scikit-learn/\n",
            "Author: UNKNOWN\n",
            "Author-email: UNKNOWN\n",
            "License: None\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: scikit-learn\n",
            "Required-by: \n",
            "-----------------------\n",
            "Name: bs4\n",
            "Version: 0.0.1\n",
            "Summary: Screen-scraping library\n",
            "Home-page: https://pypi.python.org/pypi/beautifulsoup4\n",
            "Author: Leonard Richardson\n",
            "Author-email: leonardr@segfault.org\n",
            "License: MIT\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: beautifulsoup4\n",
            "Required-by: \n",
            "-----------------------\n",
            "Name: requests\n",
            "Version: 2.23.0\n",
            "Summary: Python HTTP for Humans.\n",
            "Home-page: https://requests.readthedocs.io\n",
            "Author: Kenneth Reitz\n",
            "Author-email: me@kennethreitz.org\n",
            "License: Apache 2.0\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: idna, urllib3, certifi, chardet\n",
            "Required-by: tweepy, torchtext, tensorflow-datasets, tensorboard, Sphinx, spacy, requests-oauthlib, pymystem3, pooch, panel, pandas-datareader, kaggle, gspread, google-colab, google-api-core, gdown, folium, fix-yahoo-finance, fastai, coveralls, community, CacheControl\n",
            "-----------------------\n",
            "Name: seaborn\n",
            "Version: 0.11.2\n",
            "Summary: seaborn: statistical data visualization\n",
            "Home-page: https://seaborn.pydata.org\n",
            "Author: Michael Waskom\n",
            "Author-email: mwaskom@gmail.com\n",
            "License: BSD (3-clause)\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: numpy, scipy, pandas, matplotlib\n",
            "Required-by: missingno\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcHvuffjuVb7"
      },
      "source": [
        "## Package installation\n",
        "Running installation via terminal command\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWVnhEz7uU2d"
      },
      "source": [
        "# BeautifulSoup web scraping stack already installed\n",
        "\n",
        "#!pip install bs4\n",
        "#!pip install requests"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3RvIbIch9pB"
      },
      "source": [
        "## Imports and setup\n",
        "Package imports needed for cells below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuBrKKVHhpaa"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Make visualizations bigger and better looking - credits to Julia Evans(@b0rk)\n",
        "plt.style.use('ggplot')\n",
        "plt.rcParams['figure.figsize'] = (15,5)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Nj6pdzuxJiS"
      },
      "source": [
        "## Test data\n",
        "\n",
        "\n",
        "\n",
        "*   Originates from [Prague Water and Sewage Services](https://www.pvk.cz/vse-o-vode/cena-vodneho-a-stocneho/vyvoj-vodneho-a-stocneho-v-praze/)\n",
        "*   Table entry: *Od roku 1998 sjednoceno*\n",
        "*   In *Obdobi* (en: *Period*) kept only starting year\n",
        "*   Renamed columns to `Year, Water, Sewer`\n",
        "*   Saved resulting CSV onto `MyDrive/Hackhealth2021/WaterSewerPrague.csv`\n",
        "*   Saved same CSV as Google Sheet `WaterSewer`\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvR-sqzsec2I"
      },
      "source": [
        "## Read and preview a file stored on mounted drive\n",
        "Drive gets mounted at root with all subdirectories available\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "id": "iFVE_1gSeb5r",
        "outputId": "44a26be8-263f-4ae1-e1b6-2c28b22d9165"
      },
      "source": [
        "wsDF = pd.read_csv(f'{drivePfx}/Hackhealth2021/WaterSewerPrague.csv', index_col='Year')\n",
        "wsDF"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Water</th>\n",
              "      <th>Sewer</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>18.64</td>\n",
              "      <td>13.99</td>\n",
              "      <td>32.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>19.77</td>\n",
              "      <td>15.33</td>\n",
              "      <td>35.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>20.65</td>\n",
              "      <td>17.12</td>\n",
              "      <td>37.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>20.72</td>\n",
              "      <td>17.85</td>\n",
              "      <td>38.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>21.95</td>\n",
              "      <td>19.48</td>\n",
              "      <td>41.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>22.79</td>\n",
              "      <td>19.96</td>\n",
              "      <td>42.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>23.51</td>\n",
              "      <td>20.85</td>\n",
              "      <td>44.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>26.74</td>\n",
              "      <td>22.93</td>\n",
              "      <td>49.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>27.76</td>\n",
              "      <td>23.81</td>\n",
              "      <td>51.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>30.04</td>\n",
              "      <td>25.08</td>\n",
              "      <td>55.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>30.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>56.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>34.39</td>\n",
              "      <td>26.00</td>\n",
              "      <td>60.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>38.05</td>\n",
              "      <td>28.30</td>\n",
              "      <td>66.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>43.02</td>\n",
              "      <td>31.33</td>\n",
              "      <td>74.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>43.84</td>\n",
              "      <td>32.00</td>\n",
              "      <td>75.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>44.71</td>\n",
              "      <td>32.94</td>\n",
              "      <td>77.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>46.75</td>\n",
              "      <td>38.43</td>\n",
              "      <td>85.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>46.43</td>\n",
              "      <td>38.99</td>\n",
              "      <td>85.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>48.30</td>\n",
              "      <td>39.09</td>\n",
              "      <td>87.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>48.96</td>\n",
              "      <td>40.70</td>\n",
              "      <td>89.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>50.30</td>\n",
              "      <td>43.79</td>\n",
              "      <td>94.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>54.77</td>\n",
              "      <td>46.82</td>\n",
              "      <td>101.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Water  Sewer   Total\n",
              "Year                      \n",
              "2000  18.64  13.99   32.63\n",
              "2001  19.77  15.33   35.10\n",
              "2002  20.65  17.12   37.77\n",
              "2003  20.72  17.85   38.57\n",
              "2004  21.95  19.48   41.43\n",
              "2005  22.79  19.96   42.75\n",
              "2006  23.51  20.85   44.36\n",
              "2007  26.74  22.93   49.67\n",
              "2008  27.76  23.81   51.57\n",
              "2009  30.04  25.08   55.12\n",
              "2010  30.63  25.88   56.51\n",
              "2011  34.39  26.00   60.39\n",
              "2012  38.05  28.30   66.35\n",
              "2013  43.02  31.33   74.35\n",
              "2014  43.84  32.00   75.84\n",
              "2015  44.71  32.94   77.65\n",
              "2016  46.75  38.43   85.18\n",
              "2017  46.43  38.99   85.42\n",
              "2018  48.30  39.09   87.39\n",
              "2019  48.96  40.70   89.66\n",
              "2020  50.30  43.79   94.09\n",
              "2021  54.77  46.82  101.59"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "yn4pY0nIhec1",
        "outputId": "6909b60b-9cd6-495a-a103-2dc89e7edc91"
      },
      "source": [
        "# Long form. Year is an index\n",
        "# plt.plot(wsDF.index,wsDF['Water'],'bo-',wsDF.index,wsDF['Sewer'],'ro-');\n",
        "\n",
        "ax = plt.plot(wsDF['Water'],'bo-',wsDF['Sewer'],'ro-');\n",
        "\n",
        "plt.title('Prague: Water and Sewer cost 2000-2020')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel(r'Price ($CZK/m^3)$')\n",
        "plt.rcParams['figure.figsize'] = (21,13)\n",
        "plt.legend(['Water','Sewer'])\n",
        "\n",
        "for rowIdx,rowData in wsDF.iterrows():\n",
        "  ax[0].axes.text(rowIdx,rowData['Water']-1,rowData['Water'])\n",
        "  ax[1].axes.text(rowIdx,rowData['Sewer']-1,rowData['Sewer'])\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONVNShHkcJqv"
      },
      "source": [
        "## Write data to a file on drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wadk1OkRaJyC"
      },
      "source": [
        "# Boolean indexing does not return copy by default\n",
        "wsFiveYearDF=wsDF[wsDF.index % 5 == 0].copy()\n",
        "wsFiveYearDF['WaterIncreasePct'] = wsFiveYearDF['Water'].pct_change()*100\n",
        "wsFiveYearDF['SewerIncreasePct'] = wsFiveYearDF['Sewer'].pct_change()*100\n",
        "\n",
        "wsFiveYearDF['WaterIncreasePct'] = wsFiveYearDF['WaterIncreasePct'].round(2)\n",
        "wsFiveYearDF['SewerIncreasePct'] = wsFiveYearDF['SewerIncreasePct'].round(2)\n",
        "\n",
        "wsFiveYearDF\n",
        "wsFiveYearDF.to_csv(f'{drivePfx}/Hackhealth2021/PriceIncrease.csv')\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmoO29aXiynW"
      },
      "source": [
        "## Flush and unmount drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-xlziqKd317"
      },
      "source": [
        "# Uncomment to flush and unmount the drive\n",
        "# drive.flush_and_unmount()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Se-4HmZUjjCs"
      },
      "source": [
        "## Access data in Google Sheets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "id": "gB632i18jY7v",
        "outputId": "5a7f7e6b-4885-4f12-f944-31f59dffeb7e"
      },
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "import gspread\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n",
        "\n",
        "worksheet = gc.open('WaterSewer').sheet1\n",
        "\n",
        "# get_all_values gives a list of rows.\n",
        "rows = worksheet.get_all_values()\n",
        "print('-------------[ Row dump ]--------------')\n",
        "pprint(rows[:5])\n",
        "print('---------------------------------------')\n",
        "\n",
        "# First row contains column names\n",
        "headerRow = rows.pop(0)\n",
        "\n",
        "\n",
        "# Convert to a DataFrame and render.\n",
        "sheetDF = pd.DataFrame.from_records(rows,columns=headerRow,index='Year')\n",
        "sheetDF"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------[ Row dump ]--------------\n",
            "[['Year', 'Water', 'Sewer', 'Total'],\n",
            " ['2000', '18.64', '13.99', '32.63'],\n",
            " ['2001', '19.77', '15.33', '35.1'],\n",
            " ['2002', '20.65', '17.12', '37.77'],\n",
            " ['2003', '20.72', '17.85', '38.57']]\n",
            "---------------------------------------\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Water</th>\n",
              "      <th>Sewer</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>18.64</td>\n",
              "      <td>13.99</td>\n",
              "      <td>32.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>19.77</td>\n",
              "      <td>15.33</td>\n",
              "      <td>35.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>20.65</td>\n",
              "      <td>17.12</td>\n",
              "      <td>37.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>20.72</td>\n",
              "      <td>17.85</td>\n",
              "      <td>38.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>21.95</td>\n",
              "      <td>19.48</td>\n",
              "      <td>41.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>22.79</td>\n",
              "      <td>19.96</td>\n",
              "      <td>42.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>23.51</td>\n",
              "      <td>20.85</td>\n",
              "      <td>44.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>26.74</td>\n",
              "      <td>22.93</td>\n",
              "      <td>49.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>27.76</td>\n",
              "      <td>23.81</td>\n",
              "      <td>51.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>30.04</td>\n",
              "      <td>25.08</td>\n",
              "      <td>55.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>30.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>56.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>34.39</td>\n",
              "      <td>26</td>\n",
              "      <td>60.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>38.05</td>\n",
              "      <td>28.3</td>\n",
              "      <td>66.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>43.02</td>\n",
              "      <td>31.33</td>\n",
              "      <td>74.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>43.84</td>\n",
              "      <td>32</td>\n",
              "      <td>75.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>44.71</td>\n",
              "      <td>32.94</td>\n",
              "      <td>77.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>46.75</td>\n",
              "      <td>38.43</td>\n",
              "      <td>85.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>46.43</td>\n",
              "      <td>38.99</td>\n",
              "      <td>85.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>48.3</td>\n",
              "      <td>39.09</td>\n",
              "      <td>87.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>48.96</td>\n",
              "      <td>40.7</td>\n",
              "      <td>89.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>50.3</td>\n",
              "      <td>43.79</td>\n",
              "      <td>94.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>54.77</td>\n",
              "      <td>46.82</td>\n",
              "      <td>101.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Water  Sewer   Total\n",
              "Year                      \n",
              "2000  18.64  13.99   32.63\n",
              "2001  19.77  15.33    35.1\n",
              "2002  20.65  17.12   37.77\n",
              "2003  20.72  17.85   38.57\n",
              "2004  21.95  19.48   41.43\n",
              "2005  22.79  19.96   42.75\n",
              "2006  23.51  20.85   44.36\n",
              "2007  26.74  22.93   49.67\n",
              "2008  27.76  23.81   51.57\n",
              "2009  30.04  25.08   55.12\n",
              "2010  30.63  25.88   56.51\n",
              "2011  34.39     26   60.39\n",
              "2012  38.05   28.3   66.35\n",
              "2013  43.02  31.33   74.35\n",
              "2014  43.84     32   75.84\n",
              "2015  44.71  32.94   77.65\n",
              "2016  46.75  38.43   85.18\n",
              "2017  46.43  38.99   85.42\n",
              "2018   48.3  39.09   87.39\n",
              "2019  48.96   40.7   89.66\n",
              "2020   50.3  43.79   94.09\n",
              "2021  54.77  46.82  101.59"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOXZn7Paj--R"
      },
      "source": [
        "## Web scraping\n",
        "Scraping the data straight from the web page"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d9e1koR1kErw",
        "outputId": "6df5c8d2-aa41-4947-da8f-fa2ba24ce301"
      },
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "# Note: exception to quietly stop current and subsequent cell executions\n",
        "class StopExecution(Exception):\n",
        "    def _render_traceback_(self):\n",
        "        pass\n",
        "\n",
        "response = requests.get(\"https://www.pvk.cz/vse-o-vode/cena-vodneho-a-stocneho/vyvoj-vodneho-a-stocneho-v-praze/\")\n",
        "if (response.status_code != 200):\n",
        "  raise RuntimeError(f'Error retrieving PVK page. Received HTTP status code {response.status_code}')\n",
        "\n",
        "soup = BeautifulSoup(response.content,\"html.parser\")\n",
        "\n",
        "# Semantic selection: find data table immediately following by title\n",
        "h1 = soup.find(\"h1\",text=\"Vývoj vodného a stočného v Praze\")\n",
        "dataTable = next(tag for tag in h1.next_siblings if tag.name == \"table\")\n",
        "\n",
        "tableRows = dataTable.tbody.find_all(\"tr\")\n",
        "rowEntries = []\n",
        "for row in tableRows:\n",
        "  tableColumns = row.find_all([\"td\"])\n",
        "  colEntries = []\n",
        "  for col in tableColumns:\n",
        "    colEntries.append(col.get_text(strip=True))\n",
        "  \n",
        "  rowEntries.append(colEntries)\n",
        "\n",
        "# DeleteMe column: \n",
        "# The completely empty row in HTML table gets parsed to 5 columns\n",
        "# So we add synthetic DeleteMe column to accommodate the row\n",
        "parsedDF = pd.DataFrame(data=rowEntries,columns=[\"Period\",\"Water\",\"Sewage\",\"Total\",\"DeleteMe\"])\n",
        "parsedDF"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Period</th>\n",
              "      <th>Water</th>\n",
              "      <th>Sewage</th>\n",
              "      <th>Total</th>\n",
              "      <th>DeleteMe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>- 31.12.1990</td>\n",
              "      <td>0,60</td>\n",
              "      <td>0,20</td>\n",
              "      <td>0,80</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.1.1991 - 1.8.1992</td>\n",
              "      <td>1,50</td>\n",
              "      <td>1,50</td>\n",
              "      <td>3,00</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.9.1992 - 14.5.1993</td>\n",
              "      <td>5,00</td>\n",
              "      <td>4,00</td>\n",
              "      <td>9,00</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15.5.1993-31.1.1994</td>\n",
              "      <td>7,10</td>\n",
              "      <td>5,40</td>\n",
              "      <td>12,50</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.2.1994 - 31.1.1995</td>\n",
              "      <td>8,40</td>\n",
              "      <td>6,60</td>\n",
              "      <td>15,00</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.2.1995 -31.1.1996</td>\n",
              "      <td>9,20</td>\n",
              "      <td>7,10</td>\n",
              "      <td>16,30</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.2.1996-31.12.1996</td>\n",
              "      <td>10,61</td>\n",
              "      <td>7,96</td>\n",
              "      <td>18,57</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.1.1997 -31.12.1997</td>\n",
              "      <td>12,18</td>\n",
              "      <td>9,49</td>\n",
              "      <td>21,67</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>- 31.12.1990</td>\n",
              "      <td>3,70</td>\n",
              "      <td>2,35</td>\n",
              "      <td>6,05</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.1.1991 - 30.6.1992</td>\n",
              "      <td>4,50</td>\n",
              "      <td>3,50</td>\n",
              "      <td>8,00</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.7.1992 - 30.12.1992</td>\n",
              "      <td>6,00</td>\n",
              "      <td>4,50</td>\n",
              "      <td>10,50</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.1.1993 - 31.1.1993</td>\n",
              "      <td>6,30</td>\n",
              "      <td>4,73</td>\n",
              "      <td>11,03</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.2.1993 - 14.5.1993</td>\n",
              "      <td>8,40</td>\n",
              "      <td>6,00</td>\n",
              "      <td>14,40</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>15.5.1993 - 31.1.1994</td>\n",
              "      <td>9,60</td>\n",
              "      <td>6,00</td>\n",
              "      <td>15,60</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.2.1994 - 31.1.1995</td>\n",
              "      <td>12,20</td>\n",
              "      <td>7,60</td>\n",
              "      <td>19,80</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.2.1995 - 31.1.1996</td>\n",
              "      <td>13,35</td>\n",
              "      <td>8,23</td>\n",
              "      <td>21,58</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.2.1996 - 31.12.1996</td>\n",
              "      <td>14,18</td>\n",
              "      <td>9,14</td>\n",
              "      <td>23,32</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1.1.1997 - 31.12.1997</td>\n",
              "      <td>14,18</td>\n",
              "      <td>10,30</td>\n",
              "      <td>24,48</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1.1.1998 - 31.1.1999</td>\n",
              "      <td>14,62</td>\n",
              "      <td>11,57</td>\n",
              "      <td>26,19</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.2.1999 - 31.12.1999</td>\n",
              "      <td>16,81</td>\n",
              "      <td>13,14</td>\n",
              "      <td>29,95</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.1.2000 - 31.12.2000</td>\n",
              "      <td>18,64</td>\n",
              "      <td>13,99</td>\n",
              "      <td>32,63</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1.1.2001 - 31.12.2001</td>\n",
              "      <td>19,77</td>\n",
              "      <td>15,33</td>\n",
              "      <td>35,10</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1.1.2002 - 31.12.2002</td>\n",
              "      <td>20,65</td>\n",
              "      <td>17,12</td>\n",
              "      <td>37,77</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1.1.2003 - 31.12.2003</td>\n",
              "      <td>20,72</td>\n",
              "      <td>17,85</td>\n",
              "      <td>38,57</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1.1.2004 - 31.12.2004</td>\n",
              "      <td>21,95</td>\n",
              "      <td>19,48</td>\n",
              "      <td>41,43</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1.1.2005 - 31.12.2005</td>\n",
              "      <td>22,79</td>\n",
              "      <td>19,96</td>\n",
              "      <td>42,75</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1.1.2006 - 31.12.2006</td>\n",
              "      <td>23,51</td>\n",
              "      <td>20,85</td>\n",
              "      <td>44,36</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1.1.2007 - 31.12.2007</td>\n",
              "      <td>26,74</td>\n",
              "      <td>22,93</td>\n",
              "      <td>49,67</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1.1.2008 - 31.1.2008</td>\n",
              "      <td>27,76</td>\n",
              "      <td>23,81</td>\n",
              "      <td>51,57</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1.2.2008 - 12.1.2009</td>\n",
              "      <td>28,54</td>\n",
              "      <td>24,47</td>\n",
              "      <td>53,01</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>13.1.2009 - 31.12.2009</td>\n",
              "      <td>30,04</td>\n",
              "      <td>25,08</td>\n",
              "      <td>55,12</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1.1.2010 - 31.12.2010</td>\n",
              "      <td>30,63</td>\n",
              "      <td>25,88</td>\n",
              "      <td>56,51</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1.1.2011 - 31. 12.2011</td>\n",
              "      <td>34,39</td>\n",
              "      <td>26,00</td>\n",
              "      <td>60,39</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1.1.2012 - 31. 12.2012</td>\n",
              "      <td>38,05</td>\n",
              "      <td>28,30</td>\n",
              "      <td>66,35</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1. 1. 2013 - 31.12.2013</td>\n",
              "      <td>43,02</td>\n",
              "      <td>31,33</td>\n",
              "      <td>74,35</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1.1.2014 - 31.12.2014</td>\n",
              "      <td>43,84</td>\n",
              "      <td>32,0</td>\n",
              "      <td>75,84</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1.1.2015 - 31.12.2015</td>\n",
              "      <td>44,71</td>\n",
              "      <td>32,94</td>\n",
              "      <td>77,65</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1.1.2016 - 31.3.2016</td>\n",
              "      <td>44,14</td>\n",
              "      <td>34,86</td>\n",
              "      <td>79,00</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1. 4. 2016 - 31. 12. 2016</td>\n",
              "      <td>46,75</td>\n",
              "      <td>38,43</td>\n",
              "      <td>85,18</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1. 1. 2017 - 31.12.2017</td>\n",
              "      <td>46,43</td>\n",
              "      <td>38,99</td>\n",
              "      <td>85,42</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1.1.2018 - 31.12.2018</td>\n",
              "      <td>48,30</td>\n",
              "      <td>39,09</td>\n",
              "      <td>87,39</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1.1.2019 - 31. 12. 2019</td>\n",
              "      <td>48,96</td>\n",
              "      <td>40,70</td>\n",
              "      <td>89,66</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1. 1. 2020 - 30.4. 2020</td>\n",
              "      <td>50,92</td>\n",
              "      <td>43,17</td>\n",
              "      <td>94,09</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1. 5. 2020 -  31. 12. 2020</td>\n",
              "      <td>50,30</td>\n",
              "      <td>43,79</td>\n",
              "      <td>94,09</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>1.1.2021</td>\n",
              "      <td>54,77</td>\n",
              "      <td>46,82</td>\n",
              "      <td>101,59</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Period  Water Sewage   Total DeleteMe\n",
              "0                         None   None   None    None     None\n",
              "1                         None   None   None    None     None\n",
              "2                 - 31.12.1990   0,60   0,20    0,80     None\n",
              "3          1.1.1991 - 1.8.1992   1,50   1,50    3,00     None\n",
              "4         1.9.1992 - 14.5.1993   5,00   4,00    9,00     None\n",
              "5          15.5.1993-31.1.1994   7,10   5,40   12,50     None\n",
              "6         1.2.1994 - 31.1.1995   8,40   6,60   15,00     None\n",
              "7          1.2.1995 -31.1.1996   9,20   7,10   16,30     None\n",
              "8          1.2.1996-31.12.1996  10,61   7,96   18,57     None\n",
              "9         1.1.1997 -31.12.1997  12,18   9,49   21,67     None\n",
              "10                - 31.12.1990   3,70   2,35    6,05     None\n",
              "11        1.1.1991 - 30.6.1992   4,50   3,50    8,00     None\n",
              "12       1.7.1992 - 30.12.1992   6,00   4,50   10,50     None\n",
              "13        1.1.1993 - 31.1.1993   6,30   4,73   11,03     None\n",
              "14        1.2.1993 - 14.5.1993   8,40   6,00   14,40     None\n",
              "15       15.5.1993 - 31.1.1994   9,60   6,00   15,60     None\n",
              "16        1.2.1994 - 31.1.1995  12,20   7,60   19,80     None\n",
              "17        1.2.1995 - 31.1.1996  13,35   8,23   21,58     None\n",
              "18       1.2.1996 - 31.12.1996  14,18   9,14   23,32     None\n",
              "19       1.1.1997 - 31.12.1997  14,18  10,30   24,48     None\n",
              "20                                                           \n",
              "21        1.1.1998 - 31.1.1999  14,62  11,57   26,19     None\n",
              "22       1.2.1999 - 31.12.1999  16,81  13,14   29,95     None\n",
              "23       1.1.2000 - 31.12.2000  18,64  13,99   32,63     None\n",
              "24       1.1.2001 - 31.12.2001  19,77  15,33   35,10     None\n",
              "25       1.1.2002 - 31.12.2002  20,65  17,12   37,77     None\n",
              "26       1.1.2003 - 31.12.2003  20,72  17,85   38,57     None\n",
              "27       1.1.2004 - 31.12.2004  21,95  19,48   41,43     None\n",
              "28       1.1.2005 - 31.12.2005  22,79  19,96   42,75     None\n",
              "29       1.1.2006 - 31.12.2006  23,51  20,85   44,36     None\n",
              "30       1.1.2007 - 31.12.2007  26,74  22,93   49,67     None\n",
              "31        1.1.2008 - 31.1.2008  27,76  23,81   51,57     None\n",
              "32        1.2.2008 - 12.1.2009  28,54  24,47   53,01     None\n",
              "33      13.1.2009 - 31.12.2009  30,04  25,08   55,12     None\n",
              "34       1.1.2010 - 31.12.2010  30,63  25,88   56,51     None\n",
              "35      1.1.2011 - 31. 12.2011  34,39  26,00   60,39     None\n",
              "36      1.1.2012 - 31. 12.2012  38,05  28,30   66,35     None\n",
              "37     1. 1. 2013 - 31.12.2013  43,02  31,33   74,35     None\n",
              "38       1.1.2014 - 31.12.2014  43,84   32,0   75,84     None\n",
              "39       1.1.2015 - 31.12.2015  44,71  32,94   77,65     None\n",
              "40        1.1.2016 - 31.3.2016  44,14  34,86   79,00     None\n",
              "41   1. 4. 2016 - 31. 12. 2016  46,75  38,43   85,18     None\n",
              "42     1. 1. 2017 - 31.12.2017  46,43  38,99   85,42     None\n",
              "43       1.1.2018 - 31.12.2018  48,30  39,09   87,39     None\n",
              "44     1.1.2019 - 31. 12. 2019  48,96  40,70   89,66     None\n",
              "45     1. 1. 2020 - 30.4. 2020  50,92  43,17   94,09     None\n",
              "46  1. 5. 2020 -  31. 12. 2020  50,30  43,79   94,09     None\n",
              "47                    1.1.2021  54,77  46,82  101,59     None"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBG87SV521-k"
      },
      "source": [
        "### Cleansing 1\n",
        "Drop first two empty rows. They represent the headers and hold no data so they\n",
        "came completely empty\n",
        "\n",
        "Also drop the `DeleteMe` columns which was only needed to create the dataframe.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1BOA1jY821g4",
        "outputId": "9b173eb8-8f73-414d-aad2-12bc4e0be509"
      },
      "source": [
        "dataOnlyDF = parsedDF.drop(columns=\"DeleteMe\").drop([0,1])\n",
        "\n",
        "# reset index after dropping rows so it's easy \n",
        "# to inspect starting row position for the slice\n",
        "dataOnlyDF = dataOnlyDF.reset_index(drop=True)\n",
        "dataOnlyDF"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Period</th>\n",
              "      <th>Water</th>\n",
              "      <th>Sewage</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>- 31.12.1990</td>\n",
              "      <td>0,60</td>\n",
              "      <td>0,20</td>\n",
              "      <td>0,80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.1.1991 - 1.8.1992</td>\n",
              "      <td>1,50</td>\n",
              "      <td>1,50</td>\n",
              "      <td>3,00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.9.1992 - 14.5.1993</td>\n",
              "      <td>5,00</td>\n",
              "      <td>4,00</td>\n",
              "      <td>9,00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15.5.1993-31.1.1994</td>\n",
              "      <td>7,10</td>\n",
              "      <td>5,40</td>\n",
              "      <td>12,50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.2.1994 - 31.1.1995</td>\n",
              "      <td>8,40</td>\n",
              "      <td>6,60</td>\n",
              "      <td>15,00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.2.1995 -31.1.1996</td>\n",
              "      <td>9,20</td>\n",
              "      <td>7,10</td>\n",
              "      <td>16,30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.2.1996-31.12.1996</td>\n",
              "      <td>10,61</td>\n",
              "      <td>7,96</td>\n",
              "      <td>18,57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.1.1997 -31.12.1997</td>\n",
              "      <td>12,18</td>\n",
              "      <td>9,49</td>\n",
              "      <td>21,67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>- 31.12.1990</td>\n",
              "      <td>3,70</td>\n",
              "      <td>2,35</td>\n",
              "      <td>6,05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.1.1991 - 30.6.1992</td>\n",
              "      <td>4,50</td>\n",
              "      <td>3,50</td>\n",
              "      <td>8,00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.7.1992 - 30.12.1992</td>\n",
              "      <td>6,00</td>\n",
              "      <td>4,50</td>\n",
              "      <td>10,50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.1.1993 - 31.1.1993</td>\n",
              "      <td>6,30</td>\n",
              "      <td>4,73</td>\n",
              "      <td>11,03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.2.1993 - 14.5.1993</td>\n",
              "      <td>8,40</td>\n",
              "      <td>6,00</td>\n",
              "      <td>14,40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>15.5.1993 - 31.1.1994</td>\n",
              "      <td>9,60</td>\n",
              "      <td>6,00</td>\n",
              "      <td>15,60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.2.1994 - 31.1.1995</td>\n",
              "      <td>12,20</td>\n",
              "      <td>7,60</td>\n",
              "      <td>19,80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.2.1995 - 31.1.1996</td>\n",
              "      <td>13,35</td>\n",
              "      <td>8,23</td>\n",
              "      <td>21,58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.2.1996 - 31.12.1996</td>\n",
              "      <td>14,18</td>\n",
              "      <td>9,14</td>\n",
              "      <td>23,32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.1.1997 - 31.12.1997</td>\n",
              "      <td>14,18</td>\n",
              "      <td>10,30</td>\n",
              "      <td>24,48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1.1.1998 - 31.1.1999</td>\n",
              "      <td>14,62</td>\n",
              "      <td>11,57</td>\n",
              "      <td>26,19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.2.1999 - 31.12.1999</td>\n",
              "      <td>16,81</td>\n",
              "      <td>13,14</td>\n",
              "      <td>29,95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1.1.2000 - 31.12.2000</td>\n",
              "      <td>18,64</td>\n",
              "      <td>13,99</td>\n",
              "      <td>32,63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.1.2001 - 31.12.2001</td>\n",
              "      <td>19,77</td>\n",
              "      <td>15,33</td>\n",
              "      <td>35,10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.1.2002 - 31.12.2002</td>\n",
              "      <td>20,65</td>\n",
              "      <td>17,12</td>\n",
              "      <td>37,77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1.1.2003 - 31.12.2003</td>\n",
              "      <td>20,72</td>\n",
              "      <td>17,85</td>\n",
              "      <td>38,57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1.1.2004 - 31.12.2004</td>\n",
              "      <td>21,95</td>\n",
              "      <td>19,48</td>\n",
              "      <td>41,43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1.1.2005 - 31.12.2005</td>\n",
              "      <td>22,79</td>\n",
              "      <td>19,96</td>\n",
              "      <td>42,75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1.1.2006 - 31.12.2006</td>\n",
              "      <td>23,51</td>\n",
              "      <td>20,85</td>\n",
              "      <td>44,36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1.1.2007 - 31.12.2007</td>\n",
              "      <td>26,74</td>\n",
              "      <td>22,93</td>\n",
              "      <td>49,67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1.1.2008 - 31.1.2008</td>\n",
              "      <td>27,76</td>\n",
              "      <td>23,81</td>\n",
              "      <td>51,57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1.2.2008 - 12.1.2009</td>\n",
              "      <td>28,54</td>\n",
              "      <td>24,47</td>\n",
              "      <td>53,01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>13.1.2009 - 31.12.2009</td>\n",
              "      <td>30,04</td>\n",
              "      <td>25,08</td>\n",
              "      <td>55,12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1.1.2010 - 31.12.2010</td>\n",
              "      <td>30,63</td>\n",
              "      <td>25,88</td>\n",
              "      <td>56,51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1.1.2011 - 31. 12.2011</td>\n",
              "      <td>34,39</td>\n",
              "      <td>26,00</td>\n",
              "      <td>60,39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1.1.2012 - 31. 12.2012</td>\n",
              "      <td>38,05</td>\n",
              "      <td>28,30</td>\n",
              "      <td>66,35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1. 1. 2013 - 31.12.2013</td>\n",
              "      <td>43,02</td>\n",
              "      <td>31,33</td>\n",
              "      <td>74,35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1.1.2014 - 31.12.2014</td>\n",
              "      <td>43,84</td>\n",
              "      <td>32,0</td>\n",
              "      <td>75,84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1.1.2015 - 31.12.2015</td>\n",
              "      <td>44,71</td>\n",
              "      <td>32,94</td>\n",
              "      <td>77,65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1.1.2016 - 31.3.2016</td>\n",
              "      <td>44,14</td>\n",
              "      <td>34,86</td>\n",
              "      <td>79,00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1. 4. 2016 - 31. 12. 2016</td>\n",
              "      <td>46,75</td>\n",
              "      <td>38,43</td>\n",
              "      <td>85,18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1. 1. 2017 - 31.12.2017</td>\n",
              "      <td>46,43</td>\n",
              "      <td>38,99</td>\n",
              "      <td>85,42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1.1.2018 - 31.12.2018</td>\n",
              "      <td>48,30</td>\n",
              "      <td>39,09</td>\n",
              "      <td>87,39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1.1.2019 - 31. 12. 2019</td>\n",
              "      <td>48,96</td>\n",
              "      <td>40,70</td>\n",
              "      <td>89,66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1. 1. 2020 - 30.4. 2020</td>\n",
              "      <td>50,92</td>\n",
              "      <td>43,17</td>\n",
              "      <td>94,09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1. 5. 2020 -  31. 12. 2020</td>\n",
              "      <td>50,30</td>\n",
              "      <td>43,79</td>\n",
              "      <td>94,09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1.1.2021</td>\n",
              "      <td>54,77</td>\n",
              "      <td>46,82</td>\n",
              "      <td>101,59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Period  Water Sewage   Total\n",
              "0                 - 31.12.1990   0,60   0,20    0,80\n",
              "1          1.1.1991 - 1.8.1992   1,50   1,50    3,00\n",
              "2         1.9.1992 - 14.5.1993   5,00   4,00    9,00\n",
              "3          15.5.1993-31.1.1994   7,10   5,40   12,50\n",
              "4         1.2.1994 - 31.1.1995   8,40   6,60   15,00\n",
              "5          1.2.1995 -31.1.1996   9,20   7,10   16,30\n",
              "6          1.2.1996-31.12.1996  10,61   7,96   18,57\n",
              "7         1.1.1997 -31.12.1997  12,18   9,49   21,67\n",
              "8                 - 31.12.1990   3,70   2,35    6,05\n",
              "9         1.1.1991 - 30.6.1992   4,50   3,50    8,00\n",
              "10       1.7.1992 - 30.12.1992   6,00   4,50   10,50\n",
              "11        1.1.1993 - 31.1.1993   6,30   4,73   11,03\n",
              "12        1.2.1993 - 14.5.1993   8,40   6,00   14,40\n",
              "13       15.5.1993 - 31.1.1994   9,60   6,00   15,60\n",
              "14        1.2.1994 - 31.1.1995  12,20   7,60   19,80\n",
              "15        1.2.1995 - 31.1.1996  13,35   8,23   21,58\n",
              "16       1.2.1996 - 31.12.1996  14,18   9,14   23,32\n",
              "17       1.1.1997 - 31.12.1997  14,18  10,30   24,48\n",
              "18                                                  \n",
              "19        1.1.1998 - 31.1.1999  14,62  11,57   26,19\n",
              "20       1.2.1999 - 31.12.1999  16,81  13,14   29,95\n",
              "21       1.1.2000 - 31.12.2000  18,64  13,99   32,63\n",
              "22       1.1.2001 - 31.12.2001  19,77  15,33   35,10\n",
              "23       1.1.2002 - 31.12.2002  20,65  17,12   37,77\n",
              "24       1.1.2003 - 31.12.2003  20,72  17,85   38,57\n",
              "25       1.1.2004 - 31.12.2004  21,95  19,48   41,43\n",
              "26       1.1.2005 - 31.12.2005  22,79  19,96   42,75\n",
              "27       1.1.2006 - 31.12.2006  23,51  20,85   44,36\n",
              "28       1.1.2007 - 31.12.2007  26,74  22,93   49,67\n",
              "29        1.1.2008 - 31.1.2008  27,76  23,81   51,57\n",
              "30        1.2.2008 - 12.1.2009  28,54  24,47   53,01\n",
              "31      13.1.2009 - 31.12.2009  30,04  25,08   55,12\n",
              "32       1.1.2010 - 31.12.2010  30,63  25,88   56,51\n",
              "33      1.1.2011 - 31. 12.2011  34,39  26,00   60,39\n",
              "34      1.1.2012 - 31. 12.2012  38,05  28,30   66,35\n",
              "35     1. 1. 2013 - 31.12.2013  43,02  31,33   74,35\n",
              "36       1.1.2014 - 31.12.2014  43,84   32,0   75,84\n",
              "37       1.1.2015 - 31.12.2015  44,71  32,94   77,65\n",
              "38        1.1.2016 - 31.3.2016  44,14  34,86   79,00\n",
              "39   1. 4. 2016 - 31. 12. 2016  46,75  38,43   85,18\n",
              "40     1. 1. 2017 - 31.12.2017  46,43  38,99   85,42\n",
              "41       1.1.2018 - 31.12.2018  48,30  39,09   87,39\n",
              "42     1.1.2019 - 31. 12. 2019  48,96  40,70   89,66\n",
              "43     1. 1. 2020 - 30.4. 2020  50,92  43,17   94,09\n",
              "44  1. 5. 2020 -  31. 12. 2020  50,30  43,79   94,09\n",
              "45                    1.1.2021  54,77  46,82  101,59"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pvbPHxHi7EVu"
      },
      "source": [
        "## Cleansing 2\n",
        "Take only the period when pricing was unified between households and industrial consumers.\n",
        "\n",
        "I.e. Keep only rows under HTML table section `Od roku 1998 sjednoceno`, starting with year 1998.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3T7ofBzl9sm",
        "outputId": "78dd11bf-c4d1-43d4-e4c4-de0cfdfc1080"
      },
      "source": [
        "# Making explicit copy here as the slice seems to return view\n",
        "# judging from unifiedDF._is_view returning True without copy()\n",
        "# Using a view in subsequent cell then causes SettingWithCopyWarning\n",
        "unifiedDF = dataOnlyDF[19:].copy()\n",
        "unifiedDF._is_view"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CdUynIgTmbk"
      },
      "source": [
        "## Cleansing 3\n",
        "Replace comma in number format with dot to allow parsing to float"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "bJlwlTQb722r",
        "outputId": "11b096c2-e8c1-4622-86c1-1f7f4dc5c274"
      },
      "source": [
        "# Use apply() as we need to access the .str for string replace\n",
        "cols = [\"Water\",\"Sewage\",\"Total\"]\n",
        "unifiedDF[cols] = unifiedDF[cols].apply(lambda col : col.str.replace(\",\",\".\"))\n",
        "\n",
        "unifiedDF"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Period</th>\n",
              "      <th>Water</th>\n",
              "      <th>Sewage</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1.1.1998 - 31.1.1999</td>\n",
              "      <td>14.62</td>\n",
              "      <td>11.57</td>\n",
              "      <td>26.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.2.1999 - 31.12.1999</td>\n",
              "      <td>16.81</td>\n",
              "      <td>13.14</td>\n",
              "      <td>29.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1.1.2000 - 31.12.2000</td>\n",
              "      <td>18.64</td>\n",
              "      <td>13.99</td>\n",
              "      <td>32.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.1.2001 - 31.12.2001</td>\n",
              "      <td>19.77</td>\n",
              "      <td>15.33</td>\n",
              "      <td>35.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.1.2002 - 31.12.2002</td>\n",
              "      <td>20.65</td>\n",
              "      <td>17.12</td>\n",
              "      <td>37.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1.1.2003 - 31.12.2003</td>\n",
              "      <td>20.72</td>\n",
              "      <td>17.85</td>\n",
              "      <td>38.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1.1.2004 - 31.12.2004</td>\n",
              "      <td>21.95</td>\n",
              "      <td>19.48</td>\n",
              "      <td>41.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1.1.2005 - 31.12.2005</td>\n",
              "      <td>22.79</td>\n",
              "      <td>19.96</td>\n",
              "      <td>42.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1.1.2006 - 31.12.2006</td>\n",
              "      <td>23.51</td>\n",
              "      <td>20.85</td>\n",
              "      <td>44.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1.1.2007 - 31.12.2007</td>\n",
              "      <td>26.74</td>\n",
              "      <td>22.93</td>\n",
              "      <td>49.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1.1.2008 - 31.1.2008</td>\n",
              "      <td>27.76</td>\n",
              "      <td>23.81</td>\n",
              "      <td>51.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1.2.2008 - 12.1.2009</td>\n",
              "      <td>28.54</td>\n",
              "      <td>24.47</td>\n",
              "      <td>53.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>13.1.2009 - 31.12.2009</td>\n",
              "      <td>30.04</td>\n",
              "      <td>25.08</td>\n",
              "      <td>55.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1.1.2010 - 31.12.2010</td>\n",
              "      <td>30.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>56.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1.1.2011 - 31. 12.2011</td>\n",
              "      <td>34.39</td>\n",
              "      <td>26.00</td>\n",
              "      <td>60.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1.1.2012 - 31. 12.2012</td>\n",
              "      <td>38.05</td>\n",
              "      <td>28.30</td>\n",
              "      <td>66.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>1. 1. 2013 - 31.12.2013</td>\n",
              "      <td>43.02</td>\n",
              "      <td>31.33</td>\n",
              "      <td>74.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1.1.2014 - 31.12.2014</td>\n",
              "      <td>43.84</td>\n",
              "      <td>32.0</td>\n",
              "      <td>75.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>1.1.2015 - 31.12.2015</td>\n",
              "      <td>44.71</td>\n",
              "      <td>32.94</td>\n",
              "      <td>77.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1.1.2016 - 31.3.2016</td>\n",
              "      <td>44.14</td>\n",
              "      <td>34.86</td>\n",
              "      <td>79.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>1. 4. 2016 - 31. 12. 2016</td>\n",
              "      <td>46.75</td>\n",
              "      <td>38.43</td>\n",
              "      <td>85.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1. 1. 2017 - 31.12.2017</td>\n",
              "      <td>46.43</td>\n",
              "      <td>38.99</td>\n",
              "      <td>85.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1.1.2018 - 31.12.2018</td>\n",
              "      <td>48.30</td>\n",
              "      <td>39.09</td>\n",
              "      <td>87.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1.1.2019 - 31. 12. 2019</td>\n",
              "      <td>48.96</td>\n",
              "      <td>40.70</td>\n",
              "      <td>89.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1. 1. 2020 - 30.4. 2020</td>\n",
              "      <td>50.92</td>\n",
              "      <td>43.17</td>\n",
              "      <td>94.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1. 5. 2020 -  31. 12. 2020</td>\n",
              "      <td>50.30</td>\n",
              "      <td>43.79</td>\n",
              "      <td>94.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1.1.2021</td>\n",
              "      <td>54.77</td>\n",
              "      <td>46.82</td>\n",
              "      <td>101.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Period  Water Sewage   Total\n",
              "19        1.1.1998 - 31.1.1999  14.62  11.57   26.19\n",
              "20       1.2.1999 - 31.12.1999  16.81  13.14   29.95\n",
              "21       1.1.2000 - 31.12.2000  18.64  13.99   32.63\n",
              "22       1.1.2001 - 31.12.2001  19.77  15.33   35.10\n",
              "23       1.1.2002 - 31.12.2002  20.65  17.12   37.77\n",
              "24       1.1.2003 - 31.12.2003  20.72  17.85   38.57\n",
              "25       1.1.2004 - 31.12.2004  21.95  19.48   41.43\n",
              "26       1.1.2005 - 31.12.2005  22.79  19.96   42.75\n",
              "27       1.1.2006 - 31.12.2006  23.51  20.85   44.36\n",
              "28       1.1.2007 - 31.12.2007  26.74  22.93   49.67\n",
              "29        1.1.2008 - 31.1.2008  27.76  23.81   51.57\n",
              "30        1.2.2008 - 12.1.2009  28.54  24.47   53.01\n",
              "31      13.1.2009 - 31.12.2009  30.04  25.08   55.12\n",
              "32       1.1.2010 - 31.12.2010  30.63  25.88   56.51\n",
              "33      1.1.2011 - 31. 12.2011  34.39  26.00   60.39\n",
              "34      1.1.2012 - 31. 12.2012  38.05  28.30   66.35\n",
              "35     1. 1. 2013 - 31.12.2013  43.02  31.33   74.35\n",
              "36       1.1.2014 - 31.12.2014  43.84   32.0   75.84\n",
              "37       1.1.2015 - 31.12.2015  44.71  32.94   77.65\n",
              "38        1.1.2016 - 31.3.2016  44.14  34.86   79.00\n",
              "39   1. 4. 2016 - 31. 12. 2016  46.75  38.43   85.18\n",
              "40     1. 1. 2017 - 31.12.2017  46.43  38.99   85.42\n",
              "41       1.1.2018 - 31.12.2018  48.30  39.09   87.39\n",
              "42     1.1.2019 - 31. 12. 2019  48.96  40.70   89.66\n",
              "43     1. 1. 2020 - 30.4. 2020  50.92  43.17   94.09\n",
              "44  1. 5. 2020 -  31. 12. 2020  50.30  43.79   94.09\n",
              "45                    1.1.2021  54.77  46.82  101.59"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ct38deNWT2DU"
      },
      "source": [
        "## Cleansing 4\n",
        "Convert numeric columns into float"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dAy7uM_s9BBz",
        "outputId": "623066f4-a5be-4f49-877f-bbd6ead81ade"
      },
      "source": [
        "cols = [\"Water\",\"Sewage\",\"Total\"]\n",
        "\n",
        "# Use apply() because pandas.to_numeric() expects Series or single column\n",
        "unifiedDF[cols] = unifiedDF[cols].apply(pd.to_numeric)\n",
        "\n",
        "unifiedDF.info()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27 entries, 19 to 45\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Period  27 non-null     object \n",
            " 1   Water   27 non-null     float64\n",
            " 2   Sewage  27 non-null     float64\n",
            " 3   Total   27 non-null     float64\n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 996.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "4CDMtX3CSwMh",
        "outputId": "921c39f2-11dc-48e8-a29b-3379288d1384"
      },
      "source": [
        "# Reset index after slice\n",
        "unifiedDF = unifiedDF.reset_index(drop=True)\n",
        "\n",
        "# Fix last entry for easy parsing\n",
        "unifiedDF.iat[26,0]=\"1.1.2021 - 31.12.2021\"\n",
        "\n",
        "# Notice the second .str to allow vectorized integer indexing\n",
        "unifiedDF[\"PeriodFrom\"] = unifiedDF[\"Period\"].str.split(\"-\").str[0]\n",
        "unifiedDF"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Period</th>\n",
              "      <th>Water</th>\n",
              "      <th>Sewage</th>\n",
              "      <th>Total</th>\n",
              "      <th>PeriodFrom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.1.1998 - 31.1.1999</td>\n",
              "      <td>14.62</td>\n",
              "      <td>11.57</td>\n",
              "      <td>26.19</td>\n",
              "      <td>1.1.1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.2.1999 - 31.12.1999</td>\n",
              "      <td>16.81</td>\n",
              "      <td>13.14</td>\n",
              "      <td>29.95</td>\n",
              "      <td>1.2.1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.1.2000 - 31.12.2000</td>\n",
              "      <td>18.64</td>\n",
              "      <td>13.99</td>\n",
              "      <td>32.63</td>\n",
              "      <td>1.1.2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.1.2001 - 31.12.2001</td>\n",
              "      <td>19.77</td>\n",
              "      <td>15.33</td>\n",
              "      <td>35.10</td>\n",
              "      <td>1.1.2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.1.2002 - 31.12.2002</td>\n",
              "      <td>20.65</td>\n",
              "      <td>17.12</td>\n",
              "      <td>37.77</td>\n",
              "      <td>1.1.2002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.1.2003 - 31.12.2003</td>\n",
              "      <td>20.72</td>\n",
              "      <td>17.85</td>\n",
              "      <td>38.57</td>\n",
              "      <td>1.1.2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.1.2004 - 31.12.2004</td>\n",
              "      <td>21.95</td>\n",
              "      <td>19.48</td>\n",
              "      <td>41.43</td>\n",
              "      <td>1.1.2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.1.2005 - 31.12.2005</td>\n",
              "      <td>22.79</td>\n",
              "      <td>19.96</td>\n",
              "      <td>42.75</td>\n",
              "      <td>1.1.2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.1.2006 - 31.12.2006</td>\n",
              "      <td>23.51</td>\n",
              "      <td>20.85</td>\n",
              "      <td>44.36</td>\n",
              "      <td>1.1.2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.1.2007 - 31.12.2007</td>\n",
              "      <td>26.74</td>\n",
              "      <td>22.93</td>\n",
              "      <td>49.67</td>\n",
              "      <td>1.1.2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1.1.2008 - 31.1.2008</td>\n",
              "      <td>27.76</td>\n",
              "      <td>23.81</td>\n",
              "      <td>51.57</td>\n",
              "      <td>1.1.2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.2.2008 - 12.1.2009</td>\n",
              "      <td>28.54</td>\n",
              "      <td>24.47</td>\n",
              "      <td>53.01</td>\n",
              "      <td>1.2.2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13.1.2009 - 31.12.2009</td>\n",
              "      <td>30.04</td>\n",
              "      <td>25.08</td>\n",
              "      <td>55.12</td>\n",
              "      <td>13.1.2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.1.2010 - 31.12.2010</td>\n",
              "      <td>30.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>56.51</td>\n",
              "      <td>1.1.2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.1.2011 - 31. 12.2011</td>\n",
              "      <td>34.39</td>\n",
              "      <td>26.00</td>\n",
              "      <td>60.39</td>\n",
              "      <td>1.1.2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.1.2012 - 31. 12.2012</td>\n",
              "      <td>38.05</td>\n",
              "      <td>28.30</td>\n",
              "      <td>66.35</td>\n",
              "      <td>1.1.2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1. 1. 2013 - 31.12.2013</td>\n",
              "      <td>43.02</td>\n",
              "      <td>31.33</td>\n",
              "      <td>74.35</td>\n",
              "      <td>1. 1. 2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.1.2014 - 31.12.2014</td>\n",
              "      <td>43.84</td>\n",
              "      <td>32.00</td>\n",
              "      <td>75.84</td>\n",
              "      <td>1.1.2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.1.2015 - 31.12.2015</td>\n",
              "      <td>44.71</td>\n",
              "      <td>32.94</td>\n",
              "      <td>77.65</td>\n",
              "      <td>1.1.2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1.1.2016 - 31.3.2016</td>\n",
              "      <td>44.14</td>\n",
              "      <td>34.86</td>\n",
              "      <td>79.00</td>\n",
              "      <td>1.1.2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1. 4. 2016 - 31. 12. 2016</td>\n",
              "      <td>46.75</td>\n",
              "      <td>38.43</td>\n",
              "      <td>85.18</td>\n",
              "      <td>1. 4. 2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1. 1. 2017 - 31.12.2017</td>\n",
              "      <td>46.43</td>\n",
              "      <td>38.99</td>\n",
              "      <td>85.42</td>\n",
              "      <td>1. 1. 2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.1.2018 - 31.12.2018</td>\n",
              "      <td>48.30</td>\n",
              "      <td>39.09</td>\n",
              "      <td>87.39</td>\n",
              "      <td>1.1.2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.1.2019 - 31. 12. 2019</td>\n",
              "      <td>48.96</td>\n",
              "      <td>40.70</td>\n",
              "      <td>89.66</td>\n",
              "      <td>1.1.2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1. 1. 2020 - 30.4. 2020</td>\n",
              "      <td>50.92</td>\n",
              "      <td>43.17</td>\n",
              "      <td>94.09</td>\n",
              "      <td>1. 1. 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1. 5. 2020 -  31. 12. 2020</td>\n",
              "      <td>50.30</td>\n",
              "      <td>43.79</td>\n",
              "      <td>94.09</td>\n",
              "      <td>1. 5. 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1.1.2021 - 31.12.2021</td>\n",
              "      <td>54.77</td>\n",
              "      <td>46.82</td>\n",
              "      <td>101.59</td>\n",
              "      <td>1.1.2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Period  Water  Sewage   Total   PeriodFrom\n",
              "0         1.1.1998 - 31.1.1999  14.62   11.57   26.19    1.1.1998 \n",
              "1        1.2.1999 - 31.12.1999  16.81   13.14   29.95    1.2.1999 \n",
              "2        1.1.2000 - 31.12.2000  18.64   13.99   32.63    1.1.2000 \n",
              "3        1.1.2001 - 31.12.2001  19.77   15.33   35.10    1.1.2001 \n",
              "4        1.1.2002 - 31.12.2002  20.65   17.12   37.77    1.1.2002 \n",
              "5        1.1.2003 - 31.12.2003  20.72   17.85   38.57    1.1.2003 \n",
              "6        1.1.2004 - 31.12.2004  21.95   19.48   41.43    1.1.2004 \n",
              "7        1.1.2005 - 31.12.2005  22.79   19.96   42.75    1.1.2005 \n",
              "8        1.1.2006 - 31.12.2006  23.51   20.85   44.36    1.1.2006 \n",
              "9        1.1.2007 - 31.12.2007  26.74   22.93   49.67    1.1.2007 \n",
              "10        1.1.2008 - 31.1.2008  27.76   23.81   51.57    1.1.2008 \n",
              "11        1.2.2008 - 12.1.2009  28.54   24.47   53.01    1.2.2008 \n",
              "12      13.1.2009 - 31.12.2009  30.04   25.08   55.12   13.1.2009 \n",
              "13       1.1.2010 - 31.12.2010  30.63   25.88   56.51    1.1.2010 \n",
              "14      1.1.2011 - 31. 12.2011  34.39   26.00   60.39    1.1.2011 \n",
              "15      1.1.2012 - 31. 12.2012  38.05   28.30   66.35    1.1.2012 \n",
              "16     1. 1. 2013 - 31.12.2013  43.02   31.33   74.35  1. 1. 2013 \n",
              "17       1.1.2014 - 31.12.2014  43.84   32.00   75.84    1.1.2014 \n",
              "18       1.1.2015 - 31.12.2015  44.71   32.94   77.65    1.1.2015 \n",
              "19        1.1.2016 - 31.3.2016  44.14   34.86   79.00    1.1.2016 \n",
              "20   1. 4. 2016 - 31. 12. 2016  46.75   38.43   85.18  1. 4. 2016 \n",
              "21     1. 1. 2017 - 31.12.2017  46.43   38.99   85.42  1. 1. 2017 \n",
              "22       1.1.2018 - 31.12.2018  48.30   39.09   87.39    1.1.2018 \n",
              "23     1.1.2019 - 31. 12. 2019  48.96   40.70   89.66    1.1.2019 \n",
              "24     1. 1. 2020 - 30.4. 2020  50.92   43.17   94.09  1. 1. 2020 \n",
              "25  1. 5. 2020 -  31. 12. 2020  50.30   43.79   94.09  1. 5. 2020 \n",
              "26       1.1.2021 - 31.12.2021  54.77   46.82  101.59    1.1.2021 "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "5W7jAlscUOhj",
        "outputId": "b0ecf99e-e09d-4690-b0e6-008638459f9d"
      },
      "source": [
        "# Let's use regex for a change\n",
        "unifiedDF = unifiedDF.drop(columns=\"Period\")\n",
        "unifiedDF[\"PeriodFrom\"] = unifiedDF[\"PeriodFrom\"].str.replace(\".*\\.\",\"\",regex=True)\n",
        "unifiedDF = unifiedDF.rename(columns={\"PeriodFrom\" : \"Year\"})\n",
        "unifiedDF\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Water</th>\n",
              "      <th>Sewage</th>\n",
              "      <th>Total</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.62</td>\n",
              "      <td>11.57</td>\n",
              "      <td>26.19</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16.81</td>\n",
              "      <td>13.14</td>\n",
              "      <td>29.95</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.64</td>\n",
              "      <td>13.99</td>\n",
              "      <td>32.63</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19.77</td>\n",
              "      <td>15.33</td>\n",
              "      <td>35.10</td>\n",
              "      <td>2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.65</td>\n",
              "      <td>17.12</td>\n",
              "      <td>37.77</td>\n",
              "      <td>2002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20.72</td>\n",
              "      <td>17.85</td>\n",
              "      <td>38.57</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>21.95</td>\n",
              "      <td>19.48</td>\n",
              "      <td>41.43</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>22.79</td>\n",
              "      <td>19.96</td>\n",
              "      <td>42.75</td>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>23.51</td>\n",
              "      <td>20.85</td>\n",
              "      <td>44.36</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>26.74</td>\n",
              "      <td>22.93</td>\n",
              "      <td>49.67</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>27.76</td>\n",
              "      <td>23.81</td>\n",
              "      <td>51.57</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>28.54</td>\n",
              "      <td>24.47</td>\n",
              "      <td>53.01</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>30.04</td>\n",
              "      <td>25.08</td>\n",
              "      <td>55.12</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>30.63</td>\n",
              "      <td>25.88</td>\n",
              "      <td>56.51</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>34.39</td>\n",
              "      <td>26.00</td>\n",
              "      <td>60.39</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>38.05</td>\n",
              "      <td>28.30</td>\n",
              "      <td>66.35</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>43.02</td>\n",
              "      <td>31.33</td>\n",
              "      <td>74.35</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>43.84</td>\n",
              "      <td>32.00</td>\n",
              "      <td>75.84</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>44.71</td>\n",
              "      <td>32.94</td>\n",
              "      <td>77.65</td>\n",
              "      <td>2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>44.14</td>\n",
              "      <td>34.86</td>\n",
              "      <td>79.00</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>46.75</td>\n",
              "      <td>38.43</td>\n",
              "      <td>85.18</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>46.43</td>\n",
              "      <td>38.99</td>\n",
              "      <td>85.42</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>48.30</td>\n",
              "      <td>39.09</td>\n",
              "      <td>87.39</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>48.96</td>\n",
              "      <td>40.70</td>\n",
              "      <td>89.66</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>50.92</td>\n",
              "      <td>43.17</td>\n",
              "      <td>94.09</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>50.30</td>\n",
              "      <td>43.79</td>\n",
              "      <td>94.09</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>54.77</td>\n",
              "      <td>46.82</td>\n",
              "      <td>101.59</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Water  Sewage   Total    Year\n",
              "0   14.62   11.57   26.19   1998 \n",
              "1   16.81   13.14   29.95   1999 \n",
              "2   18.64   13.99   32.63   2000 \n",
              "3   19.77   15.33   35.10   2001 \n",
              "4   20.65   17.12   37.77   2002 \n",
              "5   20.72   17.85   38.57   2003 \n",
              "6   21.95   19.48   41.43   2004 \n",
              "7   22.79   19.96   42.75   2005 \n",
              "8   23.51   20.85   44.36   2006 \n",
              "9   26.74   22.93   49.67   2007 \n",
              "10  27.76   23.81   51.57   2008 \n",
              "11  28.54   24.47   53.01   2008 \n",
              "12  30.04   25.08   55.12   2009 \n",
              "13  30.63   25.88   56.51   2010 \n",
              "14  34.39   26.00   60.39   2011 \n",
              "15  38.05   28.30   66.35   2012 \n",
              "16  43.02   31.33   74.35   2013 \n",
              "17  43.84   32.00   75.84   2014 \n",
              "18  44.71   32.94   77.65   2015 \n",
              "19  44.14   34.86   79.00   2016 \n",
              "20  46.75   38.43   85.18   2016 \n",
              "21  46.43   38.99   85.42   2017 \n",
              "22  48.30   39.09   87.39   2018 \n",
              "23  48.96   40.70   89.66   2019 \n",
              "24  50.92   43.17   94.09   2020 \n",
              "25  50.30   43.79   94.09   2020 \n",
              "26  54.77   46.82  101.59   2021 "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxTuWTemqL9A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}