02_python/731@staatskanzlei-kanton-zuerich.ipynb

{"cells": [{"cell_type": "markdown", "id": "db7f08c1", "metadata": {}, "source": ["## Open Government Data, provided by **Canton Zurich**\n", "*Autogenerated Python starter code for data set with identifier* **731@staatskanzlei-kanton-zuerich**"]}, {"cell_type": "markdown", "id": "164c4327", "metadata": {}, "source": ["## Dataset\n", "# **Web Analytics des Webauftritts des Kantons Z\u00fcrich ab Juli 2020**"]}, {"cell_type": "markdown", "id": "46d13c8b-4ac3-4e2f-940f-0c14ec5b7c0d", "metadata": {}, "source": ["## Description\n", "\n", "Grundlage der Daten sind die Matomo-Web-Analytics (Anzahl Besuche) aller Seiten des kantonalen Webauftritts, f\u00fcr die das Statistische Amt zust\u00e4ndig ist. Siehe 'Hinweise zur Datengrundlage, Indikatoren und Themen' unter 'Weitere Informationen'. Die Daten wurden jeden Morgen um 08:30 Uhr um die Daten des Vortages erg\u00e4nzt. Wegen Problemen mit dem Bezug der Daten wird dieser Datensatz vorl\u00e4ufig nicht mehr aktualisiert."]}, {"cell_type": "markdown", "id": "cad813bb-c986-4bb4-b52b-f78bb608086f", "metadata": {}, "source": ["## Data set links\n", "\n", "[Direct data shop link for dataset](https://www.zh.ch/de/politik-staat/statistik-daten/datenkatalog.html#/datasets/731@staatskanzlei-kanton-zuerich)"]}, {"cell_type": "markdown", "id": "9d4813e9", "metadata": {}, "source": ["## Metadata\n", "- **Issued** `2021-09-27T09:12:00`\n- **Modified** `2024-03-06T11:58:55`\n- **Startdate** `2020-07-08`\n- **Enddate** `2023-10-11`\n- **Theme** `['http://publications.europa.eu/resource/authority/data-theme/GOVE', 'http://publications.europa.eu/resource/authority/data-theme/ECON']`\n- **Keyword** `['kantonale_verwaltung', 'kanton_zuerich', 'ogd']`\n- **Publisher** `['Staatskanzlei des Kantons Z\u00fcrich']`\n- **Landingpage** `https://openzh.shinyapps.io/UAstat/`\n"]}, {"cell_type": "markdown", "id": "8a857d65", "metadata": {"jp-MarkdownHeadingCollapsed": true, "tags": []}, "source": ["## Imports and helper functions"]}, {"cell_type": "code", "execution_count": null, "id": "93b39602-1c1e-46d2-ae70-1716b1481e9b", "metadata": {"tags": []}, "outputs": [], "source": ["%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "\n", "params = {\n", "    'text.color': (0.25, 0.25, 0.25),\n", "    'figure.figsize': [18, 6],\n", "   }\n", "\n", "plt.rcParams.update(params)\n", "\n", "import pandas as pd "]}, {"cell_type": "code", "execution_count": null, "id": "aa6611d7-e1c0-40a7-b0ff-601a8ef8439b", "metadata": {"tags": []}, "outputs": [], "source": ["# helper function for reading datasets with proper separator\n", "def get_dataset(url):\n", "    if url[-3:] != \"csv\":\n", "        print(\"The data set URL has no proper 'csv' extension. Reading the dataset might not have worked as expected.\\nPlease check the dataset link and adjust pandas' read_csv() parameters accordingly.\")\n", "    data = pd.read_csv(url, sep=\",\", on_bad_lines='warn', encoding_errors='ignore', low_memory=False)\n", "    # if dataframe only has one column or less the data is not comma separated, use \";\" instead\n", "    if data.shape[1] <= 1:\n", "        data = pd.read_csv(url, sep=';', on_bad_lines='warn', encoding_errors='ignore', low_memory=False)\n", "        if data.shape[1] <= 1:\n", "            print(\"The data wasn't imported properly. Very likely the correct separator couldn't be found.\\nPlease check the dataset manually and adjust the code.\")\n", "    return data"]}, {"cell_type": "markdown", "id": "02ce518f", "metadata": {}, "source": ["## Load data\n", "\n", "- The dataset has **`1` distribution(s)** in CSV format.\n", "- All available CSV distributions are listed below and can be read into a pandas dataframe."]}, {"cell_type": "code", "execution_count": null, "id": "0", "metadata": {"tags": []}, "outputs": [], "source": "# Distribution 0\n# Ktzhdistid               : 1865\n# Title                    : Web Analytics von Webseiten des Statistischen Amts im Webauftritt des Kantons Z\u00fcrich ab Juli 2020\n# Description              : Variabeln: 'date' = Datum, 'url' = alle Webseiten, bei denen unter \u2018Kontakt\u2019 (im Footer) das Statistische Amt aufgef\u00fchrt ist; die Webseiten-URLs sind auf die Basis-URLs gek\u00fcrzt und aggregiert (Basis-URL = Alle Parameter nach dem ersten Auftreten von \u201chtml\u201d werden abgeschnitten), 'theme' = die Themen entsprechen dem in der URL einer Webseite erstgenannten Thema (m\u00f6gliche erstgenannte Themen sind auf der Homepage von zh.ch aufgef\u00fchrt), 'nb_visits' = Anzahl Seitenbesuche; nach 30 Minuten Seiteninaktivit\u00e4t wird der Besuch neu gez\u00e4hlt.\n# Issued                   : 2021-08-26T11:18:16\n# Modified                 : 2021-09-27T09:14:37\n\ndf = get_dataset('https://www.web.statistik.zh.ch/UAstat/data_stat.csv')\n\n"}, {"cell_type": "markdown", "id": "4ce1f78f", "metadata": {}, "source": ["## Analyze data"]}, {"cell_type": "code", "execution_count": null, "id": "3e3dab86", "metadata": {}, "outputs": [], "source": ["# drop columns that have no values\n", "df.dropna(how='all', axis=1, inplace=True)"]}, {"cell_type": "code", "execution_count": null, "id": "841bd8d2", "metadata": {}, "outputs": [], "source": ["print(f'The dataset has {df.shape[0]:,.0f} rows (observations) and {df.shape[1]:,.0f} columns (variables).')\n", "print(f'There seem to be {df.duplicated().sum()} exact duplicates in the data.')"]}, {"cell_type": "code", "execution_count": null, "id": "75e73c96", "metadata": {}, "outputs": [], "source": ["df.info(memory_usage='deep', verbose=True)"]}, {"cell_type": "code", "execution_count": null, "id": "02f3df4d", "metadata": {}, "outputs": [], "source": ["df.head()"]}, {"cell_type": "code", "execution_count": null, "id": "a0d7d898", "metadata": {}, "outputs": [], "source": ["# display a small random sample transposed in order to see all variables\n", "df.sample(3).T"]}, {"cell_type": "code", "execution_count": null, "id": "786806dd", "metadata": {}, "outputs": [], "source": ["# describe non-numerical features\n", "try:\n", "    with pd.option_context('display.float_format', '{:,.2f}'.format):\n", "        display(df.describe(exclude='number'))\n", "except:\n", "    print(\"No categorical data in dataset.\")"]}, {"cell_type": "code", "execution_count": null, "id": "e744a6b6", "metadata": {}, "outputs": [], "source": ["# describe numerical features\n", "try:\n", "    with pd.option_context('display.float_format', '{:,.2f}'.format):\n", "        display(df.describe(include='number'))\n", "except:\n", "    print(\"No numercial data in dataset.\")"]}, {"cell_type": "code", "execution_count": null, "id": "7a65d95d", "metadata": {}, "outputs": [], "source": ["# check missing values with missingno\n", "# https://github.com/ResidentMario/missingno\n", "import missingno as msno\n", "msno.matrix(df, labels=True, sort='descending');"]}, {"cell_type": "code", "execution_count": null, "id": "fcc604b7", "metadata": {}, "outputs": [], "source": ["# plot a histogram for each numerical feature\n", "try:\n", "    df.hist(bins=25, rwidth=.9)\n", "    plt.tight_layout()\n", "    plt.show()\n", "except:\n", "    print(\"No numercial data to plot.\") "]}, {"cell_type": "code", "execution_count": null, "id": "e13cc6eb", "metadata": {}, "outputs": [], "source": ["# continue your code here..."]}, {"cell_type": "code", "execution_count": null, "id": "18886378", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "id": "59fce071", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": null, "id": "f42f25aa", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "id": "c6b87d83", "metadata": {}, "source": ["**Contact**: Statistisches Amt des Kantons Z\u00fcrich | Data Shop | datashop@statistik.zh.ch"]}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.15"}}, "nbformat": 4, "nbformat_minor": 5}