02_python/1582@bildungsstatistik-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* **1582@bildungsstatistik-kanton-zuerich**"]}, {"cell_type": "markdown", "id": "164c4327", "metadata": {}, "source": ["## Dataset\n", "# **Lernende der Regelschule im Kanton Z\u00fcrich nach Staatsangeh\u00f6rigkeit ab 2000**"]}, {"cell_type": "markdown", "id": "46d13c8b-4ac3-4e2f-940f-0c14ec5b7c0d", "metadata": {}, "source": ["## Description\n", "\n", "Sch\u00fcler/innen an \u00f6ffentlichen Regelschulen im Kanton Z\u00fcrich, die nach kantonalem Lehrplan unterrichtet werden. Mit Regelschule wird die Schulbildung w\u00e4hrend der obligatorischen Schulzeit bezeichnet. Die obligatorische Schulzeit umfasst den Kindergarten, sechs Jahre Primarschule und drei Jahre Sekundarstufe I. Nicht mitgez\u00e4hlt sind Lernende der Mittelschulen, Lernende in in Heim-/Sonderschulen, und Lernende in Privatschulen. Es gilt das Schulortprinzip, gez\u00e4hlt werden Sch\u00fclerinnen und Sch\u00fcler, welche eine Schule innerhalb des Kantons Z\u00fcrich besuchen.\n\nDie Daten werden j\u00e4hrlich f\u00fcr den Stichtag 15.9. erhoben."]}, {"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/1582@bildungsstatistik-kanton-zuerich)"]}, {"cell_type": "markdown", "id": "9d4813e9", "metadata": {}, "source": ["## Metadata\n", "- **Issued** `2022-10-24T11:15:55`\n- **Modified** `2025-01-10T13:43:23`\n- **Startdate** `2000-09-15`\n- **Enddate** `2023-09-15`\n- **Theme** `['http://publications.europa.eu/resource/authority/data-theme/EDUC']`\n- **Keyword** `['ausbildung', 'bildung', 'grossregionen', 'lernende', 'nationalitaet', 'schueler*innen', 'staatsangehoerigkeit', 'volksschule', 'bildungsdaten', 'ogd']`\n- **Publisher** `['Bildungsstatistik des Kantons Z\u00fcrich']`\n- **Landingpage** `None`\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               : 3020\n# Title                    : Lernende der Regelschule im Kanton Z\u00fcrich nach Staatsangeh\u00f6rigkeit ab 2000\n# Description              : Ausgewiesen wird die Anzahl der Lernenden in der Regelschule im Kanton Z\u00fcrich. Die Daten sind differenziert nach Jahr und Staatsangeh\u00f6rigkeit. Bei Zellen kleiner 5 wird die Staatsangeh\u00f6rigkeit zuerst auf Ebene Grossregion und, wenn n\u00f6tig, auf Ebene Kontinent aggregiert. Jahr: Das Jahr ist jeweils das Kalenderjahr der Erhebung, wobei der Stichtag der 15.9. ist. Die Daten f\u00fcr das Jahr 2023 sind somit im Schuljahr 2023/24 erhoben worden und zeigen den Datenstand am 15. September 2023. Staatsangehoerigkeit: Es wird pro Person nur eine Staatsangeh\u00f6rigkeit erhoben. Falls eine Person mehrere Staatsangeh\u00f6rigkeiten hat, und eine davon ist die Schweizerische, so wird diese priorit\u00e4r erfasst. Bei anderen mehrfachen Staatsangeh\u00f6rigkeiten wir keine Vorgabe zur Priorisierung gemacht. Staatsangehoerigkeit_ISO2_Code: Bei der Aggregation auf Grossregion und Kontinent bleibt der ISO2 Code leer. Anzahl: Anzahl der Sch\u00fclerinnen und Sch\u00fcler mit der entsprechenden Merkmalskombination. Die Daten werden im Rahmen der Statistik der Lernenden f\u00fcr das Bundesamtes f\u00fcr Statistik erhoben. Es handelt sich um eine Vollerhebung.\n# Issued                   : 2022-10-24T11:15:55\n# Modified                 : 2025-01-13T08:19:50\n\ndf = get_dataset('https://www.web.statistik.zh.ch/ogd/data/bista/ZH_Lernende_Regelschule_Staatsangehoerigkeit.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**: Bildungsstatistik des Kantons Z\u00fcrich | bista@bi.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}