3718 lines
463 KiB
Plaintext
3718 lines
463 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c2734eb9-d9b9-43b2-a9fb-c25729a0f3a3",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-5fff5091c9d505d4",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"# 7. Programmierübung: Pandas & Seaborn\n",
|
||
"\n",
|
||
"<div style=\"display:flex;\">\n",
|
||
" <div style=\"text-align: left\">\n",
|
||
" Willkommen zur siebten Programmierübung Einführung in Python 3.\n",
|
||
" </div>\n",
|
||
" <img style=\"float: right; margin: 0px 15px 15px 0px\" src=\"https://www.python.org/static/img/python-logo-large.c36dccadd999.png?1576869008\" width=\"100\" />\n",
|
||
"</div>\n",
|
||
"\n",
|
||
"Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n",
|
||
"\n",
|
||
"Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n",
|
||
"\n",
|
||
"Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8af16cfd-827f-4e7d-91a3-970e920ad212",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-fa7bc65ef436bf2f",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"# Was ist Pandas?\n",
|
||
"\n",
|
||
">_pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal._\n",
|
||
"\n",
|
||
"Pandas Keyfeature sind die beiden Datenstruckturen [Series](https://pandas.pydata.org/docs/reference/series.html) und [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n",
|
||
"\n",
|
||
"Diesem Notebook liegt zu Lernzwecken das Datenset `unis_nd.csv` bei, mit dem die Grundlegenden Funktionen Pandas gezeigt werden.\n",
|
||
"\n",
|
||
"__Für dieses Notebook schauen Sie bitte in die [Pandas Docs](https://pandas.pydata.org/docs/)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "496ec5e1-2fdb-4075-853b-c91a6144ac82",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-7fc3ec9d80a10c43",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"# Was ist Seaborn?\n",
|
||
"\n",
|
||
">_Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics._\n",
|
||
"\n",
|
||
"Seaborn bietet dabei keine neue Funktionalität gegenüber dem bekannten MatPlotLib, jedoch abstrahiert es Darstellungen die Aufwendig mit MatPlotLib darzustelln wären. Schaue dir daher gerne die [Gallerie](https://seaborn.pydata.org/examples/index.html) an.\n",
|
||
"\n",
|
||
"__Für dieses Notebook schauen Sie bitte in die [Searborn Docs](https://seaborn.pydata.org/api.html)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n",
|
||
"\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "62286603-158e-4448-98e5-e8f4aa60448a",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-d7a24465d2b7b67c",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"# Achtung\n",
|
||
"\n",
|
||
"Falls es zu Problemen kommen sollte das Seaborn nicht bekannt sei führe bitte vor **jedem** bearbeiten folgende Zeile aus:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "7198387d-3fcc-4501-9460-d07e84384a75",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-a2a83048f4c8f0b8",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: seaborn in /opt/conda/lib/python3.11/site-packages (0.13.2)\n",
|
||
"Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.3.5)\n",
|
||
"Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.3.3)\n",
|
||
"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.11/site-packages (from seaborn) (3.10.8)\n",
|
||
"Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\n",
|
||
"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
|
||
"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.60.1)\n",
|
||
"Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n",
|
||
"Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.0.0)\n",
|
||
"Requirement already satisfied: pyparsing>=3 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.5)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.3)\n",
|
||
"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"pip install seaborn"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8c6fa552-7e9c-4561-b7c3-e72268db9afb",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-430939b1ccd3736f",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"# Import Pandas\n",
|
||
"\n",
|
||
"Pandas wird vom Internet mit der Abkürzung `pd` importiert.\n",
|
||
"\n",
|
||
"Führen Sie die nächste Zelle beim neustart des Notebooks bitte immer aus."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "64315664-4ab4-46db-9600-b0dd58f95174",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-44f74bf2f8803769",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2ea5a1da-05f7-41a1-96cc-aaba1c74cf0d",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-53884904925c1212",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"# Data Frame\n",
|
||
"\n",
|
||
"Ein Pandas Data Frame `pd.DataFrame` ist eine 2-Dimensionale Datenstrucktur, vergleichbar mit einer Excel Tabelle.\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"Um aus einem Dictionary einen Pandas DataFrame zu erstellen wird das Objekt `pd.DataFrame` verwendet. Dabei ist es wichtig das das Dicitionary einer Ordnung folgt, bei dem die Schlüssel die Namen der Spalten sind. Die Reihe im Data Frame wird dann einem Schlüssel als Liste mit Werten zugeordnet.\n",
|
||
"\n",
|
||
"Schauen Sie sich dazu Folgendes Dictionary an, welches ein Subset aus dem beiliegenden Datenset `unis_nd.csv`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "30d2f79a-3f54-4397-a1f3-626b58fdf67e",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-e30f1b0549eb4481",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"unis_data = {\n",
|
||
" \t\"University name\": [ \n",
|
||
" \"Hochschule für Bildende Künste Braunschweig\",\n",
|
||
" \"Technische Universität Carolo-Wilhelmina zu Braunschweig\",\n",
|
||
" \"Hochschule 21\",\n",
|
||
" \"Technische Universität Clausthal\",\n",
|
||
" \"Hochschule Emden/Leer\",\n",
|
||
" \"PFH – Private Hochschule Göttingen\",\n",
|
||
" \"Georg-August-Universität Göttingen\"\n",
|
||
" ],\n",
|
||
" \n",
|
||
" \"Type of university\": [\n",
|
||
" \"Artistic university\",\n",
|
||
" \"University\",\n",
|
||
" \"University of Applied Sciences\",\n",
|
||
" \"University\",\n",
|
||
" \"University of Applied Sciences\",\n",
|
||
" \"University of Applied Sciences\",\n",
|
||
" \"University\"\n",
|
||
" ]\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ba23542f-9030-45a3-a7bc-9c3841ae843e",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-08a43ccd460568f5",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"Um aus dem Dictionary ein DataFrame zu erstellen, wird es einfach als Input für `pd.DataFrame` verwendet.\n",
|
||
"\n",
|
||
"Anschließend lassen wir es ausgeben. Nehmen Sie sich gerne die Zeit uns inspizieren Sie die Strucktur des beispielhaften DataFrames:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "074d682c-e936-40c3-97e1-2208b4e86a79",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-b519f770ad46c82b",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>University name</th>\n",
|
||
" <th>Type of university</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Hochschule für Bildende Künste Braunschweig</td>\n",
|
||
" <td>Artistic university</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Technische Universität Carolo-Wilhelmina zu Br...</td>\n",
|
||
" <td>University</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Hochschule 21</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Technische Universität Clausthal</td>\n",
|
||
" <td>University</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Hochschule Emden/Leer</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>PFH – Private Hochschule Göttingen</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Georg-August-Universität Göttingen</td>\n",
|
||
" <td>University</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" University name \\\n",
|
||
"0 Hochschule für Bildende Künste Braunschweig \n",
|
||
"1 Technische Universität Carolo-Wilhelmina zu Br... \n",
|
||
"2 Hochschule 21 \n",
|
||
"3 Technische Universität Clausthal \n",
|
||
"4 Hochschule Emden/Leer \n",
|
||
"5 PFH – Private Hochschule Göttingen \n",
|
||
"6 Georg-August-Universität Göttingen \n",
|
||
"\n",
|
||
" Type of university \n",
|
||
"0 Artistic university \n",
|
||
"1 University \n",
|
||
"2 University of Applied Sciences \n",
|
||
"3 University \n",
|
||
"4 University of Applied Sciences \n",
|
||
"5 University of Applied Sciences \n",
|
||
"6 University "
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis = pd.DataFrame(unis_data)\n",
|
||
"unis"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e732d3ff-463e-4411-b023-47f2fe02e4c5",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-6ed4bfb85a3f95bb",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"## Zugriffs Operationen\n",
|
||
"\n",
|
||
"Wie bereits vom Dicitonary bekannt lässt sich mittels Schlüssel (im Beispiel `University name`) auf eine Spalte zugreifen. Dabei ist wichtig zu erwähnen das jede Spalte in einem `DataFrame` eine `Series` ist. Eine `Series` ist dabei das 1-Dimensionale äquivalent zum 2-Dimensionales `DataFrame`.\n",
|
||
"\n",
|
||
"Im Folgenden Beispiel Selektieren wir die Series `University name` aus dem DataFrame `unis_nd`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "cfa48421-7e56-4c10-a4fc-3d907f429309",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-ef292cd51db1030b",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 Hochschule für Bildende Künste Braunschweig\n",
|
||
"1 Technische Universität Carolo-Wilhelmina zu Br...\n",
|
||
"2 Hochschule 21\n",
|
||
"3 Technische Universität Clausthal\n",
|
||
"4 Hochschule Emden/Leer\n",
|
||
"5 PFH – Private Hochschule Göttingen\n",
|
||
"6 Georg-August-Universität Göttingen\n",
|
||
"Name: University name, dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis[\"University name\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "62a92dad-506a-434f-b343-c020f89b19a1",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-dfdff5bf7e9be2ad",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"Um eine `Series` manuel zu definieren wird `pd.Series` verwendet. Dabei kann mittels Parameter `name` ein label für die Series gesetzt werden:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "7d54c159-119d-4350-9cf9-081ecc8f5712",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-2a1d8c078f0249d8",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 Hochschule für Bildende Künste Braunschweig\n",
|
||
"1 Technische Universität Carolo-Wilhelmina zu Br...\n",
|
||
"2 Hochschule 21\n",
|
||
"3 Technische Universität Clausthal\n",
|
||
"4 Hochschule Emden/Leer\n",
|
||
"5 PFH – Private Hochschule Göttingen\n",
|
||
"6 Georg-August-Universität Göttingen\n",
|
||
"Name: University name, dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"uni_names = pd.Series([ \n",
|
||
" \"Hochschule für Bildende Künste Braunschweig\",\n",
|
||
" \"Technische Universität Carolo-Wilhelmina zu Braunschweig\",\n",
|
||
" \"Hochschule 21\",\n",
|
||
" \"Technische Universität Clausthal\",\n",
|
||
" \"Hochschule Emden/Leer\",\n",
|
||
" \"PFH – Private Hochschule Göttingen\",\n",
|
||
" \"Georg-August-Universität Göttingen\"\n",
|
||
" ], name=\"University name\")\n",
|
||
"\n",
|
||
"uni_names"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "97930782-3e33-4036-8be2-f40c10f18794",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-e989fac8445abfe4",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"Wie Sie sehen ist der Output identisch.\n",
|
||
"\n",
|
||
"Um auf einzelne Elemente in der `Series` zuzugreifen werden, wie bereits bekannt von Listen, Index zugriffe verwendet.\n",
|
||
"\n",
|
||
"Beispiel; Selektion des 2 Elementes:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "3947baa1-1a3d-41fa-866d-a8acbd07a89a",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-07bcadf8f88e6603",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'Technische Universität Carolo-Wilhelmina zu Braunschweig'"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"uni_names[1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d8541372-36fa-4f59-b131-9c737b1e3457",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-0662b5716d2ffbf6",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"Analog dazu für den Data Frame. Bei dem zuerst die Spalte und dann Reihe selektiert wird:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "69e57b94-06ea-445b-a8ae-63952dd95358",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-d17cf7e2161c24e3",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'Technische Universität Carolo-Wilhelmina zu Braunschweig'"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis[\"University name\"][1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "720262aa-1d99-469b-896e-1bf5da4b712b",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-a5f5d2b918c6704b",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"Wie beim Dictionary lassen sich auch die bekannten Funktionen `.values`, `.keys` & `.items` ausgeben.\n",
|
||
"\n",
|
||
"Beispiel `.keys`:\n",
|
||
"\n",
|
||
"Achtung die Ausgabe ist keine Liste auch wenn es den Anschein erwirkt!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "84809ffa-64a2-4c9d-b3b1-3ff792ebda86",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-0a4876a0ab9f0672",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Schlüssel:\n",
|
||
"Index(['University name', 'Type of university'], dtype='object')\n",
|
||
"\n",
|
||
"Rückgabetype:\n",
|
||
"<class 'pandas.core.indexes.base.Index'>\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Schlüssel:\", unis.keys(), \"\\nRückgabetype:\", type(unis.keys()), sep='\\n')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "88957f18-bbc0-4ce3-981d-9b6735ea437b",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-7708becc41b11601",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"## Aufgabe - Erstellen eines Dataframes\n",
|
||
"\n",
|
||
"*2 Punkte*\n",
|
||
"\n",
|
||
"Erstelle einen Pandas Data Frame mit dem namen `uni_addr`, nachdem Schema folgender Tabelle:\n",
|
||
"\n",
|
||
"| Address | plz |\n",
|
||
"|--------------------------|----------------------------|\n",
|
||
"| Johannes-Selenka-Platz 1 | 38118 Braunschweig |\n",
|
||
"| Universitätspl. 2 | 38106 Braunschweig |\n",
|
||
"| Harburger Str. 6 | 21614 Buxtehude |\n",
|
||
"| Adolph-Roemer-Straße 2A | 38678 Clausthal-Zellerfeld |\n",
|
||
"| Constantiapl. 4 | 26723 Emden |\n",
|
||
"| Weender Landstraße 3-7 | 37073 Göttingen |\n",
|
||
"| Wilhelmsplatz 1 | 37073 Göttingen |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "2a6555ac-97ce-4d78-8d19-359b1eeb5a1a",
|
||
"metadata": {
|
||
"editable": true,
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-6ea306cdf2a57ea3",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
},
|
||
"slideshow": {
|
||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"uni_addr = None\n",
|
||
"\n",
|
||
"### BEGIN SOLUTION\n",
|
||
"uni_addr = pd.DataFrame({\n",
|
||
" \"Address\": [\"Johannes-Selenka-Platz 1\", \"Universitätspl. 2\", \"Harburger Str. 6\", \"Adolph-Roemer-Straße 2A\", \"Constantiapl. 4\", \"Weender Landstraße 3-7\", \"Wilhelmsplatz 1\"],\n",
|
||
" \"plz\": [\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",]\n",
|
||
"})\n",
|
||
"### END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "1936705b-949b-47ad-95e1-67d987662efc",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "cell-daa317b892c6c606",
|
||
"locked": true,
|
||
"points": 5,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Hier werden ihre Lösung gestestet ...\n",
|
||
"assert isinstance(uni_addr, pd.DataFrame)\n",
|
||
"assert len(uni_addr[\"Address\"]) == 7\n",
|
||
"assert len(uni_addr[\"plz\"]) == 7\n",
|
||
"### BEGIN HIDDEN TESTS\n",
|
||
"uni_addr_test = pd.DataFrame({\n",
|
||
" \"Address\": [\"Johannes-Selenka-Platz 1\", \"Universitätspl. 2\", \"Harburger Str. 6\", \"Adolph-Roemer-Straße 2A\", \"Constantiapl. 4\", \"Weender Landstraße 3-7\", \"Wilhelmsplatz 1\"],\n",
|
||
" \"plz\": [\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",]\n",
|
||
"})\n",
|
||
"\n",
|
||
"for el1, el2 in zip(uni_addr[\"Address\"], uni_addr_test[\"Address\"]):\n",
|
||
" assert el1 == el2\n",
|
||
"\n",
|
||
"for el1, el2 in zip(uni_addr[\"plz\"], uni_addr_test[\"plz\"]):\n",
|
||
" assert el1 == el2\n",
|
||
"\n",
|
||
"### END HIDDEN TESTS"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8f25a6fb-3f9b-497a-a966-3e53c2f86b9c",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-8871c67f03f141dd",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Aufgabe - Extrahieren einer Series\n",
|
||
"\n",
|
||
"*1 Punkte*\n",
|
||
"\n",
|
||
"Exthahiere die Series `plz` aus dem zuvor erstelltem Data Frame `uni_addr` und speicher dein Ergebnis in `uni_plz` mit dem richtigen Type Hint.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "2de2487d-158e-4dec-a5fa-ccd7478c161c",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-240ce62f624ce706",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"uni_plz = None\n",
|
||
"\n",
|
||
"### BEGIN SOLUTION\n",
|
||
"uni_plz = uni_addr[\"plz\"]\n",
|
||
"### END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "613f9082-d963-4129-a70c-acfab56759e0",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "cell-dbd86892a80c1f08",
|
||
"locked": true,
|
||
"points": 1,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0 38118 Braunschweig\n",
|
||
"1 38106 Braunschweig\n",
|
||
"2 21614 Buxtehude\n",
|
||
"3 38678 Clausthal-Zellerfeld\n",
|
||
"4 26723 Emden\n",
|
||
"5 37073 Göttingen\n",
|
||
"6 37073 Göttingen\n",
|
||
"Name: plz, dtype: object\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Hier werden ihre Lösungen getestet\n",
|
||
"print(uni_plz)\n",
|
||
"assert isinstance(uni_plz, pd.Series)\n",
|
||
"### BEGIN HIDDEN TESTS\n",
|
||
"uni_plz_test = pd.Series([\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",], name=\"plz\")\n",
|
||
"for el1, el2 in zip(uni_plz, uni_plz_test):\n",
|
||
" assert el1 == el2\n",
|
||
"### END HIDDEN TESTS"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f308563d-e961-40a8-bf11-79d137b02e88",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-02f5ee7a60d555b6",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"# Reading Data\n",
|
||
"\n",
|
||
"Die unweiten des Internets ermöglichen es uns Daten auf die unterschiedlichsten Arten zu Archivieren. \n",
|
||
"\n",
|
||
"Für die Speicherung und Darstellung von (Roh-)Daten werden Datenbanken ([SQL](https://en.wikipedia.org/wiki/SQL)), Transportformate ([JSON](https://en.wikipedia.org/wiki/JSON), [XML](https://en.wikipedia.org/wiki/XML)), Tabellenformate ([Excel](https://en.wikipedia.org/wiki/Microsoft_Excel), [CSV](https://en.wikipedia.org/wiki/Comma-separated_values)) und noch viele mehr verwendet.\n",
|
||
"\n",
|
||
"Ein beliebtes Betriebsystemunabhängiges Dateiformat für kleine Datensätze (bis 10GB) werden `.csv` Dateien verwendet. Jedes gängige Tabellenkalkulations- und Umfragentool kann seine Daten als _Comma-seperated values_ kurz _CSV_ Exportieren. Schauen Sie dazu gerne in das Datenset `unis_nd.csv`. Im folgenden wird sich ausschließlich auf das Einlesen von CSV-Dateien bezogen. Welche Funktionen und Dateienformate Pandas unterstüzt entnehmen Sie bitte der Dokumentation zu [input/output](https://pandas.pydata.org/docs/reference/io.html).\n",
|
||
"\n",
|
||
"Da ich ihnen Die Spannung nicht nehmen möchte lösen Sie für die folgenden Beispiele bitte nächste Aufgabe."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a6d7e62b-df8e-4da5-8f74-6c81be0f00df",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-7bf6c58e48e58743",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Aufgabe - Read CSV\n",
|
||
"\n",
|
||
"*1 Punkt*\n",
|
||
"\n",
|
||
"Lies das Datenset `unis_nd.csv` aus und speicher den resultierenden DataFrame in der Variabeln `unis_nd`.\n",
|
||
"\n",
|
||
"Falls Sie hilfe benötigen lesen Sie gerne die Dokumentation im [Getting Started](https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html) Guide.\n",
|
||
"\n",
|
||
"_Hinweis: Die Datei liegt in keinem Ordner, sondern im selben wie dieses Notebook!_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "b08cc0c4-277b-4e9f-b6e7-a607b912febe",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-3cdc8afd6dc12d44",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"unis_nd = None\n",
|
||
"\n",
|
||
"### BEGIN SOLUTION\n",
|
||
"unis_nd = pd.read_csv(\"unis_nd.csv\")\n",
|
||
"### END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "e5760557-ddec-4502-b2d3-e8cc2ef70348",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "cell-fd974b3aa3563ca0",
|
||
"locked": true,
|
||
"points": 1,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Hier werden ihre Lösungen getestet\n",
|
||
"assert isinstance(unis_nd, pd.DataFrame)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ffcf5c5c-1e35-42f9-b35b-2494ff4c421f",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-614d1dc9c4566861",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Ausgabe Operationen\n",
|
||
"\n",
|
||
"Um den gesamten Data Frame auszugeben nutzen Sie einfach die Syntax aus nächster Zelle.\n",
|
||
"\n",
|
||
"Achtung die Ausgabe ist vergleichweise Groß! Nehmen Sie sich gerne Zeit und schauen Sie sich das Dataset an."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "c9b8b9a0-0a62-4887-b6e8-c1da6086d7e4",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-ec7681e1a54af790",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>University name</th>\n",
|
||
" <th>Type of university</th>\n",
|
||
" <th>Sponsorship</th>\n",
|
||
" <th>Right of promotion</th>\n",
|
||
" <th>Founding year</th>\n",
|
||
" <th>Number of students</th>\n",
|
||
" <th>Address</th>\n",
|
||
" <th>lat</th>\n",
|
||
" <th>lon</th>\n",
|
||
" <th>plz</th>\n",
|
||
" <th>pic</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Hochschule für Bildende Künste Braunschweig</td>\n",
|
||
" <td>Artistic university</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1963</td>\n",
|
||
" <td>976.000</td>\n",
|
||
" <td>Johannes-Selenka-Platz 1</td>\n",
|
||
" <td>52.257738</td>\n",
|
||
" <td>10.502315</td>\n",
|
||
" <td>38118 Braunschweig</td>\n",
|
||
" <td>https://www.hbk-bs.de/fileadmin/_processed_/5/...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Technische Universität Carolo-Wilhelmina zu Br...</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1745</td>\n",
|
||
" <td>17709.000</td>\n",
|
||
" <td>Universitätspl. 2</td>\n",
|
||
" <td>52.273550</td>\n",
|
||
" <td>10.530097</td>\n",
|
||
" <td>38106 Braunschweig</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Hochschule 21</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2005</td>\n",
|
||
" <td>1084.000</td>\n",
|
||
" <td>Harburger Str. 6</td>\n",
|
||
" <td>53.477650</td>\n",
|
||
" <td>9.704650</td>\n",
|
||
" <td>21614 Buxtehude</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Technische Universität Clausthal</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1775</td>\n",
|
||
" <td>3446.000</td>\n",
|
||
" <td>Adolph-Roemer-Straße 2A</td>\n",
|
||
" <td>51.804840</td>\n",
|
||
" <td>10.334110</td>\n",
|
||
" <td>38678 Clausthal-Zellerfeld</td>\n",
|
||
" <td>https://www.presse.tu-clausthal.de/fileadmin/T...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Hochschule Emden/Leer</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>4481.000</td>\n",
|
||
" <td>Constantiapl. 4</td>\n",
|
||
" <td>53.368160</td>\n",
|
||
" <td>7.181410</td>\n",
|
||
" <td>26723 Emden</td>\n",
|
||
" <td>https://sta-hisweb.hs-emden-leer.de/QIS/images...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>PFH – Private Hochschule Göttingen</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1995</td>\n",
|
||
" <td>4226.000</td>\n",
|
||
" <td>Weender Landstraße 3-7</td>\n",
|
||
" <td>51.538910</td>\n",
|
||
" <td>9.933220</td>\n",
|
||
" <td>37073 Göttingen</td>\n",
|
||
" <td>https://goettingen-campus.de/fileadmin/_proces...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Georg-August-Universität Göttingen</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1737</td>\n",
|
||
" <td>28614.000</td>\n",
|
||
" <td>Wilhelmsplatz 1</td>\n",
|
||
" <td>51.534070</td>\n",
|
||
" <td>9.937850</td>\n",
|
||
" <td>37073 Göttingen</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Fachhochschule für die Wirtschaft Hannover</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1996</td>\n",
|
||
" <td>641.000</td>\n",
|
||
" <td>Freundallee 15</td>\n",
|
||
" <td>52.366200</td>\n",
|
||
" <td>9.772470</td>\n",
|
||
" <td>30173 Hannover</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Hochschule Hannover</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>9209.000</td>\n",
|
||
" <td>Ricklinger Stadtweg 120</td>\n",
|
||
" <td>52.354190</td>\n",
|
||
" <td>9.722380</td>\n",
|
||
" <td>30459 Hannover</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Hochschule für Musik, Theater und Medien Hannover</td>\n",
|
||
" <td>Artistic university</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1897</td>\n",
|
||
" <td>1409.000</td>\n",
|
||
" <td>Neues Haus 1</td>\n",
|
||
" <td>52.377380</td>\n",
|
||
" <td>9.753920</td>\n",
|
||
" <td>30175 Hannover</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>Leibniz-Fachhochschule</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1920</td>\n",
|
||
" <td>589.000</td>\n",
|
||
" <td>Expo Plaza 11</td>\n",
|
||
" <td>52.321150</td>\n",
|
||
" <td>9.818680</td>\n",
|
||
" <td>30539 Hannover</td>\n",
|
||
" <td>https://www.visit-hannover.com/var/storage/ima...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>Medizinische Hochschule Hannover (MHH)</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1963</td>\n",
|
||
" <td>3778.000</td>\n",
|
||
" <td>Carl-Neuberg-Straße 1</td>\n",
|
||
" <td>52.384050</td>\n",
|
||
" <td>9.806030</td>\n",
|
||
" <td>30625 Hannover</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>Stiftung Tierärztliche Hochschule Hannover</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1778</td>\n",
|
||
" <td>2381.000</td>\n",
|
||
" <td>Bünteweg 2</td>\n",
|
||
" <td>52.354680</td>\n",
|
||
" <td>9.797730</td>\n",
|
||
" <td>30559 Hannover</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/de/thum...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>Gottfried Wilhelm Leibniz Universität Hannover</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1831</td>\n",
|
||
" <td>28935.000</td>\n",
|
||
" <td>Welfengarten 1</td>\n",
|
||
" <td>52.382250</td>\n",
|
||
" <td>9.717770</td>\n",
|
||
" <td>30167 Hannover</td>\n",
|
||
" <td>https://www.uni-hannover.de/fileadmin/_process...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>Fachhochschule für Interkulturelle Theologie H...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2012</td>\n",
|
||
" <td>91.000</td>\n",
|
||
" <td>Missionsstraße 3-5</td>\n",
|
||
" <td>52.708843</td>\n",
|
||
" <td>10.140710</td>\n",
|
||
" <td>29320 Südheide</td>\n",
|
||
" <td>https://cdn.max-e5.info/damfiles/logo/fh_herma...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>Universität Hildesheim</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1978</td>\n",
|
||
" <td>8378.000</td>\n",
|
||
" <td>Universitätspl. 1</td>\n",
|
||
" <td>52.134010</td>\n",
|
||
" <td>9.974690</td>\n",
|
||
" <td>31141 Hildesheim</td>\n",
|
||
" <td>https://www.uni-hildesheim.de/media/_processed...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>HAWK Hochschule für angewandte Wissenschaft un...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>6495.000</td>\n",
|
||
" <td>Hohnsen 4</td>\n",
|
||
" <td>52.142460</td>\n",
|
||
" <td>9.957980</td>\n",
|
||
" <td>31134 Hildesheim</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>HAWK Hochschule für angewandte Wissenschaft un...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>6495.000</td>\n",
|
||
" <td>Haarmannpl. 3</td>\n",
|
||
" <td>51.827260</td>\n",
|
||
" <td>9.450690</td>\n",
|
||
" <td>37603 Holzminden</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>HAWK Hochschule für angewandte Wissenschaft un...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>6495.000</td>\n",
|
||
" <td>Von-Ossietzky-Straße 99</td>\n",
|
||
" <td>51.521750</td>\n",
|
||
" <td>9.969670</td>\n",
|
||
" <td>37085 Göttingen</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>Leuphana Universität Lüneburg</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>6497.000</td>\n",
|
||
" <td>Universitätsallee 1</td>\n",
|
||
" <td>53.228531</td>\n",
|
||
" <td>10.401710</td>\n",
|
||
" <td>21335 Lüneburg</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>Norddeutsche Hochschule für Rechtspflege – Nie...</td>\n",
|
||
" <td>University of Administration</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2007</td>\n",
|
||
" <td>6409.000</td>\n",
|
||
" <td>Godehardspl. 6</td>\n",
|
||
" <td>52.144840</td>\n",
|
||
" <td>9.949230</td>\n",
|
||
" <td>31134 Hildesheim</td>\n",
|
||
" <td>https://static.studycheck.de/media/images/inst...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>Kommunale Hochschule für Verwaltung in Nieders...</td>\n",
|
||
" <td>University of Administration</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2007</td>\n",
|
||
" <td>1570.000</td>\n",
|
||
" <td>Wielandstraße 8</td>\n",
|
||
" <td>52.370500</td>\n",
|
||
" <td>9.722390</td>\n",
|
||
" <td>30169 Hannover</td>\n",
|
||
" <td>https://www.nsi-hsvn.de/fileadmin/user_upload/...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>Carl von Ossietzky Universität Oldenburg\\n</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1973</td>\n",
|
||
" <td>15635.000</td>\n",
|
||
" <td>Uhlhornsweg 49-55</td>\n",
|
||
" <td>53.147340</td>\n",
|
||
" <td>8.179020</td>\n",
|
||
" <td>26129 Oldenburg</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>Hochschule Osnabrück</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>13620.000</td>\n",
|
||
" <td>Albrechtstraße 30</td>\n",
|
||
" <td>52.282680</td>\n",
|
||
" <td>8.025010</td>\n",
|
||
" <td>49076 Osnabrück</td>\n",
|
||
" <td>https://login.hs-osnabrueck.de/nidp/hsos/image...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>Universität Osnabrück</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1973</td>\n",
|
||
" <td>13640.000</td>\n",
|
||
" <td>Neuer Graben 29</td>\n",
|
||
" <td>52.271370</td>\n",
|
||
" <td>8.044540</td>\n",
|
||
" <td>49074 Osnabrück</td>\n",
|
||
" <td>https://www.eh-tabor.de/sites/default/files/st...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>Hochschule Braunschweig/Wolfenbüttel, Ostfalia...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>11577.000</td>\n",
|
||
" <td>Salzdahlumer Str. 46/48</td>\n",
|
||
" <td>52.176830</td>\n",
|
||
" <td>10.548650</td>\n",
|
||
" <td>38302 Wolfenbüttel</td>\n",
|
||
" <td>https://www.ostfalia.de/export/system/modules/...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26</th>\n",
|
||
" <td>Hochschule Wolfsburg, Ostfalia Hochschule für ...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>11577.000</td>\n",
|
||
" <td>Robert-Koch-Platz 8A</td>\n",
|
||
" <td>52.425950</td>\n",
|
||
" <td>10.787110</td>\n",
|
||
" <td>38440 Wolfsburg</td>\n",
|
||
" <td>https://www.ostfalia.de/export/system/modules/...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27</th>\n",
|
||
" <td>Hochschule Suderburg, Ostfalia Hochschule für ...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>11577.000</td>\n",
|
||
" <td>Herbert-Meyer-Straße 7</td>\n",
|
||
" <td>52.897610</td>\n",
|
||
" <td>10.446590</td>\n",
|
||
" <td>29556 Suderburg</td>\n",
|
||
" <td>https://www.ostfalia.de/export/system/modules/...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28</th>\n",
|
||
" <td>Hochschule Salzgitter, Ostfalia Hochschule für...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>11577.000</td>\n",
|
||
" <td>Karl-Scharfenberg-Straße 55/57</td>\n",
|
||
" <td>52.087240</td>\n",
|
||
" <td>10.380550</td>\n",
|
||
" <td>38229 Salzgitter</td>\n",
|
||
" <td>https://www.ostfalia.de/export/system/modules/...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>Hochschule für Künste im Sozialen, Ottersberg</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1967</td>\n",
|
||
" <td>342.000</td>\n",
|
||
" <td>Große Str. 107</td>\n",
|
||
" <td>53.106680</td>\n",
|
||
" <td>9.163100</td>\n",
|
||
" <td>28870 Ottersberg</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>30</th>\n",
|
||
" <td>Private Hochschule für Wirtschaft und Technik ...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1998</td>\n",
|
||
" <td>558.000</td>\n",
|
||
" <td>Rombergstraße 40</td>\n",
|
||
" <td>52.721250</td>\n",
|
||
" <td>8.278910</td>\n",
|
||
" <td>49377 Vechta</td>\n",
|
||
" <td>https://www.phwt.de/wp-content/uploads/2020/09...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>31</th>\n",
|
||
" <td>Private Hochschule für Wirtschaft und Technik ...</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>1998</td>\n",
|
||
" <td>558.000</td>\n",
|
||
" <td>Schlesier Str. 13A</td>\n",
|
||
" <td>52.611710</td>\n",
|
||
" <td>8.363340</td>\n",
|
||
" <td>49356 Diepholz</td>\n",
|
||
" <td>https://www.phwt.de/wp-content/uploads/2020/09...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>32</th>\n",
|
||
" <td>Universität Vechta</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1995</td>\n",
|
||
" <td>4.551</td>\n",
|
||
" <td>Driverstraße 22</td>\n",
|
||
" <td>52.721170</td>\n",
|
||
" <td>8.293800</td>\n",
|
||
" <td>49377 Vechta</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33</th>\n",
|
||
" <td>Hochschule Weserbergland</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2010</td>\n",
|
||
" <td>485.000</td>\n",
|
||
" <td>Am Stockhof 2</td>\n",
|
||
" <td>52.098750</td>\n",
|
||
" <td>9.355420</td>\n",
|
||
" <td>31785 Hameln</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>34</th>\n",
|
||
" <td>Jade Hochschule – Wilhelmshaven</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>6789.000</td>\n",
|
||
" <td>Friedrich-Paffrath-Straße 101</td>\n",
|
||
" <td>53.547870</td>\n",
|
||
" <td>8.088040</td>\n",
|
||
" <td>26389 Wilhelmshaven</td>\n",
|
||
" <td>https://www.jade-hs.de/fileadmin/layout2016/as...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35</th>\n",
|
||
" <td>Jade Hochschule – Oldenburg</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>6789.000</td>\n",
|
||
" <td>Ofener Str. 16/19</td>\n",
|
||
" <td>53.141790</td>\n",
|
||
" <td>8.202130</td>\n",
|
||
" <td>26121 Oldenburg</td>\n",
|
||
" <td>https://www.jade-hs.de/fileadmin/layout2016/as...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>36</th>\n",
|
||
" <td>Jade Hochschule – Elsfleth</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>6789.000</td>\n",
|
||
" <td>Weserstraße 52</td>\n",
|
||
" <td>53.242440</td>\n",
|
||
" <td>8.466510</td>\n",
|
||
" <td>26931 Elsfleth</td>\n",
|
||
" <td>https://www.jade-hs.de/fileadmin/layout2016/as...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>37</th>\n",
|
||
" <td>Steuerakademie Niedersachsen Rinteln</td>\n",
|
||
" <td>University of Administration</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2006</td>\n",
|
||
" <td>500.000</td>\n",
|
||
" <td>Wilhelm-Busch-Weg 29</td>\n",
|
||
" <td>52.206960</td>\n",
|
||
" <td>9.091120</td>\n",
|
||
" <td>31737 Rinteln</td>\n",
|
||
" <td>https://www.steuerakademie.niedersachsen.de/as...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>38</th>\n",
|
||
" <td>Steuerakademie Niedersachsen Bad Eilsen</td>\n",
|
||
" <td>University of Administration</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2006</td>\n",
|
||
" <td>500.000</td>\n",
|
||
" <td>Bahnhofstraße 5</td>\n",
|
||
" <td>52.239810</td>\n",
|
||
" <td>9.104230</td>\n",
|
||
" <td>31707 Bad Eilsen</td>\n",
|
||
" <td>https://www.steuerakademie.niedersachsen.de/as...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" University name \\\n",
|
||
"0 Hochschule für Bildende Künste Braunschweig \n",
|
||
"1 Technische Universität Carolo-Wilhelmina zu Br... \n",
|
||
"2 Hochschule 21 \n",
|
||
"3 Technische Universität Clausthal \n",
|
||
"4 Hochschule Emden/Leer \n",
|
||
"5 PFH – Private Hochschule Göttingen \n",
|
||
"6 Georg-August-Universität Göttingen \n",
|
||
"7 Fachhochschule für die Wirtschaft Hannover \n",
|
||
"8 Hochschule Hannover \n",
|
||
"9 Hochschule für Musik, Theater und Medien Hannover \n",
|
||
"10 Leibniz-Fachhochschule \n",
|
||
"11 Medizinische Hochschule Hannover (MHH) \n",
|
||
"12 Stiftung Tierärztliche Hochschule Hannover \n",
|
||
"13 Gottfried Wilhelm Leibniz Universität Hannover \n",
|
||
"14 Fachhochschule für Interkulturelle Theologie H... \n",
|
||
"15 Universität Hildesheim \n",
|
||
"16 HAWK Hochschule für angewandte Wissenschaft un... \n",
|
||
"17 HAWK Hochschule für angewandte Wissenschaft un... \n",
|
||
"18 HAWK Hochschule für angewandte Wissenschaft un... \n",
|
||
"19 Leuphana Universität Lüneburg \n",
|
||
"20 Norddeutsche Hochschule für Rechtspflege – Nie... \n",
|
||
"21 Kommunale Hochschule für Verwaltung in Nieders... \n",
|
||
"22 Carl von Ossietzky Universität Oldenburg\\n \n",
|
||
"23 Hochschule Osnabrück \n",
|
||
"24 Universität Osnabrück \n",
|
||
"25 Hochschule Braunschweig/Wolfenbüttel, Ostfalia... \n",
|
||
"26 Hochschule Wolfsburg, Ostfalia Hochschule für ... \n",
|
||
"27 Hochschule Suderburg, Ostfalia Hochschule für ... \n",
|
||
"28 Hochschule Salzgitter, Ostfalia Hochschule für... \n",
|
||
"29 Hochschule für Künste im Sozialen, Ottersberg \n",
|
||
"30 Private Hochschule für Wirtschaft und Technik ... \n",
|
||
"31 Private Hochschule für Wirtschaft und Technik ... \n",
|
||
"32 Universität Vechta \n",
|
||
"33 Hochschule Weserbergland \n",
|
||
"34 Jade Hochschule – Wilhelmshaven \n",
|
||
"35 Jade Hochschule – Oldenburg \n",
|
||
"36 Jade Hochschule – Elsfleth \n",
|
||
"37 Steuerakademie Niedersachsen Rinteln \n",
|
||
"38 Steuerakademie Niedersachsen Bad Eilsen \n",
|
||
"\n",
|
||
" Type of university Sponsorship Right of promotion \\\n",
|
||
"0 Artistic university public yes \n",
|
||
"1 University public yes \n",
|
||
"2 University of Applied Sciences privat no \n",
|
||
"3 University public yes \n",
|
||
"4 University of Applied Sciences public no \n",
|
||
"5 University of Applied Sciences privat no \n",
|
||
"6 University public yes \n",
|
||
"7 University of Applied Sciences privat no \n",
|
||
"8 University of Applied Sciences public no \n",
|
||
"9 Artistic university public yes \n",
|
||
"10 University of Applied Sciences privat no \n",
|
||
"11 University public yes \n",
|
||
"12 University public yes \n",
|
||
"13 University public yes \n",
|
||
"14 University of Applied Sciences privat no \n",
|
||
"15 University public yes \n",
|
||
"16 University of Applied Sciences public no \n",
|
||
"17 University of Applied Sciences public no \n",
|
||
"18 University of Applied Sciences public no \n",
|
||
"19 University public yes \n",
|
||
"20 University of Administration public no \n",
|
||
"21 University of Administration public no \n",
|
||
"22 University public yes \n",
|
||
"23 University of Applied Sciences public no \n",
|
||
"24 University public yes \n",
|
||
"25 University of Applied Sciences public no \n",
|
||
"26 University of Applied Sciences public no \n",
|
||
"27 University of Applied Sciences public no \n",
|
||
"28 University of Applied Sciences public no \n",
|
||
"29 University of Applied Sciences privat no \n",
|
||
"30 University of Applied Sciences privat no \n",
|
||
"31 University of Applied Sciences privat no \n",
|
||
"32 University public yes \n",
|
||
"33 University of Applied Sciences privat no \n",
|
||
"34 University of Applied Sciences public no \n",
|
||
"35 University of Applied Sciences public no \n",
|
||
"36 University of Applied Sciences public no \n",
|
||
"37 University of Administration public no \n",
|
||
"38 University of Administration public no \n",
|
||
"\n",
|
||
" Founding year Number of students Address \\\n",
|
||
"0 1963 976.000 Johannes-Selenka-Platz 1 \n",
|
||
"1 1745 17709.000 Universitätspl. 2 \n",
|
||
"2 2005 1084.000 Harburger Str. 6 \n",
|
||
"3 1775 3446.000 Adolph-Roemer-Straße 2A \n",
|
||
"4 2009 4481.000 Constantiapl. 4 \n",
|
||
"5 1995 4226.000 Weender Landstraße 3-7 \n",
|
||
"6 1737 28614.000 Wilhelmsplatz 1 \n",
|
||
"7 1996 641.000 Freundallee 15 \n",
|
||
"8 1971 9209.000 Ricklinger Stadtweg 120 \n",
|
||
"9 1897 1409.000 Neues Haus 1 \n",
|
||
"10 1920 589.000 Expo Plaza 11 \n",
|
||
"11 1963 3778.000 Carl-Neuberg-Straße 1 \n",
|
||
"12 1778 2381.000 Bünteweg 2 \n",
|
||
"13 1831 28935.000 Welfengarten 1 \n",
|
||
"14 2012 91.000 Missionsstraße 3-5 \n",
|
||
"15 1978 8378.000 Universitätspl. 1 \n",
|
||
"16 1971 6495.000 Hohnsen 4 \n",
|
||
"17 1971 6495.000 Haarmannpl. 3 \n",
|
||
"18 1971 6495.000 Von-Ossietzky-Straße 99 \n",
|
||
"19 1946 6497.000 Universitätsallee 1 \n",
|
||
"20 2007 6409.000 Godehardspl. 6 \n",
|
||
"21 2007 1570.000 Wielandstraße 8 \n",
|
||
"22 1973 15635.000 Uhlhornsweg 49-55 \n",
|
||
"23 1971 13620.000 Albrechtstraße 30 \n",
|
||
"24 1973 13640.000 Neuer Graben 29 \n",
|
||
"25 1971 11577.000 Salzdahlumer Str. 46/48 \n",
|
||
"26 1971 11577.000 Robert-Koch-Platz 8A \n",
|
||
"27 1971 11577.000 Herbert-Meyer-Straße 7 \n",
|
||
"28 1971 11577.000 Karl-Scharfenberg-Straße 55/57 \n",
|
||
"29 1967 342.000 Große Str. 107 \n",
|
||
"30 1998 558.000 Rombergstraße 40 \n",
|
||
"31 1998 558.000 Schlesier Str. 13A \n",
|
||
"32 1995 4.551 Driverstraße 22 \n",
|
||
"33 2010 485.000 Am Stockhof 2 \n",
|
||
"34 2009 6789.000 Friedrich-Paffrath-Straße 101 \n",
|
||
"35 2009 6789.000 Ofener Str. 16/19 \n",
|
||
"36 2009 6789.000 Weserstraße 52 \n",
|
||
"37 2006 500.000 Wilhelm-Busch-Weg 29 \n",
|
||
"38 2006 500.000 Bahnhofstraße 5 \n",
|
||
"\n",
|
||
" lat lon plz \\\n",
|
||
"0 52.257738 10.502315 38118 Braunschweig \n",
|
||
"1 52.273550 10.530097 38106 Braunschweig \n",
|
||
"2 53.477650 9.704650 21614 Buxtehude \n",
|
||
"3 51.804840 10.334110 38678 Clausthal-Zellerfeld \n",
|
||
"4 53.368160 7.181410 26723 Emden \n",
|
||
"5 51.538910 9.933220 37073 Göttingen \n",
|
||
"6 51.534070 9.937850 37073 Göttingen \n",
|
||
"7 52.366200 9.772470 30173 Hannover \n",
|
||
"8 52.354190 9.722380 30459 Hannover \n",
|
||
"9 52.377380 9.753920 30175 Hannover \n",
|
||
"10 52.321150 9.818680 30539 Hannover \n",
|
||
"11 52.384050 9.806030 30625 Hannover \n",
|
||
"12 52.354680 9.797730 30559 Hannover \n",
|
||
"13 52.382250 9.717770 30167 Hannover \n",
|
||
"14 52.708843 10.140710 29320 Südheide \n",
|
||
"15 52.134010 9.974690 31141 Hildesheim \n",
|
||
"16 52.142460 9.957980 31134 Hildesheim \n",
|
||
"17 51.827260 9.450690 37603 Holzminden \n",
|
||
"18 51.521750 9.969670 37085 Göttingen \n",
|
||
"19 53.228531 10.401710 21335 Lüneburg \n",
|
||
"20 52.144840 9.949230 31134 Hildesheim \n",
|
||
"21 52.370500 9.722390 30169 Hannover \n",
|
||
"22 53.147340 8.179020 26129 Oldenburg \n",
|
||
"23 52.282680 8.025010 49076 Osnabrück \n",
|
||
"24 52.271370 8.044540 49074 Osnabrück \n",
|
||
"25 52.176830 10.548650 38302 Wolfenbüttel \n",
|
||
"26 52.425950 10.787110 38440 Wolfsburg \n",
|
||
"27 52.897610 10.446590 29556 Suderburg \n",
|
||
"28 52.087240 10.380550 38229 Salzgitter \n",
|
||
"29 53.106680 9.163100 28870 Ottersberg \n",
|
||
"30 52.721250 8.278910 49377 Vechta \n",
|
||
"31 52.611710 8.363340 49356 Diepholz \n",
|
||
"32 52.721170 8.293800 49377 Vechta \n",
|
||
"33 52.098750 9.355420 31785 Hameln \n",
|
||
"34 53.547870 8.088040 26389 Wilhelmshaven \n",
|
||
"35 53.141790 8.202130 26121 Oldenburg \n",
|
||
"36 53.242440 8.466510 26931 Elsfleth \n",
|
||
"37 52.206960 9.091120 31737 Rinteln \n",
|
||
"38 52.239810 9.104230 31707 Bad Eilsen \n",
|
||
"\n",
|
||
" pic \n",
|
||
"0 https://www.hbk-bs.de/fileadmin/_processed_/5/... \n",
|
||
"1 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"2 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"3 https://www.presse.tu-clausthal.de/fileadmin/T... \n",
|
||
"4 https://sta-hisweb.hs-emden-leer.de/QIS/images... \n",
|
||
"5 https://goettingen-campus.de/fileadmin/_proces... \n",
|
||
"6 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"7 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"8 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"9 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"10 https://www.visit-hannover.com/var/storage/ima... \n",
|
||
"11 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"12 https://upload.wikimedia.org/wikipedia/de/thum... \n",
|
||
"13 https://www.uni-hannover.de/fileadmin/_process... \n",
|
||
"14 https://cdn.max-e5.info/damfiles/logo/fh_herma... \n",
|
||
"15 https://www.uni-hildesheim.de/media/_processed... \n",
|
||
"16 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"17 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"18 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"19 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"20 https://static.studycheck.de/media/images/inst... \n",
|
||
"21 https://www.nsi-hsvn.de/fileadmin/user_upload/... \n",
|
||
"22 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"23 https://login.hs-osnabrueck.de/nidp/hsos/image... \n",
|
||
"24 https://www.eh-tabor.de/sites/default/files/st... \n",
|
||
"25 https://www.ostfalia.de/export/system/modules/... \n",
|
||
"26 https://www.ostfalia.de/export/system/modules/... \n",
|
||
"27 https://www.ostfalia.de/export/system/modules/... \n",
|
||
"28 https://www.ostfalia.de/export/system/modules/... \n",
|
||
"29 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"30 https://www.phwt.de/wp-content/uploads/2020/09... \n",
|
||
"31 https://www.phwt.de/wp-content/uploads/2020/09... \n",
|
||
"32 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"33 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"34 https://www.jade-hs.de/fileadmin/layout2016/as... \n",
|
||
"35 https://www.jade-hs.de/fileadmin/layout2016/as... \n",
|
||
"36 https://www.jade-hs.de/fileadmin/layout2016/as... \n",
|
||
"37 https://www.steuerakademie.niedersachsen.de/as... \n",
|
||
"38 https://www.steuerakademie.niedersachsen.de/as... "
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "586d1ac8-2030-48e2-a662-531c4de9b9cf",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-dff62291b3fc7ab4",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Um die Ausgabe auf ein wenige Elemente zu beschränken können die Funktion `head` und `tail` verwendet werden, die jeweils einen Eingabeparameter nehmen zu welcher Zeile Sie von oben bzw. unten den DataFrame anzeigen sollen:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "a0243d3a-1f2f-4fe9-b134-f13b37d8fc59",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-3fd0f85fb2983ec4",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>University name</th>\n",
|
||
" <th>Type of university</th>\n",
|
||
" <th>Sponsorship</th>\n",
|
||
" <th>Right of promotion</th>\n",
|
||
" <th>Founding year</th>\n",
|
||
" <th>Number of students</th>\n",
|
||
" <th>Address</th>\n",
|
||
" <th>lat</th>\n",
|
||
" <th>lon</th>\n",
|
||
" <th>plz</th>\n",
|
||
" <th>pic</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Hochschule für Bildende Künste Braunschweig</td>\n",
|
||
" <td>Artistic university</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1963</td>\n",
|
||
" <td>976.0</td>\n",
|
||
" <td>Johannes-Selenka-Platz 1</td>\n",
|
||
" <td>52.257738</td>\n",
|
||
" <td>10.502315</td>\n",
|
||
" <td>38118 Braunschweig</td>\n",
|
||
" <td>https://www.hbk-bs.de/fileadmin/_processed_/5/...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Technische Universität Carolo-Wilhelmina zu Br...</td>\n",
|
||
" <td>University</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>yes</td>\n",
|
||
" <td>1745</td>\n",
|
||
" <td>17709.0</td>\n",
|
||
" <td>Universitätspl. 2</td>\n",
|
||
" <td>52.273550</td>\n",
|
||
" <td>10.530097</td>\n",
|
||
" <td>38106 Braunschweig</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Hochschule 21</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2005</td>\n",
|
||
" <td>1084.0</td>\n",
|
||
" <td>Harburger Str. 6</td>\n",
|
||
" <td>53.477650</td>\n",
|
||
" <td>9.704650</td>\n",
|
||
" <td>21614 Buxtehude</td>\n",
|
||
" <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" University name \\\n",
|
||
"0 Hochschule für Bildende Künste Braunschweig \n",
|
||
"1 Technische Universität Carolo-Wilhelmina zu Br... \n",
|
||
"2 Hochschule 21 \n",
|
||
"\n",
|
||
" Type of university Sponsorship Right of promotion \\\n",
|
||
"0 Artistic university public yes \n",
|
||
"1 University public yes \n",
|
||
"2 University of Applied Sciences privat no \n",
|
||
"\n",
|
||
" Founding year Number of students Address lat \\\n",
|
||
"0 1963 976.0 Johannes-Selenka-Platz 1 52.257738 \n",
|
||
"1 1745 17709.0 Universitätspl. 2 52.273550 \n",
|
||
"2 2005 1084.0 Harburger Str. 6 53.477650 \n",
|
||
"\n",
|
||
" lon plz \\\n",
|
||
"0 10.502315 38118 Braunschweig \n",
|
||
"1 10.530097 38106 Braunschweig \n",
|
||
"2 9.704650 21614 Buxtehude \n",
|
||
"\n",
|
||
" pic \n",
|
||
"0 https://www.hbk-bs.de/fileadmin/_processed_/5/... \n",
|
||
"1 https://upload.wikimedia.org/wikipedia/commons... \n",
|
||
"2 https://upload.wikimedia.org/wikipedia/commons... "
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd.head(3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "57b9dcd0-b60f-434d-86c9-8786304e213f",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-6848848e5caa4c9e",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>University name</th>\n",
|
||
" <th>Type of university</th>\n",
|
||
" <th>Sponsorship</th>\n",
|
||
" <th>Right of promotion</th>\n",
|
||
" <th>Founding year</th>\n",
|
||
" <th>Number of students</th>\n",
|
||
" <th>Address</th>\n",
|
||
" <th>lat</th>\n",
|
||
" <th>lon</th>\n",
|
||
" <th>plz</th>\n",
|
||
" <th>pic</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>36</th>\n",
|
||
" <td>Jade Hochschule – Elsfleth</td>\n",
|
||
" <td>University of Applied Sciences</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>6789.0</td>\n",
|
||
" <td>Weserstraße 52</td>\n",
|
||
" <td>53.24244</td>\n",
|
||
" <td>8.46651</td>\n",
|
||
" <td>26931 Elsfleth</td>\n",
|
||
" <td>https://www.jade-hs.de/fileadmin/layout2016/as...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>37</th>\n",
|
||
" <td>Steuerakademie Niedersachsen Rinteln</td>\n",
|
||
" <td>University of Administration</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2006</td>\n",
|
||
" <td>500.0</td>\n",
|
||
" <td>Wilhelm-Busch-Weg 29</td>\n",
|
||
" <td>52.20696</td>\n",
|
||
" <td>9.09112</td>\n",
|
||
" <td>31737 Rinteln</td>\n",
|
||
" <td>https://www.steuerakademie.niedersachsen.de/as...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>38</th>\n",
|
||
" <td>Steuerakademie Niedersachsen Bad Eilsen</td>\n",
|
||
" <td>University of Administration</td>\n",
|
||
" <td>public</td>\n",
|
||
" <td>no</td>\n",
|
||
" <td>2006</td>\n",
|
||
" <td>500.0</td>\n",
|
||
" <td>Bahnhofstraße 5</td>\n",
|
||
" <td>52.23981</td>\n",
|
||
" <td>9.10423</td>\n",
|
||
" <td>31707 Bad Eilsen</td>\n",
|
||
" <td>https://www.steuerakademie.niedersachsen.de/as...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" University name Type of university \\\n",
|
||
"36 Jade Hochschule – Elsfleth University of Applied Sciences \n",
|
||
"37 Steuerakademie Niedersachsen Rinteln University of Administration \n",
|
||
"38 Steuerakademie Niedersachsen Bad Eilsen University of Administration \n",
|
||
"\n",
|
||
" Sponsorship Right of promotion Founding year Number of students \\\n",
|
||
"36 public no 2009 6789.0 \n",
|
||
"37 public no 2006 500.0 \n",
|
||
"38 public no 2006 500.0 \n",
|
||
"\n",
|
||
" Address lat lon plz \\\n",
|
||
"36 Weserstraße 52 53.24244 8.46651 26931 Elsfleth \n",
|
||
"37 Wilhelm-Busch-Weg 29 52.20696 9.09112 31737 Rinteln \n",
|
||
"38 Bahnhofstraße 5 52.23981 9.10423 31707 Bad Eilsen \n",
|
||
"\n",
|
||
" pic \n",
|
||
"36 https://www.jade-hs.de/fileadmin/layout2016/as... \n",
|
||
"37 https://www.steuerakademie.niedersachsen.de/as... \n",
|
||
"38 https://www.steuerakademie.niedersachsen.de/as... "
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd.tail(3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7573347e-593f-4dd8-a508-5037eb422628",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-cfdfb8a099225918",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Um zu prüfen welche Typen pandas den einzelnen Spalten gegeben hat könne Sie das Attribut `dtypes` verwenden."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "36d674fe-56ff-4610-8086-adae93bdae62",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-900e418ddc2a0b14",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"University name object\n",
|
||
"Type of university object\n",
|
||
"Sponsorship object\n",
|
||
"Right of promotion object\n",
|
||
"Founding year int64\n",
|
||
"Number of students float64\n",
|
||
"Address object\n",
|
||
"lat float64\n",
|
||
"lon float64\n",
|
||
"plz object\n",
|
||
"pic object\n",
|
||
"dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd.dtypes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "093b47e6-7d60-4507-b1d4-e17be1409bed",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-502b1bd3f4ce3db1",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Um eine gesamt Übersicht des Dataframes zu bekommen nutzen Sie die Funktion `info`.\n",
|
||
"\n",
|
||
"Aus dieser können Sie entnehmen in welcher Spalte _#_ wie viele Elemente _Non-Null Count_ vorhanden sind; den Namen der Spalte _Column_ und wie Pandas die Werte der Spalte _dtype_ interpretiert."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "2ea51488-4dcb-4840-aae8-95bf54c226d0",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-c47dd110d6a97b52",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 39 entries, 0 to 38\n",
|
||
"Data columns (total 11 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 University name 39 non-null object \n",
|
||
" 1 Type of university 39 non-null object \n",
|
||
" 2 Sponsorship 39 non-null object \n",
|
||
" 3 Right of promotion 39 non-null object \n",
|
||
" 4 Founding year 39 non-null int64 \n",
|
||
" 5 Number of students 39 non-null float64\n",
|
||
" 6 Address 39 non-null object \n",
|
||
" 7 lat 39 non-null float64\n",
|
||
" 8 lon 39 non-null float64\n",
|
||
" 9 plz 39 non-null object \n",
|
||
" 10 pic 39 non-null object \n",
|
||
"dtypes: float64(3), int64(1), object(7)\n",
|
||
"memory usage: 3.5+ KB\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd.info()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "46bdc364-7bd2-4b50-bb74-b90d92312465",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-d64d555865daff61",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"# Selecting Subsets\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"Wie Sie bereits aus dem ersten Kapitel Wissen können Sie einzelne Spalten mittels Schlüsselzugriff selektieren. Um mehr als eine Spalte zu Selektieren geben Sie dem Dataframe eine Liste der Schlüssel die sie auswählen möchten mit. Für alle weiterführenden Operationen zum Selektieren von Subsets lesen Sie gerne den [Getting started](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) Guide zu Subset Data.\n",
|
||
"\n",
|
||
"Beispiel Selektion der Spalten _Sponsorship_ & _Founding year_: "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "4078322c-7354-41ae-a9fa-3af389e37d41",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-d98a6a7763f9ebc7",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Sponsorship</th>\n",
|
||
" <th>Founding year</th>\n",
|
||
" <th>University name</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1963</td>\n",
|
||
" <td>Hochschule für Bildende Künste Braunschweig</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1745</td>\n",
|
||
" <td>Technische Universität Carolo-Wilhelmina zu Br...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>2005</td>\n",
|
||
" <td>Hochschule 21</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1775</td>\n",
|
||
" <td>Technische Universität Clausthal</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>Hochschule Emden/Leer</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1995</td>\n",
|
||
" <td>PFH – Private Hochschule Göttingen</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1737</td>\n",
|
||
" <td>Georg-August-Universität Göttingen</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1996</td>\n",
|
||
" <td>Fachhochschule für die Wirtschaft Hannover</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>Hochschule Hannover</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1897</td>\n",
|
||
" <td>Hochschule für Musik, Theater und Medien Hannover</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1920</td>\n",
|
||
" <td>Leibniz-Fachhochschule</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1963</td>\n",
|
||
" <td>Medizinische Hochschule Hannover (MHH)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1778</td>\n",
|
||
" <td>Stiftung Tierärztliche Hochschule Hannover</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1831</td>\n",
|
||
" <td>Gottfried Wilhelm Leibniz Universität Hannover</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>2012</td>\n",
|
||
" <td>Fachhochschule für Interkulturelle Theologie H...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1978</td>\n",
|
||
" <td>Universität Hildesheim</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>HAWK Hochschule für angewandte Wissenschaft un...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>HAWK Hochschule für angewandte Wissenschaft un...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>HAWK Hochschule für angewandte Wissenschaft un...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>Leuphana Universität Lüneburg</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2007</td>\n",
|
||
" <td>Norddeutsche Hochschule für Rechtspflege – Nie...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2007</td>\n",
|
||
" <td>Kommunale Hochschule für Verwaltung in Nieders...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1973</td>\n",
|
||
" <td>Carl von Ossietzky Universität Oldenburg\\n</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>Hochschule Osnabrück</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1973</td>\n",
|
||
" <td>Universität Osnabrück</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>Hochschule Braunschweig/Wolfenbüttel, Ostfalia...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>Hochschule Wolfsburg, Ostfalia Hochschule für ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>Hochschule Suderburg, Ostfalia Hochschule für ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" <td>Hochschule Salzgitter, Ostfalia Hochschule für...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1967</td>\n",
|
||
" <td>Hochschule für Künste im Sozialen, Ottersberg</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>30</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1998</td>\n",
|
||
" <td>Private Hochschule für Wirtschaft und Technik ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>31</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1998</td>\n",
|
||
" <td>Private Hochschule für Wirtschaft und Technik ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>32</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1995</td>\n",
|
||
" <td>Universität Vechta</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>2010</td>\n",
|
||
" <td>Hochschule Weserbergland</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>34</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>Jade Hochschule – Wilhelmshaven</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>Jade Hochschule – Oldenburg</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>36</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>Jade Hochschule – Elsfleth</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>37</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2006</td>\n",
|
||
" <td>Steuerakademie Niedersachsen Rinteln</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>38</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2006</td>\n",
|
||
" <td>Steuerakademie Niedersachsen Bad Eilsen</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Sponsorship Founding year \\\n",
|
||
"0 public 1963 \n",
|
||
"1 public 1745 \n",
|
||
"2 privat 2005 \n",
|
||
"3 public 1775 \n",
|
||
"4 public 2009 \n",
|
||
"5 privat 1995 \n",
|
||
"6 public 1737 \n",
|
||
"7 privat 1996 \n",
|
||
"8 public 1971 \n",
|
||
"9 public 1897 \n",
|
||
"10 privat 1920 \n",
|
||
"11 public 1963 \n",
|
||
"12 public 1778 \n",
|
||
"13 public 1831 \n",
|
||
"14 privat 2012 \n",
|
||
"15 public 1978 \n",
|
||
"16 public 1971 \n",
|
||
"17 public 1971 \n",
|
||
"18 public 1971 \n",
|
||
"19 public 1946 \n",
|
||
"20 public 2007 \n",
|
||
"21 public 2007 \n",
|
||
"22 public 1973 \n",
|
||
"23 public 1971 \n",
|
||
"24 public 1973 \n",
|
||
"25 public 1971 \n",
|
||
"26 public 1971 \n",
|
||
"27 public 1971 \n",
|
||
"28 public 1971 \n",
|
||
"29 privat 1967 \n",
|
||
"30 privat 1998 \n",
|
||
"31 privat 1998 \n",
|
||
"32 public 1995 \n",
|
||
"33 privat 2010 \n",
|
||
"34 public 2009 \n",
|
||
"35 public 2009 \n",
|
||
"36 public 2009 \n",
|
||
"37 public 2006 \n",
|
||
"38 public 2006 \n",
|
||
"\n",
|
||
" University name \n",
|
||
"0 Hochschule für Bildende Künste Braunschweig \n",
|
||
"1 Technische Universität Carolo-Wilhelmina zu Br... \n",
|
||
"2 Hochschule 21 \n",
|
||
"3 Technische Universität Clausthal \n",
|
||
"4 Hochschule Emden/Leer \n",
|
||
"5 PFH – Private Hochschule Göttingen \n",
|
||
"6 Georg-August-Universität Göttingen \n",
|
||
"7 Fachhochschule für die Wirtschaft Hannover \n",
|
||
"8 Hochschule Hannover \n",
|
||
"9 Hochschule für Musik, Theater und Medien Hannover \n",
|
||
"10 Leibniz-Fachhochschule \n",
|
||
"11 Medizinische Hochschule Hannover (MHH) \n",
|
||
"12 Stiftung Tierärztliche Hochschule Hannover \n",
|
||
"13 Gottfried Wilhelm Leibniz Universität Hannover \n",
|
||
"14 Fachhochschule für Interkulturelle Theologie H... \n",
|
||
"15 Universität Hildesheim \n",
|
||
"16 HAWK Hochschule für angewandte Wissenschaft un... \n",
|
||
"17 HAWK Hochschule für angewandte Wissenschaft un... \n",
|
||
"18 HAWK Hochschule für angewandte Wissenschaft un... \n",
|
||
"19 Leuphana Universität Lüneburg \n",
|
||
"20 Norddeutsche Hochschule für Rechtspflege – Nie... \n",
|
||
"21 Kommunale Hochschule für Verwaltung in Nieders... \n",
|
||
"22 Carl von Ossietzky Universität Oldenburg\\n \n",
|
||
"23 Hochschule Osnabrück \n",
|
||
"24 Universität Osnabrück \n",
|
||
"25 Hochschule Braunschweig/Wolfenbüttel, Ostfalia... \n",
|
||
"26 Hochschule Wolfsburg, Ostfalia Hochschule für ... \n",
|
||
"27 Hochschule Suderburg, Ostfalia Hochschule für ... \n",
|
||
"28 Hochschule Salzgitter, Ostfalia Hochschule für... \n",
|
||
"29 Hochschule für Künste im Sozialen, Ottersberg \n",
|
||
"30 Private Hochschule für Wirtschaft und Technik ... \n",
|
||
"31 Private Hochschule für Wirtschaft und Technik ... \n",
|
||
"32 Universität Vechta \n",
|
||
"33 Hochschule Weserbergland \n",
|
||
"34 Jade Hochschule – Wilhelmshaven \n",
|
||
"35 Jade Hochschule – Oldenburg \n",
|
||
"36 Jade Hochschule – Elsfleth \n",
|
||
"37 Steuerakademie Niedersachsen Rinteln \n",
|
||
"38 Steuerakademie Niedersachsen Bad Eilsen "
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd[[\"Sponsorship\", \"Founding year\", \"University name\"]]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bef4ece8-00b0-486d-bba2-7f19e885cf6a",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-289af023470b6b19",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Aufgabe\n",
|
||
"\n",
|
||
"*2 Punkte*\n",
|
||
"\n",
|
||
"Selektiere die Spalten _University name_, _Founding year_ & _Number of students_, speichern sie ihr Ergebnis in der Variablen `select`.\n",
|
||
"\n",
|
||
"Gebe danach die ersten 5 Werte von Oben der Selektion aus."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "040fa689-6062-44db-a6ea-b58113e41b65",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-2d560e3a83f1c48a",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>University name</th>\n",
|
||
" <th>Founding year</th>\n",
|
||
" <th>Number of students</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Hochschule für Bildende Künste Braunschweig</td>\n",
|
||
" <td>1963</td>\n",
|
||
" <td>976.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Technische Universität Carolo-Wilhelmina zu Br...</td>\n",
|
||
" <td>1745</td>\n",
|
||
" <td>17709.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Hochschule 21</td>\n",
|
||
" <td>2005</td>\n",
|
||
" <td>1084.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Technische Universität Clausthal</td>\n",
|
||
" <td>1775</td>\n",
|
||
" <td>3446.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Hochschule Emden/Leer</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>4481.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" University name Founding year \\\n",
|
||
"0 Hochschule für Bildende Künste Braunschweig 1963 \n",
|
||
"1 Technische Universität Carolo-Wilhelmina zu Br... 1745 \n",
|
||
"2 Hochschule 21 2005 \n",
|
||
"3 Technische Universität Clausthal 1775 \n",
|
||
"4 Hochschule Emden/Leer 2009 \n",
|
||
"\n",
|
||
" Number of students \n",
|
||
"0 976.0 \n",
|
||
"1 17709.0 \n",
|
||
"2 1084.0 \n",
|
||
"3 3446.0 \n",
|
||
"4 4481.0 "
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"select = None\n",
|
||
"\n",
|
||
"### BEGIN SOLUTION\n",
|
||
"select = unis_nd[[\"University name\", \"Founding year\", \"Number of students\"]]\n",
|
||
"select.head(5)\n",
|
||
"### END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "5bd633ac-afd9-4731-9c00-b494331d2dfb",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "cell-108386a4387dbcc7",
|
||
"locked": true,
|
||
"points": 2,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Hier werden ihre Lösungen getestet\n",
|
||
"assert isinstance(select, pd.DataFrame)\n",
|
||
"assert list(select.keys()) == [\"University name\", \"Founding year\", \"Number of students\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e9751208-b181-4764-b436-a57c5313d288",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-4fa720449b4af62e",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"# Statistische Funktionen\n",
|
||
"\n",
|
||
"Wie in der Dokumentation beschrieben unterstützt Pandas verschiendene Statistische Funktion, welche direkt auf einen Data Frame angewendet werden können.\n",
|
||
"\n",
|
||
"Als Beispiel wird die Funktion `value_counts` auf die Spalte _Type of university_ gezeigt und im darauffolgenden Schritt als Kuchendiagramm geplottet. Das Ergebnis in der Variablen `count` ist eine `pd.Series`.\n",
|
||
"\n",
|
||
"Editor Side Note: Für den Plot verwende ich das Stylesheet von dhaitz, eines meiner absoluten Favouriten, dieses und mehrere finden Sie unter [github.com dhaitz](https://github.com/dhaitz/matplotlib-stylesheets). Mittels `plt.style.use` lassen sich externe Stylesheets verwenden."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "14cc37af-13fa-49ce-af37-d327b16bda73",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-daa584941e3d78fe",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Type of university\n",
|
||
"University of Applied Sciences 22\n",
|
||
"University 11\n",
|
||
"University of Administration 4\n",
|
||
"Artistic university 2\n",
|
||
"Name: count, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"count = unis_nd[\"Type of university\"].value_counts()\n",
|
||
"count"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "d8eff47d-e2e7-46c1-94ae-d344b3b62e0f",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-ea7ef2c3a9ada940",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"format = lambda s: f'Quantity: %d\\n' % s + f'In Percent: %1.1f%%' % s\n",
|
||
"\n",
|
||
"plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle') # Using my favourite Style\n",
|
||
"plt.pie(count, labels=count.keys(), autopct=format, startangle=45, explode=[0, 0, 0, 0.2])\n",
|
||
"plt.title(\"Types of Universities in lower Saxony (in %)\")\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1104848a-c149-4ef0-8835-aee9f4ad9f36",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-1c947f45fd0b2759",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Aufgabe\n",
|
||
"\n",
|
||
"*6 Punkte*\n",
|
||
"\n",
|
||
"Erstelle eine visualisierung, welche die Unterschiede zwischen staatlichen & privaten Hochschulen in Niedersachsen zeigt.\n",
|
||
"\n",
|
||
"Die dazugehörige Spalte im Datenset ist _Sponsorship_. Finde eine geeignete Darstellung.\n",
|
||
"\n",
|
||
"Erkläre anschließend deine Visualisierung und warum du dich für diese Entschieden hast. (0 Punkte bei keiner Erklärung)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "1892e3fc-4d89-4966-bb36-b691a3d16ea4",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "cell-fa553fbf09ed469b",
|
||
"locked": false,
|
||
"points": 2,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"### BEGIN SOLUTION\n",
|
||
"c = unis_nd[\"Sponsorship\"].value_counts()\n",
|
||
"plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle')\n",
|
||
"plt.bar(c.keys(), c, color=[\"black\", \"white\"])\n",
|
||
"plt.show()\n",
|
||
"### END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e7b7d860-7068-43a8-a7b6-8dfd708c3597",
|
||
"metadata": {},
|
||
"source": [
|
||
"### BEGIN SOLUTION\n",
|
||
"### END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1b420d70-9b7c-4e36-9b59-a44316af189a",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-cdcf1d5c71a5bfee",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"# Import Seaborn\n",
|
||
"\n",
|
||
"Auch hier waren die Entwickler uns einen Schritt vorraus und bevorzugen den Import von Seaborn mit dem Kürzel `sns`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "b4e4202d-cf3f-4fa0-a66e-44ac08902cf2",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-0ab00ccadf1d74a7",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt # Using it alongside sns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d10ef485-e7c4-4889-9e4f-61c765bf9384",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-25175b946c35845e",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Setting a Theme\n",
|
||
"\n",
|
||
"Auch wenn MatPlotLib es möglichmacht hat seaborn eine simpleren Weg Themes anzuwenden. Schaue dir daher gerne die vorinstallierten Styles unter [Python Graph Gallery](https://python-graph-gallery.com/104-seaborn-themes/) an. Für die Folgenden Beispiele wird das Theme `darkgrid` verwendet.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "1469effb-088e-4721-9a9c-f2fcc1423a1d",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-7587373683f9bc52",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"sns.set_style('darkgrid')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "56a7f8ff-8899-4973-be0b-7a680302bfa4",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-3151571f68fe985d",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Multidimensional Data Plotting\n",
|
||
"\n",
|
||
"Schauen wir uns im Folgenden an, wie sich verschiedene Normalverteilte Größen in einem Plot unterbringen lassen.\n",
|
||
"\n",
|
||
"Dazu gehen wir davon aus das wir 60 Bienen, 3 verschiedener Arten gefangen haben und diese auf Länge und Anzahl an Parasiten untersuchen. Zunächst lässt sich feststellen das die Messwerkzeuge zum messen der Körperlängen nur auf 1 Dezimalstelle genau sind. \n",
|
||
"Die Anzahl der Parasiten ist immer eine ganze Zahl, da wir keine Rückstände halber/toter Parasiten messen wollen. Diese werden zusammengebracht und mittels Seaborn geplottet.\n",
|
||
"\n",
|
||
"\n",
|
||
"Hierzu werden 3 Bienenarten verwendet:\n",
|
||
"\n",
|
||
"|Biene|$\\mu$|$\\sigma$|Maximale Anzahl Parasiten|\n",
|
||
"|-|-|-|-|\n",
|
||
"|Fuchsrote Lockensandbiene|13 mm|1 mm|3|\n",
|
||
"|Ackerhummel|12 mm|3 mm|4|\n",
|
||
"|Platterbsen-Mörtelbiene|14 mm|1 mm|2|\n",
|
||
"\n",
|
||
"Im folgenden samplen wir von jeder Normalverteilten Körpergrößen der einzelnen Bienenarten 60 Samples:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "4a63a9b6-4a17-46d0-8708-b3ab35e329b1",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-54d90be534e41353",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"rng = np.random.default_rng(42)\n",
|
||
"samples = 60\n",
|
||
"\n",
|
||
"# Sample lengths\n",
|
||
"lockensandbiene_len = rng.normal(13, 1, samples)\n",
|
||
"ackerhummel_len = rng.normal(12, 3, samples)\n",
|
||
"mörtelbiene_len = rng.normal(14, 1, samples)\n",
|
||
"\n",
|
||
"# Sample Parasites\n",
|
||
"lockensandbiene_par = rng.uniform(0, 3, samples)\n",
|
||
"ackerhummel_par = rng.uniform(0, 4, samples)\n",
|
||
"mörtelbiene_par = rng.uniform(0, 2, samples)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e5f1d8d4-0910-4a65-91f3-26c18afdfcbe",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-3154b2c952695029",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Aus den Samples lässt sich ein Dataset erstellen die wichtigen Attribute sind `species`, `length` & `parasites`. Nutzen wir dafür eine Dataclass erübrigt sich der Aufwand über listen."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "e5cbfbe6-bf9a-4ab8-8a82-c0f7310907d3",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-a38996eee69926d9",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from dataclasses import dataclass\n",
|
||
"\n",
|
||
"@dataclass\n",
|
||
"class Bee:\n",
|
||
" species: str\n",
|
||
" length: np.float64\n",
|
||
" parasites: np.int64\n",
|
||
"\n",
|
||
"bees = list()\n",
|
||
"\n",
|
||
"# For every samples bee append to bees\n",
|
||
"for bee_len, bee_par in zip(lockensandbiene_len, lockensandbiene_par):\n",
|
||
" b = Bee(species='Fuchsrote Lockensandbiene', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n",
|
||
" bees.append(b)\n",
|
||
"for bee_len, bee_par in zip(ackerhummel_len, ackerhummel_par):\n",
|
||
" b = Bee(species='Ackerhummel', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n",
|
||
" bees.append(b)\n",
|
||
"for bee_len, bee_par in zip(mörtelbiene_len, mörtelbiene_par):\n",
|
||
" b = Bee(species='Platterbsen-Mörtelbiene', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n",
|
||
" bees.append(b)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "453420f2-bfa5-448f-b353-7d2c150c86c8",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-1cffcdd63ad3fb6c",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Da ab diesem Schritt alle gesampleten Bienen geordnet und deklariert sind können wir mittels pandas einfach einen Dataframe erstellen und diesen speichern:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"id": "e4954edf-ecf9-4097-aab3-de0abf5bc7e5",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-b17aafac68bb15b8",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>species</th>\n",
|
||
" <th>length</th>\n",
|
||
" <th>parasites</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Fuchsrote Lockensandbiene</td>\n",
|
||
" <td>13.3</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Fuchsrote Lockensandbiene</td>\n",
|
||
" <td>12.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Fuchsrote Lockensandbiene</td>\n",
|
||
" <td>13.8</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Fuchsrote Lockensandbiene</td>\n",
|
||
" <td>13.9</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Fuchsrote Lockensandbiene</td>\n",
|
||
" <td>11.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>175</th>\n",
|
||
" <td>Platterbsen-Mörtelbiene</td>\n",
|
||
" <td>14.2</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>176</th>\n",
|
||
" <td>Platterbsen-Mörtelbiene</td>\n",
|
||
" <td>15.6</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>177</th>\n",
|
||
" <td>Platterbsen-Mörtelbiene</td>\n",
|
||
" <td>14.2</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>178</th>\n",
|
||
" <td>Platterbsen-Mörtelbiene</td>\n",
|
||
" <td>13.9</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>179</th>\n",
|
||
" <td>Platterbsen-Mörtelbiene</td>\n",
|
||
" <td>14.3</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>180 rows × 3 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" species length parasites\n",
|
||
"0 Fuchsrote Lockensandbiene 13.3 1.0\n",
|
||
"1 Fuchsrote Lockensandbiene 12.0 2.0\n",
|
||
"2 Fuchsrote Lockensandbiene 13.8 2.0\n",
|
||
"3 Fuchsrote Lockensandbiene 13.9 1.0\n",
|
||
"4 Fuchsrote Lockensandbiene 11.0 0.0\n",
|
||
".. ... ... ...\n",
|
||
"175 Platterbsen-Mörtelbiene 14.2 1.0\n",
|
||
"176 Platterbsen-Mörtelbiene 15.6 1.0\n",
|
||
"177 Platterbsen-Mörtelbiene 14.2 2.0\n",
|
||
"178 Platterbsen-Mörtelbiene 13.9 0.0\n",
|
||
"179 Platterbsen-Mörtelbiene 14.3 0.0\n",
|
||
"\n",
|
||
"[180 rows x 3 columns]"
|
||
]
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"bees_df = pd.DataFrame(bees)\n",
|
||
"bees_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c5e49fc8-e5f7-403d-9b56-3a560b92f0f4",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-b1092b46d45a70db",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Es hat Vorteile sich den Dataframe als CSV zu speichern dies versteht sich mittels:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"id": "fec83133-43d8-40ae-b3d0-43dab722a671",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-735e7a0c1e9c3f9d",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"bees_df.to_csv('Bees.csv', index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5efba824-f0d2-4134-837d-1be4ce9d2e46",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-55ef638057716b7e",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"In dem Notebook Ordner sollte eine Datei `Bees.csv` erstellt worden sein. Schaue sie dir gerne an.\n",
|
||
"\n",
|
||
"Seaborn macht es dementsprechend einfach. So verlangt `jointplot` ein Pandas DataFrame und für die `x` & `y` Koordinaten einfach den Namen der Spalten. um die Bienen voneinander zu Unterscheiden wird mit dem Parameter `hue` (Farbwert) nach Spezies gefärbt.\n",
|
||
"\n",
|
||
"Mit einer einzelenen Zeile ensteht dann folgender Plot.\n",
|
||
"Dieser Zeigt die Anzahl an Parasiten für jede gesamplete Biene und an den Seiten die Normalverteilungen dieser:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"id": "9778a0c6-0fb9-4b84-a8c5-e476dd7448a4",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-3a931b591234c528",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.jointplot(data=bees_df, x=\"length\", y=\"parasites\", hue=\"species\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "46c69a61-fbd6-4c51-a749-a141ed0c8eca",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-38cb7d6f4529d85e",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Mit dem `kind` Parameter lösst sich selbiger Plot auch als Histogramm anzeigen:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"id": "0b17470e-c9a0-4fd9-887e-eccbb3dead1f",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-d0fb66b692b5eecd",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.jointplot(data=bees_df, x=\"length\", y=\"parasites\", hue=\"species\", kind='hist')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "22c86f08-23e9-4135-a3ef-4a6b1c86554c",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-d8aa28cae2c7b366",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"Aus dem Dataset lassen sich auch Multiplots erstellen:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"id": "29b3fe01-f7ca-4a2a-822f-cb147e12eece",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-37ffc1027bae59c4",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 900x300 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"grid = sns.FacetGrid(bees_df, col='species', hue='species')\n",
|
||
"grid.map(sns.histplot, \"length\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4466fadd-e137-43ab-ba69-e4353cf54aad",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-11da2cf6fff4eafa",
|
||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": [
|
||
"## Aufgabe\n",
|
||
"\n",
|
||
"*8 Punkte*\n",
|
||
"\n",
|
||
"Erstelle das Dataset `people_in_germany`.\n",
|
||
"\n",
|
||
"Folgendes Szenario:\n",
|
||
"\n",
|
||
"Du bist Part einer Massenhaft angelegten Studie um die bereits bekannten Zahlen des Statistischen Bundesamtes zu überprüfen.\n",
|
||
"\n",
|
||
"Dazu sind 4 Größen bekannt aus den Angaben des Statistischen Bundesamt für die [männliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-maenner.html) & die [weibliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-frauen.html):\n",
|
||
"\n",
|
||
"||Körpergröße (in cm)|Gewicht (in Kg)|\n",
|
||
"|-|-|-|\n",
|
||
"|Männlich|178.9|85.8|\n",
|
||
"|Weiblich|165.8|69.2|\n",
|
||
"\n",
|
||
"Gehe dabei wie folgt vor:\n",
|
||
"- Treffe annahmen über die Verteilung und finde geeignete Werte für $\\mu$ & $\\sigma$. **Erkläre** deine Annahmen mit einem kurzen Text. Quellen sind gerne gesehen.\n",
|
||
"- Die Samplegröße beträgt 1000. Sample dementsprechend aus den gegebenen Werten.\n",
|
||
"- Speichere die gesampleten Personen nach dem Schema: `|sex|height|weight|` in der File `people_in_germany.csv` als csv.\n",
|
||
" -> Nutze für das `sex` Attribut den Datentyp `bool` mit der Kodierung: `True = female` & `False = male`.\n",
|
||
"- Stelle dein Ergebnis angemessen dar. Das theme `darkgrid` darf nicht verwendet werden.\n",
|
||
"- **Beschreibe** & **Interpretiere** den Plot. Gehe dabei von der Hypothese aus das es **keine** Unterschiede zwischen den Geschlechtern beim samplen geben sollte. (Auch hier 0 Punkte bei keiner Antwort)\n",
|
||
"\n",
|
||
"*Tipp: Dataclasses erleichtern die Aufgabe ungemein!*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"id": "68702477-b09f-45ee-871d-da9a130f3319",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-0c8bc5e7c272cd4c",
|
||
"locked": false,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 600x600 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# BEGIN SOLUTION\n",
|
||
"import numpy as np\n",
|
||
"import seaborn as sns\n",
|
||
"from dataclasses import dataclass\n",
|
||
"\n",
|
||
"samples = 1000\n",
|
||
"\n",
|
||
"@dataclass\n",
|
||
"class Individual:\n",
|
||
" sex: bool\n",
|
||
" height: np.float64\n",
|
||
" weight: np.float64\n",
|
||
" \n",
|
||
"\n",
|
||
"rng = np.random.default_rng()\n",
|
||
"individuals = list()\n",
|
||
"\n",
|
||
"for i in range(samples):\n",
|
||
" if i % 2 == 0:\n",
|
||
" sex = False\n",
|
||
" height = rng.normal(178.9, 10)\n",
|
||
" weight = rng.normal(85.8, 26)\n",
|
||
" \n",
|
||
" else:\n",
|
||
" sex = True\n",
|
||
" height = rng.normal(165.8, 11)\n",
|
||
" weight = rng.normal(69.2, 19)\n",
|
||
"\n",
|
||
" height = np.round(height, decimals=1)\n",
|
||
" weight = np.round(weight, decimals=1)\n",
|
||
" \n",
|
||
" individual = Individual(sex=sex,height=height,weight=weight)\n",
|
||
" individuals.append(individual)\n",
|
||
"\n",
|
||
"individuals = pd.DataFrame(individuals)\n",
|
||
"individuals.to_csv('people_in_germany.csv', index=False)\n",
|
||
"\n",
|
||
"sns.set_style('white')\n",
|
||
"sns.jointplot(data=individuals, x=\"height\", y=\"weight\", hue=\"sex\")\n",
|
||
"plt.show()\n",
|
||
"# END SOLUTION"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2fbcac55-ed91-4290-8026-639d215f783c",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "cell-8b5f26594c6567ed",
|
||
"locked": false,
|
||
"points": 3,
|
||
"schema_version": 3,
|
||
"solution": true,
|
||
"task": false
|
||
}
|
||
},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 149,
|
||
"id": "9d423664-b098-4fd4-9bad-5f7028424c87",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": true,
|
||
"grade_id": "cell-3060604f60a0ca6f",
|
||
"locked": true,
|
||
"points": 8,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Hier werden ihre Lösungen getestet...\n",
|
||
"test_individuals = pd.read_csv('people_in_germany.csv')\n",
|
||
"assert len(test_individuals) > 999 # Test if enough samples where taken 1 Punkt\n",
|
||
"assert list(test_individuals.columns) == ['gender', 'height', 'weight'] # Test if columns are correct 1 Punkt\n",
|
||
"\n",
|
||
"# Test gender means\n",
|
||
"male_height = np.mean(test_individuals[test_individuals['gender'] == False]['height'])\n",
|
||
"male_weight = np.mean(test_individuals[test_individuals['gender'] == False]['weight'])\n",
|
||
"female_height = np.mean(test_individuals[test_individuals['gender'] == True]['height'])\n",
|
||
"female_weight = np.mean(test_individuals[test_individuals['gender'] == True]['weight'])\n",
|
||
"\n",
|
||
"assert male_height > 176 and male_height < 182 # 1 Punkt\n",
|
||
"assert male_weight > 83 and male_weight < 89 # 1 Punkt\n",
|
||
"assert female_height > 163 and female_height < 169 # 1 Punkt\n",
|
||
"assert female_weight > 66 and female_weight < 72 # 1 Punkt\n",
|
||
"\n",
|
||
"# test cummultative mean 1 Punkt\n",
|
||
"assert test_individuals['height'].mean() > 170 and test_individuals['height'].mean() < 174 \n",
|
||
"assert test_individuals['weight'].mean() > 75 and test_individuals['weight'].mean() < 80 "
|
||
]
|
||
}
|
||
],
|
||
"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": "3.11.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|