3663 lines
457 KiB
Plaintext
3663 lines
457 KiB
Plaintext
{
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"source": [
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"# 7. Programmierübung: Pandas & Seaborn\n",
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"\n",
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"<div style=\"display:flex;\">\n",
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" <div style=\"text-align: left\">\n",
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" Willkommen zur siebten Programmierübung Einführung in Python 3.\n",
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" </div>\n",
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" <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",
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"</div>\n",
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"\n",
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"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",
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"\n",
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"Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n",
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"\n",
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"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",
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"\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8af16cfd-827f-4e7d-91a3-970e920ad212",
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"metadata": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"# Was ist Pandas?\n",
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"\n",
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">_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",
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"\n",
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"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",
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"\n",
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"Diesem Notebook liegt zu Lernzwecken das Datenset `unis_nd.csv` bei, mit dem die Grundlegenden Funktionen Pandas gezeigt werden.\n",
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"\n",
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"__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",
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"\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "496ec5e1-2fdb-4075-853b-c91a6144ac82",
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"metadata": {
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"editable": true,
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
|
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"# Was ist Seaborn?\n",
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"\n",
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">_Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics._\n",
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"\n",
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"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",
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"\n",
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"__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",
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"\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "62286603-158e-4448-98e5-e8f4aa60448a",
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"metadata": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"# Achtung\n",
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"\n",
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"Falls es zu Problemen kommen sollte das Seaborn nicht bekannt sei führe bitte vor **jedem** bearbeiten folgende Zeile aus:"
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]
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting seaborn\n",
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" Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
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"Collecting numpy!=1.24.0,>=1.20 (from seaborn)\n",
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" Downloading numpy-2.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.1/62.1 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting pandas>=1.2 (from seaborn)\n",
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" Downloading pandas-2.3.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (91 kB)\n",
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"\u001b[?25hCollecting matplotlib!=3.6.1,>=3.4 (from seaborn)\n",
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"\u001b[?25hCollecting kiwisolver>=1.3.1 (from matplotlib!=3.6.1,>=3.4->seaborn)\n",
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" Downloading kiwisolver-1.4.9-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (6.3 kB)\n",
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"Collecting pillow>=8 (from matplotlib!=3.6.1,>=3.4->seaborn)\n",
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" Downloading pyparsing-3.2.5-py3-none-any.whl.metadata (5.0 kB)\n",
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"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",
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"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n",
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"Collecting tzdata>=2022.7 (from pandas>=1.2->seaborn)\n",
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" Downloading tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
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"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",
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"\u001b[?25hInstalling collected packages: tzdata, pyparsing, pillow, numpy, kiwisolver, fonttools, cycler, pandas, contourpy, matplotlib, seaborn\n",
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"Successfully installed contourpy-1.3.3 cycler-0.12.1 fonttools-4.60.1 kiwisolver-1.4.9 matplotlib-3.10.6 numpy-2.3.3 pandas-2.3.3 pillow-11.3.0 pyparsing-3.2.5 seaborn-0.13.2 tzdata-2025.2\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
|
||
"pip install seaborn"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c6fa552-7e9c-4561-b7c3-e72268db9afb",
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"slide_type": ""
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},
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"tags": []
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},
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||
"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."
|
||
]
|
||
},
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{
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"cell_type": "code",
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"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": 3,
|
||
"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": 4,
|
||
"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": 4,
|
||
"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": 5,
|
||
"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": 5,
|
||
"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": 6,
|
||
"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": 6,
|
||
"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": 7,
|
||
"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": 7,
|
||
"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": 8,
|
||
"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": 8,
|
||
"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": 9,
|
||
"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",
|
||
"*5 Punkte*\n",
|
||
"\n",
|
||
"Erstellen Sie 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": 10,
|
||
"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": 11,
|
||
"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",
|
||
"Exthahieren Sie die Series `plz` aus dem zuvor erstelltem Data Frame `uni_addr` und speichern Sie ihr Ergebnis in `uni_plz`.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"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": 13,
|
||
"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",
|
||
"Nutzen sie die Funktion `pd.read_csv` um das Datenset `unis_nd.csv` in die Variable `unis_nd` einzulesen.\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": 14,
|
||
"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": 15,
|
||
"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": 16,
|
||
"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... "
|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"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:"
|
||
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|
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <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": 17,
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"unis_nd.head(3)"
|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
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|
||
" <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": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd.tail(3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7573347e-593f-4dd8-a508-5037eb422628",
|
||
"metadata": {
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"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": 19,
|
||
"id": "36d674fe-56ff-4610-8086-adae93bdae62",
|
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||
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|
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|
||
}
|
||
},
|
||
"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": 19,
|
||
"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": 20,
|
||
"id": "2ea51488-4dcb-4840-aae8-95bf54c226d0",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
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|
||
"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()"
|
||
]
|
||
},
|
||
{
|
||
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|
||
"id": "46bdc364-7bd2-4b50-bb74-b90d92312465",
|
||
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|
||
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|
||
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|
||
"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": 21,
|
||
"id": "4078322c-7354-41ae-a9fa-3af389e37d41",
|
||
"metadata": {
|
||
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|
||
"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",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1963</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1745</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>2005</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1775</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1995</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1737</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1996</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1897</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1920</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1963</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1778</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1831</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>2012</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1978</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1946</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2007</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2007</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1973</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1973</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1971</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1967</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>30</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1998</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>31</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>1998</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>32</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>1995</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33</th>\n",
|
||
" <td>privat</td>\n",
|
||
" <td>2010</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>34</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>36</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2009</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>37</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2006</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>38</th>\n",
|
||
" <td>public</td>\n",
|
||
" <td>2006</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"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"unis_nd[[\"Sponsorship\", \"Founding year\"]]"
|
||
]
|
||
},
|
||
{
|
||
"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",
|
||
"Selektieren Sie die Spalten _University name_, _Founding year_ & _Number of students_, speichern sie ihr Ergebnis in der Variablen `select`.\n",
|
||
"\n",
|
||
"Geben Sie danach die ersten 5 Werte von Oben der Selektion aus."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"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": 22,
|
||
"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": 23,
|
||
"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": 24,
|
||
"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": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"count = unis_nd[\"Type of university\"].value_counts()\n",
|
||
"count"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"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",
|
||
"*2 Punkte*\n",
|
||
"\n",
|
||
"Nutzen Sie die Funktion `value_counts` und erstellen Sie ein Balkendiagramm, mittels matplotlib, über die Anzahl an Staatlichen und Privaten Hochschulen Niedersachsen.\n",
|
||
"\n",
|
||
"Die dazugehörige Spalte im Datenset ist _Sponsorship_. Finden Sie eine geeignete Darstellung."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 117,
|
||
"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": "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": 29,
|
||
"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": 30,
|
||
"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": 31,
|
||
"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": 32,
|
||
"id": "e5cbfbe6-bf9a-4ab8-8a82-c0f7310907d3",
|
||
"metadata": {
|
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|
||
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|
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}
|
||
},
|
||
"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,
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||
"grade_id": "cell-1cffcdd63ad3fb6c",
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||
"locked": true,
|
||
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|
||
"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": 33,
|
||
"id": "e4954edf-ecf9-4097-aab3-de0abf5bc7e5",
|
||
"metadata": {
|
||
"nbgrader": {
|
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"grade": false,
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||
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"locked": true,
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||
"schema_version": 3,
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||
"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": 33,
|
||
"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": {
|
||
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|
||
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|
||
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|
||
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|
||
"solution": false,
|
||
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|
||
}
|
||
},
|
||
"source": [
|
||
"Es hat Vorteile sich den Dataframe als CSV zu speichern dies versteht sich mittels:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "fec83133-43d8-40ae-b3d0-43dab722a671",
|
||
"metadata": {
|
||
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|
||
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||
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||
"locked": true,
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||
"schema_version": 3,
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||
"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": {
|
||
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|
||
"grade_id": "cell-55ef638057716b7e",
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||
"locked": true,
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||
"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": 35,
|
||
"id": "9778a0c6-0fb9-4b84-a8c5-e476dd7448a4",
|
||
"metadata": {
|
||
"nbgrader": {
|
||
"grade": false,
|
||
"grade_id": "cell-3a931b591234c528",
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||
"locked": true,
|
||
"schema_version": 3,
|
||
"solution": false,
|
||
"task": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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IWhCJY0Qt1OZpdVFUZRAjkmjlUpE4yt5+xbbXiRVwYae4sVM9WGkerDQvVkbix07puOOSoRyMVz5ei5dzrJmJ6qD19hurcXo1qz41+Ooxxe6n2D12IE8h+iIJTEL0NFpjLK/CnLMRc04x5rxNqFAc7XVgj0wnftQwrJEZ6MEpXX4KqwqbfzhCHGi5CbThmpGKNYpoPeRNAO11JlqNdmUFWqPiNiocT4SqUBwzEkeFYhihGEZ9DEdRNe4fSzGCW25jYjsNrCw/8ZwA8dzET6x/KtrXtmac/cxUHo0Xs8CuZ7IZaNM6mpMxFMam2Kz40OB/DxmMP1bTf5L0axKiJ5DAJEQPoDbU4fi2mOiCMnyfr0dVRtBOA3tEOvHDh2KNzkAP6vqAtK2nHEFsNEdZu36T3VgISpdDIBecbR1GQCm000Q7TSBRQ7yleS0boy6KWRvGqI5g1oRxlNTgXlqCEUvENCvNQ2xgGtFB6cQGpxPPC9CaDkSjlZdcnLwcL+vQwATgy4JxP7VZ+4ViwcsGxYs1446xe/zAnkL0dhKYhOgGqjSYOL32XQnmnI0YG+rRCuzh6cT36o89OhN7RNoOrzTraiVYzHYEOcxyk7qLrUtaw8aFCtOEtEFddP28aWCnebDTPJDftBijNoKjPIijPIhZVk/KslKUZWO7HcQGp8PYXMyBqVjZ/mZP5SmlmGGm8U+rjPV2hEHGrgfIHXG4YMSBmowhFuu+MvjsXoORB2mG7K0x5FNbiG4hbz0hOpvWqMI6zAWbML/fhDl3E0ZhLQD2AD92QQaxY0Zgj8ogbUAawepgs/1/utt9zjpcKI6yPLu8bHWhIlimyB5t08ZbznUcpbBTPURTPUSHNXQSsuxEgCquxVlSh+PNH8i2bKxUD5GCbCIF2USHZ4JjS4CdagR4x6rk+fgmrnYN6pRSM4dB6gCbormKZe8p1n2tKDhM03+CXEknRFeTwCRER6uNYv5QnuiovbgMY2EpRmXi/mP2wEDiNNsRQ7EK0iF1q5aJHtxPZYGK8W9HhNNjXny7WGg0CCVLwJ+j8aZ3Tn3tZhrE+wWI9wsQATxOB/E15Tg31OD+sRTfd4XYToPoyGwiY/oRHpWN0+3kQDONN60KTrdz6W90zmVtDjcM2UfTb4ym8FuDBf80WP2ppuAwm5xRnd7HXwjRQAKTEG1la9TGOoyVVYnxj5ZXYiyrwNhQD5DopD0kFWvvAcSGp2EPSwNf2+651p1iaG5w1TLUNplh71oo0DZsmKcwnJA+pAc2m7XEYRAfmEZsYBpMzceojuAqrMK5voq0VxeRahpERmRy8IR+fFxg8nSsmJnuwZ1akjcDCg6zqS2GorkGc/9ukjZQU3CoTdYICU5CdDYJTELsTF0UY30txvpa1LoajHU1GKurMdbWNA7YqP1O7PwA9pgs4ocPxR6ciu7n67Cxj7rT444gy1WcP8YCmLvYurRpWWJcoX7jNEbP6Y61a5TCTvcQTs8jPCEPVR/Fta4K19pK+v1rCSdMSeOZQ+OcuiDOkDFDwNW5O5qSB6OPsqnZkAhO3z5jkjFEM+owm4whnbppIfo0CUxChOKo4nqM4vpEi9HGetSGOoyiWoyiOlRNtHFWneLEzvWjc33EJ2Zj5/mxBwYSA0P2wj/x56sYDzvq+YnlZpjetY+L6g2KytWKtCEad8deSNattN9FZGw/ImP7oeqj7L22kg8q49yrSnjx3DmE9xxE/X7DCE/IpbM6bCkFaQMT/Zuq1yeC09ePm2QXJIJT2oBO2awQfZoEJtG72RpVEUJtrMcoCaKK61Elm8NRPUZJfdNAZDTcQiTTg87yEh+Zjs72Yed4Ey1GSXhKra3KsLnMVc0wbfLTXezoHaqAjQvBl61JzUuiU3G7SPtdxMflcpId556RdTz9q2Gc+XIhgU9WYaV5qN93KPXThxIdntUpgVopSB8MaYNsKlYriuYqvnjIYMBkzd6/3KURroQQOyGBSSQ3W6PKQxgb6ojXbsD5YxlqQ0MY2liH2hRExbZ8cWiPic70otPdiVaiMZlbAlKmB53u7vaxjnqCemwucFcRUZprogEcu3AqLlwD679TuHyQOaz3hqWtjYw62L/exV3TYMLAPSlYG8SzqBj/Z6tIfXspsdwA9fsOJbjfUGL56R2+faUga7gmY6imbJliw/eKf18fZsg+iuH7a5zeDt+kEH2O0lr32k+00tLaTt+GUpCdnUJZWS3JeiR7/D5YNqokiLGuBlVYu6U/UVEtxoY6VHSrQJTixM7yNgQgLzrDjW783QNeR889daYgLc1HdTcPK1CPzQWuahYZMa6OBRi6C6fiovWw7hsDlCZ3bHKOGaQAj9dFOBTdpachojR/za7FpRWPF2Xg1wbYNq41lXgWFeNZVooRjhMdlJYIT9OGEB/QOaNR2jGo/NHFmu/imE4YcZBm8J7J9Xz0+M+lneio+nNyUjquKNEuEpjaKdnf1NCD9iEUx1hTnfhZW4Oxpga1phqjsLaxlUibCp3tRef40Nle7Gxv4vdsLynDM6mJRHvkGEat0gMCUykWF7irWaXiXBoLMGoXwlKkBtbNUZgORc5oGyNJz162NTABFDss7squY3zYwV9K0nHrrcJ53Ma9sgzPkk2JW7dELaKD0gjuNZjgXoMTA2Z2UJhXJF5LpRuDFM5VlC1XeNOh4BBN/4nJMYZTj/lcaiMJTL2PBKZ2SvY3NXTDPkSsRChaWYWxavNPdaJ/UcP27XQ3Os+P7ufDzvWh+/nQuT50hqf5U2Y9IGy0Wzfvw/+MCDNdtdhoLon5GbILYam+DIrmKkw3DJrkIG7HkvlpaHNgAvjRFePhrHp2Dzm5tSQNn27m9RqzcK8sx/PDJtzLyzAiceLZfoJT8wntPpDw2Nx2XW23OTBVVwfRQKgSCr8zqFqnCPTTjDzYJndsq+4C022S/bNVAlPvk0QNtCLpaJ3oS7SiEmNFFcaKSszlVajC2sY7zttZHnSeH2t8NvrQIdh5fnSeP3HqTHSJYiz+5qzjDUeE8baDs2M+0lp56xOtoWKVovRH8KRB9kiNw62Ihzq56B5sVNTJueV+Hsus5+yBlfy5JI0RsW1ez06TyJh+RMb0Aytx2s79Yyn+L9eS+s4ybJdJeFwu4Un9CU/IS/R7ascQFd4MKDjUpm5T4oq6718w8edohu+faHFKplN1QnQXaWFqp2T/Kwg6aB+CscYBHI0VVYlBHFdUooKJW6NqvxN7QAA9IHEZvh4QwO4f6LhgJC1Mu2ytijPbEeIVM4QbxUlxD/vbLlQrO3hHg4n7w4XKFSkDNOn5GqXa1zrTE7S3hWmzjQ6LJzPqKXXY/LLax2lVPtLtnQRRrXGU1uNeUYZrdQWudVWouI2V4iY8NhGwIqNyiA7NaHKblub2YesWpm3VlsDG+QbVhQqXX5M/JfHjy2zHDnewZP9slRam3kf+rhC7xrJRRXWJULSyEmN5ouXIKKoDGi7Lz/NjD/ATP2xo4lYg+b13nKJkE0LzoRnhVTPEV0aMAIqjLQ+HWW68rQxKtgUVaxRlK8A0IWeMjSetkwtPQv3jJleXpvB+IMLLqUFeTg1ySL2Hw+s87B5y4mzueCvVeIuW+n2HQszCVViNa00iPPnmFqHiNrbTIDYkg8jIbKJDM4kOzUiMSt7KmzWn5ELK4TahKtj0g2LtV4pVnxqk5WvyJmj6jdb4OmckBCGSlrQwtVOy/xUELeyDrRODOa6qSoxqvaqhz9Ga6i2jW6e4EoFoc4tRfiBxOq2VH9oduxPSwtSSCJr/GVHeMcN8ZEYJKk2BbXKA5WIv24VrF4JSdVEiKFkRCORC2iDdZGzGjmqd6U6dsQ91hs3nvihf+6KUOmy8tmK3sJM9Qk4mhV2Mjjha9zzEbZzFtTiLqnFuqMG5sQazIojSiT9WYnkpxPLTiA9Mwzk8m5o0N7F+Aew0zw7TjxWHqrWKitWK6iLQlsKTrhNDFQyG9EEaf3bX9nlK9s9WaWHqfaSFqa9ruO1HvKoY5+JNqLUNt/1YX4MKNwQjj5noW9Q/QGxCFnpgAHtAoOmNY0WPUo/N50aU980InzSEpHzb4AjLzd6Wk360PtRG6qG6UFG1PnG5ujcT0kZrnLs2lmWfFrANjqzzcESdmyKHxWJPnB/dcR7LqCdq1OPUMC7sZHLYyR5hFxPDTry6mYDjMIjlpxHL39KkpyJxHJvqEj+ldThK6/H8sAmzdhG+hnlsl0k824+V4yee5cfK8mFleIln+LDSvZjpHrKGeMgaYWDFoHZjw0jtaxRFcwEMTJcmJRdS+2sCeZCSqwn0Q14Hos+QwNSbaQ31scQI1yX1iRajzbf92FCXuFy/YZTrGOBMdWH386Fz/cQnZTd2wNYZnl5xT7TeTKNZpyw+N6J8Ykb4xogRUzDYNjnCcjPVdjJAty4kaQ2RWqgrVdQWQ6RaoUyNPwcCeRqn5OQ2Uyjy4w7y6xwcUQcWmkKnxSpXnJUui1dTQzybEcShYVzYwR5hF1NCLiZEnE2HKNiKdjuIDUonNih9q+2A3+Ugsq4SoyqEWRXCrA5jVofxLN2EUR3GCEYbr0rdzPK7sFPcWKke7BQ3doqbuNdFWLkJxt0E17qoX+FmY8TFOtNFzOHCyHDiG2CS0i8RoAL9Eq8VCVKit5HAlExsDcEYqjaa+KmJQk0UVRVGVUVQFWFURQijLIQqC6HKQ42tRNDQvyjDnRjQMctDfEZ+4rYfuT5SRmRRE48l7+msPqYOm+WGxRIVY74RY44RY5Nh49AwSjs4yfKwu+UkZyctSdpOdN6O1kO4RhGuglA12FEFpsabBpkjNb6M5Bi7J9mYKIbEHAyJOTioPhF8ix02P7riLHfHeTk1xNMNAWp0xMGEiJMxEScjIw4Gx8zm+0Ft5nIQz0tB57VwSseyMeqiGPVRzLoIRjCKUR9L/BuMYdSEcZTU4gnFSA3FUKHYdgGrcVWmSdzhIGa4iDmcxBxOwl4X+J2oVCdGmgMzw4kjw4GZ5cRMc4DPifY6wOtI/OtxNP6OQ15soueRPkytVRfF+cqPqOpIIlRoDRqU1ng8LsLBCNgkQo2twWr417YhrlGWhrid+LFsVNyGmA1RG2IWKmpBxEZF4hCxGvsJ7SqtgFQ3OtWVuP1Hmhud4UkEpTR38y1FCvx+D/X14eQNTEm+D0FD82K2ptRnEorHiSqIGBA0oMbUVJtQ5oSqFv7Eya+BkZWKEVUKV6xhot3wMtWgrcRLUdtgx9nhMTLdidYB06Vp5egCjRTgME3ilpWMTwPQs/ZBAxv9sDJVsyoVal3Nz5cVgowopEYV/jh44+DFwIzZmBpG1CiOXKdwtNBK1dpijFgcIxrHiMQxIzGMSMPvDdNUNI4KW4nfY3FMu+PvZ6chMUaVy0A7E//iNNFOIxG0HKrhXwPd8C+mSnz2mQZ68/8NtWW6oRJPvKHQaqvf1eZ/2dIHrMn0bY6nAmtMFtZBg1CGkj5MvUyvDkwdKTZrHvHbv+ruMkQv9cp+GVz1u0HdXYboxV7983Imr+7DA2R1Ifdnv8IYKEGnt5HAJIQQQgixE3KiWAghhBBiJyQwCSGEEELshAQmIYQQQoidkMAkhBBCCLETEpiEEEIIIXZCApMQQgghxE5IYBJCCCGE2AkJTEIIIYQQOyGBSQghhBBiJ5IqMJWUlHDllVcybdo0Jk2axLHHHsvChQu7uywhhBBC9HIt3Mqz56muruaUU05h2rRpPPbYY2RkZLB27VrS0tK6uzQhhBBC9HJJcy+5v/71r8ydO5fnnnuuu0sRQgghRB+TNIHpJz/5CdOnT6e4uJg5c+aQm5vLqaeeyi9+8YvuLk0IIYQQvVzS9GFav349zz//PEOHDuWJJ57glFNO4eabb+bVV19tcRnLsruwQiGEEKLnkO/AjpU0fZi01kyYMIHLL78cgHHjxrF8+XJeeOEFfvaznzW7TEVFPUp1bl1KQVZWCuXltSRHW932ZB96BtmHnkH2oWdI9n3oqPqzs1PavGxXfAf2Bq09xkkTmHJychgxYkSTacOHD+fdd9/d4XJd9UbTuuu21VlkH3oG2YeeQfahZ0j2feju+pP52PU0SXNKbo899mD16tVNpq1Zs4aBAwd2U0VCCCGE6CuSJjD9+te/Zv78+TzyyCOsXbuW119/nZdeeolTTz21u0sTQgghRC+XNKfkJk2axAMPPMDdd9/Ngw8+SH5+Ptdeey3HHXdcd5cmhBBCiF4uaQITwEEHHcRBBx3U3WUIIYQQoo9JmlNyQgghhBDdRQKTEEIIIcROSGASQgghhNgJCUxCCCGEEDshgUkIIYQQYickMAkhhBBC7IQEJiGESFK33HIDM2de0d1lCNEnJNU4TEIIIba45JIr0XKzMCG6hAQmIYRIUoFAoLtLEKLPkMAkhBDt8NFHH/DUU49RWFiIx+OhoGA0t99+F3fffQd1dbUUFIzmX/96iWg0xmGHHcGll16F0+kEwLZt/vGPZ/jPf16lvLycQYMGc+aZZ3HQQYc2rn/VqpU88sj9fP/9PLTWFBSM4rrrbmDgwHxuueUG6upque22u7ZbX0VFYn2//vWW9dXU1PC3v93JnDlfEQyG6NevH6ef/huOPlpuMSXEzkhgEkKINiorK+OGG67j/PMv5oADDiIYDDJ//rzG02TffjsHl8vFffc9SnHxRm699UZSU9M455wLAJg9+ynee+9trrxyJvn5g5g/fx433XQ96ekZ7L77FEpLN3Hhhb9n99334L77Hsbn87Nw4XwsK95sPZvXd9VVM5k0aSwffvhZk/U9/vjDrFmzir/+9T7S0tIpLFxPJBLpsuMlRDKTwCSEEG1UXl6GZVnMmHEweXn9ARgxYmTj406nk5kz/4TH42H48BH87nfn8OCD93H22ecRj8eZPfsp7rnnISZMmATAwIH5LFjwPa+99i92330K//rXP/H7A9x44204HImP68GDhzRbSzQabVzfxImTyM5O4eijj22yvpKSYgoKRjNmzDgA+vcf0JmHR4heRQKTEEK00ciRBUyZshdnnHEye+21N3vttTcHHngIqampjY97PJ7G+cePn0QoFGTTphKCwSDhcJjLLrugyTpjsRgFBaMBWL58GZMn79YYlnaksHB9k/UppdBaN1nf8cefxB//eDU//riMvfaaxv77H8jEiZM75FiIHsiywZSL4TuKBCYhhGgj0zS5554HWbhwPnPmfM0rr7zIrFkPMWvW0ztdNhQKAXDnnfeQk9OvyWOb+zi53e5W17L1+vr160dGhp/Kynq03rK+ffbZj5dffoOvvvofc+Z8zSWXnM8JJ/ycCy+8tNXbEcnDd/S/CL5zUneX0WtIYBJCiHZQSjFp0m5MmrQbZ575O0466Vg+/fQjAFasWE4kEsbtTrQyLV68EK/XR79+uaSmpuJyuSgpKWb33ac0u+4RIwp4++03icfjO21lGjZsWOP69thjCtnZKfj9tWw76kBGRgZHHXUMRx11DJMm7cZDD90ngamXMirD3V1CryKBSQgh2mjx4kV899037LXX3qSnZ7JkySKqqioZMmQYK1euIBaLcdttN/HrX59FcfEGnnxyFiee+AsMw8Dn83Pyyb/i/vvvRmvNpEm7UVdXx8KF3+P3BzjqqGM48cRf8MorL/KnP83k9NN/g98fYPHihYwbN57Bg4c2qWXb9R144H6sX1/CggVb1vf4448wevQYhg0bQTQa5YsvPmfIkKHN7psQoikJTEII0UZ+v5/vv5/HSy89TzBYT25uHhdeeCn77LMfH374PlOn7smgQYO58MKziUZjHHroEfz2t79vXP7ss88jPT2D2bOfYsOGIgKBFEaNGsMZZ/wGgLS0dO699xEeeuheLrzw9xiGSUHBqBb7HW29vjvvvAW/P9BkfQ6Hg0cffZCNGzfgdnuYPHk3brzx1s4/UEL0Akr34mFiS0trO30bSkF2dgplZds3fScL2YeeQfahZ+iofdh2jKSuJM9D9+uo+nNyUtq8bGj4w9R9/au2b7yPaO0xlu7zQgghhBA7IYFJCCGE6K3sJGye66GkD5MQQnSC6667obtLECIRmAzV3VX0CtLCJIQQQvRWlrQwdRQJTEIIIURvJafkOowEJiGEEKK3suzurqDXkMAkhBBC9FZySq7DSGASQggheikVlxamjiKBSQghhOitpA9Th5HAJIToNTZu3MD06VNZvnxZd5fSI82d+y3Tp0+ltrbz74LQ1U466Vheeum5xt+nT5/Kp59+3OL8fea1Ii1MHUYCkxCiS91yyw1Mnz51u5/CwvXdXVqbdWQQufDC33PvvV1/O5W+pl+/XF577R2GDRvR3aV0rri0MHUUGbhSiD4u5japVZqaeJxUh4MUrXBGrE7d5rRp+3Lttdc3mZaentGp22wLrTWWZeFwyEdlb2OaJllZ2d1dRueTFqYOI58CQvRhIa/J1StX8UlVdeO0A9PTuGPEcLyhzgtNLpez2S+r5m5Ye++9d7F8+TIeeGAWALZt8/zzs/nPf15l06YSMjIy+elPT+DXvz6rcZkNG4q47767WbJkEfn5g7nqqplMmDAJgOLijdx9950sWPA98XiMvLwBXHDBxeyzz3Tmzv2Wiy8+l1mzZnHXXXezcuUK7r77ASZMmMRDD93LBx+8RzBYz+jRY7n44ssZO3Y8Gzdu4OKLzwXgqKMOavj3GK677gZs2+Yf/3iG//znVcrLyxk0aDBnnnkWBx10aJuP3ccf/5fHH3+UoqL1ZGVlc+KJv+SUU7bcYDUajfLEE4/w3/++R3l5Of365XL66WdyzDHHb7eucDjMddddTTBYx5133ktKSgqvv/5vXnjh72zcuIG8vP6cdNLJnHDCz4HEaayf//w4brnlTl5++cVdPr6WZXHnnbcwd+63lJeXk5uby89+9nN+8YtTtnsNTJq0Gy+99ByRSJRDDjmcSy65ojG4VlZWcNttN/Htt9+QlZXF2Wef1+yxKi8v44orLmbevO/Iysrm/PMvajz2m/flqaf+QUHBaABWrVrBgw/ex4IF8/B4vOy11zQuuugK0tPTgUTr38iRBbhcLl5//TWcTic//ekJnHXWOY3brK2t5cEH7+Hzzz8hFosxevRYLrrocgoKRrXxGW8fFbORNqaOIYFJiD4q5t4+LAF8XFXNNStXcdew4Z3e0tQWjzzyAK+//m8uvvhyJk3ajbKyMtatW9NknlmzHuKCCy4lP38Qs2Y9xA03XMcLL7yKw+Hg7rvvIBaL8eCDj+HxeFizZjVer6/J8nfddRfnnnsRAwbkk5KSwkMP3cfHH3/IddfdQF5ef5577lkuv/wiXnzxVfr1y+WWW+7kuuuu5rnnXsHv9+N2ewCYPfsp3nvvba68cib5+YOYP38eN910PenpGey++5Rd3velS3/g+utn8tvf/p6DDz6MRYsWcNddt5OWlsZPfnIsADff/CcWL17A//3f/9GvXz4bNmygurpqu3XV1tZy9dWX4PX6+NvfHsLj8fDee2/z+OOPcPnlV1NQMJrly5dxxx234PV6OeqoY9p9fLXW9OuXy0033U5qahqLFi3gzjtvISsrm0MOOaxx/XPnfktWVjbPPPMMixYt4/rrZ1JQMIrjjvsZkAhVZWVl3HffIzgcDu699y9UVlZst4+PP/4w5557EZdccgXvvvsWN9xwHcOGjWDo0GHNHo+LLz6PY489nosvvpxIJMzDD9/P9df/gfvue6RxvrfffoNf/vI0Zs16mkWLFnDrrTcyadJk9txzbwD+7/+uwe1289e/3segQbk8/fRsLr30PJ5//l+kpqbt8nPebrGe9x5OVhKYhOijapXeLixt9nFVNbVKk9lJ2/7ii8857LD9G3+fNm1fbr75jp0uFwzW8/LLL3DZZVc3foEPHJjP5Mm7NZnvlFN+xb77TgfgrLPO4fTTf0FRUSFDhgylpKSYGTMOZsSIkY3Lb+viiy9mt92moTWEQiH+/e+XufbaG9hnn/0AuOaaPzJnzrG88cZrnHrqGaSkpAKQkZFJSkoKkGjpmT37Ke6556HG1peBA/NZsOB7XnvtX20KTC+++A+mTNmTM8/8HQCDBw9hzZpVPPfcbH7yk2NZt24tH374Pvfc8yCHHXYoZWW1DBiw/f5VVJRz/fUzGTRoEH/60y04nU4AnnjiUS688FJmzDgYgAEDBrJ69Spee+1fTQJTW4+vw+Fo0hozYMBAFi1awEcfvd8kMKWkpHL55VeTm5tOWlo/9tlnOt999w3HHfcz1q1by1dffcFjjz3D2LHjAfjDH67ntNNO2m4/DzroUI499ngAzj77PObM+ZqXX36RK6/8w3bzvvLKi4waNZpzzrmgcdrMmddzwglHs27dWgYPHgLAiBEF/Pa3vwdg0KDB/OtfL/Htt3PYc8+9mT//e374YTGvv/4+breL7OwULrzwUj799GM++ui//PSnJzT/xHammJyS6ygSmIToo2ri8R0+XhuPk9lJ14XsvvsUrrxyZuPvHo+3VcutWbOaaDTKlCl77nC+ESMKGv+/+dRfZWUFQ4YM5aSTTuavf72NOXO+YurUacyYcTAjRxY0WX7ixImN/y8qKiQejzNp0uTGaQ6Hg7Fjx7NmzeoWaygsXE84HOayyy5oMj0WizWeAtpVa9euZvr0GdvUOpmXXnoey7JYvvxHTNPcaRi77LILGDt2HDfeeBumaQKJYFhUVMjtt9/EnXfe0jivZVn4/YEmy7fn+L7yyku8+eZ/2LSpmEgk0nA8mp6uGjZseGNdm7exatWKxmNgmiajR49tfHzIkKEEAinb7ef48ROb/D5hwkSWL/+x2WOyYsVy5s79tkmQ36yoqLBJYNpaVlZ2Y+vWihU/EgqFOProQwBQSqG1JhKJUFRU2Ox2O5uSFqYOI4FJiD4qdScdmVMcjk7rMOr1esnPH7Td9M1fMFuLbxXsNp/q2pmtO2krlbhTu20n9uXYY49nr7325ssvP+ebb75m9uynuPDCSznppJOb1BeNtn5/mhMKhQC48857yMnp1+SxzS06Hc3tdrdqvn322Y9PPvmQNWtWN7YEhUJBINF6Nm7chCbzG0bT4NzW4/vBB+/y4IP3cuGFlzJhwkR8Pj/PPfcsS5YsbnH9m7exef2dJRQKsd9++3PeeRdv99jW/e2aq23zazYUCpKVlc399z+KUpCR4aeysh6taTbQdQlpYeowMqyAEH1UilYcmN58n4oD09NI0aqLK0pcKVdeXtZk2ooVW8bJyc8fhNvt5rvv5rRrO7m5eRx//EnceutfOPnkX/H66/9ucd6BA/NxOp0sWDC/cVo8Hmfp0iUMHToc2BKAbHvLX/PDhg3D5XJRUlJMfv6gJj+5uXltqnvIkGEsXDi/ybSFC+czaNBgTNNkxIiR2LbNvHnf7XA95557EUceeQyXXHIeq1evAiAzM4vs7Bw2bCjart4BAwbuUp0tHd+FC+czceIkTjjh54waNYb8/EEUFRXt0rqHDBmKZVksW/ZD47R169ZQV7f9kA6LFy/a7vchQ7bvvwQwatRoVq9eRV5e/+323+ttXQvo6NFjqKgoxzRN8vMHMWTIkMZ1bO443uV6YD/EZCUtTEL0Uc6IxR0jhnPNylV8vN1VciNwhnZ8yq4zTJmyJ88/P5u3336DCRMm8d57b7Nq1crGU1hut5vTTvs1Dz10Hw6Hg0mTdqOyspI1a1Y2exVYc+699y723ntfBg0aTG1tLXPnftvilygkWpuOP/4kHnroXlJTU8nNzeO5554lHA5zzDE/BSAvrz9KKb744nP23ns/3G43Pp+fk0/+FffffzdaayZN2o26ujoWLvwevz/QpE/QtqqqKrcbUDErK5uTT/4VZ599Bk8//TgHH3wYixcv5JVXXuKKKxJ9cvr3H8BRRx3Dbbf9GacT+vXLZ+PGjVRWVjbpIwRw4YWXYtsWl1xyHvff/yhDhgzlrLPO4Z57/oLfH2DatH2IxWIsXbqE2toaTj75V7TGjo5vfv5g3nnnTb7++kv69x/Au+++xdKli+nfv/WBbPDgoUybti9/+cutXHHFTEzT5L777mq2de3jjz9gzJixTJq0G++//w4//LCYP/zh/5pd74kn/oLXX/83N9xwHaeddgapqWkUFq7nv/99j2uu+WOTU4QtmTp1GuPHT2TmzCs5//yLmTx5LMuXr+F///ucGTMOYsyYca3ez46iJDB1GAlMQvRh3pDFXcOGU6s0tfE4KZvHYeqGsAQwbdo+nHnm73j44fuJRiMcffRxHHnk0axcuaJxnjPP/B2mafLEE49SVlZKVlY2xx9/Yqu3YdsWd999B6Wlm/D5/Eybtg8XX3z5Dpc599wL0drm5puvJxgMMnr0WO6++35SUxOdvXNy+nHWWefwyCP3c+utN3LkkUdz3XU3cPbZ55GensHs2U+xYUMRgUAKo0aN4YwzfrPD7b3//ju8//47Tab97nfncuaZv+PPf76Nxx9/lKeffpysrGzOOuvcxivkAK644g/MmvUgN9xwA1VVVeTm5nH66c1v7+KLr8C2bS6++Fzuv/9Rjj32eNxuD88//ywPPXQvHo+XESNG8vOfn9Ls8s3Z0fH96U9PYPnyZfzpTzMBxaGHHsHPfvZzvvrqi1avH+Daa6/njjtu5qKLfk9GRiZnn30ejz9est18v/3tOfz3v+9x9913kJWVzZ/+dAvDhg1vdp3Z2Tk8/PATPPzw/Vx22YXEYlHy8vozbdo+252SbIlSir/+9V5mzXqIW2+9kerqKjIzM5k8eQ8yMjrrEoqdiHTPe7k3UnrbDgO9SGlp5w//rxRkZ6dQVlZLsh5J2YeeQfahZ5B96BmSfR86qv6cnLb3fQoNf5jIFVOJ/WJM2wvoA1p7jKUPkxBCCNFbdVNrcW8kgUkIIYTopZQEpg4jgUkIIYTorcISmDqKBCYhhBCil1JBCUwdRQKTEEII0VsFY91dQa+RNMMK3H///TzwwANNpg0bNox33nmnhSWEEEKIvk1amDpO0gQmgIKCAp566qnG31szkJgQQgjRV6m6dt7jRzRKqsBkmiY5OTndXYYQQgiRHOrklFxHSarAtHbtWqZPn47b7Wa33XbjiiuuYMCAATtcRnXy7bA2r7+zt9OZZB96BtmHnkH2oWdI9n3oKfWrumi319BbJM1I35988gnBYJBhw4ZRWlrKgw8+SElJCa+//jqBQKDZZSzLxjSlX7sQvcnBBx/MGWecwZlnntlh6/z6668544wzmDNnTuPtTpJdb9wnsWtCwx+GgBPvgt91dym9QtK0MM2YMaPx/2PGjGHy5MkcdNBBvP322/z85z9vdpmKivouaWHKykqhvDw5h+8H2Yeeoq/tw6JFCzjvvN8xbdo+/PWv97Z6G5ZlU18foays4259VF0dBKC8vI5YTPWK52Gz8vI6otHka2JI9vdDR9Wfnd32W6MAUBejrKQapPGgRa09xkkTmLaVmprK0KFDWbdu3Q7n66o3mtZdt63OIvvQM3T1Pvji4KqNQm0UUt1EA06C7fxkaM0+vP76a5x44i95443XKC0tJTt71/ondtQxisVijevauu7e8FqC5N8Pqb8DaqiNQZq7e4voBZI2MNXX17N+/XrpBC5EO6SFbew/fkr8s8LGaeb++aTdfADVns77izQYDPLf/77PE088S0VFGW+99TpnnPHbxsc///xTnn76cVatWoHX62XSpN257ba/Nruu11//Nw8+eA8333wnU6fuxapVK3jwwftYsGAeHo+XvfaaxkUXXUF6ejoAF174e4YPH4FpOnjvvbcYPnwkv/nN2QAsW/YDDz98P2vXrmbkyFFce+31DB48FIBbbrmBurpabrvtrsZt33vvXSxfvowHHpjVuO4RI0ZiGCZvv/0GTqeTs88+j8MOO5K//e1OPvrov2RmZnLppVexzz77ATB37rdcfPG53HXX/TzyyP2sXbuWCRMmcuONt7J06Q888MDfKC0tZd99p/OHP/wfHo8HANu2+cc/nuE//3mV8vJyBg0azJlnnsVBBx3aoc+VSH6qOoKWwNRuSdNGd8cdd/DNN99QWFjI3LlzufDCCzEMg2OOOaa7SxMiKfniYP/xU/RWYQlAf1aI/cdP8XXi8C0ffvg+Q4YMZfDgoRx++E94883/sLk75RdffM511yUCxZNP/oN77nmYcePGN7uef/zjGR555H7uvvsBpk7di9raWi6++DxGjRrN44/P5q677qOiooLrr/9Dk+XefvtNnE4HDz/8BFddNbNx+qxZD3HRRZfyyiuvYJomt932513et7fffpO0tDQee+wZTjzxF9x11+383/9dw4QJk3jyyb+z5557c/PN1xMOh5ss9+STs7jssqt55JEn2LSphP/7vz/wz38+z5/+dDN/+cs9zJnzFS+//ELj/LNnP8U777zJlVfOZPbsF/nlL0/lppuuZ96873a5ZtG7qepId5fQKyRNC1NxcTGXX345VVVVZGZmMmXKFF566SUyMzO7uzQhkpKrNtqkZWlr+rNCXLVRghmuTtn2m2++xuGHHwXAtGn7UF9fx7x537HHHlN59tknOeSQwznrrHMa5y8oGLXdOh566D7effct7r9/FsOHjwDglVdeZNSo0ZxzzgWN882ceT0nnHA069atZfDgIQAMGjSI88+/pHGesrIyAH7/+/PZffcpZGen8Ktf/ZqrrrqUSCSC2936v85HjizgzDMTnWxPP/03/OMfz5CWls5xx/0MgN/85nf8+98vs2LFciZMmNi43Nlnn8ekSbsBcPTRP+XRRx/gxRf/zcCB+QAceOAhzJ37Hb/61ZlEo1Fmz36Ke+55iAkTJgEwcGA+CxZ8z2uv/Ys99pjS6npF76UVKA2qMrzzmcVOJU1g+tvf/tbdJQjRu9TuZEC7uhh0QmBat24NS5Ys5tZbE6fYHA4HBx98GG+++Rp77DGV5cuXceyxx+9wHS+88A9CoRCPP/5sY6AAWLFiOXPnfsthh+2/3TJFRYWNgWn06LHNrnfEiILG/2dnZwNQWVlJXl5eq/dv63WYpklqahojRoxsnJaZmQVAVVVFi8tlZmbi8Xia7FtmZhY//LAYgMLC9YTDYS677IIm64jFYhQUjG51raKXCzihNoaqkhamjpA0gUkI0cFSdhKGAs5O2ewbb7yGZVkcf/xRjdO01jidTi677Brcbs9O1zFp0m58+eXnfPjhB5x++pmN00OhEPvttz/nnXfxdstkZWU3/t/j8Ta7Xodj649E1VCbnfhNKbYdhSUe3/68ZdN1JJbbeppquITNtnWLy227zGablwmFQgDceec95OT0azKP09k5z5tIPto0wO9EVUgLU0eQwCREHxVNcWHun79dHyYAtX8+0Z0FqjaIx+O8885bXHjhpey1195NHps580o++OAdRowYyXffzeHoo49rcT3jxo3nxBN/wRVXXIxpmpx66ukAjBo1mk8++ZC8vP7NBo72SE/PYPXqlU2mrVixDNPs+o/RYcOG4XK5KCkpZvfd5fSbaJlOdckpuQ6SNJ2+hRAdK+gA4+YDUPvnN5mu9s/HuPmAdg8t0Jwvvvic2toajjnmeIYPH9nkZ8aMg3njjf/wm9+czQcfvMsTTzzKmjWrWblyBX//+9PbrWvixMn85S/38tRTj/HSS88BcOKJv6CmpoYbbriOH35YTFFRIV9//SW33nojlmW1q/YpU/Zk6dIfePvtN1i/fh1PPPEoq1at3PmCncDn83Pyyb/i/vvv5u2336CoqJBly5by8ssv8Pbbb3RLTaJn0ikuVHmou8voFaSFSYg+rNpj4Lv9wMQ4THUxCDiJprio7aRPhjfeeI2pU/dqdnT+Aw88mOeee5bU1DRuuul2nn76cf7+96fx+/1Mnrx7s+ubPHk3/vKXe7jqqkswDIOTTjqZhx9+gocfvp/LLruQWCxKXl5/pk3bB8No39+H06btw5ln/o6HH76faDTC0Ucfx5FHHs3KlSvatd62Ovvs80hPz2D27KfYsKGIQCCFUaPGcMYZv+mWekTPpFMlMHWUpLk1SluUlnbcSMAtUSoxSmhZWXKORguyDz2F7EPPIPvQMyT7PnRU/Tk5bR/pu36PJ7H3yMVYUUnwnz9texG9XGuPsZySE0IIIXopneZGlUkLU0eQwCSEEEL0UjrdjQrGoT7W3aUkPQlMQgghRC+l0xODrqrSYDdXkvwkMAkhhBC9lM5IBCajRAJTe0lgEkIIIXopnZYYCFZtksDUXhKYhBBCiN7KaaDT3BjFdd1dSdKTwCSEEEL0YnaGG1UsLUztJYFJCCGE6MV0pge1UVqY2ksCkxCix3jrrdc58sgDu7uM7dxyyw3MnHlFd5fRIZ544lHOPPNUAE466djG28p0hrlzv2X69KnU1iYGEW7N87t1faJj6EwvhgSmdpPAJIToUrfccgPTp09l+vSpHHjg3vzyl8fz1FOPEY/H27S+5r70e2rw6mhvvfU606dP5bTTTtrusbfffpv99pvKSScd22T6Kaeczr33PgTAY489y3HHnbDT7WwbfDrT1vWJjqGzPKiSIMTt7i4lqcm95IQQXW7atH259trricVifPnl/7j77jtwOBxkZWV3d2lNWJaFUqq7y9ghr9dLZWUlixYtYMKESY3TX375ZXJz87ab3+fzAT4AMjIydrr+tgbZttq6PtExdLYXZWlUaRDdf/v7OIrWkRYmIfo4p3JhRr3Y1V7MqBencnX6Nl0uJ1lZ2eTl9ednPzuJqVP34vPPP91uvqKiQv7wh8s59tjDOeyw/fnd785gzpyvGx+/8MLfU1y8kfvuu7ux1Wru3G+59dYbqaura5z2xBOPAhCNRnnggXs4/vijOPTQ6Zx99q+ZO/fbxvW99dbrHHHEgfz3v//ltNN+zsEH70tJSXHj408+OYtjjjmUww+fwV/+ciux2JbRkz/66APOOOOXHHzwfvzkJ4dwySXnEwptuSXF66//m9NOO4mDD96XU089kX/965+Nj23cuIHp06fyyScfctFF53DIIfvx61+fwqJFC3Z6LE3T5LDDjuDNN//TOG3TphK++eYbDjvsyO3mf/XVl/nFL37KgQfuzSmnnMA777zZ5PHp06fy6qsvc801l3HoodO5446bufjicwE46qiDmD59KrfccgMAtm0ze/ZT/Pznx3HwwYmaP/rog53W/OmnH3PyyT/j4IP35fLLL2xyjJs7JdcRx27+/O85//zfcfDB+3HCCUdzzz1/afL89GY62wuAUSSn5dpDWpiE6MNceJnzjxgbf7Aap/Ufq9jzVC9Ruu7LxO12U11dvd30YDDI3nvvx+9/fz5Op4t33nmTa665nOeee4W8vDxuvfUvnHnmqRx33M849tjjAUhNTePii6/giSce4bnnXgHA6020WPztb3eyZs0qbrzxVrKzc/jkk4+48sqLeeaZFxg0aDAA4XCYxx57jD/84Y+kpqaRkZEJwLffzsHlcnHffY9SXLyRW2+9kdTUNM455wLKysq44YbrOP/8iznggIMIBoPMnz+Pzfc2f++9t3n88Ue4/PKrKSgYzfLly7jjjlvwer0cddQxjfs7a9ZDXHDBpeTnD2LWrIe44YbreOGFV3E4dvxRffTRx3HRRedwySVX4vF4eOut19l///3JzMxsMt8nn3zEvff+lYsvvoKpU/fiiy8+47bb/ky/frnsscfUxvmefHIW5557IRdffAWmaTJ9+gFcd93VPPfcK/j9ftzuxNg+s2c/xXvvvc2VV84kP38Q8+fP46abric9PYPdd5/SbK3hcJhnn32SP/7xRhwOJ3fddTs33HAtDz/8ZLPzv/tu+49dUVEhV155EWeffR4zZ15PVVUlf/vbnfztb3dy7bV/2uGx7Q10lhetQBXVwtTtWx1F60gLkxB9lFO5mPNcjI0/NL2V+sYfNHOei3VJS5PWmjlzvuabb75iypQ9t3u8oGAUxx9/IsOHj2TQoMGcffZ5DBw4kP/97xMgEY4Mw8Dn85GVlU1WVjZOp5NAIIBSqnGaz+ejuLiYt956nZtuuoPJk3dn4MB8Tj31dCZO3I233nq9cZvxeJwbbriBiRMnM3jwUDyeRDhwOp3MnPknhg8fwb77Tud3vzuHl19+Edu2KS8vw7IsZsw4mP79BzBixEhOOOHnDaeXEq0mF154KTNmHMyAAQOZMeNgfvGLU3jttX812d9TTvkV++47ncGDh3DWWedQXLyRoqLCnR7HUaPGMGDAQD766AO01rz11huceOKJ2833wguzOeqoYznhhJ8zePAQTj75VxxwwEE8//zsJvMddtgRHH30cQwcmE9eXn9SUlIByMjIJCsrm0AgQDQaZfbsp5g583qmTduHgQPz+clPjuXww4/abr+2Fo/Hueyyq5kwYRJjxozlj3+8kYULF7BkyaJm5++IYzd79lMcdtiR/OIXpzJo0GAmTpzMJZdcxTvvvEkkEtnp8U16DgOd6cEolBam9pAWJiH6KDtiNmlZ2trGHzR2xIROykxffPE5hx22P/F4HNu2OeywI/ntb3+/3emcYDDIk0/O4ssvP28MJZFIpMkpnNZatWoFlmVxyilNOzlHo1HS0tIaf3c6nYwePZry8qZfLiNHFjSGJ4Dx4ycRCgXZtKmEkSMLmDJlL84442T22mtv9tprbw488BBSU1MJhUIUFRVy++03ceedtzQub1kWfn/T/iQjRhQ0/n9zf67KygqGDBnKYYft3/jY4YcfxVVXXdtk2aOPPo633nqd3Nw8wuEQM2bMYOnSFU3mWbNmzXadvCdOnMw///lCk2ljxoxjZwoL1xMOh7nssguaTI/FYhQUjG5xOdM0GTt2y/qHDBlKIJDC2rVrGDduQpN5g8Fghxy7FSuWs3Llct5//53GebTW2LbNxo0bGDp02E73N9npHB9GYed32u/NJDAJ0UfFdnLGLRYCo5MC0+67T+HKK2ficDjJzs5u8ZTTgw/ew5w5XzeeanG73fzxj9cQi+16R+RQKIhpmjzxxGwMw2zymNfrbfy/2+3e5Y7epmlyzz0PsnDhfObM+ZpXXnmRWbMeYtaspxtD1jXX/HG7QGAYTRv5tz4Om2uw7cSVTU89teVKQL/fv10Nhx9+FA89dD9PPjmLI474yU5P4+2Ix+Pd6Tyb+//ceec95OT0a/KY0+ls87a3FgwmBlts77ELhYL89KcncNJJJ2+3jeY6xvdGOtuLWlfT3WUkNQlMQvRRzp18Jzq90Hz7U/t5vV7y8wftdL6FC+fzk58cy4wZBwGJL9Di4g3Alv4xDocTy2p6uXRz0woKRmNZFpWVlUyevPsu17xixXIikXBj/53Fixfi9fro1y8XSHxJT5q0G5Mm7caZZ/6Ok046lk8//YiTT/4V2dk5bNhQxOGHH7XL291sZ8crNTWN6dMP4MMP3+fqq69tdp6hQ4eyYMH8Jn1/Fi6cz7BhO25h2RyAbHvLK2LYsGG4XC5KSopb7K/UHMuyWLp0SWMAWrduDXV1tQwZMnS7ebOzszvk2I0aNYbVq1e36jXXW+kcH+a3JaA19PArP3sq6cMkRB9luC36j23+g7P/WIXh7qy41Hr5+YP55JMPWb58GcuX/8iNN16HbTftc9W/f3/mz59LaekmqqqqGqeFQkG+/fYbqqqqCIfDDB48hMMPP4qbb/4Tn3zyIRs2FLFkySJmz36KL774fKe1xGIxbrvtJlavXsWXX37Ok0/O4sQTf4FhGCxevIhnn32SpUuXUFxczCeffERVVSVDhiSCyFlnncPs2U/xz3++wLp1a1m5cgVvvvkfXnjh7x16vK677k+8+eYHzYYPgFNOOYO3336dV199mfXr1/HCC39vDHU7kpfXH6UUX3zxOZWVlQSDQXw+Pyef/Cvuv/9u3n77DYqKClm2bCkvv/wCb7/9Rovrcjgc/O1vf2Hx4kUsXfoDt9xyI+PHT9yuBWmzjjh2p532axYtms/dd9/B8uXLWL9+HZ999jF3331Hq9eR7Ox+XlQ4jirrG1cGdgZpYRKij4rpKHue6t2u43fiKjknUd39H6wXXXQZt932Z84997ekpaVz2mm/pr6+vsk8Z511Ln/5y6388pfHE41G+fzzb5k4cTLHH38if/rTTKqrq/nNb87mrLPO4dpr/8QzzzzBAw/cQ2npJtLS0hk/fiL77rt/CxVsMXXqngwaNJgLLzybaDTGoYcewW9/+3sgcYrs++/n8dJLzxMM1pObm8eFF17KPvvsB8Cxxx6P2+3h+eef5aGH7sXj8TJixEh+/vNTOvR4ud2exhaw5hxwwIFccsmVPP/8bO6996/07z+AmTOvb3KFXHNycvpx1lnn8Mgj93PrrTdy5JFHc911N3D22eeRnp7B7NlPsWFDEYFACqNGjeGMM37T4ro8Hg+/+tWvufHG6ygrK2XSpN34wx+ub3H+445r/7EbObKABx6YxaxZD3H++WcDmgED8jnkkMNavY5kp/slTuOq9bXoHBnnqi2U3nzday9UWtr5HdyUguzsFMrKaknWIyn70DN01z44lQs7YhILJU7DGW6LmI62aV3yPPQMsg/dr6Pqz8lJafOy9Xs8SeTWhj8GYjaeSz8kcs004j8r2PGCfUxrj7G0MAnRx8V0FFyJDt4WYCXhl5MQYiecRuJKOen43WbSh0kIIYToA3Q/H8ZaCUxtJYFJCCGE6APsXAlM7SGBSQghhOgDdK4PtaEOot1/BWwyksAkhBBC9AE614+ytYz43UYSmIQQQoheaEN60+u67LyGoQVWb3+ja7FzEpiEEEKIXuiAP40gzlaXvaa40AGn9GNqIwlMQgghRC+17V0X7Vw/xhppYWoLCUxCCCFELxWj6cBqOs+PsUoCU1tIYBJCCCF6qe1amPJ8GGurYZubU4udk8AkhBBC9FLxbVuY+gdQURtVXN/CEqIlEpiEEEKIXiqqmv6uG66UM+RKuV0mgUkIIYTopaLbtjBluNEeh/RjagMJTEIIIUQvFd12glLY/f0Yq6q6oZrkJoFJCCGE6KUi27QwQcOVciurur6YJCeBSQghhOilQqqZwDTAj7GuBuztHxMtk8AkhBBC9FLhZlqY7P4BVNhK3IhXtJoEJiGEEKKXaq6FyR7QcKWc9GPaJRKYRIczDLXzmdrA4ejdL9fmjpthKFTnHE4hRB9Q30wLE2lutN8p/Zh2UdJ+A82aNYvRo0dzyy23dHcpgsQXe2rYJn11LSmfFpG+ro6UiEa189veNA0ygjaZq2oIvLeWzFU1ZARtTDNpX7rb8cU06eURAp9uwPpkHalBi5SYJr0kRMqnRaQtrSI1ZGN2UhAVQvRezQamzVfKSWDaJY7uLqAtFixYwAsvvMDo0aO7uxRBQ1iqjGL99i3ihbWN09XwdNIeO4rqFBPdhr6FhmGQXhElevZb6K3urq2GppH+2FFUpruw7eQe3j81quG2L4m/tQoAy2XgevAI9OPfE/9645YZ092kPHk0tYP8WNJRUwjRSnXNnJID0AMCGCuquraYJJd0f6bX19dz1VVXcfPNN5OWltbd5QjAF7KwLngPvVVYAtCrqrCu+hBftG1f8KnBONFLP2gSlgD0mmqil31AanDbuyQlF6epUP9Zjt0QlgDMo0divbUSe+uwBFAVIX7mGwTqknufhRBdq0Y1/0elPSCQuFIuZnVxRckr6VqY/vznPzNjxgz23XdfHn744Z3O39n9PzavP5n7mbR3HxzVUeLLK5t9TM8twVkXQ2W6dnm9Rk2U+JKy5te7qAyjJoryeoDkfB68dRbW4wuaTDMPHUr0sg+aX6AmilpVhTEpG92WJrsukIzPw7ZkH3qGZN+HnlJ/jdLQTA16YABlacw1NdijMrq+sCSUVIHpzTffZMmSJbz88sutmj8z099lfV2yslK6ZDudqa37YK3Z8aWpZtgiO3vX122tqt3xDMH4dutNpufBLqolUhne/oFoy6cZVXE9WQcN7byiOkgyPQ8tkX3oGZJ9H7q7/nqHIs3n2266HuskBqRuCuPYN7mPcVdJmsC0ceNGbrnlFp588kncbnerlqmoqO+SFqasrBTKy2vb1E+nJ2jvPqSl7+D5cBhYAScVZTsJP83IyPAk/jJqriZDQbqbsob1JuPzEMBGjcxAr9jSOqdDccj0QEUzQQrQozJ79D4m4/OwLdmHniHZ96Gj6m/LH5tb22TFqK4ONvuYO9tL/dwNxKb3b9c2kl1rj3HSBKbFixdTXl7OCSec0DjNsizmzJnDP/7xDxYuXIhpmtst11VvNK27bludpa37EA04cBwzAvuNlds9ZvxyDOGAo03rjac4MY8difWfFds9Zv60gHiKc7v1JtPzEPQ6SLlmb+Jnv904zXp5KY7fTib+16+3X2BsFlaun2To555Mz0NLZB96hmTfh+6uvxy7+T86aejH9GNlUh/frpQ0gWnvvffm9ddfbzJt5syZDB8+nLPPPrvZsCS6RtBUpF6zN0a6B/ulpRC1wOPAPH089hkTCLexla/GhMwrp0GaG+ulpRCxwG1i/mIs5u8mU5HkT7ll2UTGZeK65xCsW76E0iD2VxswThyN4//2I37ft1AdAUNhHD4U4w/7UO1RLX74CSHEtsqVjUajmunIpPMDmF9u6IaqklPSBKZAIMCoUaOaTPP5fKSnp283XXS9GpfCfdEeeH4zERW20B4H4RQHkXb+6VLhVvgv3AP3GRMhGAOfk0i6k+pecml9yKGITR+I75/HY9THML1OQh6DuMvAc/BgjPo42m0SDTgIGUhYEkLskrBKjMUUaCYw2fkpOCsjqPIQOsvbDdUll6QJTKLni6CJBBwQaHhZdVA7bz2a+lQHpDast5eEpc3ilk2N10D53GRnpxAqq0VbNlGfCb4kb0YTQnS7YmUzUm9/AZTOT/TdMZZVYO07sKvLSjpJHZhmz57d3SUIIYQQPVqxshipt/+611ketM+B8WOlBKZWSLqBK4UQQgixc6lBC0PDxhYGr0Qp7IEpmEvLu7awJCWBSQghhOiFTFuTiUGhank0bz0oBWNZRRdWlbwkMAkhhBC9VI42WL+DwGQPTsHYUA81kS6sKjlJYBJCCCF6qX7aYI2xg8A0KBUAU1qZdkoCkxBCCNFL5WmDNSqO3cKYJDrXh/aYGD9IYNoZCUxCCCFELzVAm0TUDjp+Gwp7UCrmD9Lxe2ckMAkhhBC91EA7MZbbChVvcR57cArG4rKuKilpSWASQggheqlMFD6tWGa0HJj00DSMkiCqPNSFlSUfCUxCCCFEL6VQDNYGPxixFuexhyY6fhtL5LTcjkhgEkIIIXqxwbaDxTs4JaczPehUF6acltshCUxCCCFELzZUmxQZNlW0POK3NTQNY5EEph2RwCSEEEL0YsMbOn4v3MFpOT00NdHCZLUQqoQEJiGEEKI364dBilZ8v6N+TMPTUME4xurqLqwsuUhgEkIIIXoxhWKkbfLdjgLTkDS0oTAWymm5lkhgEkIIIXq5UdrBAiNGtIURv3Gb6IEBzIWlXVtYEpHAJIQQQvRyo2wHEQWLdzAekz08DXO+BKaWSGASQggherkh2sSn4Wsj2uI89vB0jMJaVEW4CytLHhKYhBBCiF7ORDHadvDVjgLTiHQADDkt1ywJTEIIIUQfMFY7+d6IEWyhH5PO9GBnejDnb+riypKDBCYhhBCiD5hgO4gp+HaHp+WkH1NLJDAJIYQQfUCeNsjWBp+bOz4tZywth3DLncP7KglMQgghRB+gUEywHXxiRFqcxx6RjoprjB/kRrzbksAkhBBC9BGTbCeFhs2aFm7GqwcE0F4H5vfSj2lbEpiEEEKIPmKc7cCp4dOW+jEZSvoxtUACkxBCCNFHuFGM0Q4+MndwWm54GuaCUrBbGBW8j5LAJIQQQvQhky0n3xkxarGbfdweno6qj2Gsqurawno4CUxCCCFEH7Kb7cRS8L8Wrpazh6ahTYWxQE7LbU0CkxBCCNGHZGEw2Db5sKWr5dwmdn5K4rScaCSBSQghhOhjJtkOPjOjxFsa9XtYmgSmbUhgEkIIIfqY3W0nNUrzvRFr9nF7eBpGUZ3ciHcrEpiEEEKIPmaoNknTio9bGF7AHpYGgLFIWpk2k8AkhBBC9DEGikm2s8XhBXSmBzvdjblYRvzeTAKTEEII0QftZjtYY1isbW7Ub6Wwh6RiLJQWps0kMAkhhBB90DjbiWMHo37rIamYSytkAMsGEpiEEEKIPsiDYrR28FkLp+Xsoamo+hhqXU0XV9YzSWASQggh+qiJtoM5RoxQM8ML2INTARKtTEICkxBCCNFXTbSdRBV829xpOZ8TO8eLsVQ6foMEJiGEEKLP6q8NsrTi8xZuk6LzUzCWVXZxVT2TBCYhhBCij1IoxtlOvmhpPKb8AObyStDS8VsCkxBCCNGHjbMdrDIsSrG2e8wemIKqjaI2Bbuhsp5FApMQQgjRh421HQB8bW5/mxQ9MACAsbKqK0vqkSQwCSGEEH1YGgYDbYNvmjktpzM8aLeJsaq6GyrrWSQwCSGEEH3cKO3g2+ZuxGso7Dw/xmoJTBKYRK9gGKrLt6mU6pbtGoZCbbVZpVq3/x1Rb1u2tblew9h++81NczpNTHPnH03bHofm5wGHw9hqftXsPhiGapyvuX1ojdYeGyF6otG2g7Ut9GPSuT6MtRKYHN1dQGs999xzPP/88xQVFQFQUFDA+eefz4wZM7q5MtGdfHGNqzaGXlKOleoibXg69T6TWCd+bzkBX9CClVVQHUGNzSaa5iTo6LyNKgWBsMbcFESvrUb1D2APDIChMIrq0EW1qMGpWLl+6jwGeqsrWhxK4a+Po1ZXoyvDqNGZxNLd1DtbX69pKvx1FkZhLbqkHjU8nXi2l3q3anLxjBOFXVhD6tJyVF0UNSoLXR/FitvUDvTzvddmiMdDbljjrYmifygHw0CNyQSPiVETw164CfwujDGZhFJdhLTd5DiEXSZlOs7yYJA8l4vBbjeBqI291e0bHA4Dd72FKqxBb6zHOzSNsmw3P3ht8j0eNoYjZLuc9FcmKVVRrJVV6KoI3nFZxDPcrAuFWWyFCdk2E/x+UmxwRG2aYxoKf72Fsb4GvSmIGpFOPMtLnUvCk0geBQ39mOYaMY6wzSaP6Vwf5ieF3VFWj5I0gSkvL48rr7ySIUOGoLXm3//+NxdccAGvvvoqBQUF3V2e6AYpUY2691viLy8DSPxd5Dbx33sowck5RDuh/dSpwb+kgvgF70Foyw0rHceMJPWaadR0wpekUpBWb2Od+w7xZVuNuJvnx3XnQcRmfoIuqk3MOzydtMePojpgojU4FfiXVxE/5x2o29Lcbhw6lNQ/7deqek1TkbIxRPw3b2KXhbbUNakfafcfSpUncaBdGnzflxK5+H2IbPkr1Tx6BOZ++fgv+YAxjx3Ol94gR721kfj937F5cGHXyz/DemEJ0YbnMjHRxH3nQai98wg2lBn0Orhg2Y98W1vXOFuW08Fz48cxMK6wbY3DYeArDWOf+SYU16NIbCZzdCZD7j2QE0oXcfnQwSyqquW0Qht94QcYwcRzqQHjqOGsvWQiv924unEbZw3I47y8/rjCTf/6Ng1FyoZg4thUhLccmym5pN19CNUeacQXySETg2xtMN+IcYTtafKYzvGhaqJQG4UUVzdV2P2S5t188MEHM2PGDIYOHcqwYcO47LLL8Pl8fP/9991dmugGDoeB8cl67K2/YAEiFvEL3sNX08y5+A7gr4sT//07TcISgP3GCtRbK3G24lTSrvLGNNZ1n6CXbXN7guJ6Ytd9guP8PRon6VVVWFd9iC+qG+q1iP/2rSZhCUB/sAZe+AFXK04hNa5jq7AEoBdswr7tS7wNDS++mhjx899tEpYArDdXostDqAEpZP3+fY6JezDv2xKWjGNGYi8uw9r2uYxaxC//L57yxH2ubJfB7WvXNQlLAOWxOKcu/oFaV+LYu0MW+uy3obi+yXxqWQWDbvqac9Oy+eOKVfzGTMF37nsQbPpcqrdXMe69Ig5IS22c9sSGYr6pr9vuVKG/Lk78zDdhq7AEoL8rQf9tDh4ZukYkkRG2ybxmrpSzs70AGIW1XV1Sj5I0gWlrlmXx5ptvEgwG2X333Xc4r1Kd/9NV25F92PLjrY9jPzKvhReIRr+zEqfT7NBtOp0G+uN1EG/+1Iz12Hw89fEOfx5cdXH0F0XNblOvr0VlesBUW6Z9V4KzLobDYaC/3bhdgNnMfnZRq+o1iuugtPkxWOz31uCui+N0muh3VoHVfEKwnl+C44RRUBrEub4WfFsatx2njsN6akGzy2FrrNeWEwi4qTPhP6Vlzc5WHouxPhrBMMAoC6HXt/DB/r8iDlc+xvn92J8VQgun2XxPLeJcd0aTafcXFhF0qC3HxQBjXQ1Uhptdh/3GStztfD30pfd0b9yHjqi/3VTrf0ZqBz+oOFGlm0zXOQ2Bqbi+249pZz1PrZE0p+QAli1bxsknn0wkEsHn8/Hggw8ycuTIFufPzPS3qvNoR8jKSumS7XSmZNoHe2MdkZL6lmdYXU16uq/Dtxvd0V27y0K4TIPs7PYdx22fB7ukjHgL8wLo2ih4HU1akcyIRUaGn2hhXcsL1kZxsvN6499savlBW2PGbdLTfUTXtnxs9KYgZDQ0828KQsC1pWXH50Tv6LksrMXtdhAPxZvpjrrFpniM/XMzCC6qYEefgWYoTqbTgbGuquWZKsOkbfP3ZHEkiuk2yU7d8rqKl25oeR1xG0dct/v10B7J9J5uSbLvQ3fX7/e7Wz3veNsgZoVYn+JgD2PLcjrVS8zrIFAdw9mNr+fullSBadiwYfz73/+mtraWd999l2uuuYa///3vLYamior6jknoO6BU4g1RXl6btCPHJ+M++NAYE3PQc0uan2HffCor67Gs5lsQ2sIwFIG9+sOzi5p9XI3JJIKmvqxtzdYtPQ+pPhNcJkSbjwsq2wv1WzWjOwysgJPK8jpSd+vX8vaGpBI1NHU7qTdtSGrLDwacxD0m9ZX1+PcZCK8sa3Y2NT4bvfmy5OFpTVpl9KZ6jAk52HM2Nr/sXv0JBqO4tSbNYVIdb/44jPB4KCurJWVAgBafdZdJ0O9gdXUYe8/+8OTC5rdZkMFKu+mpickpflTEoqx2y/FKG5He0pYgzU3MZVDbxtdDeyTje3pbyb4PHVV/ewN3fX2k1fPmoHE64ctgLSOspu8zd7qb0OpKqrvh9dzZWnuMk+qUnMvlYsiQIUyYMIErrriCMWPG8Oyzz+5wGa07/6ertiP7sOUn5DIwr967+Sc92wt79icetzt0m5alYUIO5Pmb3az5h30Iuo0Ofx7CASfmmROb3aZxwCDseSVs7g8EYJwylnDAiW1r7BHpqMHNBx7z6r0J+hw7rSme4Ubt1b/5dZyzO6GAg3jchql5iWPfDMdZk7FeXII9rT/hvKYtf7G75+C4dE+abRbK9GDsP4hQKEaKpbh0UH6z65+aEiDbSOxLNM2FPnhws/NFzhjPk7Fq6i0Le1wWakCg2fmqrpjKffVb7tBuAFcPGYwjajU9Ntle1OTmQ6l50RRCAae8p/vwPnRE/e2mW//j0IrB2mSBim33mE73oIrru/2Ydtbz1BpJFZi2Zds20WjzNwwUvZtta0KDAzgePbLJl57aqz+OfxxHXYq5g6XbrtZv4ph9LGq/gVsm5vpx3H8YkVHpTS5t7ygRNPYZ4zEumgJ+Z2Kiy8A8eSyOC6cQf6qhlcTrwDx3d/TvdyPckKDqfCbm00ejZgzaEkiyvZh/OYjobjmtaoGrcyqMvx6M8bNRsHmsohQX5lXTsH5WQLRhn2v9Jo7njsOYNqBxWdXfj/PWGdifrSc+rT8rb9qH2yPlqL8fi9rcOrO0HLu4FtfDRzR5Lo0pebj+fhz1WYmrcuyYxbHpmfxp2BDSHInn1wR+lpPNg6ML8DT01QorMG46APuXY6ChIzh+J+ELdueLE4ewwopy/5gCLqvcQPiZo2H/rUJYPx/q3kN5d7CTFaFEJ/fBHjd/Hz+WAba53YdrvVth3ncoxrEjt/QjS3VhXrcv1pHDiNkd18IpRFcYZpvMb2YAS53mQm1z4Udfo7TelXzVfe666y4OOOAA+vfvT319PW+88QaPPfYYTzzxBPvtt1+zy5SWdn7ToVKJ5ryysuRsNobk3geHw8BbF8esj2G6HYQ9JkGnojNf1kqBNwau+hjEbGy/k2DAJN5Ch+ddWe+OngeXMvDWxVChONpjEg4kwpOnLoYKW2ivg1DAQXSbhZUCTxzc9XGIWth+B8GAk/gunq70aHDXxRPb8jkIpTgaw9JmhqHIcDiwSoOouI3yOLC1xlaKqjQHlSb4MUixwV0Xw6yLgaGwAk7sNBfusgiqLgoOg3iqk3q3uV2oM5wGtSYEbRu3YRCwwWim87bbNHDWxFDhONrnpDzNQb2ZOI5Ry8KtDNJsjSOmcdbE0FELHXBhpTqJex2UhiPEbU2KYeKP6R2GS49WuOtiqIiF7XMQCji7NSwl83t6s2Tfh46qPyen7afkxr35CXc7s3dpmS+NKLOcQf4XyiZ9qzYVx2srML/fRPA/J7S5np6qtcc4afowlZeXc80117Bp0yZSUlIYPXr0DsOS6BvicZtaj4HyusnOTiFYVtupYQkSTbhBBwTTnFsmtjMstUZU20T9Jvg3t54lthkJOCDg2FLcNrSGkAmhVAeNb/k29O0KKwinOCClYR3NtKZprTHSPFTEYtuVYsQ1WXEAixgQcxvg3qpDajBG0GeAb6sxYJqp047Z+GOQODHa8n5ELJvIVsfLHbVxN1nGIg7EadivhmOjonGyU71Y1eGGfbB22NkcIKz0NsdGWpZEchqmE++XRUaM6fZWHb9TXKiqSOIDpbM7B/dQSROYbr311u4uQQghhOjVcrVBQCsWGPEmgYlUFypiJa5u9TtbXkEvltR9mIQQQgjRAg2xXex2pFAMb6Yfk24ISaq69Vfd9TYSmIQQQoheKtyGrrzDtYMFRgx7q8tvdUACkwQmIYQQopeyorve32ikbVKjNKvUVr33fA2BqbbvXpkugUkIIYTopeJtaBAarh0YGuZudVpOexu6PEtgEkIIIURvE2/D0EleFEO0ybfGVuHI03AVaX3n3Ng8GUhgEkIIIXqpXe30vdko28E3Zgy9uR+TodAeMzFOWh8lgUkIIYTopSI7uK/1jozTDkqVzeqt+jFpj2PLTbP7IAlMQgghRC8VD4K9s5FXmzHaduDQ8MXWp+XcJioop+SEEEII0esowjW7vpQbxSjt4FNzq17jLhPCbUhfvYQEJiGEEKK3MjWhyrbdymSy7eQbI0Z9w+2EtMtEheWUnBBCCCF6GXcAguVtW3Z3y0FMwWdmw2k5pwERaWESQgghRC/jSYP6irb1Y8rBZJht8q4ZTkxwGqiQtDAJIYQQopfxZmiwFPWb2rb8nraTj40o9dhopwFRaWESQgghRC/j9IAroKkqals/pr0sFzHgPTMCThMlp+SEEEII0Rv5c6C+tG2DWGZhME47eMUMgcOAmAQmIYQQQvRCvmyNMqFiddtamfa3XMwz46zIMqXTtxBCCCF6J8OAlDyoXA/x8K4vv4ftJFUrnh/rRcXsji8wSUhgEkIIIXq5lDyNYcCmZbveyuREcYDl4l8jXNQqCUxCCCGE6KUME9LyoaZIEazY9eUPttxETPjnHikdX1yS6JDAVFdXxwcffMDKlSs7YnVCCCGE6GCBfhpXQLNhgdrlcZkyMNi71OaJGRnEdN9sZWpTYLrkkkv4+9//DkA4HObEE0/k0ksv5bjjjuPdd9/t0AKFEEII0TEyR2jiEShZvOun5o7aaFGS7uTN6qqOLywJtCkwffvtt0ydOhWA999/H601c+bM4brrruPhhx/u0AKFEEII0TGcHsgcCtWFiqr1uxaa8qMw9cd6HivdRFzrzimwB2tTYKqtrSUtLQ2Azz77jMMPPxyv18uBBx7I2rVrO7RAIYQQQnQcf7bGn6spXrSL95kzDU74XwXrY1He6oOtTG0KTP3792fevHkEg0E+++wz9ttvPwBqampwuVwdWqAQQgghOlbGEI07FQrnKiK1rVzIVAwtibK7x8fDpcXE+lgrU5sC0xlnnMFVV13FjBkzyMnJYdq0aQDMmTOHUaNGdWiBQgghhOhYSkF2gcZ0wbpvFJH6VixkJiLDT30pFMVivFbVhsvtkpijLQuddtppTJo0ieLiYvbdd18MI3EQBw0axKWXXtqR9QkhhBCiExgm9BujKVmiWPe1YvA0jdu/gwXMRJ+nwdrJNJ+fh0tLOCYtA4/RN0YoavNeTpw4kRkzZlBSUkI8HgfgwAMPZMqUKR1WnBBCCCE6j+GAfmM1SsG6rxWRuh3M3BCYsGx+mp5JRTzOCxW70gkqubUpMIVCIa699lp22203jjnmGDZu3AjATTfdxKxZszq0QCGEEEJ0HtMJuWM1yoC1XylCVS3N2BCYoha5Tif7B1J4rGwTNVbfuL9cmwLTXXfdxdKlS3n22Wdxu92N0/fZZx/eeuutDitOCCGEEJ3PaAhNDleipamutJmZGvowYSU6ex+TlkFE2zxVtqnrCu1GbQpM//3vf7n++usbx2LarKCggHXr1nVIYUIIIYToOoYDcsZqPKlQ+K2ial3TcZq0o+H3hhvwZjgcHJqSxj8qyiiNxbq63C7XpsBUUVFBVlbWdtNDoRBK7frooUIIIYTofoaRuHoukAvFixSblioaRw8wzcS/8S2n4I5KS8NUill9oJWpTYFpwoQJfPzxx9tN/+c//8luu+3WzpKEEEII0W1UYpymtCGailVQNLfh3nMN19Wr2JZ7yfkMkyNT03mlspzCaLR76u0ibRpW4LLLLuPss89mxYoVWJbFs88+y8qVK5k3bx6zZ8/u6BqFEEII0cVS8zROD5StUKz5UjF4jIkLGk/JbXZISiof1FYzq7SEPw8c1C21doU2tTBNnTqV1157DcuyGDVqFP/73//IzMzkhRdeYMKECR1doxBCCCG6gTcd8sZprAis/a7hlNw2gcltGByVms5/qitZH410fZFdpE0tTACDBw/m5ptv7shahBBCCNHDOH2QN15TujTRRzlYbOPZZp4ZgRTerqniibJN3DCgd7YytamFaezYsZSXbz9YVWVlJWPHjm13UUIIIYToOQwn9BuX+P+mhZrixU0v8HIZBoenpPGfqkpKeukVc20KTLqFG+5Fo1GcTme7ChJCCCFEz6NMhTYNUjIt1n2tWPfNVlfQATNSUnEqg+cqyrqvyE60S6fknn32WQCUUvzzn//E5/M1PmbbNnPmzGH48OEdW6EQQgghegRtKlKyLPr11xQvUsQjMGy/xCjhXsNg/0AKL1dWcG5OLt5edo+5XQpMTz/9NJBoYXrhhRcab7oL4HQ6yc/P58Ybb+zQAoUQQgjRQ5gGKm6TOVRjumDjArAtxYgDEqHp4JRU3qut5r2aKn6antnd1XaoXQpMH374IQCnn346DzzwAGlpaZ1SlBBCCCF6Hm2qxnGY0gYkQtKG7xXKUAyfrslxOhnv8fJKZUWvC0xtai+bPXu2hCUhhBCirzENsLYMK5CaB/0nQfkKKJyb6Ai+rz/A96EgRb1sIMtWtzDddtttXHLJJfh8Pm677bYdzjtz5sx2FyaEEEKInkU3nJLbWtoAjRVRbFyg8GbAbsP8OCvKeL+mmjOzc7qp0o7X6sC0ZMkS4vF44/9bIveSE0IIIXonbaom95LbLGOoJlwDa/4HE3IMxnm8fFTbRwPT1rc8kdufiNYwjER41lqjlMK2mx+OQnQch8NAa7C2ajI3jMSlv0ol/h/f5q/DpvNpTDNxpt6y7O2eN6XY4XPpcBgN8ymUYqfP+eb5bdvGMAzicXurenXjtC01GhgGW82XmEepxDSHw2hc17brbW4fXC4Ty7KxbRrr3fy6NU0Tw4Bo1NpuvU1rUg3b2n5fd3YcNu9DCyO17JLmtrX5WMp7T3SYZlqYIPH+yR2vCVUpVv9PMWE/Ly9UlhO0LXyG2Q2Fdrw2j/S9tbq6Or766iuGDRvGiBEjOmKV23n00Ud57733WLVqFR6Ph913350rr7xShjHogbyWxl0XhyVlKIeBGp6BXVIH6R7i2V7q3apDviDEFqkxjaM2hl6wCTwO1LhsLK+JqonCD+WogAsjPwWrsBYjz4+d4abKnQgagbDGLA3CyiqMvACkudAbalHDM9joVxR5DQY4nShgdSTCxkiUkT4v/UwHnoiF1hD2OSi34hRHQgyu06y1I9RbFuN9PtK0whlt+gEb85jUGpr5tXUYSjEpEMAVMfDF3FSt1zhdikCOorbcxpdq4EqBuK2pLLSxQoq0fEXEa+G1TSrWaVQMMgYa1JfbaAPcmRDHRleY1JXbBPoZ+DI0da4YqyNhRse9OEMOypZYuAMmKQMMokaY1NIQujJK7WAn9ZUOPGkmtgXlm2z8mQZOL9RXaNJyFdpjUeqMsai+HrdhMNbnxR/TGHGNaRgYcTe1JZpIPaQPVJheizhRlIKQ22STFWNlMMxAj5t8p4tA1G5TsHFq8AUt+LES6qOocdlE073UltrUrHNjWZCZr8AVJ65754CCogsZCmU1/zo1HZA7TlP4rcGAai8WMD8YZJ9AStfW2EnaFJguueQS9txzT371q18RDoc58cQTKSoqQmvN3XffzRFHHNHRdfLNN99w2mmnMXHiRCzL4u677+ass87izTffbDIelOhegZjGeGYR8cfmb5noMHBcNQ29ohL1YwWp9x5Ktbd3jc/RndIjGv34fKLPLmqc5vzrweiP1mK9uXLLjB4HzpsOIP7cElCQ+ef9sS1N/Px3if+w1cj9OT5cdxxE7Jy3yTlqBFUnDOeX5Su4vWAE5y9bTnnDKL6TA34eHT0KZSiuWr4Sj2lyRFYm581bSNjeEpB+3i+Ha/LzcYUTzfgRn4NnS0p4YH0Rmz92Hxw0ipzPU1jzxZZOooYD9vyFk9VfxQnXaaad4uKrx6LYcRi+j0nWYIOP/xlFb5XFCvZ3kJKjWPZ0nH1Od/HlM1GCVYmtpA9QDPmNZpTTxw+v2KxfuCU8OFwWM041sL7bQPHIkXx7v8V+v3HzxbNRqjdu+XLwZSj2/pWLjx6MMnwfk9pJcS5YvTxxzJXiLwUjOMSbSnijk08eiRLf6rZa+ZMM9viFh0pPjLOWLmNJfbDxsVyXi+fHj6VfbNdaYl0afPNKiV/yAUQTx9c+fTJF++zON/8Jbzk2CiYcYTJ0PxdxeldHXNG1tNH8KbnN/NngSdfoxS684wwWh0O9JjC16Vvr22+/ZerUqQC8//77aK2ZM2cO1113HQ8//HCHFrjZE088wQknnEBBQQFjxozh9ttvZ8OGDSxevLhTtid2nWEoHAvLsLcOSwBxm/htX2IeNgy9tBz9l6/xSAtTh3A6DdS8EqytwpKxZ3/02mrsrcMSQDhObObHOH49AfvjdehFpVg3fg4/bHObo9Igses+xnH+FOyH5zFqbZCAaXLLqjWckz+gcbb5dfW8Xl7OExs28nlVNWf0z+XqH1c0CUsA/9xUynvVVZimgcNh8EMoxP1bhaWJAT/56wKs+aLpcnYcvnk+xsj9HJSutFn6YYzdj3dgOmHI7g7mvBhrEpYAln8Wx+kBw4RvXogy/sgtfxNWbdAUv2IQLzZYv7DpcvEofPKCIvyT8fzv3waj9new+N1Yk7AEEKzUfPN8lPFHOFjwRpyhdX5GehN31YppzaU/roCok48ebBqWAAoX2Kz60ualktImYQmgJBrl9CU/UO/atT6gvuoY8QveawxL+ByEjhrP1//WTY+NhkXvWNRtNBpPIQrRJi2ckttMKUjPh9oNinyHi5WRcBcW17naFJhqa2sbhxX47LPPOPzww/F6vRx44IGsXbu2QwvcUQ3AToc3SPRX6NyfrtpOT98Hb9TGenhei8+F9c4qjIOHYL+zCnddvEfuQ3f/7Oo++Oss4o/Pb3KczZ+NIv7CD80/CXEb+7ti1OR+KJcD/cm6ZmfTG+tRfie4TPRj87khPZcFdfWM9jdtzc33evj7xhL2TEvli+oaWvq786HCDdQ7IeQyeLiwqMljv04dwIYPm19Oa9iwxCK3wGDV1xa5o0wGjDdZ9328hS3B8s8thk1zUFOi8WeoJo+VLNWJy6KbkTvSYMXXNmjIGmpQ8mPzXwp1ZRpvaqK/0LIP4szMG9r42Cifl6KlFnYL5S370GJ3I7XZx4oiUUotq9XPvdNpYr+1ErbuY3bYcJYubPn2VIvftTC1s9tf533lPd0R9beX6uAfbagdBiYAf7YGFOkxB2sjkW5/HjrqGLfplFz//v2ZN28eaWlpfPbZZ9x9990A1NTU4HK52rLKXWLbNrfeeit77LEHo0aNanG+zEx/YwfWzpaVlfxNju3dB3tTPZGS+hYf15vqUYNSwdI44prs7I4/Zn3tebDXVRMvbnrMVZobSoMtLAF6UxCV4YFIHHbQ0qcrw+B3ojfVk2Ml3kfRbVqPTKUI2jaZDgclkZZP9RRHo5guB5atKdlmbJYch5PyqpYLCVVr3AGFFQNtgycAdRU7md+f+H88AsqgSWuL1UKZ7oCivqGxzW75jAMA0RCYLghWa3LZ8pmX6XRSX9hybbEQ+Gi5A2yVFWdKdusH+4uurW7yu53tp77GAJr/QgtVa9xOF+lp7lZvo7sl+3u6u+v3eDv2O9l0OVDBKAF/y68h7Qdlxki33ay3azvls747tCkwnXHGGVx11VX4fD4GDBjAtGnTAJgzZ84OA0xHufHGG1m+fDnPPffcDuerqKjvkIS+I0ol3hDl5bVJ25G5o/bBAzh364f97upmHzcm5GDPK4EUF3G3QU1Zbds3to2++jykuA2MiTnYH65tnGavrkaNy0Yvaf4GmMa4bOJPLwCXCV4HhJpvDlF5fqiJoA4YxAIjhknijuRbq4nHyXe7WREKcVr/XF7ZVNrsuiYF/BCx8BiKyYEAq0JbmumXROoZPdhD2YrmdzpzkMGaOXG8aYmLBaqLNf1GGmxc0nwoyBysqClJrMvlU03CkuEARwuf8zXFmiF7GBQuBMNIzNtSS5EnJXEPrayhBoutLa/j1aEw2cMNVrYQWFL6KTbakWYfA+jvdFHWyveFaRr4982Hfy9vnGYsKyP3qDibljf/h2LWMEXEilJf1vM7fyf7e7qj6m9v2AiHOrbPmkJjxuLU1bf8OgZQSuGMQqUZa/Vruru09hi3qfnltNNO48UXX+TWW2/lueeea7zUdtCgQVx66aVtWWWr/fnPf+bjjz/mmWeeIS8vb6fza935P121nZ6+D2EDjAungNlMSk1xYew1APvrDZgX7EEoxdkj96G7f3Z1H+oc4LhoCmzVL8V6cQmOsyc3/4bI9aP6+dBra9B+J+bvmp/P2Hcg9pLyxBUxZ0/m/0qKOCE3h/+WVzaZzwSuHjqY5cEQgz0ecpzNnw66bugQ3DEbQnEuGDQQ51Z/yTxVuYFBx5Bo79+GJwXS+isqizSTjnaw/LM4m1bY9Btp4vRuP78yYNT+TlZ9HWfQbibFPzZtKhp+kIHT3ULrS41m4Ghw+WDtPIuR+zX/9+TACQalKy0MB4w6yMGthVvCati2yM43CGQ3/5fa7j9zUuVqPqwcnJFOGkarn/t43IY9+0P2lgOh/7eeYSNizYZCZcD4wx3ErFi3v877ynu6I+pvL93BP5gKWrhKbjPbAjuu8DgMIlpj2brbn4uOOMZtPl81YcIEDjvsMPx+f+O0Aw88kClTprR1lTuktebPf/4z77//Ps888wyDBg3qlO2IttMagjkeHM8cgxqW1jhdTcrBdc+hxB76FvPafbCOGUHU2vE5cNE6tg3hXB+uJ3+CGpo45np9LfbaGpz3HwZ5W96fxj4Dcd1xILFH5uG8/UCsQSnYvxiDecVeEGgIOk4D86cFOM6YiPXuKhxPH83DqpZf5vXjpH45vFBcAkCqaXLd0MHsH0hln0CAOwqGc9/aQu4bU8A+aVv66Ax0u3hy7GiGG87Gq79ytMHzE8cxypf4ot8UjfG2Ucr+5zvxZ20JGjkjDPY9083Ct2Ps+Usnqf0VK79IBKAlH0Q55GI3mUO3zJ/ST7Hfb1ys+DLOsL1Mxhzs4MdPEk1EDjdMONqEvaMscFSz3xkm3rTNnUyg/1jFoWeB65YPOPzUGLWbbDIHGYw91NEYPgwHjNjHZMS+DooWWRx6qZt/hDdQ13Caco+UAK9MnAAqwkEXORkwwWgMgd5U2PdMJ6n5cY5KT+fqIYMImIlTc06lODWvH7cPH44rspNzgduoDZg4njsONa2hM74G96wvOeIiB5mDtxyb1FzFIZe4UF65Qk60T2v6MIUbzhS7fIn3fCef6OkySuu2Zdji4mL++9//snHjRmKxpn8xdcatUW644QbeeOMNHnroIYYNG9Y4PSUlBY/H0+wypaWd3wyoVKI5r6wsOZuNoeP3wTQNfME4Rl0MwzQSHYejFrbLIJTiJNYJYamvPw9Op0mgOoqqi4FpYAecBAMOPLUxjPoYhsMAh5E4/eZ1UJ/uIhJLfDm7DIW3No4KxzGcZqK1KhwjHnBRnOHA0pBqKWwFtYYmbNv4DYMUS2E3rMPpNqkwIaxtvIZJXNvELY1fGaTE9XaDZTqdBtVORa1tYyiFzzRQGnwhB2YocSNP05G4es10gumNEbUM7IiBtkB5IOSJ4Yk6IKxQtsLhTIzVpAyI+SyitsYfdmBHwXCB6YkT0TZ1hsZjGKTWOoiHE0FIe21qnTGyqy2cEYu4z0vEdqAcoDGwYmA6FUpp4lFweDS4Y1QrmzqtMRX4tYEnlhhLSSkwcULMgRUHh0tjO6ONA4oaToMaE0K2jccwCNgKFd21sLTldaPwxjSu+hg6bmP7nYRTnHhML8GaxDhZpsvGdkSxdtIy0JMk+3u6o+rPyWn7Kblxb3zCTbXZbd94M7zfb8C1ppKyS/dvcZ6SJYraEqg8uooXq8qZN25Sh9bQ0Vp7jNvUh+nLL7/kvPPOY9CgQaxatYqCgoLGcZjGjRvXllXu1PPPPw/A6aef3mT6bbfdxgknnNAp2xRtY1k2tW4D3FudF/A1NGZKy1KniMUsKn0m+LbqUByziHoM8Gz1PPjNxsc2i9qaqN/c8hhAIPF/XyjxfGkSfyWmNvyA3aSXTixikQKkKsjO9m71JWHRXDegWMzGFwNf49o3ry2O5d78P8BN4sq7zd0lzMSPtsETbJjLkVhDrOFxDZgh8AK2ioE7sXY7kmhS31x/2IzD5gY4GzwRqPMYKK9BdraXuq2/6Ey2XAHo2VKTJ/FrA6txL7SGODFwxMDRUNtWeciO2QRiENi88XbQWhN0QDBty+lQFbfwpCvqIiG0bthC2/KYEE1oQ6Gsll9MVhyqiyB3LGzEbmxJ7Q3aFJjuuusufvvb33LxxRez++67c//995OZmcmVV17J/vu3nDrbY9myZZ2yXiGEEEK0kql2mPEr1iQutMgdp6mOWGSZHXJDkR6hTX2YVq5cyfHHHw+Aw+EgHA7j9/u55JJLePzxxzuyPiGEEEL0ENowWhzpOxqEilWQNx5cfiiPx8lr4UKQZNSmwOTz+Rr7LeXk5LBu3ZbB7yorKzumMiGEEEL0LAaoZm7fozUUL1I4PTBgt8Tjm+IxhrqTZ8yvnWlTW9nkyZP57rvvGDFiBDNmzOCOO+7gxx9/5P3332fy5MkdXaMQQgghegLDSNx8V+smw2RXrFYEy2HMkRrTCTFtUxyLMdzV/EVZyahNgWnmzJnU1ydGF77ooouor6/nrbfeYujQofzhD3/o0AKFEEII0TPozWO+2bpxzL1gJZT+CP0nQWrDCBdF0Rg2MMbTzIBpSWqXA5NlWRQXFzN69GggcXruz3/+c4cXJoQQQogeZnNgsmwwDeIR2DBPEciBgXtsOVW3MhLGqRRjWhj2Jxntch8m0zT57W9/S3V19c5nFkIIIUSvoRvu7KEsja1hw/cKDBh5sGbrOyctj4QZ6/FudzulZNamPSkoKKCwsLCjaxFCCCFET9YwrJKyLEqXKUKVUHCwxuXbMovWmh8jYfb0+ZtfR5JqU2C69NJLueOOO/joo4/YtGkTdXV1TX6EEEII0ftsbmGqL4HK1YpBe0FKbtN5imIxqi2Laf5AN1TYedrU6fv3v/89AOeddx5qq17yWmuUUvzwww8dU50QQggheo6GZpayHzTpQzW547YfYmBxOIhLKXbrZS1MbQpMzz77bEfXIYQQQoiezkwkJgObYfvrrUcWaLQwFGJPnx9PL+q/BG0MTHvttVdH1yGEEEKIHi4cVKQCA8dbOJu5AC5s2ywPh7gyb0CX19bZ2nWTl1AoxIYNGxpH/d5szJgx7SpKCCGEED1PXalJPyAtz2b7k3GwJBwiDkwPpHRxZZ2vTYGpoqKCmTNn8umnnzb7uPRhEkIIIXqXWBAioYZhBeLNB6aFoSCDXS4Gu3rPLVE2a9MJxltuuYWamhpeeuklPB4Pjz/+OLfffjtDhgzh4Ycf7ugahRBCiP9v787j5Krq/P+/zr23qrp6X7N09oV0QkIWthCMyKogItsXvoOyqEEGFRVnGMHxpw6LEsRRcdwwKrIoDMMwfl1Ah1UB2WVPSALZt05v6b2We+/5/VGd7jTpTnc6nVQv7+fjUY+kb9069Tm3tnede+peybK2BgPRjklLfrjX9dZa3mxv4wP5hYe4skNjQCNMzz//PD/+8Y854ogjMMZQWVnJ+973PvLz87n99ts58cQTB7lMERERyaZ0K8SKOgJTeu/AtDWdpj4IRuTuOBjgCFNbWxulpaUAFBUVUV9fD8CsWbNYuXLl4FUnIiIiQ0IQgJPTsUuuh8D0ZnsbMWM4coQdTmC3AQWmadOmsX79egCqqqr4z//8T6qrq7nvvvuoqKgY1AJFREQk+1wX0n5HbOghMK1KtHNkbh6xEXY4gd0GtEvu0ksvpaamBoCrrrqKyy+/nN/97ndEIhFuueWWQS1QREREsi+aB03bDdYAftDtOt9a1iYTfLZibM83HgEGFJjOPvvszv/PnTuXJ554gnXr1jF+/PjOXXUiIiIycsTLLI1bDNZ1MKnuI0wbkkmS1nLsCDsdyp4GfBym//qv/+LOO+9kw4YNAEydOpXLLruMCy64YLBqExERkSEikgO5FRC86xK0dg9Ma5MJ4sZQlRPPUnUH34AC02233cavfvUrLr74YhYuXAjAq6++yre+9S22bdvGF7/4xcGsUURERIaAkskW+4KhfnVIYRrcSGb5u8kE8+K5eD2dK2WEGFBguvfee7nxxhv5yEc+0rnslFNOoaqqihtvvFGBSUREZARyPDA5DmEi5O0/GWadZonkwMZUinOKS7Jd3kE1oKnsvu8zb968vZbPnTuXIAh6uIWIiIiMBCbiUFgekGiElb837KwNqA98Zo/g3XEwwMB09tlnc++99+61/P777+ess8464KJERERkaLKuQ8QETFmSOTnK357JnE/2sGgPZ+MdQQY86fuBBx7gmWeeYcGCBQC8/vrrbNu2jXPOOYebb765c72vfOUrB16liIiIDA2eg0kHRHNh8hLL29tTmBC23xNnzEctBSP0yAIDCkxr1qzh8MMPB2DTpk0AFBcXU1xczJo1azrXMyN48peIiMhoZF0H0pnpN64LqXFpxqaj+I2Gv/3IMOlYy8yTLNERdsDvAQWmu+++e7DrEBERkWHAegazx4Er622aykiUueeEVK80bH3FsPVVw7TjLVOPt3gjZE/dgHfJiYiIyOhjXRe3PdX5dyMBc0wMx4XxR1jKZ1q2v25496+GDc8aph5vmXKcJTLM54QrMImIiEj/eabbueSabUCp6YoTkThMXmwZN8+y/Q3Dur8Y1j9tmHS0ZcrxlnhRNoo+cApMIiIi0m/WdbrtkmsloKiHOBHNgynHWSoXWKrfMmx+ybDxOcPYuZlddcUTD2XVB06BSURERPrPczAdI0yBtSSxFBq319UjcZh4tGX8AkvNGsPOlYYdbzgUTbBMWWIZN9fiDIM0MgxKFBERkaEiM8KUCUwJMv/m7iMw7eZGYNxcy9g5ll2bYecqh9cfcHj7YcvEoyyTjrHEiw9m5QdGgUlERET6zboOdOySS5E5eGV8P46DbRwomQIlU0Lad8HOVZlddeueMlQcBpMXh5TPzKw3lCgwiYiISP+5pnOEKd0xwhRlYMddjBfDlCWWiUdb6t411Kw2vHy3S05RZsRp4pGWWMFgFX5gFJhERESk/xwHY4EwZPfU78gBHqjajcCY2ZaKKktrLdS8bXj3ScM7jxvGzIHJx4aUToNsHg9bgUlERET6zTodqSWwhG5ml5w7wBGm9zIG8isgv8Iy6VhL7TuZUacX73DJLbNMXmyZsDA7x3RSYBIREZF+s25HOApDgo653oMVmPbkxTomiR9uad6RGXVa/SfDmkcMExZmwtOhPG+dApOIiIj0X8cIkwksNpIZYTqYe8qMgcLxUDjekmqz1Kw27HjLsPlFh7LplqnvCyk/7ODvrlNgEhERkf7bHUxC2/EbOXAOamTqEs2FCYss4+dbGjYYqldmJonnVVimvz+z3On7CAcDosAkIiIi/WY7hnLMHoHpUM/Fdlwom2EpnW5pqYYdbzi88aDD2sctMz5gmbBo8IOTApOIiIjsPwthR2TK1iGTjIGCcVAwLqStHra/Znjr/zms+6tl1mmZ89kN1q66IXZYKBERERnS9kggu0eYDsak7/2VWwozTrLMPScglg+v3e/w/AqHpu2D074Ck4iIiPSftZ3/DRjcwwoMhtxSOOy0kKrTA5It8OxPHdY+agiDvm+7LwpMMmI5jsG8Zyw2s6zrb2P2XtZfxhicjl+LDLSNQyEadXHd7jvze9o2+2t3G3tuh932XOZ5Do6TWf+963meg+c5nY/D7nZ3t919+xo8zyUW8/boV1cbe95XTk6EnJwIjkO361x37/syxuC6DtGo27nee+XkeHh7TGDwPIjHI+/pM3v1z3GcHtvrj562V38ft/fe7kAMl+e5HEJdM707D1zpDcEnRmElHP7RkPHzLe/+1fDCLx2SLQNvb1jNYXrxxRf5xS9+wZtvvklNTQ0/+tGPOPXUU7NdlgwxeWlLpD6JfbsWyuIwvRjrOTg17dh36jHj8rGTCwk9g7u1BbuhETO5kLAyn5ZclzC0+2w/giG31ce80wC7kjizy7DtaWzCJ5xSRGueS9BHG4dCScJimpKEr9dgimI4s0vxcyOYmjbs6jpMeS52WqZefz/KzfUt0aY04ZYWUtPH0NTs0d5iKZ7g4OWHbE4keNemqIzH2OWHtCdSVNl82rYCPpRMMniRkDDlUL8mAM+heIJDW2OIEzek8n0iOPg7HZJNloIJhjA/JMf32LU5JF5owESp2RESL4qSW2iwoSVMQV2NJa/UweQDIaRaI7Q2hBRUOPgutDVaCscb2l0fN+Hit1viBS5N1SE5BZkwULMjZFdRmuKxcRwM7Q2wbUdIQXmE/IrMOs3bLTW1lsKxEfJKDMYN2bXD0t5oKRrvkFNosb7Dri2WZIuldLJDJNeSor3P7euZCKQ86jdlzuBeMtFgvBA/4dCwOXPAvqJxhtBLEdiur8zGgGdjpJod6naEpMoCckpyMusF4X4/f3Y/z1m/i/TYEtqjuTRUW+IlhrwyCLwUYbj/7crwZzoSkzUGv+P/Az01ysHmuDDhSEthpeXdJzO76I75VEi8aP/bGlaBqa2tjaqqKs4//3yuuuqqbJcjQ1Bh0hJ+9a/4f92cWRD3iP74Q6S/+zz+G7VdK5bFif77yaRvfAb77i4AzIQCCu/4ME0l0V5DUwTIe7MW/3OPQMLvXO6cOhX3jOmEVz9GwR1n0jwhN6uhqTRh8W/+G+Gf1nctjLlEbzuVcFeS4LonM8sKouT/4sO0TC3At33XW5C2mH9/EX9TE01fOpUnbrOkE+nO68fNMeSfl2Z8aYyr3l7L4pwizqoex6MPpLG7P1sNzDnZI5ZvefX/BUCAcWD+mRESzSHVay1Hne/yt58nSSdg7uku5VNcnrw7wfs+EeOl/0pTt6Hj/FV5cMLlMV64L01Tdab+vDLDkoujPHNnivZdXX0qGm846v9EeeQ7SRZ+NILjhcTyHf780wTHXxbl5QfS1K7vaDcX3n95jJf+K0Xj9q42cosNx18W5bl70rTWZ5YXjjWc+JkYr/42QcNmGDvLoepEj7/dmcRPdm27yrkOx16US8K29bp9I0R59y+w8pFU57Ill0bZttKy8aWuZW4Eli6LkD+RztAUCeM8tSJNw+bdz0ufnAI46fMx3PwEQdD/52PUQu7rNfj/8jj+jz7KX38boX5T1+Mcy4eTr4rhFCQVmkaj3TnddUh1vLBjQ3yHVcE4mHNmyNsPO7x8t8NxV4R40f1rY2j38D0+8IEP8KUvfYnTTjst26XIEGTTAdz1FnZ3WALc/zMb/9dvYfcMSwB17aSueRzvc0d13X5rM8Hn/pfc9t53dOc1+fj/+OduYQkgfHQDdt0unFml+J/8I3ktfi8tHHx5MY/wd2u7hyWAZEDqqkdwF46F3XvomlP9rtdzHZxHNxL+zxrSVy/l8ftc0onu6+xYZXGei/HIzgbebWvngshYVt9vu8ISgIVVj/nkFBjyyjLfSm0Ir/0+zdhZDq11llWPpjns/R44MPVoj7+uSDHlKI93n/M7wxLAnJMjvPq7rrAEcMQZEZ67p3tYAmjcbln5v2kOW+rx/L1pyqe5/PX2FJMWumx4KegMSwBVJ3q88XC6W1gCaNtlee7XKead3vVds6na8uzdKZZcEgPg8NMiPP3LVLewBLDtrZC1TwXEot135e3mOIamrQ4rH+l6/lVMd2hrsGx8qftzMkjDX3+WxqQy7/gRE+HvD/g0bO5eb6IZnvxRGseP9XifvcltTuN/5s/Yc+bwyqv51G/qfn2yBZ74UQrX389PHBkRTEdItq4hicVh6I4w7SlWkJnb1FoL65/a/3qHVWAaCGMO/uVQ3Y/6sO+LrWkj+M1b3R5/930TCR/f2POTo7Y9c8Ta/K4PMLu2Aa8p1WP7kYiDfXIj+D1/o/bvW4V77ixoSOBsasJxsvM4xOpT+He+0XOf/ZDgkfW4Xzima1lrGvN2Ha5r9tluvM0n/NmrmMmF1DbF8FM938W6pwPmUcAZpWXUP9P7W8y7f/OZdkz3uVUbXgqYtMBl26qQMTMdZix22fJ6gA0zIzSbX+0eHEommm5BxziZ0Y+Wup5HU3asDimf5jDlKIetb4aEAUyY57Lp793bLZvisHNtz49zS60lXth9Lk/NuyHGZEaxdm0NCXvJn2v+6mMTkR63r2sjvPWn7nVMW+yy9qmeG7MhbH4tJBJxsWmPLW/0XG97o6V9174f272e549ugMCSPvkwNr3Z87ZMNENbvRnQ83y0vC8NRv0HyhyMy+7Rc9ehnZBcXBxjDs59DfIlryRzkt9Nzxuw+7eNh9Uuuf1VWpqH6x6aTFhWVnBI7udgGu59CDc2Qmu6+0I/hH3tGqtrh/wotHTdzkkElJf3vC1Sm5v32ZYpyHzjNjXtlJVN7nftezrQxyFs2ZUJg72wW5pwjq2k20fz9lZKS/P33e72FpLVrZi5FbQ29f4u4ychZh0qnCipht7ba2u0xPK6t9PeaCmf6mSO7xJAXqmhtSP82IDuI1WwVzDxopBq3Wc3CH3IK3E62wX2+vVMX7+mSbWDG6XbKJKfhFieob2x9+dbuh2wpsfnV1tjSFtj92GpvtprqbEUF+fS0Bp2TcTt6X5bYfys/j+vUhubAAiss9c231OyBSrnHNz3jeH+vpTt+nPigz8K6BqDdQz5hXH8tmYKrUdRUe6g38/BUnlYQPVbPnEnj/zy/meEER2Y6utbByWh74sxmRdEXV0z/ZgCMiSNlD6U5HiYiQXYLV2hxgYhFEShuefhEDOhAOr3CBcGwqIoDbV7ByPHMRQsroS73uy5rdllmdAG2BnF1PbQRl99GIzHocRxMIeXY1fW9ni9c0wlwV/es4/l8LI+7zcXi3NEBXZjE+VjQ7r263WXV2qoJcXKZAunzLCwpuf2yiY7NFaHey/b0TW3oHptyIzjPd55JiAMMnOLUntMAXI8ME5XkEonIV7U+4ve8cC4mXYPP9Vj9ZPgpzKjUnv+esY4mXV7GynKKTDdwpLjQjTX0FxjmbGk9zfggjEGnJDa2r1TnedEKJ9m2NTQ9SA0VWcmjNdt7Dm1jK1yaGhowXgxvBh77QbcLbeMfj8fHceQv2QC3LcKL5EkEo9ngl5P/anof7v7a7i/Lw1W/b19eeuvRHsvQ8EHINaexvMcWlqTNPhJiqxDY2Pvc/OGmqZdBnBobGklQf+38YjfJWftwb8cqvtRH/Z9ccbm4X55cbfHP/h/a/Eumdfjc8M5ehzhhkZIdX0YOWcfRjIv0mP7QWCxc8thXF6P7XlXLCS4bxVm0Vj88njWHoeWIo/INcf2/IIYl4ezYAzh797pWlZVSjChgDDcd7vtUQf32uOgIUF+0y4Kx/YcTGaf7bIrJ8VTjY0ULAqJ5Oy9jnFg5vs8NrzYNZTjRTO7x7avDDnshMx11WtCSiYYcosN657zmX1y9/k/W14LmHH8HsHNQs26gMp5Pb+1HbbUY+PLAc01ISUTDXmlhnf/5jPnlO7tbvp7wMz39fx9csI8h53vdh+CmrnUpbUhpL3R4kYgv7znbbPw7AjEUj1uXz9MM+90D7NH6Wuf9jn8tJ7riBcZyqYafN9CxGfuB3teb2yVwY0H/X7+BIGF+RUwNo/of77KEe/vOaxVzDR4eaHelw5y/QfKHoSL8QNsNPN8a7YBJcY7KPdzsC61aw35Yy3RvP3bxiM+MMnokjpqLO63T4LyOADhk5tgThneV46Dwo5hi4iDe94svK8ej7/ilcyyHA/3U/Phn4+lfR/nH2rOc/HuOQvz/omdy8z4PCI3nUD40nbMorE43z+V1thBHtrch1QqIDislMhtp2LGd4U75/gJxO78CKmfdvTZMTinT8O7/XRacvquNwwt7VMK8H52OpGfPc/JZ7YxaW7XHICcAlh8aYTXK3ZxWG4uX546mRvq1nHE5w1lU7reagrHGk75fJRNr6Y7R0RKJxvef3mMN/6U5oizPCpmms75Sq/+Ns0pX4gRBplQNf8jEaIdo//vPh8weaHL3A95eB3zmt9+wueI0yMcttTF6cgQkRw4/DSP4kpDa73llM/HWPlompOuimXON2UzYSbWsbk2vBwwYZ7DEWd6nYHP8WDWCS5zT4/w9hOZoScvBod/0GPOKRGe+Gnmm/zrD6U44YooE+Y57J4HGy8yLLk0QsnkkFSq5/191oLJTXHKF6OdYbStwbJre8gH/jFKbknXYzRutuGUqyMEXmbWfTrwmXwsLDrHIxLvqNeFGUscFl8cwWf/Rhma8z28e86ClM/Ups0c9SHbuc2NA9MWOyy5LEKaXoa0ZEQz6RAby7xRNhFQbnr+IcNQtPNtQ+NWw2En7/+vO421g5FhD43W1lY2bcrsSjjnnHP4yle+wuLFiykqKqKysnKv9WtqDs5Q8Z6MyQzn1dYOz2FjGHl9cB2H3BYf05bGRF2SeR5BxCGnOY3T7mNjLsn8CKGBeIuPSfjYHI9EvkeyHxvAGIj7lmirD2mLyXGxNnMSymSeR2KAWWmwH4d4PEK8NoFpSUPMJcyN0JZniDX6mHYfE3VJ5Hkk9vNrk+c5xFt8nHafMC+HpBPF9w1u1EI0DfkRahMp4q5Luw1JW0tpOkokYbABuNEQE/Nx2138pAOuwYkawsASetCSkyYSOsQSLqTBxiCRkyI/FcMmAQeMyYysuC64JvNVPMQhCMB1AZPJKtaCH2QONGkt2ADIsTTnpMlr93BSBjdqCFLgOJnbpX1wImCiPhHPELS4+EnwYgY3N8CxhnS7g5+0eDGI5oUkUgGkPcI0uFGLjaZxQ48w6RL4mWBFTopUqu9fI7quwQmiBEkns2swEkAkgJRHkHQyuxUjAT6pvZ4nnuti0hGClCEadwicJOlgYL/YNAbiaYi2pbGeSzKaSyowHffvkw7TfTdyAIb7+9Jg1V9RMfBdcof/4S/c2Fw+8DvvRe7fNuK2p6hftpivpjbwMW8MH4+MGfT7GWy1aw3rnzZMOsYy96yuB6W/23hYzWF68803ufTSSzv/vvnmmwE499xzWb58ebbKkiHGD0Ka4g7E9/gpdRCSynUhd/fwkQULqTwX8jqW9fNdzVpocw1thUP7W1V7e5r2PfsHkLYku22H/ef7Ic05DuREgRBIgJf5nwmgPJKL35jA2pCu6aYJAo/O9UgBbgB7zhPteDeKdxyqIHCAjocwmoAUbZkDYe0WzbTV7WPbhb3Gb1y6xlfcTMnxNgjxCSNkDtq5R7smBqXlBdTWtuGnO26T29FGco92Opaldh9awU2D29E/H3yCTLuRjtv2c5AnCCwByc6+B3R00gSQ09F+L09VPwjACTBxKCgvoLZ24Ie3sBbaPPZ4nnc9zujQS6Oak/IJcyIkbEgbIWOH+AhTGMCWlwzVbzlMPDrk8DMHlmCHVWBavHgxq1evznYZIiIio5ZJBYRFOdTazNeVSmf/jvN1KLXUwIanHRKNMPvDIVOOswP+MdiwCkwiIiKSXSbpE8Qj1HQEpklm6B3ANNUGW1821K41FIyHJReFFI4/sDYVmERERKTfnISPzY1STZoiXArM0IkSfgK2v2nYudLgRmDOmZZJx9jMjzsO0NDppYiIiAxt1mKSPmFehJ02xRTTw3FDsiDVBtVvGXa+nTni+NTjLdOW2s5fjQ4GBSYRERHpF5P0MaElyI+x3SZZ7BRmtZ62Bqh+01D3rsGJwJTjLFOPt8T2feKCAVFgEhERkX5x2jLzlpL5UXbaFqY7h36EyYawazPsXOXQtM0QK7Acdopl0rG2xwPlDhYFJhEREekX0xGYthe4hMCMQ7hLLt0ONWsMNasNqRZD0UTL/P8TMm7e4MxR6osCk4iIiPSL257GGtgcDzGWgz7CZC00b4ea1YaGjQbjwLgjLFMWhxRNOKh3vRcFJhEREekXpzVFmBdjq0lTSZS4OThDO+l2qH3HULvakGgy5JVbqj5kqVzYdZqeQ02BSURERPrFtKUIi2JssylmmUH8CRqZ0aSmbVC72tCwyWAMjJ1rmXRMSMkUBnzAycGiwCQiIiL94ram8UvibLMpTnKLB6XNdBvUrDXUrjEkmw15FZaq0y2VC7I3mtQTBSYRERHpF6c1RfXsEtoJmXkA85d2z03a+bbDrk1k5ibNy4wmFU/K/mhSTxSYREREpG/W4rQm2TQ2BgTMcPZ/l5yfzMxNqlnVMTepwjL7jMxo0mAeZPJgUGASERGRPpm2NCawbCn2KADK9iNCtDdA9crMASZtCGMPt0xePDTmJvWXApOIiIj0yW1JArAtD6aaGKaPpLN7Enf1mw6NWw3RfMv0EywTj7bkFByKigeXApOIiIj0yW1OAVAdCTnKyet1PRtCwwbD9jcMbXWGgnGW+ed3HGByGKeOYVy6iIiIHCpOc4J0QYyd+EzqYcK3tVC/zrDtNUNil6FsumXuWQGl04fPbrd9UWASERGRPjktSWon5JPGMtFEO5dbC41bYMtLDu0NhopZloUXBhRPymKxB4ECk4iIiPTJaUlRPbUEgMqOwNS+CzY9lzkJbslUy4ILRl5Q2k2BSURERPrkNiXZWREDoCKMsuUVw443DDnFcOTHAipmj4xdb71RYBIREZF9MkkfJ+lTVxShwIas+4NH+y6YfkLml29uJNsVHnwKTCIiIrJPbnPmkAK1jktuk4txYcmVIYXjs1zYIaTAJCIiIvvkNCUA2Jl0KMt3WHJliBft40YjjAKTiIiI7JO/LUkqEsWWWiaWuaMuLAE42S5AREREhq72XWBrk/hledj8kALXzXZJWaHAJCIiIj0Kfahfb4iHSSLT4yRtSK4zOqPD6Oy1iIiI9Klph8EGEEslYGweKWvJUWASERER6WChdSeUjk1hUgHh2FwCa4mM5IMt7YMCk4iIiOwl2QZh2lAUy/xCzo7JI7DgosAkIiIiAoDfnglGsVQS64Atj2OxI/po3vuiwCQiIiJ7CQPAWJzGBLYsDhEHg8HabFeWHQpMIiIishfHBazB1CewY/OAzLniQkZnYlJgEhERkb14OR3BqD5JODYXABfwR+kQkwKTiIiI7CWaB8aEOE1J7O7AZIwCk4iIiMhuxkBhTgJjLeG4fAA8DL52yYmIiIh0KfAyhxRojWRGmDxjSGuESURERKRL3G8n7UXYtj4GgGsgFSowiYiIiHRyGxOkS3Kp32BINHXMYdIuOREREZEublMCJuTjRWHbawYXTfoWERER6WIzB630x+RROg3q3gEbwCjdI6fAJCIiIntzWpI4fkgwJp+iyRY3CqkWdGoUERERkd3chswv5PyKPFwXSqZCKgFhOrt1ZYsCk4iIiOzF3dVOGI8Q5md+IVc82WINJGpH5xCTApOIiIjsxWtoxx+T37kPzvXAxkLS1W7mxLyjjAKTSD8YY3CczJuG4xjMENqJb8zumvpar6sPB8vu9vfeXpl/MxfwPAdnj3ef3dfvXn/3xfO6v0X11K7nOZ3tvbfd3cv2bHdPmfUzl/fe157rv7cPva3X17bZXf97292XPbfNvtoVGWzurnbSY/O7LQuiFjfhUL8+S0VlkZftAvbXr3/9a37xi19QU1PD7Nmz+drXvsb8+fOzXZaMUJ6BvNYAs6ERkxfFRF3C1XXY0jjMKKY138XP0i9GHMeQ3xrgbGvBbm7CTC0iGJdHS9zF7vmzX9ehNWpY295OXcpnbn4upbhEk4P3FTE/bfHqk9hVtaQqcnGnFrKjJEKTtbzb3sr4WIwJsRhjG9NEtrQQrN+FO7UYJhawqzjK2kQ7nmMoiHqsam0l13WZHs9hY6KdMdEYE9wIOa0ejdss6XYomeiQTlrSaXDKQkr8BNHNzdh3G3AmF8G0ItojcVp2WFpqQwrHRzDFltfcFqbE4xSlI/gthl0NIUWlLs21lra2kLJJDpHckHBTI/lr63HLcyEZkA4MqQll1G0HawwlkwwRJ02sqR37Zg1EXczhZbTleaTek10ixEi3OtRvtcQLDbnFhpa6kLxSh3S7JQghr8wSekmCoOtxcxwH14/SWgdtDZaicQ7RwhDfJLF2z3ZDcgoMBWMNgZckDMNBe1xl9DJ+iNOUwB9X0G15wgnIx2XnakP5zNH1c7lhFZgeeughbr75Zq6//noWLFjAnXfeybJly/jTn/5EWVlZtsuTEcYzhvyNzfiXP0zkxhMIfvkG4VObu1bI9ci//XRaZ5WQPsQHcnMcQ2FdiuBTf8Tf1tK53EwroujnH6axwMuEJtfwNik+8crbtO3xQXpKaTHLp00j1n7goakoaQm/+hf8p7Z0LovlRyj76Qe5LtrA860tHJYb5+Gyqbifehi7qQl3d70TCij61YdpKAj4U3U9v62p7Wwjagw3zJzGO40JTm4q57Vfpgj2mGw6aYHLpEUuL//C55SPO5h/exrW1GMmFtB+29k8dp9Pe+PuxyWkaLxh6RUFrGxoZ/3vHWwapi/2+NOKZLd2J843HFPVStxzSV/zOMGHZrF+ShWv3uXTmUMNLPygYfrGjTg/eC6zzHOIf+sDmKWVJN1MaooS57m70uxc63e2H4nD8ZdGee6eNEXjDBUzHJ69I81Jn8/ByU0QhhbHcbAtMf73P1Ikux5eSicbll4eB2N54dc+O97eo90cOPGzMaJlSUKr0CQHxm1ow1hI7xGYQmtpJaQs7tKw3sAoO4DlsNold8cdd3DhhRdy/vnnM3PmTK6//npycnL47//+72yXJiNQXquP/8k/4hw1jvCV6u5hCaDNx1/2MHktfs8NHMza2gOCz/wZu0dYArDrGwmueZzcVOYDsznqcMmbq7qFJYDH6ndx986dOJEDewuIGgO/egO7R1gCoCVN/uV/5qb8MQD8dOxk3C8+ht3U1L3erc04Vz3C3ITTLSwBpKzlX9eu44LcsbyyIuwWagA2vxbQvDOkaLzDY7928P/p/QCk/2kpTz7g7RGWMhq3W9Y9E5L7ag7bXg+Zc0qEZ+9O7dXultct65tL8J/agq1rp2XxTF55zKHbsfosvPpnS/ORM6AsnlnmhwRffoJ4fRKAiOux8n8Ddq7tXke6Hf52Z4ojzvBY/0KAn4KcQsOTP07jBh2nn/CjPPHD7mEJoH6TZfOrIWueCNnx9nvaTcATP0zh+DFEDpRb14Z1DUF51y65ZgIsMDbHo6UGRlsuHzaBKZVK8dZbb3H88cd3LnMch+OPP55XXnml19sZc/Avh+p+1IdD1wfXNZjV9dCSxj1zBsEDb/fyxAywL2zD85xD2ge3MYVdv6vHkuwr1URa0niewwtNTSR7OSrvHdt20OweWL3xVp/g3pU9b5uET9lbDUzOiTG5DexbtT2uZlfXk9OQ7PG6BQX5bH096PWNee3TPtOP80i2QnM0D0piJMeW0FLXc59LJnisfSKgZJKhbmPY68TVlU9ZUgsnwpmzWPlqtOeVgLdejWA/UtVtWXjfKmIRB1IR1j3b8x2kE5BotsSLDe887TNtsUdbgyXZnJnj1b7LkGju+T7zSxzeebrndv1UJlS5HSNc2X5NDqXX9HCt/0CZAV68+jb8inzYY25fg818ORwfiWADg5/I/jYerMepP4bNLrmGhgaCINhr11tZWRnr1q3r8TalpXm47qHJhGVlBX2vNMSpD92lt2dmNZqYB82p3lfc2kJJSd6g3W9/+hCsb9nn9W4yoKQkj8276npdpzkIMJ5LecHAaw9bGgnaex9h87a3UDItBu19HLillzZKIxHae85ZACSaINoxwNPeYigtzyPZ3vv6XgxSbZldW227et+dkGoFmxslLM+nranX1WhrhLAst3MXIwCbmynIidGUdAj3MfjY3miJ5UJLnSWWl3nXTrfB2BkFbFjf+w2NkwlGvbbbANNLMqMCek1nX7brz4n3Hvj3JdKQIJhRRn5e14hlczIBPkyPF7AGS3FRHrnFw2bc5YANm8A0EPX1rYOS0PfFmMwLoq6umeF6eh31oef2Cg/PhHNb3YqZVoRd39jzukeOpa6upftE6wHeZ3/7UFSyj90uEYcgP0JDXQtHFeT3utq0eA6OH1Bb28tQRj/ku2AmFGC39txG8ogKNrfvxI6PZU5zHvTQMcdgS3Jgx95XvdPWTtksw/qner7/kkmGpp2ZNovKQuymRvLye994bbssheMMTdWWKUf2/uZQXGlwtjbivL2TiuMOo35zz+uOmWDxXq3pNpPDHD+BxkQKTIR4IbT3EriKxjm01vuUTXFo2pEZQouXQG1tM/kV8V5rS7VbcksMbQ0997N0sqG+vpnSUr2ms2mw6i8vP7DAlWjfR7rujR+SU99K+5LJtLd2jf5u8tsowyPYFQAOjS2ttB36GQmDrr/beNhEw5KSElzXpa6u+zfmuro6ysvLe72dtQf/cqjuR304dH0IQwjG58HsUvz/XIV3xaIen19mciHhjGLC0B7SPqTyPZyPzOyxJudjc0nkRwhDy8xYDlNzcnpc7/+bOoW89IHV257v4Xx5cc/bZnoxa8ZGqPd9/uIkCS+o6nE9e85hpEti9BRJWoKAwimZX5b1ZO5pEd552qdiKuS8Uw3JgMjzG5h4eI+rEyYD5p7r0tZgicYNeaU9t3vkBy3RhmZ4bD1Vc9O4kb3XcTyoWpDG/vndroWFUcxpU0mnA0IvxfyP9nBDoHiCIdVuSSdhzske7z7rUznXwc0JsBa83JAxh/Vcm+PConN6/q5bNN4QL7XsnrKW7dfkUHpND9f6D5QdwMWtz0z4TlUWdmtrq00y04nT3gSRuMWLZX8bD9bj1B/DJjBFo1Hmzp3Ls88+27ksDEOeffZZFi3q+cNM5EC05Dh4Pz0dM7OYcHUdkX9bCmNyM1caMKdMwf3VmTTH3X03dBC0uQauXYzziSMg1nH/eRGczx2JvXw+iY4xj9xkyG/mzuGDpaWdL/ax0Sg/qjqMhbE4QXBgszZ9PyR99Fjcb58E5R2jIo6BD02j+ecf4quNmWGjL2xaT+KqIwn/cQHEOz7sczzCZfNJ/tPR/KB2B7dVHcbknK6RsyVFhfzH7MP41+p3OO6LHuPmdL1d5ZYYjrs4yo7VARUzDUvPCXB/mPm1mrPiJY49KcWsE1ycjruK5MD80w258wP+ml/HkmUR3nokzXEfjzL+cIfdaS232PD+ZRGK33oXpzyO98n5RP/jKT54sU/JxK4AUzLB8KEveOTc/SKkM9vQHDUW796zaS7I3GkQhIyZHXLsxzxyOr7AGgcmLXQ58twoa/7qc/xlUTa8HDBxgcvR/+CStpnRgDRJllwaYfpxDk7HwxvNhSPP9yibaSmZHnLcJZGudg1MWujwgc9E8J2e54OJ9Jdb00IYdQjGdI1QW2vZYlPMcuK0VBuKJmSxwCwx9kD3IxxCDz30ENdeey033HAD8+fP58477+Thhx/m4Ycf7nGUqaZm4Lsa+suYzHBebe3wHDYG9aEvORZiLT4YcDDYVAARh2SeR8IdnG+BMLA+xIwhp8WHhA85Hu35EVLvmSFtDPgRh1Yn88uzXONQkLYHHJb25LkOuS0+Tlsa4h6N+R4tOQ4BkApCoq6DayAvgPyGVGbOUo5HujBK0liaXbDG4BhIhhbXGDwH2sOQmHEoD8CmHGzawwbgRgwWSwi0xQNiNqCkLonpaDdZHCEwHiRc/BS4UUgUBDQSkGchzzr4KY8wZYhGDEEIvm9xcyyRqE9+aAhq28BzMNZiA0jlxklZDws40RAiPrHmNG6bD64hnevRHnV471uq57qYdIQgZXAjmUndftriegZrLRYwkTTpHiY8eY4H6QhhGtyoxUbS+EGwV7uOBybqk+44yZde09k3WPVXVAx8l9zhf/gLNzb3vgemN/lPrgMDDZcd3bmsxqb5VnozN5mpxO4rpup0y9Qlw/CB6UF/t/GwmsP04Q9/mPr6en7wgx9QU1PDnDlz+PnPf77PXXIiByphIFGw50tljxGlLL9fJK0lmedCXkdNPfyczFpwUyFdg+sBg31WAz8IaYo7mNxYZj5AbTN5bXveS9f/W+Iu7B6V8zPLC/aYE77nrKv8jtumdrfhpcGjW/2xjgGVhgIPdj9OoQXSEElDBHzAJKC44zYpQsCHKJm2XTBu5mapFDjlBdT76fd80KU7Lh33n4Z0jgM5e0yq7eGT0Q8CcALIgc5HJ9q9D/SSXf3QB9cHt2OVPW60V7uj7CfecpBYi1fTQvtRE7stXhu24wDj1+VTD4yfNzLC0v4YVoEJ4OKLL+biiy/OdhkiIiIjjtOSxGlPk5pU1G356rCdKuI0v+Exbp4lNrx/vDggw2YOk4iIiBxc3s5WrIH0xOLOZb61rLHtzKsrIt0GM04cfaNLoMAkIiIiHbzqFvyKPGy86xeea207CUImv1zEpMWW/IosFphFCkwiIiICQGRnC6kppd2WvRK0MKYtyqR0DrNOGZ2jS6DAJCIiIoBpS+E2JUhPKelclrIhrwWtLHinlPnnWbyeD+s2KigwiYiICJEdmUPxpKZ2BaYXmltJOZazK4opn5GtyoaGYfcrORERERl83o5m0mPysLmZQ2Wk2uFvyWZmJ/NZeuLAzkk3kmiESUREZLSzluiOZlLTM+fQDHx4bWWK7UUJPjmjrPOI86OZApOIiMgo5zQncVpSpKaVYi1sf83w9/JGyo3HqRWFfTcwCigwiYiIjHKRbU1Y15CaUkL1KkPtLp93xrfw8YpyIqbnE0GPNgpMIiIio1xkezPpCcXUb42wa6Nh03FNRIzhgpLSvm88SigwiYiIjGZhiLejiaZxZex8G0oXBLwQa+T8klIKXf02bDcFJhERkVHMq2nFSYdsaiujdBqsmdFIMgy5tEwntt+TApOIiMgo5m5uIh2JYCcXMeF9AY80N3J2cQnjIjqUwJ4UmEREREapMARnQzONpWUcdprlqUQTLWHAsvIx2S5tyFFgEhERGaUaV6WJt7WSc0IZNhry56ZGPlpUwsRoLNulDTkKTCIiIqNQ03ZDZGsz1oB7TDlPtjTRFAR8ukKjSz1RYBIRERllki3QuBnK2IWdXEgyz+NPTY2cVVTCJI0u9UiBSUREZBQJQ6h715BTaMnd1UQwLzO61BIE/KNGl3qlwCQiIjKK7NpoCNMwcXwLJhHQdngpf2rU3KW+KDCJiIiMEslmaN1pGDMbYtsbsXkRniiD1jDg0xVjs13ekKbAJCIiMhpYqN9gyCm2FE+2OBubaJtXxp+bmzpGl3TcpX1RYBIRERkFWmoMfhuMnWcxbWmc6jaePKqI5iDgcs1d6pMCk4iIyAhnQ2jcCgUTIF4IzsYmfAceKrJ8uKhYv4zrBwUmERGREa6lJjPRu3ymBcDZ0MhfT6hglw24XEf17hcFJhERkZHMQvMOKBgHsTwgtLC5mYcWFnBifiHTYznZrnBYUGASEREZwRKNECQMJVMzo0umpo1XK6NsyzF8qrwiy9UNHwpMIiIiI1hLrSGab4kXZ/52NjXz8LHFzM+JsyA3L6u1DScKTCIiIiNUGEL7LiiaAMZklm1pamPl5DiXlGl0aX8oMImIiIxQySYgMOSPy+yOIx3y6ASPijScXFiU1dqGGwUmERGRESqxy+Dl2sxkbyBZ3czfZudzQayQyO4hJ+kXBSYREZERKtEEeeVdfz+fTpCKGM6ZXpm9ooYpBSYREZERym83nZO9AZ4uNSzd5jNOp0HZbwpMIiIiI1i8ODN/qdr3WTsmykdtPMsVDU9etgsQERGRg8SxRHMz/30h0UquG/CBaROyW9MwpREmERGRESqaC6bjk/7FWMApb7YQm1ma3aKGKQUmERGRESra8eu4agI25zt8sB5w9Ou4gVBgEhERGaEiHdOVXnXSRNMhS4p07KWBUmASEREZobxY5t/XwxTHvd1CfE75vm8gvVJgEhERGaHcGCSxrIkEfOCNZoK5CkwDpcAkIiIyQrlRyzvGx3cMS+osFOj4SwOlwCQiIjJCuRFY7fgUtwVMLy/MdjnDmgKTiIjICOW68I7xOXpNK3ZWSbbLGdYUmEREREYoL9+y3gQctaaVcJaOv3QgFJhERERGIgM73JCEA/PXtxHOLM52RcPasDk1yk9+8hP+8pe/sGrVKiKRCC+99FK2SxIRERnSNpkAgDmNIbZM55A7EMNmhCmdTnP66adz0UUXZbsUERGRYWGzCahsCsgfrwnfB2rYjDB94QtfAODBBx/MciUiIiLDwzYTcNj2JOE0HeH7QA2bwDRQ5iCfMmd3+wf7fg4m9WFoUB+GBvVhaBjufRgq9e8wIUdvbMNOrcx6LcPdiA5MpaV5uO6h2etYVlZwSO7nYFIfhgb1YWhQH4aG4d6HbNbvu4ZGEzJte4KCD47FLR/e2zLbshqYvvOd77BixYp9rvPQQw8xY8aMAbVfX996SEaYysoKqKtrxtqDe18Hi/owNKgPQ4P6MDQM9z4MVv3lBxBymuMuAFOqU+wq9LC1zQMvZATr7zbOamD61Kc+xbnnnrvPdSZNmnRA93GoXmjWHrr7OljUh6FBfRga1IehYbj3YSjUP6E+TTguD4bxdhwKshqYSktLKS3VgbREREQOlrGeR+ANmx/FD1nDZg7Ttm3baGxsZNu2bQRBwKpVqwCYPHkyeXl5Wa5ORERk6ClpD4mMzSPIdiEjwLAJTD/4wQ/4n//5n86/zznnHADuuusuFi9enKWqREREhq7yZh87Lj/bZYwIwyYwLV++nOXLl2e7DBERkWFjTEMaOzY322WMCNqpKSIiMkKVNaQJKxSYBoMCk4iIyAhV0uxjy3UOucGgwCQiIjJCFbf6OunuIFFgEhERGaEK2wJsWU62yxgRFJhERERGqIK2EFuiwDQYFJhERERGqNwQyBk2P4gf0hSYRERERqi462a7hBFDgUlERGSEyo0oMA0WBSYREZERKhbV7rjBosAkIiIyQuVo/tKgUWASEREZoaI5kWyXMGIoMImIiIxAk6uTlLoaYRosCkwiIiIj0JPXrSY3psA0WBSYRERERiibp11yg0WBSUREZKTSpO9Bo8AkIiIyQlkFpkGjwCQiIjJSxRWYBosCk4iIyAhlYzrS92BRYBIRERmpFJgGjQKTiIjICGV1LrlBo8AkIiIyUsX0MT9YtCVFRERGKo0wDRoFJhERkRHKRvUxP1i0JUVEREYqTx/zg0VbUkREZKRSYBo02pIiIiIjlQLToNGWFBERGaGsAtOg0ZYUEREZqVyT7QpGDAUmERGRkUojTINGW1JERGQESp8xTadGGUQ6jbGIiMgIlPy392W7hBFFI0wiIiIifVBgEhEREemDApOIiIhIHxSYRERERPqgwCQiIiLSBwUmERERkT4oMImIiIj0QYFJREREpA8KTCIiIiJ9UGASERER6YMCk4iIiEgfFJhERERE+qDAJCIiItIHL9sF9MeWLVv48Y9/zHPPPUdtbS1jxozhox/9KFdeeSXRaDTb5YmIiMgINywC07p167DWcsMNNzBlyhTWrFnD1772Ndrb27n22muzXZ6IiIiMcMMiMJ1wwgmccMIJnX9PmjSJ9evXc++99yowiYiIyEE3LAJTT5qbmykqKupzPWMObh272z/Y93MwqQ9Dg/owNKgPQ8Nw78NQqT/b9z+SGGutzXYR+2vjxo2cd955XHvttVx44YW9rhcEIa6ree0iIjL66DNwcGV1hOk73/kOK1as2Oc6Dz30EDNmzOj8u7q6mssvv5zTTz99n2EJ0BNFRERGLX0GDq6sjjDV19fT0NCwz3UmTZrU+Uu46upqLr30UhYsWMDy5ctxHD0ZRERE5OAbNrvkdoeluXPncuutt+K6brZLEhERkVFiWASm6upqLrnkEiorK7nlllu6jSxVVFRksTIREREZDYbFr+SeeeYZNm7cyMaNG7sdXgBg9erVWapKRERERothMQnovPPOY/Xq1T1ehoKf/exnVFVV8c1vfjPbpeyX6upqrrnmGhYvXsz8+fM566yzeOONN7JdVr8FQcD3v/99Tj75ZObPn8+pp57Kj370I4b6oOmLL77IlVdeydKlS6mqquLRRx/tdr21lttuu42lS5cyf/58PvGJT7Bhw4bsFNuDfdWfTqe59dZbOeuss1i4cCFLly7ly1/+MtXV1VmseG99PQZ7+vrXv05VVRW/+tWvDl2B/dCfPrz77rtceeWVHHXUUSxcuJDzzz+fbdu2ZaHanvXVh9bWVm644QZOOOEE5s+fz4c//GHuvffeLFXbs9tvv53zzz+fRYsWsWTJEj772c+ybt26buskk0muv/56Fi9ezKJFi/j85z9PbW1tliqWgRoWgWkoe/3117nvvvuoqqrKdin7pbGxkYsuuohIJMKKFSv44x//yLXXXtuvY1sNFStWrODee+/l61//Og899BDXXHMNP//5z7n77ruzXdo+tbW1UVVVxTe+8Y0er1+xYgV33303//Zv/8b9999PPB5n2bJlJJPJQ1xpz/ZVfyKRYOXKlXzmM5/hwQcf5Ic//CHr16/nM5/5TBYq7V1fj8FujzzyCK+99hpjxow5RJX1X1992LRpEx/72MeYPn06d999N7/73e/47Gc/SywWO8SV9q6vPixfvpynnnqKW2+9lYceeojLLruMG2+8kccee+wQV9q7F154gY9//OPcf//93HHHHfi+z7Jly2hra+tc51vf+hZPPPEE3//+97n77rvZuXMnV111VRarlgGxMmAtLS32gx/8oH3mmWfsxRdfbG+66aZsl9Rvt956q73ooouyXcYBueKKK+xXvvKVbsuuuuoq+8///M9Zqmj/zZo1yz7yyCOdf4dhaN/3vvfZn//8553Lmpqa7Lx58+wf/vCHbJS4T++tvyevvfaanTVrlt26deshqmr/9NaHHTt22Pe///12zZo19qSTTrJ33HHHoS+un3rqw9VXX22vueaaLFW0/3rqw5lnnml/+MMfdlt27rnn2u9+97uHsrT9UldXZ2fNmmVfeOEFa23m9Tt37lz78MMPd67zzjvv2FmzZtlXXnklS1XKQGiE6QDccMMNfOADH+D444/Pdin77fHHH2fevHl84QtfYMmSJZxzzjncf//92S5rvyxatIjnnnuO9evXA/D222/z8ssv7zXPbTjZsmULNTU13Z5TBQUFLFiwgFdeeSWLlQ1cS0sLxhgKCwuzXUq/hWHIv/zLv7Bs2TIOO+ywbJez38Iw5Mknn2Tq1KksW7aMJUuWcMEFF+xz1+NQtGjRIh5//HGqq6ux1na+3pcuXZrt0nrV3NwM0Dla/+abb5JOp7u9pmfMmEFlZSWvvvpqNkqUARoWk76Hoj/+8Y+sXLmSBx54INulDMjmzZu59957+eQnP8mVV17JG2+8wU033UQkEuHcc8/Ndnn9csUVV9DS0sIZZ5yB67oEQcCXvvQlPvrRj2a7tAGrqakBoKysrNvysrKyYTnnIZlM8p3vfIczzzyT/Pz8bJfTbytWrMDzPC699NJslzIgdXV1tLW1sWLFCq6++mquueYannrqKa666iruuusujj322GyX2C9f+9rX+NrXvsYJJ5yA53kYY7jppps45phjsl1aj8Iw5Fvf+hZHHnkks2bNAqC2tpZIJLLXF4aysrLO17sMDwpMA7B9+3a++c1v8stf/nJIzQfYH9Za5s2bxz/90z8BcPjhh7N27Vruu+++YROYHn74YX7/+9/z7//+78ycOZNVq1Zx8803M2bMmGHTh5EsnU7zxS9+EWst119/fbbL6bc333yTu+66iwcffBAzTE/EFYYhAKeccgqf+MQnAJgzZw5///vfue+++4ZNYLr77rt59dVX+clPfkJlZSUvvfQS119/PWPGjBmSI/vXX389a9eu5Te/+U22S5GDQIFpAN566y3q6uo477zzOpcFQcCLL77Ir3/9a954440hf2DNioqKbqecAZg+fTp//vOfs1TR/vv2t7/NFVdcwZlnnglAVVUV27Zt4/bbbx+2gWn3ccXq6uq6TTSuq6tj9uzZ2Sprv6XTaa6++mq2bdvGnXfeOaxGl1566SXq6uo46aSTOpcFQcAtt9zCXXfdxeOPP57F6vqnpKQEz/P2eo3PmDGDl19+OUtV7Z9EIsH3vvc9fvjDH3LiiScCMHv2bFatWsUvfvGLIReYbrjhBp588knuuecexo0b17m8vLycdDpNU1NTt1Gmuro6HUdwmFFgGoDjjjuO3//+992WfeUrX2H69Ol8+tOfHvJhCeDII4/snPuz24YNG5gwYUKWKtp/iURirxEA13WH/GEF9mXixIlUVFTw7LPPMmfOHCAzB+i1117joosuynJ1/bM7LG3cuJG77rqLkpKSbJe0X84+++y9PoyXLVvG2Wef3e1L0lAWjUY54ogjhvVr3Pd90un0kH+NW2u58cYbeeSRR7j77ruZNGlSt+vnzZtHJBLh2Wef5UMf+hAA69atY9u2bSxcuDALFctAKTANQH5+fuf+6d1yc3MpLi7ea/lQddlll3HRRRfx05/+lDPOOIPXX3+d+++/nxtuuCHbpfXbSSedxE9/+lMqKys7d8ndcccdnH/++dkubZ9aW1vZtGlT599btmxh1apVFBUVUVlZyaWXXspPfvITpkyZwsSJE7ntttsYM2YMp556ahar7rKv+isqKvjCF77AypUruf322wmCoHOeRlFRUed5IbOtr8fgvSEvEolQXl7O9OnTD3WpveqrD8uWLeNLX/oSxxxzDIsXL+app57iiSee4K677spi1d311Ydjjz2WW2+9lZycHCorK3nxxRf57W9/y3XXXZfFqru7/vrr+cMf/sCPf/xj8vLyOp/vBQUF5OTkUFBQwPnnn8/y5cspKioiPz+fm266iUWLFikwDTPD4tQow8Ell1zC7Nmz+epXv5rtUvrtiSee4Lvf/S4bNmxg4sSJfPKTn+TCCy/Mdln91tLSwm233cajjz7auQvrzDPP5HOf+9yQ+WDuyfPPP9/jZOJzzz2X5cuXY63lBz/4Affffz9NTU0cddRRfOMb32DatGlZqHZv+6r/qquu4pRTTunxdnfddReLFy8+2OX1S1+PwXudfPLJXHrppZ3zgYaC/vThgQce4Gc/+xk7duxg2rRpfP7znx8ywRv67kNNTQ3f/e53efrpp2lsbKSyspL/+3//L5/4xCeGzPyy3o7Bd/PNN3eOSCaTSZYvX84f//hHUqkUS5cu5Rvf+IZ2yQ0zCkwiIiIifdBxmERERET6oMAkIiIi0gcFJhEREZE+KDCJiIiI9EGBSURERKQPCkwiIiIifVBgEhEREemDApOIiIhIHxSYREaJSy65hG9+85vZLoPnn3+eqqoqmpqasl2KiEi/KTCJyEEzVEKaiMiBUmASERER6YMCk8golEqluOWWW3j/+9/PwoULueCCC3j++ec7r3/wwQc5+uijeeqppzjjjDNYtGgRy5YtY+fOnZ3r+L7PTTfdxNFHH83ixYu59dZbufbaa/nsZz8LwHXXXccLL7zAXXfdRVVVFVVVVWzZsqXz9m+99RbnnXceCxYs4B/+4R9Yt27dodsAIiL7SYFJZBS64YYbeOWVV/je977H7373O04//XQuv/xyNmzY0LlOIpHgl7/8Jd/+9re555572L59O7fcckvn9StWrOD3v/89N998M7/5zW9oaWnh0Ucf7bz+q1/9KosWLeLCCy/k6aef5umnn2b8+PGd13/ve9/juuuu47//+79xXZd//dd/PSR9FxEZCAUmkVFm27ZtPPjgg9x2220cffTRTJ48mWXLlnHUUUfx4IMPdq6XTqe5/vrrOeKII5g7dy4f//jHee655zqvv+eee7jiiis47bTTmDFjBl//+tcpLCzsvL6goIBIJEJOTg4VFRVUVFTgum7n9V/60pc49thjmTlzJldccQWvvPIKyWTy0GwEEZH95GW7ABE5tNasWUMQBJx++undlqdSKYqLizv/jsfjTJ48ufPvMWPGUFdXB0BzczO1tbXMnz+/83rXdZk7dy5hGParjqqqqs7/V1RUAFBXV0dlZeV+90lE5GBTYBIZZdra2nBdt3NX2J5yc3M7/+953d8ejDFYawetjj3bN8YA9DtsiYgcatolJzLKzJkzhyAIqK+vZ8qUKd0uu0d6+lJQUEB5eTlvvPFG57IgCFi5cmW39SKRiEKQiIwIGmESGWWmTZvGWWedxZe//GWuu+465syZQ0NDA88++yxVVVWceOKJ/Wrn4osv5vbbb2fy5MlMnz6de+65h8bGxs7RIoAJEybw2muvsWXLFnJzc7vt8hMRGU4UmERGoZtvvpmf/OQnLF++nJ07d1JcXMzChQv7HZYAPv3pT1NbW8u1116L67pceOGFLF26tNtuvk996lNcd911nHnmmSQSCR577LGD0BsRkYPP2MGclCAio1YYhpxxxhmcccYZXH311dkuR0RkUGmESUQGZOvWrTzzzDMcc8wxpFIpfv3rX7N161bOOuusbJcmIjLoFJhEZEAcx+HBBx/klltuwVrLrFmzuOOOO5gxY0a2SxMRGXTaJSciIiLSBx1WQERERKQPCkwiIiIifVBgEhEREemDApOIiIhIHxSYRERERPqgwCQiIiLSBwUmERERkT4oMImIiIj0QYFJREREpA//PyFrgVZim6dMAAAAAElFTkSuQmCC",
|
||
"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": 36,
|
||
"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": 98,
|
||
"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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cs2LFird8RaJly5aaOnWq5fHN+yM3J06cUGpqqtWZmeyYzWbL75nfu0pMTFT9+vXVr18/TZ06VV999ZWCgoL00EMPqVGjRlb9/fz8LL+fOnVKaWlpVvvE3d1d/v7+On78eI4x5GffF9SJEyfUrl07q2XNmjVTdHS00tPTdfjwYbm6uuree+/NdZznnntO/v7+WrhwoVxdXaW/zyyeOnVKL7/8siZNmmRpe+PGjSzfh7iV/bt27Vpt3LhRZ8+e1fXr15WWlpZl/zdo0MASV+ZzHD16VJJ0/Phxubm5qUmTJpb19evXV4UKFbJs583z+CUpICAgx7tp/vLLL9qzZ0+WPvr7PVC3bt0s254ZW+ZVxCNHjig5OVktW7a0apOSkmKZdnS7ISfm7cSJE/rxxx+1fPlySZKbm5s6d+6sDRs2WN5Lhw8fVlhYWK7jREVF6dq1a9q4caPuuusuy/L8vrcbN26c7bjZfd4TEhIKdFXj5jFcXV1VsWJF+fj4WJZVrVrVMm5O/apUqaIyZcpYbVvVqlX1448/SkWUc8FnOL927NihwMBApaWlyTAMPfLII3rxxRezbZuUlKRly5Zpx44dio+PV3p6ulJSUqymUObX0aNHlZ6ero4dO1otT01Ntdomd3f3LH+/lMdrl9trb6tjhpvzUteuXTV9+nSrvj169NDGjRtVvXp1Xbt2TaGhoVqzZo1VmxMnTmS50UrmsdHNbj5uyElh84ibm5vVsWPmccnx48fl7+9v1dZW++6XX37RkSNHtGXLFksbwzCUkZGhuLi4bGct3YrbvshzdXVVVFSU9u3bp6+//loxMTFauHCh1q9fb/WHKSeZ82hXrFiR5Uv3Hh4ekmS5nJwf+RkvNDRUX375pXbu3Kmvv/5azzzzjHr37q3x48dnO6YtpjWUKVNGtWvXzrLcZDJZTQHR32/6TKVKlcr1eTPd/Icic7pTRkaGJCksLEzBwcHasWOHvv76a7355psaP3681XdI8lt05iY/+97W8vveCA0N1eeff65ff/3VkkAy43311VfVtGlTq/YuLtZfty3s/v344481Z84cjR8/XoGBgSpXrpzeeustHTx40Gr8f07hyO59YWvJyclq06aNxo4dm2XdzTdoyS22pKQkeXt7KyYmJssYJeHGAUWBnPg/ueXEDRs26MaNGwoJCbEsMwxDHh4emjx5sjw9PfO1jS1atNCOHTu0detWqzt05ve9nVPeu/k9/8/Pe145O7sxMvtlN+4/x/pnm+zGyYzFHjn3dsBn+H/yOq7JPHnt7u6uatWq5ToVcc6cOdq9e7fGjx+vu+++W6VLl9aIESMKdTOQ5ORkubq6auPGjVYnR3XTdE79vY8LerOV3F77zHxxq8cMN393rnz58lli6Nq1q+bNm6dly5bp0UcfvaUb4f3zqznZKY48YqvjreTkZPXs2dPq+DVT9erVbRLrzW77Ik9/vwjNmzdX8+bNNWzYMLVp00bbt2/Xs88+K/19tj8lJcWS1A4cOKCyZcuqevXq8vLykoeHh86ePZvjvF+z2azNmzcrLS0tz7Ne9evXz3M8/X0nom7duqlbt25at26d5s6dm2My7NmzZ7a3tb1ZtWrVcl2fWxzHjh2zWnb48GHLdtapU0elS5fWN998k68/LjmpXr26evXqpV69emn+/Plav359th8SSbr77rvl7u6uffv2qWbNmtLfZ3R+/PFH9e/fX7rpQ5ienm7pl999XxD16tXTvn37rJbt27dPderUkaurq3x8fJSRkaG9e/cqKCgox3HGjh2rcuXK6ZlnnlFMTIwaNGigqlWrqlq1ajp9+rQeffTRW4ozp/27b98+BQYGqnfv3pa2Bb3CVa9ePd24cUOHDh2ynB07ceJEtrdAPnDggB5//HHL44MHD+Z4Jq5x48b67LPPVLNmzUL/EWncuLEuXrwoV1dX1apVq1BjOCNyYs458caNG/rPf/6jiIgIy/c6Mw0bNkwfffSRevXqJR8fH8XGxqpHjx45Poefn5969+6tgQMHytXV1XLzFFu8t3OSV84uTkWRc/E/fIbzPq7J6eR1dvbv369u3bqpQ4cO0t8nCM+cOWPVxt3d3XIwf/Oym48zJMnX11fp6elKTEws1L9XyO21Ux6vvS2OGfLaZxUrVlTbtm21detWqxlgN8s8Nrr5u2z79u1TgwYNch3blsduOR2XZHc1zVbHW/fcc49+/fXXfL/vbtVtX+QdPHhQsbGxat26tapUqaKDBw8qMTFR9erVs7RJTU3Vyy+/rKFDh+rMmTNaunSp+vTpIxcXF5UvX17PPfecIiMjZRiGmjdvritXrmjfvn0qX768unXrpt69eysmJkajR4/W4MGD5enpqQMHDsjf39/qefT3WZG8xlu8eLEaN26shg0bKjU1VTt27Mj1Eq8tpjXk5P7779dbb72lDz74QAEBAfrwww917Ngx3XPPPdLfV/IGDRqkefPmyd3dXc2aNVNiYqKOHTuW51SmTDNnztQDDzygOnXq6PLly9qzZ0+u21u2bFn16tVLc+fOlZeXl2rUqKFVq1YpJSVFTzzxhCSpZs2aMplM2rFjh0JDQ1WqVKl87fucJCYmZplW6O3treeee05PPPGEli9frs6dO+vAgQNau3atpkyZIkmqVauWunXrpokTJ+qVV16R2WzW2bNnlZCQYHVjE0kaP3680tPT1b9/f0VHR6t+/foaMWKEZsyYIU9PT4WEhCg1NVWHDh3S5cuXLX/Mb2X/1q5dWx988IF27dqlWrVq6T//+Y9+/PHHAhVE9erVU0hIiKZMmaKpU6fK1dVVs2bNyvZM8KeffqomTZqoefPm2rJli3744Ycc/wfh008/rfXr12v06NEaOHCgKlasqJMnT+qTTz7RjBkzspwhzU5QUJACAgI0bNgwvfTSS6pTp44uXLignTt3qn379lZTOW4X5MTc7dixQ3/99ZeeeOKJLFd7H3roIW3YsEG9evXS8OHD9cwzz+juu+9Wly5ddOPGDe3cuTPL/9Rr1qyZ3nzzTQ0aNEiurq565plnbPLezkleObs43UrORc74DNte7dq1tW3bNrVt21Ymk0mLFi3KUtDVrFlTe/fuVZcuXeTu7q7KlSurZs2aSk5OVmxsrGWaZd26ddW1a1eNGzdOERER8vX11aVLlyxtHnzwwVxjye21y+u1t8UxQ37Mnj1bU6ZMUaVKlbJdP3DgQIWHh8vX11dBQUH68ssvtW3bNkVFReU6ri2P3dzd3fXqq6/qlVdekaurq1599VUFBARkmaqZyRb7btCgQXrqqac0ffp0hYWFqUyZMvr111+1e/duTZ48OV9jFMRtX+SVL19ee/fu1erVq3X16lXVqFFDERERCg0NtbRp1aqVateurd69eys1NTXLvO3w8HBVrlxZK1asUFxcnDw9PXXPPfdoyJAhkqRKlSpp9erVmjdvnvr27SsXFxf5+vqqefPm2caU13ju7u5asGCBzpw5o9KlS6t58+ZasGBBke+r7ISEhOiFF17QvHnzdP36dfXo0UOPP/645ftYkvTCCy/I1dVVS5Ys0YULF+Tt7a2ePXvm+zkyMjI0ffp0nTt3TuXLl1dISIgmTJiQa5+xY8fKMAyNGzdOSUlJatKkiVatWmX5TsIdd9yhF198UfPnz9eECRP0+OOPa/bs2Xnu+5x89NFH+uijj6yWjRw5Ui+88IIWLVqkJUuW6I033pC3t7dGjBhhubOmJE2dOlULFizQ1KlT9eeff6pGjRo5/p+eiRMnKiMjQ/3791dMTIzCwsJUunRpvfXWW5o7d67Kli0rHx8fyxXLW92/PXv21OHDhzVq1CiZTCZ16dJFTz/9tP7f//t/+R5fkiIjI/XKK6+oT58+qlq1qkaOHKklS5Zkaffiiy/qk08+0bRp0+Tt7a358+fneGbvjjvu0HvvvafXXntNAwYMUGpqqmrUqKGQkJAs0ydyYjKZ9Oabb2rRokWaMGGCLl26pKpVq6pFixaW7xzdbsiJuduwYYOCgoKync778MMPa9WqVfrll1/UsmVLLV68WK+//rrefPNNlS9fPsfv3rZo0UJvvvmmBg8eLFdXV/Xt2/eW39s5yU/OLk6FzbnIGZ9h24uIiNDEiRPVs2dPVapUSYMGDVJSUpJVmxEjRmjy5Mlq3769UlNTdeTIETVr1kw9e/ZUeHi4/vzzTw0fPlwvvviiIiMj9cYbb2j27Nm6cOGCKlasqICAgDwLPOXx2uX12tvimCE/SpcuneuU3vbt22vixIl6++23NWvWLNWsWVOzZs3K8v34f7LlsVvp0qU1aNAgjRkzRufPn1eLFi1yPKksG+27Ro0aKSYmRosWLdLTTz8tSbrrrruynNS3FZNR1F+cKeEiIiJ0+fJlvf766/YOBQDsjpwIlGx8hoHbA/8MHQAAAACcCEUeAAAAADgRpmsCAAAAgBPhSh4AAAAAOBGKPAAAAABwIhR5AAAAAOBEKPIAAAAAwIlQ5AEAAACAE6HIAwAAAAAnQpEHAAAAAE6EIg8AAAAAnAhFHgAAAAA4EYo8AAAAAHAiFHkAAAAA4EQo8gAAAADAiVDkAQAAAIATocgDAAAAACdCkQfYUYZhFGl7AAAAezIyCnfsUth++B+TYXDUCNjTopOnFZeSkme7WqVLK7z2XcUSEwAAgK388HGqkhLzX3KUq2ySfxePIo3J2bnZOwDgdheXkqLfruVd5AEAAJRESYmGrsRzXak4MV0TAAAAAJwIRR4AAAAAOBGKPAAAAABwIhR5AAAAAOBEKPIAAAAAwIlQ5AEAAACAE7Frkbd3714NGTJEwcHBMpvN2r59u9X6iIgImc1mq58BAwbkOe7atWvVtm1b+fn5KSwsTD/88EMRbgUAAAAAOA67FnnJyckym82aMmVKjm1CQkL01VdfWX4WLFiQ65iffPKJIiMjNWzYMG3evFmNGjXSgAEDlJCQUARbAAAAAACOxa7/DD00NFShoaG5tvHw8JC3t3e+x4yKitKTTz6pHj16SJKmTZumHTt2aOPGjRo8ePAtxwwAAAAAjszhv5P37bffqlWrVnr44Yc1ZcoUXbp0Kce2qamp+umnnxQUFGRZ5uLioqCgIO3fv7+YIgYAAAAA+7Hrlby8hISEqEOHDqpVq5ZOnz6tBQsWaNCgQfr3v/8tV1fXLO0vXbqk9PR0ValSxWp5lSpVdOLEiWKMHAAAAADsw6GLvC5dulh+z7zxSvv27S1X9wAAAAAA1hx+uubN7rrrLlWqVEknT57Mdn2lSpXk6uqa5SYrCQkJqlq1ajFFCQAAAAD2U6KKvHPnzunPP//M8UYsHh4eaty4sWJjYy3LMjIyFBsbq8DAwGKMFAAAAADsw67TNZOSknTq1CnL47i4OB0+fFheXl7y8vLSsmXL9PDDD6tq1ao6ffq05s2bp9q1ayskJMTSp3///urQoYP69OkjSXr22Wc1fvx4NWnSRP7+/lq9erWuXbum7t2722UbAQAAAKA42bXIO3TokPr162d5HBkZKUnq1q2bpk6dqqNHj+qDDz7QlStXVK1aNbVu3VojR46Uh4eHpc/p06et7rjZuXNnJSYmasmSJYqPj5evr69WrVrFdE0AAAAAtwWTYRiGvYMAbmdjjxzTb9dS8mxXt0xpvWZuWCwxAQAA2EpszHVdic9/yeHpbVKrvqWKNCZnV6K+kwcAAAAAyB1FHgAAAAA4EYo8AAAAAHAiFHkAAAAA4EQo8gAAAADAiVDkAQAAAIATocgDADgMI8Mx/quPo8QBAEBh2PWfoQMAcDOTi0lpy7+XceaK/WKo6Sn3Yc3t9vwAANwqijwAgEMxzlyR8ftf9g4DAIASi+maAAAAAOBEKPIAAAAAwIlQ5AEAAACAE6HIAwAAAAAnQpEHAAAAAE6EIg8AAAAAnAhFHgAAAAA4EYo8AAAAAHAiFHkAAAAA4EQo8gAAAADAiVDkAQAAAIATsWuRt3fvXg0ZMkTBwcEym83avn27ZV1aWprmzZunrl27KiAgQMHBwRo3bpzOnz+f65hLly6V2Wy2+unYsWMxbA0AAAAA2J+bPZ88OTlZZrNZPXr00PDhw63WpaSk6Oeff9bQoUPVqFEjXb58WTNnztTQoUO1adOmXMdt2LChoqKiLI9dXV2LbBsAAAAAwJHYtcgLDQ1VaGhotus8PT2tCjVJmjRpksLCwnT27FnVqFEjx3FdXV3l7e1t83gBAAAAwNHZtcgrqKtXr8pkMqlChQq5tjt58qSCg4NVqlQpBQQEaMyYMbkWhQAAAADgLEpMkXf9+nW99tpr6tKli8qXL59jO39/f0VGRqpu3bqKj4/X8uXL1bt3b23ZsiXXfgAAAADgDEpEkZeWlqaRI0fKMAxNmzYt17Y3T/9s1KiRmjZtqjZt2mjr1q0KCwsrhmgBAAAAwH4cvshLS0tTeHi4zp49q9WrVxf4alyFChVUp04dnTp1qshiBAAAAABH4dBFXmaBd/LkSUVHR6tSpUoFHiMpKUmnT5/mRiwAAAAAbgt2LfKSkpKsrrDFxcXp8OHD8vLykre3t0aMGKGff/5ZK1asUHp6uuLj4yVJXl5e8vDwkCT1799fHTp0UJ8+fSRJc+bMUZs2bVSjRg1duHBBS5culYuLix555BE7bSUAAAAAFB+7FnmHDh1Sv379LI8jIyMlSd26ddPw4cP1xRdfSJIee+wxq37R0dFq2bKlJOn06dO6dOmSZd25c+c0evRo/fnnn6pcubKaN2+u9evXq3LlysW0VQAAAABgP3Yt8lq2bKkjR47kuD63dZkyC8FMCxcutElsAAAAAFASudg7AAD5U9HNTRmGUaA+BW0PQJJXKRkZ9v/sOEIMAJzHreQU8lHJ49A3XgHwf8q5usrFZNKik6cVl5KSZ/tapUsrvPZdxRIb4ExM5dxlcjEpbfn3Ms5csU8MNT3lPqy5XZ4bgHMyuZj0w8epSkosWMFWrrJJ/l08iiwuFA2KPKCEiUtJ0W/X8i7yANwa48wVGb//Ze8wAMBmkhINXYnnqtztgOmaAAAAAOBEKPIAAAAAwIlQ5AEAAACAE6HIAwAAAAAnQpEHAAAAAE6EIg8AAAAAnAhFHgAAAAA4EYo8AAAAAHAiFHkAAAAA4EQo8gAAAADAiVDkAbnIMIwibQ8A2fIqJSPDMfKJo8QBwD48ypIHSiI3ewcAODIXk0mLTp5WXEpKnm0DK3iqd/U7C9weAP7JVM5dJheT0pZ/L+PMFfvFUdNT7sOa2+35AdifWymTTC4m/fBxqpISC1bsVanjIp8Q9yKLDTmjyAPyEJeSot+u5V201SxVqlDtASAnxpkrMn7/y95hAICSEg1diS9YkVe2ElcA7YXpmgAAAADgRCjyAAAAAMCJUOQBAAAAgBOhyAMAAAAAJ0KRBwAAAABOxK5F3t69ezVkyBAFBwfLbDZr+/btVusNw9DixYsVHBwsf39/PfPMM/r999/zHHft2rVq27at/Pz8FBYWph9++KEItwIAAAAAHIddi7zk5GSZzWZNmTIl2/UrV65UTEyMpk6dqvXr16tMmTIaMGCArl+/nuOYn3zyiSIjIzVs2DBt3rxZjRo10oABA5SQkFCEWwIAAAAAjsGuRV5oaKhGjRqlDh06ZFlnGIaio6M1dOhQtW/fXo0aNdLcuXN14cKFLFf8bhYVFaUnn3xSPXr0UIMGDTRt2jSVLl1aGzduLOKtAQAAAAD7c9jv5MXFxSk+Pl5BQUGWZZ6enmratKn279+fbZ/U1FT99NNPVn1cXFwUFBSUYx8AAAAAcCYOW+TFx8dLkqpUqWK1vEqVKrp48WK2fS5duqT09PQC9QEAAADgODzKSkaGUej+t9LXWbjZOwAAAAAAyORWyiSTi0k/fJyqpMSCFWzlKpvk38WjyGIrKQp1Ja9du3a6dOlSluWXL19Wu3btbBGXvL29JSnLDVMSEhJUtWrVbPtUqlRJrq6uBeoDAAAAwPEkJRq6El+wn4IWhc6qUEXemTNnlJGRkWV5amqqzp8/b4u4VKtWLXl7eys2Ntay7OrVqzp48KACAwOz7ePh4aHGjRtb9cnIyFBsbGyOfQAAAADAmRRouuZ///tfy++7du2Sp6en5XFmMVWzZs18j5eUlKRTp05ZHsfFxenw4cPy8vJSjRo11K9fP73xxhuqXbu2atWqpcWLF6tatWpq3769pU///v3VoUMH9enTR5L07LPPavz48WrSpIn8/f21evVqXbt2Td27dy/IpgIAAABAiVSgIm/YsGGSJJPJpIiICOuB3NxUs2bNLMtzc+jQIfXr18/yODIyUpLUrVs3zZ49W4MGDdK1a9c0efJkXb58Wc2bN9eqVatUqlQpS5/Tp09bTR3t3LmzEhMTtWTJEsXHx8vX11erVq1iuiYAAACA20KBirxffvlFktS2bVtt2LBBlStXvqUnb9mypY4cOZLjepPJpJEjR2rkyJE5tvniiy+yLOvTp4/lyh4AAAAA3E4KdXfN7AorAAAAAID9FfpfKMTGxio2NlYJCQlZbsKSOe0SAAAAAFC8ClXkLVu2TMuXL1eTJk3k7e0tk8lk+8gAAAAAAAVWqCJv3bp1ioyM1OOPP277iAAAAAAAhVao/5OXlpamZs2a2T4aAAAAAMAtKVSR98QTT2jLli22jwYAAAAAcEsKNV3z+vXrWr9+vWJjY2U2m+XmZj3MhAkTbBUfAAAAAKAAClXkHTlyRI0aNZIkHT161GodN2EBAAAAAPspVJEXExNj+0gAAAAAALesUN/JAwAAAAA4pkJdyevbt2+u0zKjo6NvJSYAAAAAQCEVqsjz9fW1enzjxg0dPnxYx44d43/nAQAAAIAdFarImzhxYrbLly5dquTk5FuNCQAAAABQSDb9Tt6jjz6qjRs32nJIAAAAAEAB2LTI279/vzw8PGw5JAAAAACgAAo1XXP48OFWjw3DUHx8vA4dOqQXXnjBVrEBAAAAAAqoUEWep6en1WOTyaS6detqxIgRCg4OtlVsAAAAAIACKlSRFxkZaftIAAAAAAC3rFBFXqZDhw7p+PHjkqSGDRvqnnvusVVcAAAAAIBCKFSRl5CQoFGjRunbb79VhQoVJEmXL19Wy5YttXDhQlWuXNnWcQIAAAAA8qFQd9d89dVXlZSUpI8//ljffvutvv32W3300Ue6evWqZsyYYdMA27ZtK7PZnOVn2rRp2bbftGlTlrZ+fn42jQkAAAAAHFWhruTt2rVLUVFRql+/vmVZgwYNNGXKFD333HO2jE8bNmxQenq65fGxY8f07LPPqmPHjjn2KV++vD799FPLY5PJZNOYAAAAAMBRFarIy8jIkLu7e9bB3NyUkZFhi7gs/jn1880339Tdd9+t++67L8c+JpNJ3t7eNo0DAAAAAEqCQk3XvP/++zVz5kydP3/esuz8+fOKjIxUq1atbBmfldTUVH344Yfq0aNHrlfnkpOT1aZNG4WGhmro0KE6duxYkcUEAAAAAI6kUFfyJk+erKFDh6pdu3a68847JUnnzp1Tw4YNNW/ePFvHaLF9+3ZduXJF3bp1y7FN3bp1NWvWLJnNZl25ckVvv/22evbsqY8//tgSKwAAAAA4q0IVedWrV9fmzZu1e/dunThxQpJUv359BQUF2To+Kxs3btQDDzygO+64I8c2gYGBCgwMtHrcuXNnrVu3TuHh4UUaHwAAAADYW4Gma8bGxqpz5866evWqTCaTWrdurb59+6pv377y8/NTly5d9N133xVJoGfOnNHu3bv1xBNPFKifu7u7fH19derUqSKJCwAAAAAcSYGKvNWrV+vJJ59U+fLls6zz9PTUU089paioKFvGZ7Fp0yZVqVJFDz74YIH6paen6+jRo9yIBQAAAMBtoUBF3pEjRxQSEpLj+tatW+unn36yRVxWMjIytGnTJj3++ONyc7OeYTpu3DjNnz/f8njZsmX66quvdPr0af3000966aWXdPbsWYWFhdk8LgAAAABwNAX6Tt7FixezFFlWg7m5KTEx0RZxWdm9e7fOnj2rHj16ZFn3xx9/yMXl/2rVy5cva9KkSYqPj5eXl5caN26sdevWqUGDBjaPCwAAAAAcTYGKvDvuuEPHjh1T7dq1s11/5MiRIpkWGRwcrCNHjmS7LiYmxurxxIkTNXHiRJvHAAAAAAAlQYGma4aGhmrx4sW6fv16lnUpKSlaunSp2rRpY8v4AMDpGRmGvUNwiBjggLxKOcx7w1HiAICSoEBX8oYOHarPP/9cDz/8sHr37q26detKkk6cOKF3331X6enpGjJkSFHFCgBOyeRiUtry72WcuWKf529aTe5P3WPXGG6OA47DVM7d7u9PSTLV9JT7sOZ2e34AKGkKVORVrVpV69at09SpU7VgwQIZxv/OqplMJgUHB2vy5MmqWrVqUcUKAE7LOHNFxu9/2eW5TTXK2z2Gm+OA47H3ewMAUDAF/mfoNWvW1MqVK/XXX3/p5MmTkqTatWvLy8urKOIDAAAAABRAgYu8TF5eXvL397dtNAAAAACAW1KgG68AAAAAABwbRR4AAAAAOBGKPAAAAABwIhR5AAAAAOBEKPIAAAAAwIlQ5AEAAACAE6HIAwAAAAAnQpEHAAAAAE6EIg8AAAAAnAhFHgAAAAA4EYo8AAAAAHAiFHkAAAAA4EQo8gAAAADAiVDkAQAAAIATocgDAAAAACdCkQcAAAAATsShi7ylS5fKbDZb/XTs2DHXPlu3blXHjh3l5+enrl27aufOncUWLwAAAADYm5u9A8hLw4YNFRUVZXns6uqaY9t9+/ZpzJgxGj16tNq0aaMtW7Zo2LBh2rRpk3x8fIopYgAAAACwH4e+kqe/izpvb2/LT+XKlXNsGx0drZCQEA0cOFD169dXeHi47rnnHq1Zs6ZYYwYAAAAAe3H4Iu/kyZMKDg5Wu3btNGbMGJ09ezbHtgcOHFCrVq2slgUHB+vAgQPFECkAAAAA2J9DT9f09/dXZGSk6tatq/j4eC1fvly9e/fWli1bVL58+SztL168qKpVq1otq1Klii5evFiMUQMAAACA/Th0kRcaGmr5vVGjRmratKnatGmjrVu3KiwszK6xAQAAAIAjcvjpmjerUKGC6tSpo1OnTmW7vmrVqlmu2iUkJGS5ugcAAAAAzqpEFXlJSUk6ffq0vL29s10fEBCgb775xmrZ7t27FRAQUEwRAgAAAIB9OXSRN2fOHH377beKi4vTvn37NHz4cLm4uOiRRx6RJI0bN07z58+3tO/Xr5927dqlt99+W8ePH9fSpUt16NAh9enTx45bAQAAAADFx6G/k3fu3DmNHj1af/75pypXrqzmzZtr/fr1ln+j8Mcff8jF5f/q1GbNmum1117TokWLtGDBAtWpU0fLly/nf+QBAAAAuG04dJG3cOHCXNfHxMRkWdapUyd16tSpCKMCAAAAAMfl0NM1AQAAAAAFQ5EHAAAAAE6EIg8AAAAAnAhFHgAAAAA4EYo8AAAAAHAiFHkAAAAA4EQo8gAnVdHNTRmGUaA+BW0PAADgSDzKSkZG4Y9nbqWvI3Ho/5MHoPDKubrKxWTSopOnFZeSkmf7WqVLK7z2XcUSGwAAQFFwK2WSycWkHz5OVVJiwQq2cpVN8u/iUWSxFSeKPMDJxaWk6LdreRd5AAAAziIp0dCVeOe4KlcYTNcEAAAAACdCkQcAAAAAToQiDwAAAACcCEUeAAAAADgRijwAAAAAcCIUeQAAAADgRCjyAAAAAMCJUOQBAAAAgBOhyAMAAAAAJ0KRBwAAAABOhCIPt5UMw7B3CAAAAIViZHAcU5Q8yhZ+Hzvaa+Nm7wBys2LFCn3++ec6ceKESpcurcDAQI0dO1b16tXLsc+mTZs0YcIEq2UeHh768ccfiyFiODoXk0mLTp5WXEpKnm0DK3iqd/U7iyUuAACAvJhcTPrh41QlJRasoKhSx0U+Ie5FFpezcCtlKtQ+LlfZJP8uHkUaW0E5dJH37bffqnfv3vLz81N6eroWLFigAQMG6OOPP1bZsmVz7Fe+fHl9+umnlscmk6mYIkZJEJeSot+u5V3k1SxVqljiAQAAyK+kRENX4gtW5JWt5FhXmRxdYfaxo3HoIu+tt96yejx79my1atVKP/30k+69994c+5lMJnl7exdDhAAAAADgWBy6yPunK1euSJK8vLxybZecnKw2bdooIyND99xzj0aPHq2GDRsWU5QAAAAAYD8l5sYrGRkZmjVrlpo1ayYfH58c29WtW1ezZs3S66+/rnnz5skwDPXs2VPnzp0r1ngBAAAAwB5KzJW8adOm6dixY3r33XdzbRcYGKjAwECrx507d9a6desUHh5eDJECAAAAgP2UiCJv+vTp2rFjh9asWaM77yzY3Q7d3d3l6+urU6dOFVl8AAAAAOAoHHq6pmEYmj59urZt26bVq1frrrvuKvAY6enpOnr0KDdiAQAAAHBbcOgredOmTdNHH32k119/XeXKlVN8fLwkydPTU6VLl5YkjRs3TnfccYfGjBkjSVq2bJkCAgJUu3ZtXb58WW+99ZbOnj2rsLAwu24LAAAAABQHhy7y3nvvPUlS3759rZZHRkaqe/fukqQ//vhDLi7/d0Hy8uXLmjRpkuLj4+Xl5aXGjRtr3bp1atCgQTFHDwAAAADFz6GLvCNHjuTZJiYmxurxxIkTNXHixCKMCgAAAAAcl0N/Jw/IS4Zh2DsEAEBR8yolI8P++d4RYgCA/HDoK3lAXlxMJi06eVpxKSl5tg2s4Kne1Qt2d1YAgP2ZyrnL5GJS2vLvZZy5Yp8YanrKfVhzuzw3ABQURR5KvLiUFP12Le8ir2apUsUSDwCgaBhnrsj4/S97hwEADo/pmgAAAADgRCjyAAAAAMCJUOQBAAAAgBOhyAMAAAAAJ0KRBwAAAABOhCIPAAAAAJwIRR4AAAAAOBGKPAAAAABwIhR5AAAAAOBEKPIAAAAAwIlQ5AGQJFV0c1OGYRS4X2H6AAAAOAuPspKRUfjjoVvpmxM3m48IoEQq5+oqF5NJi06eVlxKSr761CpdWuG17yry2AAAAByVWymTTC4m/fBxqpISC1awlatskn8XD9vHZPMRAZRocSkp+u1a/oo8AAAA/E9SoqEr8Y4xw4npmgAAAADgRCjyAAAAAMCJUOQBAAAAgBOhyAMAAAAAJ0KRBwAAAABOpEQUeWvXrlXbtm3l5+ensLAw/fDDD7m237p1qzp27Cg/Pz917dpVO3fuLLZYAQAAAMCeHL7I++STTxQZGalhw4Zp8+bNatSokQYMGKCEhIRs2+/bt09jxozRE088oQ8++EDt2rXTsGHDdPTo0WKPHQAAAACKm8MXeVFRUXryySfVo0cPNWjQQNOmTVPp0qW1cePGbNtHR0crJCREAwcOVP369RUeHq577rlHa9asKfbYAQAAAKC4OXSRl5qaqp9++klBQUGWZS4uLgoKCtL+/fuz7XPgwAG1atXKallwcLAOHDhQ5PECAAAAgL252TuA3Fy6dEnp6emqUqWK1fIqVaroxIkT2fa5ePGiqlatmqX9xYsXizRW2E+t0qXz1a5aKQ/a27B9Qdsid6aanvZ7cu+y9o+BOBwvBuKwYvd9APytXGVTgfuU8TKVqL63U7yF6ZMfJsMwjCIZ2QbOnz+vBx54QOvWrVNgYKBl+dy5c7V37169//77Wfo0adJEs2fP1iOPPGJZtnbtWi1fvly7d+8uttgBAAAAwB4cerpmpUqV5OrqmuUmKwkJCVmu1mWqWrVqlqt2ubUHAAAAAGfi0EWeh4eHGjdurNjYWMuyjIwMxcbGWl3Zu1lAQIC++eYbq2W7d+9WQEBAkccLAAAAAPbm0EWeJD377LNav369Nm/erOPHj2vq1Km6du2aunfvLkkaN26c5s+fb2nfr18/7dq1S2+//baOHz+upUuX6tChQ+rTp48dtwIAAAAAiodD33hFkjp37qzExEQtWbJE8fHx8vX11apVqyzTL//44w+5uPxfrdqsWTO99tprWrRokRYsWKA6depo+fLl8vHxseNWAAAAAEDxcPgreZLUp08fffnllzp06JDef/99NW3a1LIuJiZGs2fPtmrfqVMnffbZZzp06JA++ugjhYaG2iHqW5Oenq5Fixapbdu28vf3V/v27bV8+XI54n1y9u7dqyFDhig4OFhms1nbt2+3Wm8YhhYvXqzg4GD5+/vrmWee0e+//263eG+WW+xpaWmaN2+eunbtqoCAAAUHB2vcuHE6f/68XWPOlNd+v9nkyZNlNpv1zjvvFGuMOclP7MePH9eQIUPUvHlzBQQEqEePHjp79qxd4i3pSlI+ETnFbsgpyK+SlFPIJ/ZDTrGvElHk3Y5Wrlyp9957T5MnT9Ynn3yisWPHatWqVYqJibF3aFkkJyfLbDZrypQp2a5fuXKlYmJiNHXqVK1fv15lypTRgAEDdP369WKP9Z9yiz0lJUU///yzhg4dqk2bNmnZsmX67bffNHToULvE+k957fdM27Zt08GDB1WtWrViiy0vecV+6tQpPf3006pXr55iYmL04Ycf6oUXXlCpUqWKPVZnUJLyicgpdkNOQX6VpJxCPrEfcoqdGXBIgwcPNiZMmGC1bPjw4caYMWPsFlN++Pj4GNu2bbM8zsjIMFq3bm2sWrXKsuzy5ctGkyZNjI8++shOUWbvn7Fn5+DBg4aPj49x5syZYosrP3KK/dy5c0ZISIhx9OhRo02bNkZUVJRd4stNdrGHh4cbY8eOtVtMzqak5hODnGI35BTkpqTmFPKJ/ZBTih9X8hxUYGCgvvnmG/3222+SpF9++UXff/+9HnjgAXuHViBxcXGKj49XUFCQZZmnp6eaNm2q/fv32zW2wrh69apMJpMqVKhg71DylJGRoZdeekkDBgxQw4YN7R1OvmVkZGjHjh2qU6eOBgwYoFatWiksLCzXaR7InbPkE5FT7IqcgkzOklPIJ/ZFTilaDn/jldvV4MGDdfXqVXXq1Emurq5KT0/XqFGj9Oijj9o7tAKJj4+XJFWpUsVqeZUqVbL8P0NHd/36db322mvq0qWLypcvb+9w8rRy5Uq5ubmpX79+9g6lQBISEpScnKyVK1cqPDxcY8eO1a5duzR8+HBFR0frvvvus3eIJY6z5BORU+yKnIJMzpJTyCf2RU4pWhR5Dmrr1q3asmWL5s+frwYNGujw4cOKjIxUtWrV1K1bN3uHd9tJS0vTyJEjZRiGpk2bZu9w8nTo0CFFR0dr06ZNMplM9g6nQDIyMiRJ7dq10zPPPCNJ8vX11b59+7Ru3TqHSZ4lCfnE8ZBTig85xfbIKY6lpOUTkVOKBUWeg5o7d64GDx6sLl26SJLMZrPOnj2rFStWlKgE6u3tLf191uPmL9QmJCSoUaNGdows/9LS0hQeHq6zZ89q9erVJeIM2XfffaeEhAS1adPGsiw9PV1z5sxRdHS0vvjiC7vGl5tKlSrJzc1N9evXt1pev359ff/993aLqyRzlnwicordkFNwM2fJKeQT+yGnFD2KPAeVkpKS5cyGq6urQ96eODe1atWSt7e3YmNj5evrK/09Z/zgwYPq1auXvcPLU2byPHnypKKjo1WpUiV7h5Qvjz32mNV3DCRpwIABeuyxx9S9e3e7xZUfHh4e8vPzs3zXI9Pvv/+umjVr2i2uksxZ8onIKXZDTsHNnCWnkE/sh5xS9CjyHFSbNm30r3/9SzVq1LBMhYiKilKPHj3sHVoWSUlJOnXqlOVxXFycDh8+LC8vL9WoUUP9+vXTG2+8odq1a6tWrVpavHixqlWrpvbt29s1buURu7e3t0aMGKGff/5ZK1asUHp6umX+vpeXlzw8POwYed77/Z/J3t3dXVWrVlW9evXsEK21vGIfMGCARo0apXvvvVctW7bUrl279OWXXyo6OtqucZdUJSmfiJxiN+QU5FdJyinkE/shp9iXyShpp11uE1evXtXixYu1fft2yzSCLl26aNiwYQ7xwb3Znj17sv3SbLdu3TR79mwZhqElS5Zo/fr1unz5spo3b64pU6aobt26don3ZrnFPnz4cLVr1y7bftHR0WrZsmUxRJizvPb7P7Vt21b9+vWzzB+3p/zEvmHDBr355ps6d+6c6tatqxdffNEh/uiWRCUpn4icYjfkFORXScop5BP7IafYF0UeAAAAADgR/k8eAAAAADgRijwAAAAAcCIUeQAAAADgRCjyAAAAAMCJUOQBAAAAgBOhyAMAAAAAJ0KRBwAAAABOhCIPAAAAAJwIRR5KrL59+2rmzJn2DkN79uyR2WzW5cuX7R0KgFtATgFgK+QT2BtFHlAAjpK0ATgHcgoAWyGf4GYUeQAAAADgRCjy4BRSU1M1Z84chYSEKCAgQGFhYdqzZ49l/aZNm9SiRQvt2rVLnTp1UmBgoAYMGKALFy5Y2ty4cUMzZsxQixYt1LJlS82bN0/jx4/XCy+8IEmKiIjQt99+q+joaJnNZpnNZsXFxVn6//TTT+revbuaNm2qnj176sSJE8W8FwDYCjkFgK2QT2APFHlwCtOnT9f+/fu1cOFCffjhh+rYsaMGDhyo33//3dImJSVFb7/9tubOnas1a9bojz/+0Jw5cyzrV65cqS1btigyMlLvvvuurl69qu3bt1vWv/zyywoMDNSTTz6pr776Sl999ZWqV69uWb9w4UJFRERo48aNcnV11cSJE4txDwCwJXIKAFshn8AeKPJQ4p09e1abNm3S4sWL1aJFC919990aMGCAmjdvrk2bNlnapaWladq0afLz81Pjxo3Vu3dvffPNN5b1a9as0eDBg9WhQwfVr19fkydPVoUKFSzrPT095e7urtKlS8vb21ve3t5ydXW1rB81apTuu+8+NWjQQIMHD9b+/ft1/fr1YtwTAGyBnALAVsgnsBc3ewcA3KqjR48qPT1dHTt2tFqempqqihUrWh6XKVNGd999t+VxtWrVlJCQIEm6cuWKLl68KH9/f8t6V1dXNW7cWBkZGfmKw2w2W3739vaWJCUkJKhGjRq3sHUAihs5BYCtkE9gLxR5KPGSk5Pl6upqmYJws7Jly1p+d3OzfrubTCYZhmGzOG4e32QySVK+ky8Ax0FOAWAr5BPYC9M1UeL5+voqPT1diYmJql27ttVP5tmqvHh6eqpq1ar68ccfLcvS09P1888/W7Vzd3cnKQJOjpwCwFbIJ7AXruShxKtbt666du2qcePGKSIiQr6+vrp06ZJiY2NlNpv14IMP5mucPn36aMWKFbr77rtVr149rVmzRn/99ZfljJck1axZUwcPHlRcXJzKli1rNdUCgHMgpwCwFfIJ7IUiD04hMjJSb7zxhmbPnq0LFy6oYsWKCggIyHfylKRBgwbp4sWLGj9+vFxdXfXkk08qODjYanrFc889p4iICHXp0kUpKSn673//W0RbBMCeyCkAbIV8AnswGbac8As4kYyMDHXq1EmdOnVSeHi4vcMBUMKRUwDYCvkEeeFKHvC3M2fO6Ouvv9a9996r1NRUrV27VmfOnFHXrl3tHRqAEoicAsBWyCcoKIo84G8uLi7atGmT5syZI8Mw5OPjo6ioKNWvX9/eoQEogcgpAGyFfIKCYromAAAAADgR/oUCAAAAADgRijwAAAAAcCIUeQAAAADgRCjyAAAAAMCJUOQBAAAAgBOhyAMAAAAAJ0KRBwAAAABOhCIPAAAAAJwIRR4AAAAAOJH/D6oMv1Gr1RQpAAAAAElFTkSuQmCC",
|
||
"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.\n",
|
||
"- Die Samplegröße beträgt 1000. Sample dementsprechend aus den gegebenen Werten.\n",
|
||
"- Speichere die gesampleten Personen nach dem Schema: |gender|height|weight| in der File `people_in_germany.csv` als csv.\n",
|
||
" -> Nutze für das `gender` 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.\n",
|
||
"\n",
|
||
"*Tipp: Dataclasses erleichtern die Aufgabe ungemein!*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 148,
|
||
"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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",
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"<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",
|
||
" gender: 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",
|
||
" gender = False\n",
|
||
" height = rng.normal(178.9, 10)\n",
|
||
" weight = rng.normal(85.8, 26)\n",
|
||
" \n",
|
||
" else:\n",
|
||
" gender = 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(gender=gender,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=\"gender\")\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
|
||
}
|