tranquillo/opendata-dresden-jupyter-notebook
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init
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{
"cells": [
{
"cell_type": "code",
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"execution_count": 26,
init
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"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/rob/data/OpenDataPortalDresden/csv\n"
]
}
],
"source": [
"%cd /home/rob/data/OpenDataPortalDresden/csv/"
]
},
{
"cell_type": "code",
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"execution_count": 27,
init
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"steuerstatistik_dresden_quartale_md1.csv\r\n"
]
}
],
"source": [
"%ls steuerstatistik_dresden_quartale_md1.csv"
]
},
{
"cell_type": "code",
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"execution_count": 28,
init
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import Data"
]
},
{
"cell_type": "code",
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"execution_count": 29,
init
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"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('./steuerstatistik_dresden_quartale_md1.csv',sep=';')"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Einzelne Spalten anzeigen"
]
},
init
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{
"cell_type": "code",
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"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"years = df['Jahr']\n",
"tax = df['Grundsteuer A und B']\n",
"#tax oder years ausgeben .."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ganze Tabelle anzeigen"
]
},
{
"cell_type": "code",
"execution_count": 31,
init
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"metadata": {},
"outputs": [
{
"data": {
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"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>Jahr</th>\n",
" <th>Quartal</th>\n",
" <th>Steuereinnahmen (netto)</th>\n",
" <th>Steuereinnahmen (brutto)</th>\n",
" <th>Grundsteuer A und B</th>\n",
" <th>Gewerbesteuer (brutto)</th>\n",
" <th>Sonstige Gemeindesteuern</th>\n",
" <th>Spielautomatensteuer</th>\n",
" <th>Hundesteuer</th>\n",
" <th>Zweitwohnungssteuer</th>\n",
" <th>Beherbergungssteuer</th>\n",
" <th>Gemeindeanteil an der Einkommenssteuer</th>\n",
" <th>Gewerbesteuerumlage</th>\n",
" <th>Gemeindeanteil Umsatzsteuer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1991</td>\n",
" <td>1. Quartal</td>\n",
" <td>4122</td>\n",
" <td>934</td>\n",
" <td>934</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3188</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1991</td>\n",
" <td>2. Quartal</td>\n",
" <td>8377</td>\n",
" <td>4666</td>\n",
" <td>4636</td>\n",
" <td>30</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3711</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1991</td>\n",
" <td>3. Quartal</td>\n",
" <td>4962</td>\n",
" <td>4962</td>\n",
" <td>824</td>\n",
" <td>4138</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1991</td>\n",
" <td>4. Quartal</td>\n",
" <td>33760</td>\n",
" <td>16842</td>\n",
" <td>1052</td>\n",
" <td>15790</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16918</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992</td>\n",
" <td>1. Quartal</td>\n",
" <td>12473</td>\n",
" <td>12473</td>\n",
" <td>2615</td>\n",
" <td>9858</td>\n",
" <td>138</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
init
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"text/plain": [
build graphs from tax values
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" Jahr Quartal Steuereinnahmen (netto) Steuereinnahmen (brutto) \\\n",
"0 1991 1. Quartal 4122 934 \n",
"1 1991 2. Quartal 8377 4666 \n",
"2 1991 3. Quartal 4962 4962 \n",
"3 1991 4. Quartal 33760 16842 \n",
"4 1992 1. Quartal 12473 12473 \n",
"\n",
" Grundsteuer A und B Gewerbesteuer (brutto) Sonstige Gemeindesteuern \\\n",
"0 934 0 60 \n",
"1 4636 30 47 \n",
"2 824 4138 60 \n",
"3 1052 15790 35 \n",
"4 2615 9858 138 \n",
"\n",
" Spielautomatensteuer Hundesteuer Zweitwohnungssteuer \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Beherbergungssteuer Gemeindeanteil an der Einkommenssteuer \\\n",
"0 0 3188 \n",
"1 0 3711 \n",
"2 0 0 \n",
"3 0 16918 \n",
"4 0 0 \n",
"\n",
" Gewerbesteuerumlage Gemeindeanteil Umsatzsteuer \n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 "
init
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]
},
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"execution_count": 31,
init
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# To sum up, get a less columns"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"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>Jahr</th>\n",
" <th>Quartal</th>\n",
" <th>Steuereinnahmen (netto)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1991</td>\n",
" <td>1. Quartal</td>\n",
" <td>4122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1991</td>\n",
" <td>2. Quartal</td>\n",
" <td>8377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1991</td>\n",
" <td>3. Quartal</td>\n",
" <td>4962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1991</td>\n",
" <td>4. Quartal</td>\n",
" <td>33760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992</td>\n",
" <td>1. Quartal</td>\n",
" <td>12473</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Jahr Quartal Steuereinnahmen (netto)\n",
"0 1991 1. Quartal 4122\n",
"1 1991 2. Quartal 8377\n",
"2 1991 3. Quartal 4962\n",
"3 1991 4. Quartal 33760\n",
"4 1992 1. Quartal 12473"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[:,['Jahr','Quartal','Steuereinnahmen (netto)']].head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sum up each group"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"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>Steuereinnahmen (netto)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Jahr</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td>444032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
" <td>462857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>526787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>637082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018</th>\n",
" <td>619476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Steuereinnahmen (netto)\n",
"Jahr \n",
"2014 444032\n",
"2015 462857\n",
"2016 526787\n",
"2017 637082\n",
"2018 619476"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[:,['Jahr','Steuereinnahmen (netto)']].groupby(['Jahr']).sum().tail(5)"
init
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
build graphs from tax values
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"# Print graph with all values"
init
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]
},
{
"cell_type": "code",
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"execution_count": 88,
init
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"metadata": {},
"outputs": [
{
"data": {
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init
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"text/plain": [
build graphs from tax values
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"<Figure size 1462.62x648 with 1 Axes>"
init
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
build graphs from tax values
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"#select columns und sum the number-column \n",
"tax_net = df.loc[:,['Jahr','Steuereinnahmen (netto)']].groupby(['Jahr']).sum()\n",
"tax_gross = df.loc[:,['Jahr','Steuereinnahmen (brutto)']].groupby(['Jahr']).sum()\n",
"\n",
"# merge together both data sets\n",
"tax = tax_net\n",
"tax['Steuereinnahmen (brutto)'] = tax_gross['Steuereinnahmen (brutto)']\n",
"\n",
"# spit it out\n",
"ax = sns.relplot(data=tax, height=9, aspect=2, kind=\"line\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# add one more data column"
init
2019-07-05 14:42:35 +02:00
]
},
{
"cell_type": "code",
build graphs from tax values
2019-07-30 22:42:06 +02:00
"execution_count": 92,
init
2019-07-05 14:42:35 +02:00
"metadata": {},
build graphs from tax values
2019-07-30 22:42:06 +02:00
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1546.25x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tax_municipality = df.loc[:,['Jahr','Gemeindeanteil an der Einkommenssteuer']].groupby(['Jahr']).sum()\n",
"tax['Gemeindeanteil an der Einkommenssteuer'] = tax_municipality\n",
"\n",
"# spit it out\n",
"ax = sns.relplot(data=tax, height=9, aspect=2, kind=\"line\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# add even two more interesting columns"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1546.25x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tax_property = df.loc[:,['Jahr','Grundsteuer A und B']].groupby(['Jahr']).sum()\n",
"tax_trade_gross = df.loc[:,['Jahr','Gewerbesteuer (brutto)']].groupby(['Jahr']).sum()\n",
"\n",
"#even merge these two in our tax data set\n",
"tax['Grundsteuer A und B'] = tax_property\n",
"tax['Gewerbesteuer (brutto)'] = tax_trade_gross\n",
"\n",
"# spit it out\n",
"ax = sns.relplot(data=tax, height=9, aspect=2, kind=\"line\")\n"
]
init
2019-07-05 14:42:35 +02:00
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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",
build graphs from tax values
2019-07-30 22:42:06 +02:00
"version": "3.7.4"
init
2019-07-05 14:42:35 +02:00
}
},
"nbformat": 4,
"nbformat_minor": 2
}