650 lines
64 KiB
Text
650 lines
64 KiB
Text
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d74b2c0a",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Twitter - Pressemitteilung Vergleich"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "8d048755",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"from tqdm import tqdm # Fortschrittsanzeige für pandas\n",
|
||
"from datetime import datetime\n",
|
||
"tqdm.pandas()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "dc238dc3",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tweets_csv = '../mod_data/copbird_table_tweet_ext_state.csv'\n",
|
||
"users_csv = '../mod_data/copbird_table_user_ext.csv'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "88746ce4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"limit = None\n",
|
||
"tweets = pd.read_csv(tweets_csv, nrows=limit)\n",
|
||
"users = pd.read_csv(users_csv, nrows=limit)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "a9d777a1",
|
||
"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>user_id</th>\n",
|
||
" <th>name</th>\n",
|
||
" <th>handle</th>\n",
|
||
" <th>stadt</th>\n",
|
||
" <th>bundesland</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1032561433102434304</td>\n",
|
||
" <td>Polizei Wittlich</td>\n",
|
||
" <td>PolizeiWittlich</td>\n",
|
||
" <td>Wittlich</td>\n",
|
||
" <td>Rheinland-Pfalz</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1143867545226764293</td>\n",
|
||
" <td>Bayerisches Landeskriminalamt</td>\n",
|
||
" <td>LKA_Bayern</td>\n",
|
||
" <td>München</td>\n",
|
||
" <td>Bayern</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1169206134189830145</td>\n",
|
||
" <td>Polizei Stendal</td>\n",
|
||
" <td>Polizei_SDL</td>\n",
|
||
" <td>Stendal</td>\n",
|
||
" <td>Sachsen-Anhalt</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1184024283342950401</td>\n",
|
||
" <td>Polizei Ravensburg</td>\n",
|
||
" <td>PolizeiRV</td>\n",
|
||
" <td>Ravensburg</td>\n",
|
||
" <td>Baden-Württemberg</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1232548941889228808</td>\n",
|
||
" <td>Polizei Bad Nenndorf</td>\n",
|
||
" <td>Polizei_BadN</td>\n",
|
||
" <td>Bad Nenndorf</td>\n",
|
||
" <td>Niedersachsen</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" user_id name handle \\\n",
|
||
"0 1032561433102434304 Polizei Wittlich PolizeiWittlich \n",
|
||
"1 1143867545226764293 Bayerisches Landeskriminalamt LKA_Bayern \n",
|
||
"2 1169206134189830145 Polizei Stendal Polizei_SDL \n",
|
||
"3 1184024283342950401 Polizei Ravensburg PolizeiRV \n",
|
||
"4 1232548941889228808 Polizei Bad Nenndorf Polizei_BadN \n",
|
||
"\n",
|
||
" stadt bundesland \n",
|
||
"0 Wittlich Rheinland-Pfalz \n",
|
||
"1 München Bayern \n",
|
||
"2 Stendal Sachsen-Anhalt \n",
|
||
"3 Ravensburg Baden-Württemberg \n",
|
||
"4 Bad Nenndorf Niedersachsen "
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"users.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "f1cde0ae",
|
||
"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>tweet_id</th>\n",
|
||
" <th>tweet_text</th>\n",
|
||
" <th>created_at</th>\n",
|
||
" <th>user_id</th>\n",
|
||
" <th>user_name</th>\n",
|
||
" <th>handle</th>\n",
|
||
" <th>stadt</th>\n",
|
||
" <th>bundesland</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1321021123463663616</td>\n",
|
||
" <td>@mahanna196 Da die Stadt keine Ausnahme für Ra...</td>\n",
|
||
" <td>2020-10-27 09:29:13</td>\n",
|
||
" <td>778895426007203840</td>\n",
|
||
" <td>Polizei Oldenburg-Stadt/Ammerl</td>\n",
|
||
" <td>Polizei_OL</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1321023114071969792</td>\n",
|
||
" <td>#Zeugengesucht\\r\\nDie Hintergründe zu dem Tötu...</td>\n",
|
||
" <td>2020-10-27 09:37:08</td>\n",
|
||
" <td>2397974054</td>\n",
|
||
" <td>Polizei Berlin</td>\n",
|
||
" <td>polizeiberlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1321025127388188673</td>\n",
|
||
" <td>RT @bka: EUROPE´S MOST WANTED – Sexualstraftät...</td>\n",
|
||
" <td>2020-10-27 09:45:08</td>\n",
|
||
" <td>2397974054</td>\n",
|
||
" <td>Polizei Berlin</td>\n",
|
||
" <td>polizeiberlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1321028108665950208</td>\n",
|
||
" <td>@StrupeitVolker Wir verstehen nicht so recht w...</td>\n",
|
||
" <td>2020-10-27 09:56:59</td>\n",
|
||
" <td>2810902381</td>\n",
|
||
" <td>Polizei München</td>\n",
|
||
" <td>PolizeiMuenchen</td>\n",
|
||
" <td>München</td>\n",
|
||
" <td>Bayern</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1321029199998656513</td>\n",
|
||
" <td>Wir unterstützen das @bka bei der #Öffentlichk...</td>\n",
|
||
" <td>2020-10-27 10:01:19</td>\n",
|
||
" <td>223758384</td>\n",
|
||
" <td>Polizei Sachsen</td>\n",
|
||
" <td>PolizeiSachsen</td>\n",
|
||
" <td>Dresden</td>\n",
|
||
" <td>Sachsen</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" tweet_id tweet_text \\\n",
|
||
"0 1321021123463663616 @mahanna196 Da die Stadt keine Ausnahme für Ra... \n",
|
||
"1 1321023114071969792 #Zeugengesucht\\r\\nDie Hintergründe zu dem Tötu... \n",
|
||
"2 1321025127388188673 RT @bka: EUROPE´S MOST WANTED – Sexualstraftät... \n",
|
||
"3 1321028108665950208 @StrupeitVolker Wir verstehen nicht so recht w... \n",
|
||
"4 1321029199998656513 Wir unterstützen das @bka bei der #Öffentlichk... \n",
|
||
"\n",
|
||
" created_at user_id user_name \\\n",
|
||
"0 2020-10-27 09:29:13 778895426007203840 Polizei Oldenburg-Stadt/Ammerl \n",
|
||
"1 2020-10-27 09:37:08 2397974054 Polizei Berlin \n",
|
||
"2 2020-10-27 09:45:08 2397974054 Polizei Berlin \n",
|
||
"3 2020-10-27 09:56:59 2810902381 Polizei München \n",
|
||
"4 2020-10-27 10:01:19 223758384 Polizei Sachsen \n",
|
||
"\n",
|
||
" handle stadt bundesland \n",
|
||
"0 Polizei_OL NaN NaN \n",
|
||
"1 polizeiberlin Berlin Berlin \n",
|
||
"2 polizeiberlin Berlin Berlin \n",
|
||
"3 PolizeiMuenchen München Bayern \n",
|
||
"4 PolizeiSachsen Dresden Sachsen "
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tweets.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ce184d1d",
|
||
"metadata": {},
|
||
"source": [
|
||
"Selektiere Tweets mit PM-Links"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "5e20da3c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def has_pm_link(txt):\n",
|
||
" return \"https://t.co/\" in txt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "7902d91c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tweets['has_pm'] = tweets['tweet_text'].apply(lambda x: has_pm_link(x))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "fe6a144e",
|
||
"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>tweet_id</th>\n",
|
||
" <th>tweet_text</th>\n",
|
||
" <th>created_at</th>\n",
|
||
" <th>user_id</th>\n",
|
||
" <th>user_name</th>\n",
|
||
" <th>handle</th>\n",
|
||
" <th>stadt</th>\n",
|
||
" <th>bundesland</th>\n",
|
||
" <th>has_pm</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1321021123463663616</td>\n",
|
||
" <td>@mahanna196 Da die Stadt keine Ausnahme für Ra...</td>\n",
|
||
" <td>2020-10-27 09:29:13</td>\n",
|
||
" <td>778895426007203840</td>\n",
|
||
" <td>Polizei Oldenburg-Stadt/Ammerl</td>\n",
|
||
" <td>Polizei_OL</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1321023114071969792</td>\n",
|
||
" <td>#Zeugengesucht\\r\\nDie Hintergründe zu dem Tötu...</td>\n",
|
||
" <td>2020-10-27 09:37:08</td>\n",
|
||
" <td>2397974054</td>\n",
|
||
" <td>Polizei Berlin</td>\n",
|
||
" <td>polizeiberlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1321025127388188673</td>\n",
|
||
" <td>RT @bka: EUROPE´S MOST WANTED – Sexualstraftät...</td>\n",
|
||
" <td>2020-10-27 09:45:08</td>\n",
|
||
" <td>2397974054</td>\n",
|
||
" <td>Polizei Berlin</td>\n",
|
||
" <td>polizeiberlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" <td>Berlin</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1321028108665950208</td>\n",
|
||
" <td>@StrupeitVolker Wir verstehen nicht so recht w...</td>\n",
|
||
" <td>2020-10-27 09:56:59</td>\n",
|
||
" <td>2810902381</td>\n",
|
||
" <td>Polizei München</td>\n",
|
||
" <td>PolizeiMuenchen</td>\n",
|
||
" <td>München</td>\n",
|
||
" <td>Bayern</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1321029199998656513</td>\n",
|
||
" <td>Wir unterstützen das @bka bei der #Öffentlichk...</td>\n",
|
||
" <td>2020-10-27 10:01:19</td>\n",
|
||
" <td>223758384</td>\n",
|
||
" <td>Polizei Sachsen</td>\n",
|
||
" <td>PolizeiSachsen</td>\n",
|
||
" <td>Dresden</td>\n",
|
||
" <td>Sachsen</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" tweet_id tweet_text \\\n",
|
||
"0 1321021123463663616 @mahanna196 Da die Stadt keine Ausnahme für Ra... \n",
|
||
"1 1321023114071969792 #Zeugengesucht\\r\\nDie Hintergründe zu dem Tötu... \n",
|
||
"2 1321025127388188673 RT @bka: EUROPE´S MOST WANTED – Sexualstraftät... \n",
|
||
"3 1321028108665950208 @StrupeitVolker Wir verstehen nicht so recht w... \n",
|
||
"4 1321029199998656513 Wir unterstützen das @bka bei der #Öffentlichk... \n",
|
||
"\n",
|
||
" created_at user_id user_name \\\n",
|
||
"0 2020-10-27 09:29:13 778895426007203840 Polizei Oldenburg-Stadt/Ammerl \n",
|
||
"1 2020-10-27 09:37:08 2397974054 Polizei Berlin \n",
|
||
"2 2020-10-27 09:45:08 2397974054 Polizei Berlin \n",
|
||
"3 2020-10-27 09:56:59 2810902381 Polizei München \n",
|
||
"4 2020-10-27 10:01:19 223758384 Polizei Sachsen \n",
|
||
"\n",
|
||
" handle stadt bundesland has_pm \n",
|
||
"0 Polizei_OL NaN NaN False \n",
|
||
"1 polizeiberlin Berlin Berlin True \n",
|
||
"2 polizeiberlin Berlin Berlin True \n",
|
||
"3 PolizeiMuenchen München Bayern False \n",
|
||
"4 PolizeiSachsen Dresden Sachsen True "
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tweets.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "09c2ed78",
|
||
"metadata": {},
|
||
"source": [
|
||
"Erzeuge einen gesamten DataFrame"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "299ab987",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tweets_with_pm = tweets.loc[tweets['has_pm'] == True]\n",
|
||
"tweets_without_pm = tweets.loc[tweets['has_pm'] == False]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "41cf83cb",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tweets_with_pm_count = tweets_with_pm.groupby('bundesland').size().reset_index(name='count').sort_values(by='count', ascending=False)\n",
|
||
"tweets_without_pm_count = tweets_without_pm.groupby('bundesland').size().reset_index(name='count').sort_values(by='count', ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "8fc83c56",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tweets_with_pm_count['pm'] = tweets_with_pm_count['count'].apply(lambda x: 'mit PM')\n",
|
||
"tweets_without_pm_count['pm'] = tweets_with_pm_count['count'].apply(lambda x: 'ohne PM')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "a674d7e3",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"result = pd.concat([tweets_with_pm_count, tweets_without_pm_count])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "7ca5c2a2",
|
||
"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>bundesland</th>\n",
|
||
" <th>count</th>\n",
|
||
" <th>pm</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>Nordrhein-Westfalen</td>\n",
|
||
" <td>9242</td>\n",
|
||
" <td>mit PM</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Niedersachsen</td>\n",
|
||
" <td>2773</td>\n",
|
||
" <td>mit PM</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Baden-Württemberg</td>\n",
|
||
" <td>2759</td>\n",
|
||
" <td>mit PM</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Bayern</td>\n",
|
||
" <td>2264</td>\n",
|
||
" <td>mit PM</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Hessen</td>\n",
|
||
" <td>1737</td>\n",
|
||
" <td>mit PM</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" bundesland count pm\n",
|
||
"10 Nordrhein-Westfalen 9242 mit PM\n",
|
||
"9 Niedersachsen 2773 mit PM\n",
|
||
"1 Baden-Württemberg 2759 mit PM\n",
|
||
"2 Bayern 2264 mit PM\n",
|
||
"7 Hessen 1737 mit PM"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"result.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e1185d0c",
|
||
"metadata": {},
|
||
"source": [
|
||
"Visualisiere die Ergebnisse"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "ad21d5c9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[]"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 1152x1152 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.set(style=\"darkgrid\")\n",
|
||
"plt.figure(figsize=(16, 16))\n",
|
||
"g = sns.barplot(x=\"bundesland\", y=\"count\", hue=\"pm\", data=result, ci=None)\n",
|
||
"g.set_xticklabels(g.get_xticklabels(), rotation=90, ha='right')\n",
|
||
"plt.plot()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "copbird-env",
|
||
"language": "python",
|
||
"name": "copbird-env"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.8.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|