peter/copbird_aufarbeitung
1
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity
copbird_aufarbeitung/ergebnisse_hackathon_repo/team-16/notebooks/tweet-pm-counts.ipynb

651 lines
64 KiB
Text
Raw Normal View History

init
2023-03-26 16:36:49 +00:00
{
"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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
"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
}
Reference in a new issue Copy permalink