1031 lines
38 KiB
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1031 lines
38 KiB
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"cells": [
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{
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"cell_type": "markdown",
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"id": "83885e86-1ccb-46ec-bee9-a33f3b541569",
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"metadata": {},
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"source": [
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"# Zusammenfassung der Analysen vom Hackathon für die Webside\n",
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"\n",
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"- womöglich zur Darstellung auf der Webside\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9bd1686f-9bbc-4c05-a5f5-e0c4ce653fb2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import altair as alt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "81780c9a-7721-438b-9726-ff5a70910ce8",
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"metadata": {},
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"source": [
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"## Daten aufbereitung\n",
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"\n",
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"Dump der Datenbank vom 25.03.2023. Die verschiedene Tabellen der Datenbank werden einzeln eingelesen. Zusätzlich werden alle direkt zu einem Tweet zugehörige Information in ein Datenobjekt gesammelt. Die Informationen zu den GIS-Daten zu den einzelnen Polizeistadtion (\"police_stations\") sind noch unvollständig und müssen gegebenfalls nocheinmal überprüft werden.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 119,
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"id": "fcc48831-7999-4d79-b722-736715b1ced6",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"((479991, 3), (151690, 8), (151690, 4), (13327, 5))"
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]
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},
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"execution_count": 119,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tweets_meta = pd.concat([pd.read_csv(\"data/entity_old.tsv\", sep = \"\\t\"), # data from old scraper\n",
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" pd.read_csv(\"data/tweets.csv\")]) # data from new scraper\n",
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"\n",
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"tweets_text = pd.concat([pd.read_csv(\"data/tweet_old.tsv\", sep = \"\\t\")[['id', \n",
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" 'tweet_text', \n",
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" 'created_at', \n",
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" 'user_id']].rename(columns = {\"id\":\"tweet_id\"}),\n",
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" pd.read_csv(\"data/tweets-1679742698645.csv\")])\n",
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"\n",
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"tweets_statistics = pd.concat([pd.read_csv(\"data/tweet_old.tsv\", sep = \"\\t\")[['id', \n",
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" 'like_count', \n",
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" 'retweet_count', \n",
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" 'reply_count', \n",
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" 'quote_count']].rename(columns = {\"id\":\"tweet_id\"}),\n",
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" pd.read_csv(\"data/tweets-1679742620302.csv\")])\n",
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"\n",
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"tweets_user = pd.read_csv(\"data/user_old.tsv\", \n",
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" sep = \"\\t\").rename(columns = {\"id\":\"user_id\",\"name\": \"user_name\"}\n",
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" ).merge(pd.read_csv(\"data/tweets-1679742702794.csv\"\n",
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" ).rename(columns = {\"username\":\"handle\", \"handle\": \"user_name\"}),\n",
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" on = \"user_id\",\n",
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" how = \"outer\",\n",
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" suffixes = [\"_2021\", \"_2022\"])\n",
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"\n",
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"tweets_meta.shape, tweets_statistics.shape, tweets_text.shape, tweets_user.shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f7b2b95-0a6c-42c6-a308-5f68d4ba94b9",
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"metadata": {},
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"source": [
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"Jetzt können noch alle Tweet bezogenen informationen in einem Data Frame gespeichert werden:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 150,
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"id": "cf409591-74a0-48dc-8f9e-66f7229f58cd",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tweet_id int64\n",
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"like_count int64\n",
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"retweet_count int64\n",
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"reply_count int64\n",
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"quote_count int64\n",
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"measured_at object\n",
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"is_deleted float64\n",
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"tweet_text object\n",
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"created_at object\n",
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"user_id int64\n",
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"user_name_2021 object\n",
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"handle_2021 object\n",
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"handle_2022 object\n",
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"user_name_2022 object\n",
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"dtype: object"
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]
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},
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"execution_count": 150,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tweets_combined = pd.merge(tweets_statistics, \n",
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" tweets_text,\n",
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" on = 'tweet_id').merge(tweets_user, on = 'user_id'\n",
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" ).drop(['id'], axis = 1) # drop unascessary id column (redundant to index)\n",
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" \n",
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"# Convert Counts to integer values\n",
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"tweets_combined[['like_count', 'retweet_count', 'reply_count', 'quote_count']] = tweets_combined[['like_count', 'retweet_count', 'reply_count', 'quote_count']].fillna(-99).astype(int)\n",
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"tweets_combined.dtypes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"id": "e312a975-3921-44ee-a7c5-37736678bc3f",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>user_id</th>\n",
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" <th>handle</th>\n",
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" <th>username</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1000004686156652545</td>\n",
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" <td>6jannik9</td>\n",
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" <td>Systemstratege:</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1000043230870867969</td>\n",
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" <td>lsollik</td>\n",
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" <td>Physiolucy</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1000405847460151296</td>\n",
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" <td>achim1949hans</td>\n",
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" <td>Systemstratege:</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1000460805719121921</td>\n",
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" <td>wahrew</td>\n",
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" <td>WahreWorte</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1000744009638252544</td>\n",
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" <td>derd1ck3</td>\n",
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" <td>Ⓓ①ⓒⓚ①③ (🏡)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11554</th>\n",
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" <td>99931264</td>\n",
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" <td>havok1975</td>\n",
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" <td>Systemstratege:</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11555</th>\n",
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" <td>999542638226403328</td>\n",
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" <td>madame_de_saxe</td>\n",
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" <td>Systemstratege:</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11556</th>\n",
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" <td>999901133282754560</td>\n",
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" <td>tungstendie74</td>\n",
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" <td>Systemstratege:</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11557</th>\n",
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" <td>999904275080794112</td>\n",
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" <td>_danielheim</td>\n",
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" <td>Systemstratege:</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11558</th>\n",
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" <td>999955376454930432</td>\n",
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" <td>amyman6010</td>\n",
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" <td>Systemstratege:</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>11559 rows × 3 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" user_id handle username\n",
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"0 1000004686156652545 6jannik9 Systemstratege: \n",
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"1 1000043230870867969 lsollik Physiolucy\n",
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"2 1000405847460151296 achim1949hans Systemstratege: \n",
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"3 1000460805719121921 wahrew WahreWorte\n",
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"4 1000744009638252544 derd1ck3 Ⓓ①ⓒⓚ①③ (🏡)\n",
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"... ... ... ...\n",
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"11554 99931264 havok1975 Systemstratege: \n",
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"11555 999542638226403328 madame_de_saxe Systemstratege: \n",
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"11556 999901133282754560 tungstendie74 Systemstratege: \n",
|
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"11557 999904275080794112 _danielheim Systemstratege: \n",
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"11558 999955376454930432 amyman6010 Systemstratege: \n",
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"\n",
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"[11559 rows x 3 columns]"
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]
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},
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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|
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"tweets_meta = pd.read_csv(\"data/tweets.csv\")\n",
|
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"tweets_time = pd.read_csv(\"data/tweets-1679742620302.csv\")\n",
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"tweets_text = pd.read_csv(\"data/tweets-1679742698645.csv\")\n",
|
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"tweets_user = pd.read_csv(\"data/tweets-1679742702794.csv\"\n",
|
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" ).rename(columns = {\"username\":\"handle\", # rename columns\n",
|
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" \"handle\": \"username\"})\n",
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"tweets_user = tweets_user.assign(handle = tweets_user['handle'].str.lower()) # convert handles to lower case\n",
|
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"tweets_combined = pd.merge(tweets_time, # merge the two tweet related data frames\n",
|
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" tweets_text, \n",
|
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" how = 'inner', \n",
|
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" on = 'tweet_id'\n",
|
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" ).drop(['id'], # drop unascessary id column (redundant to index)\n",
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" axis = 1)\n",
|
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"tweets_combined = tweets_combined.assign(measured_at = pd.to_datetime(tweets_combined['measured_at']), # change date to date format\n",
|
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|
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" created_at = pd.to_datetime(tweets_combined['created_at']))\n",
|
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"police_stations = pd.read_csv(\"data/polizei_accounts_geo.csv\", sep = \"\\t\" # addiditional on police stations\n",
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" ).rename(columns = {\"Polizei Account\": \"handle\"})\n",
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"tweets_user"
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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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|
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"id": "91dfb8bb-15dc-4b2c-9c5f-3eab18d78ef8",
|
|||
|
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"metadata": {
|
|||
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|
"tags": []
|
|||
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},
|
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|
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"source": [
|
|||
|
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"### Adjazenzmatrix mentions\n",
|
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" \n",
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"Information, welche nicht direkt enthalten ist: welche Accounts werden erwähnt. Ist nur im Tweet mit @handle gekennzeichnet."
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]
|
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},
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|
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{
|
|||
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"cell_type": "code",
|
|||
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"execution_count": 53,
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|||
|
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"id": "5d8bf730-3c8f-4143-b405-c95f1914f54b",
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"metadata": {
|
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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|
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"0 Auch wir schließen uns dem Apell an! \\n\\n#Ukra...\n",
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"1 @BWeltenbummler Sehr schwer zu sagen. Die Evak...\n",
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"2 Halten Sie durch – die Evakuierung ist fast ab...\n",
|
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"3 Halten Sie durch – die Evakuierung ist fast ab...\n",
|
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|
|
"4 RT @drkberlin_iuk: 🚨 In enger Abstimmung mit d...\n",
|
|||
|
|
"Name: tweet_text, dtype: object"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 53,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# TODO"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "0c242090-0748-488c-b604-f521030f468f",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Metadaten \n",
|
|||
|
|
"\n",
|
|||
|
|
"Welche Daten bilden die Grundlage?"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"id": "0e5eb455-6b12-4572-8f5e-f328a94bd797",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"hashtag 157145\n",
|
|||
|
|
"url 88322\n",
|
|||
|
|
"mention 36815\n",
|
|||
|
|
"Name: entity_type, dtype: int64"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tweets_meta[\"entity_type\"].value_counts()\n",
|
|||
|
|
"# tweets_meta[tweets_meta['entity_type'] == \"mention\"]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "ef440301-cf89-4e80-8801-eb853d636190",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Insgesamt haben wir 84794 einzigartige Tweets:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"id": "5a438e7f-8735-40bb-b450-2ce168f0f67a",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"84794"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tweets_combined[\"tweet_id\"].value_counts().shape[0] # Anzahl an Tweets"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"id": "4f1e8c6c-3610-436e-899e-4d0307259230",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Die Tweets wurden vom 2022-02-24 bis zum: 2023-03-16 gesammelt. Also genau insgesamt: 384 Tage.\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print(\"Die Tweets wurden vom \", tweets_combined['created_at'].min().date(), \"bis zum:\", tweets_combined['created_at'].max().date(), \"gesammelt.\", \"Also genau insgesamt:\", (tweets_combined['created_at'].max() - tweets_combined['created_at'].min()).days, \"Tage.\")\n",
|
|||
|
|
"# tweets_combined[tweets_combined['created_at'] == tweets_combined['created_at'].max()] # Tweets vom letzten Tag"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "d8b47a60-1535-4d03-913a-73e897bc18df",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Welche Polizei Accounts haben am meisten getweetet?"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 43,
|
|||
|
|
"id": "9373552e-6baf-46df-ae16-c63603e20a83",
|
|||
|
|
"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>handle</th>\n",
|
|||
|
|
" <th>count</th>\n",
|
|||
|
|
" <th>Name</th>\n",
|
|||
|
|
" <th>Typ</th>\n",
|
|||
|
|
" <th>Bundesland</th>\n",
|
|||
|
|
" <th>Stadt</th>\n",
|
|||
|
|
" <th>LAT</th>\n",
|
|||
|
|
" <th>LONG</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>11</th>\n",
|
|||
|
|
" <td>polizei_ffm</td>\n",
|
|||
|
|
" <td>2993</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>polizei_nrw_do</td>\n",
|
|||
|
|
" <td>2860</td>\n",
|
|||
|
|
" <td>Polizei NRW DO</td>\n",
|
|||
|
|
" <td>Polizei</td>\n",
|
|||
|
|
" <td>Nordrhein-Westfalen</td>\n",
|
|||
|
|
" <td>Dortmund</td>\n",
|
|||
|
|
" <td>51.5142273</td>\n",
|
|||
|
|
" <td>7.4652789</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>polizeisachsen</td>\n",
|
|||
|
|
" <td>2700</td>\n",
|
|||
|
|
" <td>Polizei Sachsen</td>\n",
|
|||
|
|
" <td>Polizei</td>\n",
|
|||
|
|
" <td>Sachsen</td>\n",
|
|||
|
|
" <td>Dresden</td>\n",
|
|||
|
|
" <td>51.0493286</td>\n",
|
|||
|
|
" <td>13.7381437</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>91</th>\n",
|
|||
|
|
" <td>polizeibb</td>\n",
|
|||
|
|
" <td>2310</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>61</th>\n",
|
|||
|
|
" <td>polizeihamburg</td>\n",
|
|||
|
|
" <td>2093</td>\n",
|
|||
|
|
" <td>Polizei Hamburg</td>\n",
|
|||
|
|
" <td>Polizei</td>\n",
|
|||
|
|
" <td>Hamburg</td>\n",
|
|||
|
|
" <td>Hamburg</td>\n",
|
|||
|
|
" <td>53.550341</td>\n",
|
|||
|
|
" <td>10.000654</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" handle count Name Typ Bundesland \\\n",
|
|||
|
|
"11 polizei_ffm 2993 NaN NaN NaN \n",
|
|||
|
|
"3 polizei_nrw_do 2860 Polizei NRW DO Polizei Nordrhein-Westfalen \n",
|
|||
|
|
"0 polizeisachsen 2700 Polizei Sachsen Polizei Sachsen \n",
|
|||
|
|
"91 polizeibb 2310 NaN NaN NaN \n",
|
|||
|
|
"61 polizeihamburg 2093 Polizei Hamburg Polizei Hamburg \n",
|
|||
|
|
"\n",
|
|||
|
|
" Stadt LAT LONG \n",
|
|||
|
|
"11 NaN NaN NaN \n",
|
|||
|
|
"3 Dortmund 51.5142273 7.4652789 \n",
|
|||
|
|
"0 Dresden 51.0493286 13.7381437 \n",
|
|||
|
|
"91 NaN NaN NaN \n",
|
|||
|
|
"61 Hamburg 53.550341 10.000654 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 43,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tweets_agg = tweets_combined.merge(tweets_user,\n",
|
|||
|
|
" on = \"user_id\"\n",
|
|||
|
|
" ).groupby(by = [\"user_id\", \"handle\", \"username\"]\n",
|
|||
|
|
" )[\"user_id\"].aggregate(['count']\n",
|
|||
|
|
" ).merge(police_stations, \n",
|
|||
|
|
" on = \"handle\",\n",
|
|||
|
|
" how = \"left\"\n",
|
|||
|
|
" ).sort_values(['count'], \n",
|
|||
|
|
" ascending=False)\n",
|
|||
|
|
"tweets_agg.shape\n",
|
|||
|
|
"activy_police_vis = tweets_agg[0:50]\n",
|
|||
|
|
"activy_police_vis.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "9cf5f544-706b-41af-b785-7023f04e3ecb",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Visualisierung aktivste Polizeistadtionen:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 47,
|
|||
|
|
"id": "b1c39196-d1cc-4f82-8e01-7529e7b3046f",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
"<div id=\"altair-viz-a660bd38b72240eaae654b5e471932a6\"></div>\n",
|
|||
|
|
"<script type=\"text/javascript\">\n",
|
|||
|
|
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
|
|||
|
|
" (function(spec, embedOpt){\n",
|
|||
|
|
" let outputDiv = document.currentScript.previousElementSibling;\n",
|
|||
|
|
" if (outputDiv.id !== \"altair-viz-a660bd38b72240eaae654b5e471932a6\") {\n",
|
|||
|
|
" outputDiv = document.getElementById(\"altair-viz-a660bd38b72240eaae654b5e471932a6\");\n",
|
|||
|
|
" }\n",
|
|||
|
|
" const paths = {\n",
|
|||
|
|
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
|
|||
|
|
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
|
|||
|
|
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
|
|||
|
|
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
|
|||
|
|
" };\n",
|
|||
|
|
"\n",
|
|||
|
|
" function maybeLoadScript(lib, version) {\n",
|
|||
|
|
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
|
|||
|
|
" return (VEGA_DEBUG[key] == version) ?\n",
|
|||
|
|
" Promise.resolve(paths[lib]) :\n",
|
|||
|
|
" new Promise(function(resolve, reject) {\n",
|
|||
|
|
" var s = document.createElement('script');\n",
|
|||
|
|
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
|
|||
|
|
" s.async = true;\n",
|
|||
|
|
" s.onload = () => {\n",
|
|||
|
|
" VEGA_DEBUG[key] = version;\n",
|
|||
|
|
" return resolve(paths[lib]);\n",
|
|||
|
|
" };\n",
|
|||
|
|
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
|
|||
|
|
" s.src = paths[lib];\n",
|
|||
|
|
" });\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" function showError(err) {\n",
|
|||
|
|
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
|
|||
|
|
" throw err;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" function displayChart(vegaEmbed) {\n",
|
|||
|
|
" vegaEmbed(outputDiv, spec, embedOpt)\n",
|
|||
|
|
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" if(typeof define === \"function\" && define.amd) {\n",
|
|||
|
|
" requirejs.config({paths});\n",
|
|||
|
|
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
|
|||
|
|
" } else {\n",
|
|||
|
|
" maybeLoadScript(\"vega\", \"5\")\n",
|
|||
|
|
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
|
|||
|
|
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
|
|||
|
|
" .catch(showError)\n",
|
|||
|
|
" .then(() => displayChart(vegaEmbed));\n",
|
|||
|
|
" }\n",
|
|||
|
|
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-da2bacd5b3a57271f77be4dc435a345f\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"count\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"handle\", \"sort\": \"-x\", \"type\": \"nominal\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-da2bacd5b3a57271f77be4dc435a345f\": [{\"handle\": \"polizei_ffm\", \"count\": 2993, \"Name\": null, \"Typ\": null, \"Bundesland\": null, \"Stadt\": null, \"LAT\": null, \"LONG\": null}, {\"handle\": \"polizei_nrw_do\", \"count\": 2860, \"Name\": \"Polizei NRW DO\", \"Typ\": \"Polizei\", \"Bundesland\": \"Nordrhein-Westfalen\", \"Stadt\": \"Dortmund\", \"LAT\": \"51.5142273\", \"LONG\": \"7.4652789\"}, {\"handle\": \"polizeisachsen\", \"count\": 2700, \"Name\": \"Polizei Sachsen\", \"Typ\": \"Polizei\", \"Bundesland\": \"Sachsen\", \"Stadt\": \"Dresden\", \"LAT\": \"51.0493286\", \"LONG\": \"13.7381437\"}, {\"handle\": \"polizeibb\", \"count\": 2310, \"Name\": null, \"Typ\": null, \"Bundesland\": null, \"Stadt\": null, \"LAT\": null, \"LONG\": null}, {\"handle\": \"polizeihamburg\", \"count\": 2093, \"Name\": \"Polizei Hamburg\", \"Typ\": \"Polizei\", \"Bundesland\": \"Hamburg\", \"Stadt\": \"Hamburg\", \"LAT\": \"53.550341\", \"LONG\": \"10.000654\"}, {\"handle\": \"polizeimuenchen\", \"count\": 2021, \"Name\": \"Polizei M\\u00fcnchen\", \"Typ\": \"Polizei\", \"Bundesland\": \"Bayern\", \"Stadt\": \"M\\u00fcnchen\", \"LAT\": \"48.135125\", \"LONG\": \"11.581981\"}, {\"handle\": \"polizeimfr\", \"count\": 1892, \"Name\": \"Polizei Mittelfranken\", \"Typ\": \"Polizei\", \"Bundesland\": \"Bayern\", \"Stadt\": \"N\\u00fcrnberg\", \"LAT\": \"49.453872\", \"LONG\": \"11.077298\"}, {\"handle\": \"polizeimannheim\", \"count\": 1835, \"Name\": \"Polizei Mannheim\", \"Typ\": \"Polizei\", \"Bundesland\": \"Baden-W\\u00fcrttemberg\", \"Stadt\": \"Mannheim\", \"LAT\": \"49.4892913\", \"LONG\": \"8.4673098\"}, {\"handle\": \"polizei_nrw_bi\", \"count\": 1794, \"Name\": \"Polizei NRW BI\", \"Typ\": \"Polizei\", \"Bundesland\": \"Nordrhein-Westfalen\", \"Stadt\": \"Bielefeld\", \"LAT\": \"52.0191005\", \"LONG\": \"8.531007\"}, {\"handle\": \"polizei_nrw_k\", \"count\": 1540, \"Name\": \"Polizei NRW K\", \"Typ\": \"Polizei\", \"Bundesland\": \"Nordrhein-Westfalen\", \"Stadt\": \"K\\u00f6ln\", \"LAT\": \"50.938361\", \"LONG\": \"6.959974\"}, {\"handle\": \"bremenpolizei\", \"count\": 1417, \"Name\": null, \"Typ\": null, \"Bundesland\": null, \"Stadt\": null, \"LAT\": null, \"LONG\": null}, {\"handle\": \"polizei_kl\", \"count\": 1380, \"Name\": \"Polizei Kaiserslautern\", \"Typ\": \"Polizei\", \"Bundesland\": \"Rheinland-Pfalz\", \"Stadt\": \"Kaiserslautern\", \"LAT\": \"49.4432174\", \"LONG\": \"7.7689951\"}, {\"handle\": \"polizei_md\", \"count\": 1365, \"Name\": \"Polizei Magdeburg\", \"Typ\": \"Polizei\", \"Bundesland\": \"Sachsen-Anhalt\", \"Stadt\": \"Magdeburg\", \"LAT\": \"52.1315889\", \"LONG\": \"11.6399609\"}, {\"handle\": \"polizei_ka\", \"count\": 1356, \"Name\": \"Polizei Karlsruhe\", \"Typ\": \"Polizei\", \"Bundesland\": \"Baden-W\\u00fcrttemberg\", \"Stadt\": \"Karlsruhe\", \"LAT\": \"49.0068705\", \"LONG\": \"8.4034195\"}, {\"handle\": \"polizeiberlin\", \"count\": 1351, \"Name\": null, \"Typ\": null, \"Bundesland\": null, \"Stadt\": null, \"LAT\": null, \"LONG\": null}]}}, {\"mode\": \"vega-lite\"});\n",
|
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|
"</script>"
|
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],
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"execution_count": 47,
|
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}
|
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],
|
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"source": [
|
|||
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|
"barchart = alt.Chart(activy_police_vis[0:15]).mark_bar().encode(\n",
|
|||
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|
" x = 'count:Q',\n",
|
|||
|
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" y = alt.Y('handle:N', sort = '-x'),\n",
|
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")\n",
|
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"barchart "
|
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|
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},
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{
|
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"cell_type": "markdown",
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"id": "90f686ff-93c6-44d9-9761-feb35dfe9d1d",
|
|||
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"metadata": {
|
|||
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"tags": []
|
|||
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},
|
|||
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"source": [
|
|||
|
|
"Welche Tweets ziehen besonders viel Aufmerksamkeit auf sich?"
|
|||
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]
|
|||
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},
|
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{
|
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"cell_type": "code",
|
|||
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"execution_count": 90,
|
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"id": "d0549250-b11f-4762-8500-1134c53303b4",
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|
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"tags": []
|
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{
|
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"data": {
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"text/plain": [
|
|||
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"0 Die Gewalt, die unsere Kolleginnen & Kollegen in der Silvesternacht erleben mussten, ist une...\n",
|
|||
|
|
"1 WICHTIGE Info:\\nÜber das Internet wird derzeit ein Video verbreitet, in dem von einem Überfall a...\n",
|
|||
|
|
"2 Die Experten gehen derzeit davon aus, dass es sich um ein absichtliches \"Fake-Video\" handelt, da...\n",
|
|||
|
|
"3 Auf unserem #A45 in #lichterfelde) befindet sich gerade diese Fundhündin. Sie wurde am Hindenbur...\n",
|
|||
|
|
"4 @nexta_tv Wir haben das Video gesichert und leiten den Sachverhalt an die zuständigen Kolleginne...\n",
|
|||
|
|
" ... \n",
|
|||
|
|
"84789 #Polizeimeldungen #Tagesticker\\n \\nAnhalt-Bitterfeld\\nhttps://t.co/tNLEzztL1o\\n \\nDessau-Roßlau\\...\n",
|
|||
|
|
"84790 Am Mittwoch erhielten wir mehrere Anrufe über einen auffälligen Pkw-Fahrer (Reifen quietschen un...\n",
|
|||
|
|
"84791 @Jonas5Luisa Kleiner Pro-Tipp von uns: Einfach mal auf den link klicken! ;)*cl\n",
|
|||
|
|
"84792 Vermisstensuche nach 27-Jährigem aus Bendorf-Mühlhofen: Wer hat Tobias Wißmann gesehen? Ein Foto...\n",
|
|||
|
|
"84793 #PolizeiNRW #Köln #Leverkusen : XXX - Infos unter https://t.co/SeWShP2tZE https://t.co/Kopy7w8W3B\n",
|
|||
|
|
"Name: tweet_text, Length: 84794, dtype: object"
|
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|
|
]
|
|||
|
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},
|
|||
|
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"execution_count": 90,
|
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|
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|
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|
|
}
|
|||
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|
],
|
|||
|
|
"source": [
|
|||
|
|
"tweets_attention = tweets_combined.merge(tweets_user,\n",
|
|||
|
|
" on = \"user_id\",\n",
|
|||
|
|
" how = \"left\"\n",
|
|||
|
|
" ).merge(police_stations,\n",
|
|||
|
|
" on = \"handle\",\n",
|
|||
|
|
" how = \"left\")\n",
|
|||
|
|
"pd.options.display.max_colwidth = 100\n",
|
|||
|
|
"tweets_attention.sort_values('like_count', ascending = False).reset_index()['tweet_text']\n",
|
|||
|
|
"\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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|||
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|||
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|||
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|
|||
|
|
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|||
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|
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|
|||
|
|
"old = pd.read_csv(\"data/user_old.tsv\",sep = \"\\t\").rename(columns = {\"id\":\"user_id\",\"name\": \"user_name\"} )\n",
|
|||
|
|
"new = pd.read_csv(\"data/tweets-1679742702794.csv\").rename(columns = {\"username\":\"handle\", \"handle\": \"user_name\"})\n",
|
|||
|
|
"new"
|
|||
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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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|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>151685</th>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>151686</th>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>151687</th>\n",
|
|||
|
|
" <td>6</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>151688</th>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>151689</th>\n",
|
|||
|
|
" <td>10</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>151690 rows × 4 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" like_count retweet_count reply_count quote_count\n",
|
|||
|
|
"0 2 1 2 0\n",
|
|||
|
|
"1 2 0 0 0\n",
|
|||
|
|
"2 19 3 3 0\n",
|
|||
|
|
"3 0 0 0 0\n",
|
|||
|
|
"4 2 0 0 0\n",
|
|||
|
|
"... ... ... ... ...\n",
|
|||
|
|
"151685 5 1 1 0\n",
|
|||
|
|
"151686 2 0 0 0\n",
|
|||
|
|
"151687 6 0 0 0\n",
|
|||
|
|
"151688 2 0 0 0\n",
|
|||
|
|
"151689 10 1 0 0\n",
|
|||
|
|
"\n",
|
|||
|
|
"[151690 rows x 4 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 148,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 142,
|
|||
|
|
"id": "dac4e5fc-22ca-466d-bc3c-586e68696d03",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"like_count\n",
|
|||
|
|
"False 147573\n",
|
|||
|
|
"True 4117\n",
|
|||
|
|
"dtype: int64"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 142,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "python-scientific kernel",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python-scientific"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.10.9"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|