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Add notebook
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‎Outbreaks_Headlines_1.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_fwf('data/headlines.txt')\n",
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"df.columns = ['headline']"
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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": 29,
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"metadata": {},
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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>headline</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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" <td>0</td>\n",
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" <td>Could Zika Reach New York City?</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <td>First Case of Zika in Miami Beach</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2</td>\n",
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" <td>Mystery Virus Spreads in Recife, Brazil</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>3</td>\n",
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" <td>Dallas man comes down with case of Zika</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>4</td>\n",
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" <td>Trinidad confirms first Zika case</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" headline\n",
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"0 Could Zika Reach New York City?\n",
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"1 First Case of Zika in Miami Beach\n",
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"2 Mystery Virus Spreads in Recife, Brazil\n",
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"3 Dallas man comes down with case of Zika\n",
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"4 Trinidad confirms first Zika case"
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]
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},
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"execution_count": 29,
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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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"df.head()"
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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": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"import geonamescache\n",
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"\n",
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"gc = geonamescache.GeonamesCache()\n",
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"countries = gc.get_countries()\n",
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"cities = gc.get_cities()"
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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": 58,
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"metadata": {},
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"outputs": [],
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"source": [
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"country_names = []\n",
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"country_ids = list(countries.keys())\n",
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"for country_id in country_ids:\n",
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" country_names.append(countries[country_id]['name'])"
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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": 60,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"252"
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]
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},
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"execution_count": 60,
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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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"len(country_names)"
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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": 73,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Andorra',\n",
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" 'United Arab Emirates',\n",
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" 'Afghanistan',\n",
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" 'Antigua and Barbuda',\n",
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" 'Anguilla',\n",
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" 'Albania',\n",
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" 'Armenia',\n",
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" 'Angola',\n",
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" 'Antarctica',\n",
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" 'Argentina',\n",
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" 'American Samoa',\n",
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" 'Austria',\n",
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" 'Australia',\n",
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" 'Aruba',\n",
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" 'Aland Islands',\n",
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" 'Azerbaijan',\n",
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" 'Bosnia and Herzegovina',\n",
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" 'Barbados',\n",
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" 'Bangladesh',\n",
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" 'Belgium']"
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]
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},
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"execution_count": 73,
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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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"country_names[:20]"
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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": 68,
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"metadata": {},
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"outputs": [],
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"source": [
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"city_names = []\n",
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"city_ids = list(cities.keys())\n",
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"for city_id in city_ids:\n",
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" city_names.append(cities[city_id]['name'])"
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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": 70,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"24336"
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]
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},
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"execution_count": 70,
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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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"len(city_names)"
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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": 94,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Andorra la Vella',\n",
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" 'Umm Al Quwain City',\n",
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" 'Ras Al Khaimah City',\n",
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" 'Zayed City',\n",
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" 'Khawr Fakkān',\n",
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" 'Dubai',\n",
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" 'Dibba Al-Fujairah',\n",
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" 'Dibba Al-Hisn',\n",
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" 'Sharjah',\n",
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" 'Ar Ruways',\n",
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" 'Al Fujairah City',\n",
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" 'Al Ain City',\n",
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" 'Ajman City',\n",
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" 'Adh Dhayd',\n",
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" 'Abu Dhabi',\n",
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" 'Khalifah A City',\n",
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" 'Bani Yas City',\n",
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" 'Musaffah',\n",
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" 'Al Shamkhah City',\n",
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" 'Reef Al Fujairah City']"
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]
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},
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"execution_count": 94,
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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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"city_names[:20]"
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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": 89,
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"metadata": {},
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"outputs": [],
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"source": [
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"from unidecode import unidecode\n",
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"\n",
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"city_names_unidecoded = [unidecode(city) for city in city_names]"
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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": 93,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Andorra la Vella',\n",
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" 'Umm Al Quwain City',\n",
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" 'Ras Al Khaimah City',\n",
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" 'Zayed City',\n",
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" 'Khawr Fakkan',\n",
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" 'Dubai',\n",
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" 'Dibba Al-Fujairah',\n",
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" 'Dibba Al-Hisn',\n",
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" 'Sharjah',\n",
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" 'Ar Ruways',\n",
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" 'Al Fujairah City',\n",
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" 'Al Ain City',\n",
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" 'Ajman City',\n",
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" 'Adh Dhayd',\n",
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" 'Abu Dhabi',\n",
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" 'Khalifah A City',\n",
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" 'Bani Yas City',\n",
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" 'Musaffah',\n",
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" 'Al Shamkhah City',\n",
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" 'Reef Al Fujairah City']"
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]
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},
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"execution_count": 93,
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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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"city_names_unidecoded[:20]"
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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": 104,
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"metadata": {},
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"outputs": [],
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"source": [
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"pattern_country = '|'.join(country_names)\n",
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"pattern_city = '|'.join(city_names)\n",
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"\n",
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"def pattern_searcher(search_str:str, search_list:str):\n",
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" search_obj = re.search(search_list, search_str)\n",
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" if search_obj :\n",
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" return_str = search_str[search_obj.start(): search_obj.end()]\n",
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" else:\n",
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" return_str = 'NA'\n",
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" return return_str\n",
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"\n",
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"df['country'] = df['headline'].apply(lambda x: pattern_searcher(search_str=x, search_list=pattern_country))\n",
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"df['city'] = df['headline'].apply(lambda x: pattern_searcher(search_str=x, search_list=pattern_city))"
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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": 107,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df.replace('NA', np.nan)"
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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": 108,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"headline 0\n",
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"country 633\n",
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"city 40\n",
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"dtype: int64"
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]
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},
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"execution_count": 108,
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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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"df.isnull().sum()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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