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Created May 25, 2020 07:46
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CapstoneWeek1Project.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Alcoholic spaces Scanner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this project I have pointed out the places in Bucharest that are alcoholic or not for teenagers less than 18 years old.\n",
"\n",
"There are many parents who want their children to be safe and practice the rules and regulations of the country. they should not be drinking before the legal age of the country. In this project I have made an analysis about various places that are suitable for the youth as well as the drinking sites.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"Data was available at wikipedia for Bucharest\n",
"\n",
"Links are:\n",
"\n",
"- \"https://en.wikipedia.org/wiki/Category:Districts_of_Bucharest\"\n",
"\n",
"- \"https://en.wikipedia.org/wiki/Sectors_of_Bucharest\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installing libraries and packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: geopy in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (1.22.0)\n",
"Requirement already satisfied: geographiclib<2,>=1.49 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geopy) (1.50)\n",
"Requirement already satisfied: folium in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (0.5.0)\n",
"Requirement already satisfied: requests in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from folium) (2.23.0)\n",
"Requirement already satisfied: six in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from folium) (1.14.0)\n",
"Requirement already satisfied: branca in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from folium) (0.4.1)\n",
"Requirement already satisfied: jinja2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from folium) (2.11.2)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->folium) (2020.4.5.1)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->folium) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->folium) (1.25.9)\n",
"Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->folium) (2.9)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from jinja2->folium) (1.1.1)\n",
"Requirement already satisfied: geocoder in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (1.38.1)\n",
"Requirement already satisfied: click in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (7.1.2)\n",
"Requirement already satisfied: requests in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (2.23.0)\n",
"Requirement already satisfied: six in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (1.14.0)\n",
"Requirement already satisfied: ratelim in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (0.1.6)\n",
"Requirement already satisfied: future in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from geocoder) (0.18.2)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (1.25.9)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (2020.4.5.1)\n",
"Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->geocoder) (2.9)\n",
"Requirement already satisfied: decorator in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from ratelim->geocoder) (4.4.2)\n"
]
}
],
"source": [
"#install libraries \n",
"!pip install geopy \n",
"!pip install folium \n",
"!pip install geocoder"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: lxml in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (4.5.1)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install lxml"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: BeautifulSoup4 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (4.9.1)\n",
"Requirement already satisfied: soupsieve>1.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from BeautifulSoup4) (2.0.1)\n"
]
}
],
"source": [
"!pip install BeautifulSoup4"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Libraries imported.\n"
]
}
],
"source": [
"#import libraries \n",
"import numpy as np # library to handle data in a vectorized manner\n",
"\n",
"import pandas as pd # library for data analsysis\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', None)\n",
"\n",
"import matplotlib.pyplot as plt # for graphical usage \n",
"\n",
"import json # library to handle JSON files\n",
"\n",
"from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
"\n",
"from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
"import geocoder # to get coordinates\n",
"\n",
"import requests # library to handle requests\n",
"from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
"\n",
"# Matplotlib and associated plotting modules\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"\n",
"# import k-means from clustering stage\n",
"from sklearn.cluster import KMeans\n",
"\n",
"import folium # map rendering library\n",
"from folium import plugins\n",
"from folium.plugins import HeatMap\n",
"\n",
"# main documentation page: http://beautiful-soup-4.readthedocs.io/en/latest/\n",
"# how to use the BeautifulSoup package: https://www.youtube.com/watch?v=ng2o98k983k video\n",
"from bs4 import BeautifulSoup \n",
"import pandas as pd\n",
"import requests\n",
"\n",
"print('Libraries imported.')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The geograpical coordinate of Bucharest, Romania are 44.4361414, 26.1027202.\n"
]
}
],
"source": [
"# get coordinates of Bucharest\n",
"bucharest_address = 'Bucharest, Romania'\n",
"\n",
"geolocator = Nominatim(user_agent=\"bucharest1_explorer\")\n",
"location = geolocator.geocode(bucharest_address)\n",
"latitude = location.latitude\n",
"longitude = location.longitude\n",
"bucharest_center = [latitude, longitude ]\n",
"print('The geograpical coordinate of {} are {}, {}.'.format(bucharest_address, latitude, longitude))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 40 neighborhoods in Bucharest, Romania\n"
]
}
],
"source": [
"# Read Bucharest neighborhood data \n",
"url = \"https://en.wikipedia.org/wiki/Category:Districts of Bucharest\"\n",
"source = requests.get(url).text\n",
"soup = BeautifulSoup(source,'lxml')\n",
"\n",
"neighborhoodList = []\n",
"\n",
"# append the data into the list\n",
"for row in soup.find_all(\"div\", class_=\"mw-category\")[0].findAll(\"li\"):\n",
" neighborhoodList.append(row.text.replace(', Bucharest',''))\n",
" \n",
"df_neighborhood = pd.DataFrame({\"Neighborhood\": neighborhoodList})\n",
"print(\"There are {} neighborhoods in {}\".format(df_neighborhood.shape[0], bucharest_address))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 6 Sector in Bucharest, Romania\n"
]
},
{
"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>Sector</th>\n",
" <th>Neigborhoods</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Sector 1</td>\n",
" <td>Dorobanți, Băneasa, Aviației, Pipera, Aviator...</td>\n",
" <td>225,454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Sector 2</td>\n",
" <td>Pantelimon, Colentina, Iancului, Tei, Floreas...</td>\n",
" <td>345,370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sector 3</td>\n",
" <td>Vitan, Dudești, Titan, Centrul Civic, Balta A...</td>\n",
" <td>385,439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sector 4</td>\n",
" <td>Berceni, Olteniței, Văcărești, Timpuri Noi, T...</td>\n",
" <td>287,828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sector 5</td>\n",
" <td>Rahova, Ferentari, Giurgiului, Cotroceni, 13 ...</td>\n",
" <td>271,575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Sector 6</td>\n",
" <td>Giulești, Crângași, Drumul Taberei, Militari,...</td>\n",
" <td>367,760</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sector Neigborhoods Population\n",
"0 Sector 1 Dorobanți, Băneasa, Aviației, Pipera, Aviator... 225,454\n",
"1 Sector 2 Pantelimon, Colentina, Iancului, Tei, Floreas... 345,370\n",
"2 Sector 3 Vitan, Dudești, Titan, Centrul Civic, Balta A... 385,439\n",
"3 Sector 4 Berceni, Olteniței, Văcărești, Timpuri Noi, T... 287,828\n",
"4 Sector 5 Rahova, Ferentari, Giurgiului, Cotroceni, 13 ... 271,575\n",
"5 Sector 6 Giulești, Crângași, Drumul Taberei, Militari,... 367,760"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read Bucharest sector data from wikipedia\n",
"url = \"https://en.wikipedia.org/wiki/Sectors_of_Bucharest\"\n",
"source = requests.get(url).text\n",
"soup = BeautifulSoup(source,'lxml')\n",
"\n",
"sectorPopList = []\n",
"sectorPopulationList = []\n",
"\n",
"for row in soup.find_all(\"tbody\"):\n",
" header = str(row.findAll(\"th\"))\n",
" if \"Population (October 2011)\" in header:\n",
" i = 0\n",
" for td in row.find_all(\"td\"):\n",
" i+=1\n",
" if i==2: \n",
" sectorPopList.append(td.text.replace(\"\\n\",\"\"))\n",
" if i==3: \n",
" sectorPopulationList.append(td.text.replace(\"\\n\",\"\")) \n",
" i=0\n",
"\n",
"df_sectorPop = pd.DataFrame({\"Sector\": sectorPopList, \"Population\": sectorPopulationList})\n",
"\n",
"sectorNeigList =[]\n",
"sectorNeigborList =[]\n",
"\n",
"for row in soup.find_all(\"ul\"):\n",
" if sectorPopList[0] in row.text:\n",
" for s in row.text.split(\"\\n\"):\n",
" sectorNeigList.append(s.split(\":\")[0])\n",
" sectorNeigborList.append(s.split(\":\")[1])\n",
" \n",
"df_sector= pd.DataFrame({\"Sector\": sectorNeigList, \"Neigborhoods\": sectorNeigborList}).merge(df_sectorPop,on='Sector' )\n",
"\n",
"print(\"There are {} Sector in {}\".format(df_sector.shape[0], bucharest_address))\n",
"df_sector"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>Neighborhood</th>\n",
" <th>Sector</th>\n",
" <th>SectorPopulation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aviației</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Băneasa</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Berceni</td>\n",
" <td>Sector 4</td>\n",
" <td>287,828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bucureștii Noi</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Centrul Civic</td>\n",
" <td>Sector 3</td>\n",
" <td>385,439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Sector SectorPopulation\n",
"0 Aviației Sector 1 225,454\n",
"1 Băneasa Sector 1 225,454\n",
"2 Berceni Sector 4 287,828\n",
"3 Bucureștii Noi Sector 1 225,454\n",
"4 Centrul Civic Sector 3 385,439"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def getSector(row):\n",
" for i in range(df_sector.shape[0]):\n",
" if row[\"Neighborhood\"] in df_sector.iloc[i].Neigborhoods:\n",
" return pd.Series([df_sector.iloc[i].Sector, df_sector.iloc[i].Population], index = ['Sector','SectorPopulation'])\n",
"\n",
"df_neighborhood[[\"Sector\",\"SectorPopulation\"]] =df_neighborhood.apply(getSector, axis=1)\n",
"df_neighborhood.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Geographical coordinates of five neighborhoods are as below\n"
]
},
{
"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>Neighborhood</th>\n",
" <th>Sector</th>\n",
" <th>SectorPopulation</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aviației</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Băneasa</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" <td>44.494012</td>\n",
" <td>26.080358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Berceni</td>\n",
" <td>Sector 4</td>\n",
" <td>287,828</td>\n",
" <td>44.386430</td>\n",
" <td>26.128490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bucureștii Noi</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" <td>44.480413</td>\n",
" <td>26.042807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Centrul Civic</td>\n",
" <td>Sector 3</td>\n",
" <td>385,439</td>\n",
" <td>44.434300</td>\n",
" <td>26.094660</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Sector SectorPopulation Latitude Longitude\n",
"0 Aviației Sector 1 225,454 44.485790 26.101219\n",
"1 Băneasa Sector 1 225,454 44.494012 26.080358\n",
"2 Berceni Sector 4 287,828 44.386430 26.128490\n",
"3 Bucureștii Noi Sector 1 225,454 44.480413 26.042807\n",
"4 Centrul Civic Sector 3 385,439 44.434300 26.094660"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# define a function to get coordinates\n",
"def get_latlng(neighborhood):\n",
" # initialize your variable to None\n",
" lat_lng_coords = None\n",
" # loop until you get the coordinates\n",
" while(lat_lng_coords is None):\n",
" g = geocoder.arcgis('{}, {}'.format(neighborhood,bucharest_address))\n",
" lat_lng_coords = g.latlng\n",
" return lat_lng_coords\n",
"\n",
"coords = [ get_latlng(neighborhood) for neighborhood in df_neighborhood[\"Neighborhood\"].tolist() ]\n",
"\n",
"df_coords = pd.DataFrame(coords, columns=['Latitude', 'Longitude'])\n",
"\n",
"# merge the coordinates into the original dataframe\n",
"df_neighborhood['Latitude'] = df_coords['Latitude']\n",
"df_neighborhood['Longitude'] = df_coords['Longitude']\n",
"print(\"Geographical coordinates of five neighborhoods are as below\")\n",
"df_neighborhood.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fc585987cc0>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#create map of Bucharest neighborhoods using latitude and longitude values\n",
"map_bucharest= folium.Map(location=[latitude, longitude], zoom_start=11)\n",
"\n",
"# add markers to map\n",
"for lat, lng, neighborhood in zip(df_neighborhood['Latitude'], df_neighborhood['Longitude'], df_neighborhood['Neighborhood']):\n",
" label = '{}'.format(neighborhood)\n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7,\n",
" parse_html=False).add_to(map_bucharest) \n",
" \n",
"map_bucharest"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# @hidden_cell\n",
" CLIENT_ID=\"Q5SAY0XAYHJ2KXJ3AIRKQJ2IPWSWFCPR5FOA5OFWCBE2DEUF\"\n",
" CLIENT_SECRET=\"HK1XAI1VDMPCZWIXOUIUPVJ0ECGRR1OJTE0MEKZIBVRH5PGW\"\n",
" VERSION=\"20180605\"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 224 unique venue categories. Some of them are as below:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\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>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>VenueName</th>\n",
" <th>VenueId</th>\n",
" <th>VenueLatitude</th>\n",
" <th>VenueLongitude</th>\n",
" <th>VenueDistance</th>\n",
" <th>VenueCategory</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aviației</td>\n",
" <td>44.48579</td>\n",
" <td>26.101219</td>\n",
" <td>ibis Styles Bucharest Erbas</td>\n",
" <td>5bbb782175dcb7002cc15ee7</td>\n",
" <td>44.483963</td>\n",
" <td>26.097134</td>\n",
" <td>382</td>\n",
" <td>Hotel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Aviației</td>\n",
" <td>44.48579</td>\n",
" <td>26.101219</td>\n",
" <td>LIDL</td>\n",
" <td>583956246d349d0574eb02ac</td>\n",
" <td>44.488396</td>\n",
" <td>26.094375</td>\n",
" <td>616</td>\n",
" <td>Supermarket</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Aviației</td>\n",
" <td>44.48579</td>\n",
" <td>26.101219</td>\n",
" <td>Mega Image Concept Store</td>\n",
" <td>56348b62498e53f51a0a4e0e</td>\n",
" <td>44.479783</td>\n",
" <td>26.102568</td>\n",
" <td>677</td>\n",
" <td>Supermarket</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Aviației</td>\n",
" <td>44.48579</td>\n",
" <td>26.101219</td>\n",
" <td>Starbucks</td>\n",
" <td>525fd077498eed1c5a52c1d6</td>\n",
" <td>44.478522</td>\n",
" <td>26.102503</td>\n",
" <td>815</td>\n",
" <td>Coffee Shop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Aviației</td>\n",
" <td>44.48579</td>\n",
" <td>26.101219</td>\n",
" <td>Flying Pig</td>\n",
" <td>58a2fc95d0bb3e516a2363b7</td>\n",
" <td>44.479454</td>\n",
" <td>26.102837</td>\n",
" <td>716</td>\n",
" <td>Burger Joint</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Latitude Longitude VenueName \\\n",
"0 Aviației 44.48579 26.101219 ibis Styles Bucharest Erbas \n",
"1 Aviației 44.48579 26.101219 LIDL \n",
"2 Aviației 44.48579 26.101219 Mega Image Concept Store \n",
"3 Aviației 44.48579 26.101219 Starbucks \n",
"4 Aviației 44.48579 26.101219 Flying Pig \n",
"\n",
" VenueId VenueLatitude VenueLongitude VenueDistance \\\n",
"0 5bbb782175dcb7002cc15ee7 44.483963 26.097134 382 \n",
"1 583956246d349d0574eb02ac 44.488396 26.094375 616 \n",
"2 56348b62498e53f51a0a4e0e 44.479783 26.102568 677 \n",
"3 525fd077498eed1c5a52c1d6 44.478522 26.102503 815 \n",
"4 58a2fc95d0bb3e516a2363b7 44.479454 26.102837 716 \n",
"\n",
" VenueCategory \n",
"0 Hotel \n",
"1 Supermarket \n",
"2 Supermarket \n",
"3 Coffee Shop \n",
"4 Burger Joint "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"LIMIT = 100\n",
"\n",
"def getNeighborhoodVenues( latitude, longitude,neighborhood, radius=1000 ):\n",
" venues = []\n",
" for lat, long, neighborhood in zip(latitude, longitude ,neighborhood):\n",
"\n",
" # create the API request URL\n",
" url = \"https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}\".format(\n",
" CLIENT_ID,\n",
" CLIENT_SECRET,\n",
" VERSION,\n",
" lat,\n",
" long,\n",
" radius, \n",
" LIMIT)\n",
"\n",
" # make the GET request\n",
" results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
"\n",
" # return only relevant information for each nearby venue\n",
" for venue in results:\n",
" venues.append((\n",
" neighborhood,\n",
" lat, \n",
" long, \n",
" venue['venue']['name'], \n",
" venue['venue']['id'], \n",
" venue['venue']['location']['lat'], \n",
" venue['venue']['location']['lng'], \n",
" venue['venue']['location']['distance'], \n",
" venue['venue']['categories'][0]['name']))\n",
" \n",
" # convert the venues list into a DataFrame\n",
" venues = pd.DataFrame(venues)\n",
" # define the column names\n",
" venues.columns = ['Neighborhood', 'Latitude', 'Longitude', 'VenueName', 'VenueId', 'VenueLatitude', 'VenueLongitude','VenueDistance','VenueCategory']\n",
" \n",
" return venues \n",
"\n",
"neighborhood_venues = getNeighborhoodVenues (df_neighborhood['Latitude'], df_neighborhood['Longitude'], df_neighborhood['Neighborhood'] )\n",
"\n",
"print('There are {} unique venue categories. Some of them are as below:'.format(len(neighborhood_venues['VenueCategory'].unique())))\n",
"neighborhood_venues.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 10 distinct venue counts are as below\n"
]
},
{
"data": {
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" <td>65</td>\n",
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" <th>Italian Restaurant</th>\n",
" <td>56</td>\n",
" </tr>\n",
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" <th>Pizza Place</th>\n",
" <td>52</td>\n",
" </tr>\n",
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" <th>Supermarket</th>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gym</th>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hotel</th>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Romanian Restaurant</th>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gym / Fitness Center</th>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burger Joint</th>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Park</th>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Clothing Store</th>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bakery</th>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pub</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dessert Shop</th>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lounge</th>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bistro</th>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fried Chicken Joint</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eastern European Restaurant</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bus Station</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pharmacy</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Plaza</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Grocery Store</th>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cosmetics Shop</th>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sandwich Place</th>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Farmers Market</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Department Store</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electronics Store</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bar</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fast Food Restaurant</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chinese Restaurant</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Salon / Barbershop</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cupcake Shop</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Doner Restaurant</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Furniture / Home Store</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanese Restaurant</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bookstore</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Steakhouse</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sushi Restaurant</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ice Cream Shop</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pool</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkish Restaurant</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nightclub</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mediterranean Restaurant</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Spa</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beer Garden</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hostel</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tea Room</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wine Bar</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mobile Phone Shop</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soccer Field</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Shopping Mall</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Theater</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Seafood Restaurant</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greek Restaurant</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Casino</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Health &amp; Beauty Service</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Light Rail Station</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cocktail Bar</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sporting Goods Shop</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Snack Place</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Roof Deck</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bagel Shop</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gastropub</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Salad Place</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Multiplex</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Garden</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>French Restaurant</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>American Restaurant</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Diner</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asian Restaurant</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vietnamese Restaurant</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tram Station</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Metro Station</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>History Museum</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Modern European Restaurant</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>German Restaurant</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Market</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Accessories Store</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hardware Store</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Historic Site</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Women's Store</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Shoe Store</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chocolate Shop</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Art Gallery</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Art Museum</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wine Shop</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Toy / Game Store</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tennis Court</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beer Bar</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Spanish Restaurant</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Palace</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dance Studio</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Concert Hall</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Convenience Store</th>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Count\n",
"VenueCategory \n",
"Café 69\n",
"Restaurant 65\n",
"Coffee Shop 59\n",
"Italian Restaurant 56\n",
"Pizza Place 52\n",
"Supermarket 52\n",
"Gym 39\n",
"Hotel 39\n",
"Romanian Restaurant 38\n",
"Gym / Fitness Center 36\n",
"Burger Joint 34\n",
"Park 32\n",
"Clothing Store 30\n",
"Bakery 27\n",
"Pub 26\n",
"Dessert Shop 24\n",
"Lounge 24\n",
"Bistro 22\n",
"Fried Chicken Joint 19\n",
"Eastern European Restaurant 19\n",
"Bus Station 17\n",
"Pharmacy 17\n",
"Plaza 16\n",
"Grocery Store 15\n",
"Cosmetics Shop 15\n",
"Sandwich Place 15\n",
"Farmers Market 13\n",
"Department Store 13\n",
"Electronics Store 13\n",
"Bar 13\n",
"Fast Food Restaurant 13\n",
"Chinese Restaurant 13\n",
"Middle Eastern Restaurant 12\n",
"Salon / Barbershop 12\n",
"Cupcake Shop 11\n",
"Doner Restaurant 11\n",
"Furniture / Home Store 11\n",
"Lebanese Restaurant 11\n",
"Bookstore 10\n",
"Steakhouse 10\n",
"Sushi Restaurant 10\n",
"Ice Cream Shop 10\n",
"Pool 9\n",
"Turkish Restaurant 9\n",
"Nightclub 9\n",
"Mediterranean Restaurant 9\n",
"Spa 8\n",
"Beer Garden 8\n",
"Hostel 7\n",
"Tea Room 7\n",
"Wine Bar 7\n",
"Mobile Phone Shop 7\n",
"Soccer Field 7\n",
"Shopping Mall 7\n",
"Theater 7\n",
"Seafood Restaurant 7\n",
"Greek Restaurant 6\n",
"Casino 6\n",
"Health & Beauty Service 6\n",
"Light Rail Station 6\n",
"Cocktail Bar 6\n",
"Sporting Goods Shop 6\n",
"Snack Place 5\n",
"Roof Deck 5\n",
"Bagel Shop 5\n",
"Gastropub 5\n",
"Food & Drink Shop 5\n",
"Salad Place 5\n",
"Multiplex 5\n",
"Garden 5\n",
"French Restaurant 4\n",
"American Restaurant 4\n",
"Diner 4\n",
"Asian Restaurant 4\n",
"Vietnamese Restaurant 4\n",
"Tram Station 4\n",
"Metro Station 4\n",
"History Museum 4\n",
"Modern European Restaurant 3\n",
"German Restaurant 3\n",
"Market 3\n",
"Accessories Store 3\n",
"Hardware Store 3\n",
"Historic Site 3\n",
"Women's Store 3\n",
"Shoe Store 3\n",
"Chocolate Shop 3\n",
"Art Gallery 3\n",
"Art Museum 3\n",
"Arts & Crafts Store 3\n",
"Wine Shop 3\n",
"Vegetarian / Vegan Restaurant 3\n",
"Toy / Game Store 3\n",
"Tennis Court 3\n",
"Beer Bar 3\n",
"Spanish Restaurant 3\n",
"Palace 3\n",
"Dance Studio 3\n",
"Concert Hall 3\n",
"Convenience Store 3"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Top 100 distinct venue counts are as below')\n",
"neighborhood_venues[['VenueId','VenueCategory']].drop_duplicates().groupby('VenueCategory').count()[['VenueId']].rename(columns={\"VenueId\": \"Count\"}).sort_values(by=['Count'], ascending=False)[:100]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of youth enjoyment places: 179\n",
"Total number of alcoholic places : 36\n",
"Percentage of alcoholic places: 20.11%\n"
]
}
],
"source": [
"restaurant_list =['Convenience Store', 'Dance Studio','Bar',\n",
" 'Casino','Theater','Gym / Fitness Center','Gym','Beer Bar','Wine Shop','Chocolate Shop','Garden','Multiplex',\n",
" 'Food & Drink Shop', 'Cocktail Bar']\n",
"turkish_restaurant_list = ['Bar','Beer Bar','Wine Shop','Food & Drink Shop', 'Cocktail Bar', 'Casino']\n",
"\n",
"# Filter restaurants \n",
" \n",
"neighborhood_venues['RestFlag']=False\n",
"for restCat in restaurant_list:\n",
" neighborhood_venues['RestFlag'] = neighborhood_venues['RestFlag'] | neighborhood_venues['VenueCategory'].str.contains(restCat)\n",
" \n",
"neighborhood_restaurants = neighborhood_venues[neighborhood_venues['RestFlag'] == True].iloc[:,:-1]\n",
"turkish_restaurants = neighborhood_restaurants[ neighborhood_restaurants['VenueCategory'].isin(turkish_restaurant_list) ]\n",
"other_restaurants = neighborhood_restaurants[ ~neighborhood_restaurants['VenueCategory'].isin(turkish_restaurant_list) ]\n",
"\n",
"print('Total number of youth enjoyment places:', len(neighborhood_restaurants['VenueId'].unique()))\n",
"print('Total number of alcoholic places :', len(turkish_restaurants['VenueId'].unique()))\n",
"print('Percentage of alcoholic places: {:.2f}%'.format(len(turkish_restaurants['VenueId'].unique()) / len(neighborhood_restaurants['VenueId'].unique()) * 100))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11 neighborhoods do not have any alcoholic places\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# get counts of restaurants in each Neighborhood\n",
"df_rest_counts = neighborhood_restaurants.groupby(['Neighborhood']).count().rename(columns={\"VenueCategory\": \"EnjoyingPlaces\"})[['EnjoyingPlaces']]\n",
"\n",
"#find neighborhoods that does not have any restaurant \n",
"noRestList = list(set(neighborhood_venues['Neighborhood']) - set(neighborhood_restaurants['Neighborhood']))\n",
"\n",
"#if exists , append neighborhoods without any restaurant to df_rest_counts\n",
"if noRestList != []:\n",
" df_rest_counts = df_rest_counts.append (pd.DataFrame( {'Neighborhood' : noRestList , 'EnjoyingPlaces': [0] * len(noRestList) } ).set_index('Neighborhood'))\n",
"\n",
"df_rest_counts.reset_index(inplace=True)\n",
"\n",
"#####\n",
"# get counts of Turkish restaurants in each Neighborhood\n",
"df_turk_rest_counts = turkish_restaurants.groupby(['Neighborhood']).count().rename(columns={\"VenueCategory\": \"AlcoholicPlaces\"})[['AlcoholicPlaces']]\n",
"\n",
"#find neighborhoods that does not have any restaurant \n",
"noRestList = list(set(neighborhood_venues['Neighborhood']) - set(turkish_restaurants['Neighborhood']))\n",
"\n",
"#if exists , append neighborhoods without any restaurant to df_rest_counts\n",
"if noRestList != []:\n",
" df_turk_rest_counts = df_turk_rest_counts.append (pd.DataFrame( {'Neighborhood' : noRestList , 'AlcoholicPlaces': [0] * len(noRestList) } ).set_index('Neighborhood'))\n",
"\n",
"df_turk_rest_counts.reset_index(inplace=True)\n",
"df_rest_counts= df_rest_counts.merge(df_turk_rest_counts).set_index('Neighborhood')\n",
"df_rest_counts= df_rest_counts.sort_values(by=['EnjoyingPlaces'],ascending =False)\n",
"\n",
"print('{} neighborhoods do not have any alcoholic places'.format(len(noRestList)))\n",
"######\n",
"#Draw graph\n",
"df_rest_counts[['EnjoyingPlaces','AlcoholicPlaces']].plot(kind='bar',figsize=(15,5))\n",
"plt.title('Alcoholic Counts of Neighborhoods')\n",
"plt.xlabel('Neighborhood')\n",
"plt.ylabel('Alcoholic Counts')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>VenueId</th>\n",
" <th>VenueRating</th>\n",
" <th>VenueLikes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4d7731aebc888cfac2f5e573</td>\n",
" <td>7.6</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5abf8bf49fca567ea381ee43</td>\n",
" <td>7.5</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5dde6a94af03f30008bfeb22</td>\n",
" <td>7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>529e0ff211d2ee9c61e157e7</td>\n",
" <td>7.7</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>525d091a498edd52e72a51e4</td>\n",
" <td>6.6</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VenueId VenueRating VenueLikes\n",
"0 4d7731aebc888cfac2f5e573 7.6 4.0\n",
"1 5abf8bf49fca567ea381ee43 7.5 23.0\n",
"2 5dde6a94af03f30008bfeb22 7.5 0.0\n",
"3 529e0ff211d2ee9c61e157e7 7.7 55.0\n",
"4 525d091a498edd52e72a51e4 6.6 8.0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def getVenueRaitings(venues):\n",
" \n",
" raitings = []\n",
" likes =[]\n",
" for venueId in venues:\n",
"\n",
" # create the API request URL\n",
" url = 'https://api.foursquare.com/v2/venues/{}?client_id={}&client_secret={}&v={}'.format(venueId, CLIENT_ID, CLIENT_SECRET, VERSION)\n",
" result = requests.get(url).json()\n",
" try:\n",
" rating = result['response']['venue']['rating']\n",
" likes = result['response']['venue']['likes']['count']\n",
" except:\n",
" rating = None\n",
" likes = None\n",
" \n",
" raitings.append((venueId, rating,likes))\n",
" \n",
" # convert the venues list into a DataFrame\n",
" rating = pd.DataFrame(raitings)\n",
" # define the column names\n",
" rating.columns = ['VenueId', 'VenueRating','VenueLikes']\n",
"\n",
" return rating \n",
"\n",
"restaurants_raitings = getVenueRaitings(neighborhood_restaurants['VenueId'].drop_duplicates())\n",
"restaurants_raitings.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>VenueName</th>\n",
" <th>VenueId</th>\n",
" <th>VenueLatitude</th>\n",
" <th>VenueLongitude</th>\n",
" <th>VenueDistance</th>\n",
" <th>VenueCategory</th>\n",
" <th>VenueRating</th>\n",
" <th>VenueLikes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aviației</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" <td>Toni&amp;Guy</td>\n",
" <td>4d7731aebc888cfac2f5e573</td>\n",
" <td>44.484375</td>\n",
" <td>26.092021</td>\n",
" <td>747</td>\n",
" <td>Salon / Barbershop</td>\n",
" <td>7.6</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Aviației</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" <td>Hangar Gastrobar</td>\n",
" <td>5abf8bf49fca567ea381ee43</td>\n",
" <td>44.480385</td>\n",
" <td>26.105761</td>\n",
" <td>701</td>\n",
" <td>Beer Garden</td>\n",
" <td>7.5</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Floreasca</td>\n",
" <td>44.476308</td>\n",
" <td>26.103289</td>\n",
" <td>Hangar Gastrobar</td>\n",
" <td>5abf8bf49fca567ea381ee43</td>\n",
" <td>44.480385</td>\n",
" <td>26.105761</td>\n",
" <td>494</td>\n",
" <td>Beer Garden</td>\n",
" <td>7.5</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Aviației</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" <td>World Class Romania</td>\n",
" <td>5dde6a94af03f30008bfeb22</td>\n",
" <td>44.480482</td>\n",
" <td>26.106822</td>\n",
" <td>739</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Floreasca</td>\n",
" <td>44.476308</td>\n",
" <td>26.103289</td>\n",
" <td>World Class Romania</td>\n",
" <td>5dde6a94af03f30008bfeb22</td>\n",
" <td>44.480482</td>\n",
" <td>26.106822</td>\n",
" <td>542</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Latitude Longitude VenueName \\\n",
"0 Aviației 44.485790 26.101219 Toni&Guy \n",
"1 Aviației 44.485790 26.101219 Hangar Gastrobar \n",
"2 Floreasca 44.476308 26.103289 Hangar Gastrobar \n",
"3 Aviației 44.485790 26.101219 World Class Romania \n",
"4 Floreasca 44.476308 26.103289 World Class Romania \n",
"\n",
" VenueId VenueLatitude VenueLongitude VenueDistance \\\n",
"0 4d7731aebc888cfac2f5e573 44.484375 26.092021 747 \n",
"1 5abf8bf49fca567ea381ee43 44.480385 26.105761 701 \n",
"2 5abf8bf49fca567ea381ee43 44.480385 26.105761 494 \n",
"3 5dde6a94af03f30008bfeb22 44.480482 26.106822 739 \n",
"4 5dde6a94af03f30008bfeb22 44.480482 26.106822 542 \n",
"\n",
" VenueCategory VenueRating VenueLikes \n",
"0 Salon / Barbershop 7.6 4.0 \n",
"1 Beer Garden 7.5 23.0 \n",
"2 Beer Garden 7.5 23.0 \n",
"3 Gym / Fitness Center 7.5 0.0 \n",
"4 Gym / Fitness Center 7.5 0.0 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#merge raitings to restaurants \n",
"neighborhood_restaurants = neighborhood_restaurants.merge(restaurants_raitings)\n",
"neighborhood_restaurants.head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>VenueName</th>\n",
" <th>VenueId</th>\n",
" <th>VenueLatitude</th>\n",
" <th>VenueLongitude</th>\n",
" <th>VenueDistance</th>\n",
" <th>VenueCategory</th>\n",
" <th>VenueRating</th>\n",
" <th>VenueLikes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aviației</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" <td>Toni&amp;Guy</td>\n",
" <td>4d7731aebc888cfac2f5e573</td>\n",
" <td>44.484375</td>\n",
" <td>26.092021</td>\n",
" <td>747</td>\n",
" <td>Salon / Barbershop</td>\n",
" <td>7.6</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Aviației</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" <td>Hangar Gastrobar</td>\n",
" <td>5abf8bf49fca567ea381ee43</td>\n",
" <td>44.480385</td>\n",
" <td>26.105761</td>\n",
" <td>701</td>\n",
" <td>Beer Garden</td>\n",
" <td>7.5</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Floreasca</td>\n",
" <td>44.476308</td>\n",
" <td>26.103289</td>\n",
" <td>Hangar Gastrobar</td>\n",
" <td>5abf8bf49fca567ea381ee43</td>\n",
" <td>44.480385</td>\n",
" <td>26.105761</td>\n",
" <td>494</td>\n",
" <td>Beer Garden</td>\n",
" <td>7.5</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Aviației</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" <td>World Class Romania</td>\n",
" <td>5dde6a94af03f30008bfeb22</td>\n",
" <td>44.480482</td>\n",
" <td>26.106822</td>\n",
" <td>739</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Floreasca</td>\n",
" <td>44.476308</td>\n",
" <td>26.103289</td>\n",
" <td>World Class Romania</td>\n",
" <td>5dde6a94af03f30008bfeb22</td>\n",
" <td>44.480482</td>\n",
" <td>26.106822</td>\n",
" <td>542</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Latitude Longitude VenueName \\\n",
"0 Aviației 44.485790 26.101219 Toni&Guy \n",
"1 Aviației 44.485790 26.101219 Hangar Gastrobar \n",
"2 Floreasca 44.476308 26.103289 Hangar Gastrobar \n",
"3 Aviației 44.485790 26.101219 World Class Romania \n",
"4 Floreasca 44.476308 26.103289 World Class Romania \n",
"\n",
" VenueId VenueLatitude VenueLongitude VenueDistance \\\n",
"0 4d7731aebc888cfac2f5e573 44.484375 26.092021 747 \n",
"1 5abf8bf49fca567ea381ee43 44.480385 26.105761 701 \n",
"2 5abf8bf49fca567ea381ee43 44.480385 26.105761 494 \n",
"3 5dde6a94af03f30008bfeb22 44.480482 26.106822 739 \n",
"4 5dde6a94af03f30008bfeb22 44.480482 26.106822 542 \n",
"\n",
" VenueCategory VenueRating VenueLikes \n",
"0 Salon / Barbershop 7.6 4.0 \n",
"1 Beer Garden 7.5 23.0 \n",
"2 Beer Garden 7.5 23.0 \n",
"3 Gym / Fitness Center 7.5 0.0 \n",
"4 Gym / Fitness Center 7.5 0.0 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#merge raitings to restaurants \n",
"neighborhood_restaurants = neighborhood_restaurants.merge(restaurants_raitings)\n",
"neighborhood_restaurants.head()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"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>VenueLatitude</th>\n",
" <th>VenueLongitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>44.484375</td>\n",
" <td>26.092021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>44.480385</td>\n",
" <td>26.105761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44.480385</td>\n",
" <td>26.105761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>44.480482</td>\n",
" <td>26.106822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>44.480482</td>\n",
" <td>26.106822</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VenueLatitude VenueLongitude\n",
"0 44.484375 26.092021\n",
"1 44.480385 26.105761\n",
"2 44.480385 26.105761\n",
"3 44.480482 26.106822\n",
"4 44.480482 26.106822"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data=neighborhood_restaurants[['VenueLatitude','VenueLongitude']]\n",
"data.head()\n",
"#data.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "must be real number, not str",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-65-bd4704df7651>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTileLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cartodbpositron'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_bucharest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mHeatMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_restaurant\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMarker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbucharest_center\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_bucharest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCircle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbucharest_center\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_restaurant\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/folium/plugins/heat_map.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, name, min_opacity, max_zoom, max_val, radius, blur, gradient, overlay)\u001b[0m\n\u001b[1;32m 43\u001b[0m max_val=1.0, radius=25, blur=15, gradient=None, overlay=True):\n\u001b[1;32m 44\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTileLayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 46\u001b[0m raise ValueError('data cannot contain NaNs, '\n\u001b[1;32m 47\u001b[0m 'got:\\n{!r}'.format(data))\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/folium/utilities.py\u001b[0m in \u001b[0;36m_isnan\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;34m\"\"\"Check if there are NaNs values in the iterable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/folium/utilities.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;34m\"\"\"Check if there are NaNs values in the iterable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: must be real number, not str"
]
}
],
"source": [
"bucharest_center = [latitude, longitude]\n",
"map_restaurant= folium.Map(location=bucharest_center, zoom_start=12)\n",
"df_neighborhood_noturkrest = df_neighborhood[df_neighborhood['Neighborhood'].isin(noRestList)]\n",
"\n",
"folium.TileLayer('cartodbpositron').add_to(map_bucharest) \n",
"HeatMap(data).add_to(map_restaurant)\n",
"folium.Marker(bucharest_center).add_to(map_bucharest)\n",
"folium.Circle(bucharest_center, radius=2000, fill=False, color='white').add_to(map_restaurant)\n",
"folium.Circle(bucharest_center, radius=4000, fill=False, color='white').add_to(map_restaurant)\n",
"folium.Circle(bucharest_center, radius=6000, fill=False, color='white').add_to(map_restaurant)\n",
"folium.Circle(bucharest_center, radius=10000, fill=False, color='black').add_to(map_restaurant)\n",
"for lat, lon, neig, name in zip(turkish_restaurants['VenueLatitude'], turkish_restaurants['VenueLongitude'], turkish_restaurants['Neighborhood'], turkish_restaurants['VenueName']):\n",
" label = folium.Popup(str(name) + ' - ' + str(neig), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color='red',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7).add_to(map_restaurant)\n",
" \n",
"raiting_lt_7 = turkish_restaurants[turkish_restaurants['VenueRating'] <7]\n",
"for lat, lon, neig, name in zip(raiting_lt_7['VenueLatitude'], raiting_lt_7['VenueLongitude'], raiting_lt_7['Neighborhood'], raiting_lt_7['VenueName']):\n",
" label = folium.Popup(str(name) + ' - ' + str(neig), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7).add_to(map_restaurant) \n",
"map_restaurant"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "must be real number, not str",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-66-c0a05efaf496>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTileLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cartodbpositron'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_bucharest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mHeatMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneighborhood_restaurants\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'VenueLatitude'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'VenueLongitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_restaurant\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMarker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbucharest_center\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_bucharest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mfolium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCircle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbucharest_center\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_restaurant\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/folium/plugins/heat_map.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, name, min_opacity, max_zoom, max_val, radius, blur, gradient, overlay)\u001b[0m\n\u001b[1;32m 43\u001b[0m max_val=1.0, radius=25, blur=15, gradient=None, overlay=True):\n\u001b[1;32m 44\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTileLayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 46\u001b[0m raise ValueError('data cannot contain NaNs, '\n\u001b[1;32m 47\u001b[0m 'got:\\n{!r}'.format(data))\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/folium/utilities.py\u001b[0m in \u001b[0;36m_isnan\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;34m\"\"\"Check if there are NaNs values in the iterable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/folium/utilities.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_isnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;34m\"\"\"Check if there are NaNs values in the iterable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: must be real number, not str"
]
}
],
"source": [
"bucharest_center = [latitude, longitude]\n",
"map_restaurant= folium.Map(location=bucharest_center, zoom_start=12)\n",
"\n",
"df_neighborhood_noturkrest = df_neighborhood[df_neighborhood['Neighborhood'].isin(noRestList)]\n",
"\n",
"folium.TileLayer('cartodbpositron').add_to(map_bucharest) \n",
"HeatMap(neighborhood_restaurants[['VenueLatitude','VenueLongitude']]).add_to(map_restaurant)\n",
"folium.Marker(bucharest_center).add_to(map_bucharest)\n",
"folium.Circle(bucharest_center, radius=2000, fill=False, color='white').add_to(map_restaurant)\n",
"folium.Circle(bucharest_center, radius=4000, fill=False, color='white').add_to(map_restaurant)\n",
"folium.Circle(bucharest_center, radius=6000, fill=False, color='white').add_to(map_restaurant)\n",
"folium.Circle(bucharest_center, radius=10000, fill=False, color='black').add_to(map_restaurant)\n",
"for lat, lon, neig in zip(df_neighborhood_noturkrest['Latitude'], df_neighborhood_noturkrest['Longitude'], df_neighborhood_noturkrest['Neighborhood']):\n",
" label = folium.Popup(str(neig), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color='cyan',\n",
" fill=True,\n",
" fill_color='cyan',\n",
" fill_opacity=0.7).add_to(map_restaurant)\n",
"map_restaurant"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"40 neighborhoods' venue category are shown in 224 columns as below\n"
]
},
{
"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>Neighborhood</th>\n",
" <th>Hotel</th>\n",
" <th>Supermarket</th>\n",
" <th>Coffee Shop</th>\n",
" <th>Burger Joint</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Roof Deck</th>\n",
" <th>Grocery Store</th>\n",
" <th>Vietnamese Restaurant</th>\n",
" <th>Salad Place</th>\n",
" <th>Salon / Barbershop</th>\n",
" <th>Bookstore</th>\n",
" <th>Beer Garden</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Restaurant</th>\n",
" <th>Steakhouse</th>\n",
" <th>Café</th>\n",
" <th>Pie Shop</th>\n",
" <th>Bakery</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Clothing Store</th>\n",
" <th>Sushi Restaurant</th>\n",
" <th>Turkish Restaurant</th>\n",
" <th>Toy / Game Store</th>\n",
" <th>Lebanese Restaurant</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Spanish Restaurant</th>\n",
" <th>Casino</th>\n",
" <th>Pizza Place</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Event Space</th>\n",
" <th>Indoor Play Area</th>\n",
" <th>Lounge</th>\n",
" <th>Eastern European Restaurant</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Farmers Market</th>\n",
" <th>Stadium</th>\n",
" <th>Gym</th>\n",
" <th>Romanian Restaurant</th>\n",
" <th>Lighting Store</th>\n",
" <th>Mongolian Restaurant</th>\n",
" <th>Tennis Stadium</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Sporting Goods Shop</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Doner Restaurant</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Lake</th>\n",
" <th>Nightclub</th>\n",
" <th>Park</th>\n",
" <th>Theme Restaurant</th>\n",
" <th>Tunnel</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Soccer Field</th>\n",
" <th>Electronics Store</th>\n",
" <th>Nature Preserve</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Pharmacy</th>\n",
" <th>Fountain</th>\n",
" <th>Metro Station</th>\n",
" <th>Gas Station</th>\n",
" <th>Mobile Phone Shop</th>\n",
" <th>Spa</th>\n",
" <th>Korean Restaurant</th>\n",
" <th>Shop &amp; Service</th>\n",
" <th>Bistro</th>\n",
" <th>Outdoor Sculpture</th>\n",
" <th>Gastropub</th>\n",
" <th>Theater</th>\n",
" <th>Monastery</th>\n",
" <th>Indie Theater</th>\n",
" <th>Skating Rink</th>\n",
" <th>Beer Bar</th>\n",
" <th>Used Bookstore</th>\n",
" <th>Chocolate Shop</th>\n",
" <th>Tea Room</th>\n",
" <th>Art Museum</th>\n",
" <th>Bar</th>\n",
" <th>Wine Bar</th>\n",
" <th>Concert Hall</th>\n",
" <th>Plaza</th>\n",
" <th>Cosmetics Shop</th>\n",
" <th>Rock Club</th>\n",
" <th>Swiss Restaurant</th>\n",
" <th>Hostel</th>\n",
" <th>Historic Site</th>\n",
" <th>Art Gallery</th>\n",
" <th>Tattoo Parlor</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Church</th>\n",
" <th>History Museum</th>\n",
" <th>Gourmet Shop</th>\n",
" <th>Boutique</th>\n",
" <th>French Restaurant</th>\n",
" <th>Garden</th>\n",
" <th>Hardware Store</th>\n",
" <th>Discount Store</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Bus Station</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gift Shop</th>\n",
" <th>Tennis Court</th>\n",
" <th>Accessories Store</th>\n",
" <th>Jazz Club</th>\n",
" <th>Market</th>\n",
" <th>Opera House</th>\n",
" <th>Hotel Bar</th>\n",
" <th>Pub</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Music Store</th>\n",
" <th>Pool</th>\n",
" <th>Pedestrian Plaza</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Australian Restaurant</th>\n",
" <th>Martial Arts Dojo</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Department Store</th>\n",
" <th>Climbing Gym</th>\n",
" <th>Soccer Stadium</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Sake Bar</th>\n",
" <th>Convenience Store</th>\n",
" <th>Outlet Mall</th>\n",
" <th>Veterinarian</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Flower Shop</th>\n",
" <th>Fish Market</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Juice Bar</th>\n",
" <th>Scandinavian Restaurant</th>\n",
" <th>Cheese Shop</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Modern European Restaurant</th>\n",
" <th>Seafood Restaurant</th>\n",
" <th>Creperie</th>\n",
" <th>Wine Shop</th>\n",
" <th>German Restaurant</th>\n",
" <th>Molecular Gastronomy Restaurant</th>\n",
" <th>Hookah Bar</th>\n",
" <th>American Restaurant</th>\n",
" <th>Drugstore</th>\n",
" <th>Auto Workshop</th>\n",
" <th>Pet Store</th>\n",
" <th>Multiplex</th>\n",
" <th>Eye Doctor</th>\n",
" <th>Skate Park</th>\n",
" <th>Greek Restaurant</th>\n",
" <th>Water Park</th>\n",
" <th>Brewery</th>\n",
" <th>Playground</th>\n",
" <th>Ballroom</th>\n",
" <th>Tourist Information Center</th>\n",
" <th>Bus Line</th>\n",
" <th>Butcher</th>\n",
" <th>Health &amp; Beauty Service</th>\n",
" <th>Health Food Store</th>\n",
" <th>Camera Store</th>\n",
" <th>Bagel Shop</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Diner</th>\n",
" <th>Auto Dealership</th>\n",
" <th>Portuguese Restaurant</th>\n",
" <th>Science Museum</th>\n",
" <th>Perfume Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Lingerie Store</th>\n",
" <th>Ramen Restaurant</th>\n",
" <th>Cafeteria</th>\n",
" <th>Tram Station</th>\n",
" <th>Boxing Gym</th>\n",
" <th>Museum</th>\n",
" <th>Food Truck</th>\n",
" <th>Buffet</th>\n",
" <th>Movie Theater</th>\n",
" <th>Kebab Restaurant</th>\n",
" <th>Exhibit</th>\n",
" <th>Public Art</th>\n",
" <th>Pool Hall</th>\n",
" <th>RV Park</th>\n",
" <th>Women's Store</th>\n",
" <th>Palace</th>\n",
" <th>Cultural Center</th>\n",
" <th>Football Stadium</th>\n",
" <th>Shoe Store</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Hungarian Restaurant</th>\n",
" <th>Karaoke Bar</th>\n",
" <th>Donut Shop</th>\n",
" <th>Beach</th>\n",
" <th>Auto Garage</th>\n",
" <th>Currency Exchange</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>Sports Club</th>\n",
" <th>Food</th>\n",
" <th>Go Kart Track</th>\n",
" <th>IT Services</th>\n",
" <th>Souvlaki Shop</th>\n",
" <th>Track</th>\n",
" <th>Gym Pool</th>\n",
" <th>Recreation Center</th>\n",
" <th>Bike Shop</th>\n",
" <th>Basketball Court</th>\n",
" <th>Paper / Office Supplies Store</th>\n",
" <th>Bowling Alley</th>\n",
" <th>Sports Bar</th>\n",
" <th>Laundromat</th>\n",
" <th>Baby Store</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Taco Place</th>\n",
" <th>Watch Shop</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Fish &amp; Chips Shop</th>\n",
" <th>Betting Shop</th>\n",
" <th>ATM</th>\n",
" <th>Soup Place</th>\n",
" <th>Circus</th>\n",
" <th>Dance Studio</th>\n",
" <th>Pet Café</th>\n",
" <th>Leather Goods Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Recording Studio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>13 Septembrie</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Aviației</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Berceni</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bucureștii Noi</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Băneasa</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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],
"text/plain": [
" Neighborhood Hotel Supermarket Coffee Shop Burger Joint \\\n",
"0 13 Septembrie 2 3 1 0 \n",
"1 Aviației 4 4 3 3 \n",
"2 Berceni 1 0 2 0 \n",
"3 Bucureștii Noi 0 1 0 0 \n",
"4 Băneasa 2 1 0 0 \n",
"\n",
" Dessert Shop Roof Deck Grocery Store Vietnamese Restaurant Salad Place \\\n",
"0 0 0 0 0 0 \n",
"1 2 1 1 1 1 \n",
"2 0 0 2 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Salon / Barbershop Bookstore Beer Garden Gym / Fitness Center \\\n",
"0 0 0 0 2 \n",
"1 1 1 1 2 \n",
"2 0 0 1 2 \n",
"3 0 0 0 0 \n",
"4 0 0 0 1 \n",
"\n",
" Restaurant Steakhouse Café Pie Shop Bakery Shopping Mall \\\n",
"0 5 1 4 0 0 0 \n",
"1 7 3 5 1 5 1 \n",
"2 0 0 0 0 0 0 \n",
"3 1 0 0 0 0 0 \n",
"4 2 0 2 0 0 0 \n",
"\n",
" Italian Restaurant Clothing Store Sushi Restaurant Turkish Restaurant \\\n",
"0 1 2 0 0 \n",
"1 1 4 2 1 \n",
"2 1 0 0 0 \n",
"3 0 0 0 0 \n",
"4 2 0 0 0 \n",
"\n",
" Toy / Game Store Lebanese Restaurant Sandwich Place Spanish Restaurant \\\n",
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"1 1 2 1 1 \n",
"2 0 1 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 1 0 \n",
"\n",
" Casino Pizza Place Middle Eastern Restaurant Event Space \\\n",
"0 1 0 0 0 \n",
"1 1 4 1 1 \n",
"2 0 3 0 0 \n",
"3 0 0 0 0 \n",
"4 0 1 1 0 \n",
"\n",
" Indoor Play Area Lounge Eastern European Restaurant Fried Chicken Joint \\\n",
"0 0 2 0 1 \n",
"1 2 1 1 1 \n",
"2 0 0 2 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Farmers Market Stadium Gym Romanian Restaurant Lighting Store \\\n",
"0 1 0 1 2 0 \n",
"1 1 1 2 1 1 \n",
"2 2 0 2 0 0 \n",
"3 1 0 1 0 0 \n",
"4 1 0 0 2 0 \n",
"\n",
" Mongolian Restaurant Tennis Stadium Chinese Restaurant Ice Cream Shop \\\n",
"0 0 0 0 0 \n",
"1 1 1 1 1 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Sporting Goods Shop Cocktail Bar Cupcake Shop Food & Drink Shop \\\n",
"0 0 1 0 0 \n",
"1 1 1 1 1 \n",
"2 0 0 0 1 \n",
"3 0 0 0 0 \n",
"4 0 0 0 1 \n",
"\n",
" Doner Restaurant Vegetarian / Vegan Restaurant Lake Nightclub Park \\\n",
"0 0 0 0 0 1 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 1 \n",
"3 0 0 0 0 1 \n",
"4 1 1 1 1 1 \n",
"\n",
" Theme Restaurant Tunnel Bed & Breakfast Soccer Field Electronics Store \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 1 1 1 1 1 \n",
"\n",
" Nature Preserve Fast Food Restaurant Pharmacy Fountain Metro Station \\\n",
"0 0 1 2 0 0 \n",
"1 0 0 0 0 0 \n",
"2 1 1 2 1 1 \n",
"3 0 0 0 0 1 \n",
"4 0 0 0 0 0 \n",
"\n",
" Gas Station Mobile Phone Shop Spa Korean Restaurant Shop & Service \\\n",
"0 0 0 1 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 1 1 1 1 1 \n",
"4 0 0 0 0 0 \n",
"\n",
" Bistro Outdoor Sculpture Gastropub Theater Monastery Indie Theater \\\n",
"0 1 0 0 1 0 0 \n",
"1 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 \n",
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"\n",
" Skating Rink Beer Bar Used Bookstore Chocolate Shop Tea Room \\\n",
"0 0 0 0 1 2 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Art Museum Bar Wine Bar Concert Hall Plaza Cosmetics Shop Rock Club \\\n",
"0 1 1 0 0 4 0 0 \n",
"1 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 \n",
"\n",
" Swiss Restaurant Hostel Historic Site Art Gallery Tattoo Parlor \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Mediterranean Restaurant Church History Museum Gourmet Shop Boutique \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" French Restaurant Garden Hardware Store Discount Store \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Arts & Crafts Store Bus Station Furniture / Home Store Gift Shop \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Tennis Court Accessories Store Jazz Club Market Opera House Hotel Bar \\\n",
"0 1 1 1 0 0 1 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Pub Indian Restaurant Music Store Pool Pedestrian Plaza \\\n",
"0 3 1 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Indie Movie Theater Australian Restaurant Martial Arts Dojo \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Light Rail Station Department Store Climbing Gym Soccer Stadium \\\n",
"0 1 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Miscellaneous Shop Sake Bar Convenience Store Outlet Mall Veterinarian \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Mexican Restaurant Flower Shop Fish Market Japanese Restaurant \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Juice Bar Scandinavian Restaurant Cheese Shop Asian Restaurant \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Modern European Restaurant Seafood Restaurant Creperie Wine Shop \\\n",
"0 0 2 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" German Restaurant Molecular Gastronomy Restaurant Hookah Bar \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" American Restaurant Drugstore Auto Workshop Pet Store Multiplex \\\n",
"0 1 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Eye Doctor Skate Park Greek Restaurant Water Park Brewery Playground \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Ballroom Tourist Information Center Bus Line Butcher \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Health & Beauty Service Health Food Store Camera Store Bagel Shop \\\n",
"0 0 0 0 1 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Falafel Restaurant Diner Auto Dealership Portuguese Restaurant \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Science Museum Perfume Shop Snack Place Lingerie Store \\\n",
"0 0 0 1 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Ramen Restaurant Cafeteria Tram Station Boxing Gym Museum Food Truck \\\n",
"0 0 0 1 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Buffet Movie Theater Kebab Restaurant Exhibit Public Art Pool Hall \\\n",
"0 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" RV Park Women's Store Palace Cultural Center Football Stadium \\\n",
"0 0 0 1 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Shoe Store BBQ Joint Hungarian Restaurant Karaoke Bar Donut Shop \\\n",
"0 0 1 0 0 0 \n",
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"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
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" Beach Auto Garage Currency Exchange Athletics & Sports Sports Club \\\n",
"0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
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" Food Go Kart Track IT Services Souvlaki Shop Track Gym Pool \\\n",
"0 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Recreation Center Bike Shop Basketball Court \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Paper / Office Supplies Store Bowling Alley Sports Bar Laundromat \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Baby Store Comfort Food Restaurant Taco Place Watch Shop Smoke Shop \\\n",
"0 0 2 1 1 1 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Fish & Chips Shop Betting Shop ATM Soup Place Circus Dance Studio \\\n",
"0 1 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Pet Café Leather Goods Store Gaming Cafe Recording Studio \n",
"0 0 0 0 0 \n",
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"3 0 0 0 0 \n",
"4 0 0 0 0 "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# one hot encoding\n",
"onehot = pd.get_dummies(neighborhood_venues[['VenueCategory']], prefix=\"\", prefix_sep=\"\")\n",
"# add neighborhood column back to dataframe\n",
"onehot['Neighborhood'] = neighborhood_venues['Neighborhood']\n",
"\n",
"venues_grouped = onehot.groupby([\"Neighborhood\"]).sum().reset_index()\n",
"\n",
"# move neighborhood column to the first column and filter only restaurant columns \n",
"fixed_columns =['Neighborhood'] + list(neighborhood_venues['VenueCategory'].unique())\n",
"venues_grouped = venues_grouped[fixed_columns]\n",
"\n",
"print(\"{} neighborhoods' venue category are shown in {} columns as below\".format(venues_grouped.shape[0],venues_grouped.shape[1]-1))\n",
"venues_grouped.head()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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>Neighborhood</th>\n",
" <th>Total Number of Venues</th>\n",
" <th>1st Most Common Restaurant</th>\n",
" <th>2nd Most Common Restaurant</th>\n",
" <th>3rd Most Common Restaurant</th>\n",
" <th>4th Most Common Restaurant</th>\n",
" <th>5th Most Common Restaurant</th>\n",
" <th>6th Most Common Restaurant</th>\n",
" <th>7th Most Common Restaurant</th>\n",
" <th>8th Most Common Restaurant</th>\n",
" <th>9th Most Common Restaurant</th>\n",
" <th>10th Most Common Restaurant</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>13 Septembrie</td>\n",
" <td>69</td>\n",
" <td>Restaurant</td>\n",
" <td>Plaza</td>\n",
" <td>Café</td>\n",
" <td>Supermarket</td>\n",
" <td>Pub</td>\n",
" <td>Hotel</td>\n",
" <td>Lounge</td>\n",
" <td>Seafood Restaurant</td>\n",
" <td>Tea Room</td>\n",
" <td>Romanian Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Aviației</td>\n",
" <td>86</td>\n",
" <td>Restaurant</td>\n",
" <td>Bakery</td>\n",
" <td>Café</td>\n",
" <td>Hotel</td>\n",
" <td>Supermarket</td>\n",
" <td>Pizza Place</td>\n",
" <td>Clothing Store</td>\n",
" <td>Steakhouse</td>\n",
" <td>Burger Joint</td>\n",
" <td>Coffee Shop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Berceni</td>\n",
" <td>27</td>\n",
" <td>Pizza Place</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Pharmacy</td>\n",
" <td>Grocery Store</td>\n",
" <td>Gym</td>\n",
" <td>Farmers Market</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>Hotel</td>\n",
" <td>Italian Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bucureștii Noi</td>\n",
" <td>11</td>\n",
" <td>Spa</td>\n",
" <td>Supermarket</td>\n",
" <td>Gym</td>\n",
" <td>Metro Station</td>\n",
" <td>Park</td>\n",
" <td>Gas Station</td>\n",
" <td>Mobile Phone Shop</td>\n",
" <td>Farmers Market</td>\n",
" <td>Korean Restaurant</td>\n",
" <td>Shop &amp; Service</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Băneasa</td>\n",
" <td>27</td>\n",
" <td>Hotel</td>\n",
" <td>Café</td>\n",
" <td>Restaurant</td>\n",
" <td>Romanian Restaurant</td>\n",
" <td>Italian Restaurant</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Pizza Place</td>\n",
" <td>Middle Eastern Restaurant</td>\n",
" <td>Farmers Market</td>\n",
" <td>Food &amp; Drink Shop</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Total Number of Venues 1st Most Common Restaurant \\\n",
"0 13 Septembrie 69 Restaurant \n",
"1 Aviației 86 Restaurant \n",
"2 Berceni 27 Pizza Place \n",
"3 Bucureștii Noi 11 Spa \n",
"4 Băneasa 27 Hotel \n",
"\n",
" 2nd Most Common Restaurant 3rd Most Common Restaurant \\\n",
"0 Plaza Café \n",
"1 Bakery Café \n",
"2 Coffee Shop Pharmacy \n",
"3 Supermarket Gym \n",
"4 Café Restaurant \n",
"\n",
" 4th Most Common Restaurant 5th Most Common Restaurant \\\n",
"0 Supermarket Pub \n",
"1 Hotel Supermarket \n",
"2 Grocery Store Gym \n",
"3 Metro Station Park \n",
"4 Romanian Restaurant Italian Restaurant \n",
"\n",
" 6th Most Common Restaurant 7th Most Common Restaurant \\\n",
"0 Hotel Lounge \n",
"1 Pizza Place Clothing Store \n",
"2 Farmers Market Eastern European Restaurant \n",
"3 Gas Station Mobile Phone Shop \n",
"4 Doner Restaurant Pizza Place \n",
"\n",
" 8th Most Common Restaurant 9th Most Common Restaurant \\\n",
"0 Seafood Restaurant Tea Room \n",
"1 Steakhouse Burger Joint \n",
"2 Gym / Fitness Center Hotel \n",
"3 Farmers Market Korean Restaurant \n",
"4 Middle Eastern Restaurant Farmers Market \n",
"\n",
" 10th Most Common Restaurant \n",
"0 Romanian Restaurant \n",
"1 Coffee Shop \n",
"2 Italian Restaurant \n",
"3 Shop & Service \n",
"4 Food & Drink Shop "
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def return_most_common_venues(row, num_top_venues):\n",
" row_categories = row\n",
" row_categories_sorted = row_categories.sort_values(ascending=False)\n",
" \n",
" return row_categories_sorted.index.values[0:num_top_venues]\n",
"\n",
"num_top_venues = 10\n",
"\n",
"columns = ['Neighborhood','Total Number of Venues']\n",
"indicators = ['st', 'nd', 'rd']\n",
"# create columns according to number of top venues\n",
"\n",
"for ind in np.arange(num_top_venues):\n",
" try:\n",
" columns.append('{}{} Most Common Restaurant'.format(ind+1, indicators[ind]))\n",
" except:\n",
" columns.append('{}th Most Common Restaurant'.format(ind+1))\n",
"\n",
"# create a new dataframe\n",
"venues_most = pd.DataFrame(columns = columns)\n",
"\n",
"for ind in range(venues_grouped.shape[0]):\n",
" venues_most.loc[ind, 'Neighborhood'] = venues_grouped.iloc[ind].Neighborhood\n",
" venues_most.loc[ind, 'Total Number of Venues'] = venues_grouped.iloc[ind,1:].sum()\n",
" venues_most.iloc[ind, 2:] = return_most_common_venues(venues_grouped.iloc[ind, 1:], num_top_venues)\n",
"\n",
"venues_most.head()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import scipy.cluster.hierarchy as shc\n",
"\n",
"data = venues_grouped.iloc[:,3:]\n",
"plt.figure(figsize=(10, 6)) \n",
"plt.title('Hierarchical Clustering Dendrogram')\n",
"plt.xlabel('Neighborhoods')\n",
"plt.ylabel('Distance')\n",
"plt.axhline(y=20, c='k')\n",
"dend = shc.dendrogram(shc.linkage(data, method='ward'))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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>Neighborhood</th>\n",
" <th>Sector</th>\n",
" <th>SectorPopulation</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>NeighborhoodCluster</th>\n",
" <th>1st Most Common Restaurant</th>\n",
" <th>2nd Most Common Restaurant</th>\n",
" <th>3rd Most Common Restaurant</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aviației</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" <td>44.485790</td>\n",
" <td>26.101219</td>\n",
" <td>0</td>\n",
" <td>Restaurant</td>\n",
" <td>Bakery</td>\n",
" <td>Café</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Băneasa</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" <td>44.494012</td>\n",
" <td>26.080358</td>\n",
" <td>2</td>\n",
" <td>Hotel</td>\n",
" <td>Café</td>\n",
" <td>Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Berceni</td>\n",
" <td>Sector 4</td>\n",
" <td>287,828</td>\n",
" <td>44.386430</td>\n",
" <td>26.128490</td>\n",
" <td>2</td>\n",
" <td>Pizza Place</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Pharmacy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bucureștii Noi</td>\n",
" <td>Sector 1</td>\n",
" <td>225,454</td>\n",
" <td>44.480413</td>\n",
" <td>26.042807</td>\n",
" <td>2</td>\n",
" <td>Spa</td>\n",
" <td>Supermarket</td>\n",
" <td>Gym</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Centrul Civic</td>\n",
" <td>Sector 3</td>\n",
" <td>385,439</td>\n",
" <td>44.434300</td>\n",
" <td>26.094660</td>\n",
" <td>3</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Hotel</td>\n",
" <td>Theater</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Sector SectorPopulation Latitude Longitude \\\n",
"0 Aviației Sector 1 225,454 44.485790 26.101219 \n",
"1 Băneasa Sector 1 225,454 44.494012 26.080358 \n",
"2 Berceni Sector 4 287,828 44.386430 26.128490 \n",
"3 Bucureștii Noi Sector 1 225,454 44.480413 26.042807 \n",
"4 Centrul Civic Sector 3 385,439 44.434300 26.094660 \n",
"\n",
" NeighborhoodCluster 1st Most Common Restaurant 2nd Most Common Restaurant \\\n",
"0 0 Restaurant Bakery \n",
"1 2 Hotel Café \n",
"2 2 Pizza Place Coffee Shop \n",
"3 2 Spa Supermarket \n",
"4 3 Coffee Shop Hotel \n",
"\n",
" 3rd Most Common Restaurant \n",
"0 Café \n",
"1 Restaurant \n",
"2 Pharmacy \n",
"3 Gym \n",
"4 Theater "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"from sklearn.cluster import AgglomerativeClustering\n",
"\n",
"kclusters= 6\n",
"cluster = AgglomerativeClustering(n_clusters=kclusters, affinity='euclidean', linkage='ward') \n",
"clusterresult = cluster.fit_predict(data)\n",
"\n",
"venues_grouped['NeighborhoodCluster'] = clusterresult\n",
"venues_cluster= df_neighborhood.merge(venues_grouped[['Neighborhood','NeighborhoodCluster']])\n",
"venues_cluster =venues_cluster.merge(venues_most[['Neighborhood','1st Most Common Restaurant','2nd Most Common Restaurant','3rd Most Common Restaurant']])\n",
"venues_cluster.head()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fc585268080>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map\n",
"map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(kclusters)\n",
"ys = [i+x+(i*x)**2 for i in range(kclusters)]\n",
"colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
"\n",
"# add markers to the map\n",
"for lat, lon, poi, cluster, sector,SectorPopulation in zip(venues_cluster['Latitude'], venues_cluster['Longitude'], venues_cluster['Neighborhood'], venues_cluster['NeighborhoodCluster'], venues_cluster['Sector'],venues_cluster['SectorPopulation']):\n",
" label = folium.Popup(str(poi) + ' - Cluster ' + str(cluster)+ ' ' + str(sector) + ' ' + str(SectorPopulation), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=5,\n",
" popup=label,\n",
" color=rainbow[cluster-1],\n",
" fill=True,\n",
" fill_color=rainbow[cluster-1],\n",
" fill_opacity=0.7).add_to(map_clusters)\n",
"folium.Marker(bucharest_center).add_to(map_clusters)\n",
"folium.Circle(bucharest_center, radius=2000, fill=False, color='white').add_to(map_clusters)\n",
"folium.Circle(bucharest_center, radius=4000, fill=False, color='white').add_to(map_clusters)\n",
"folium.Circle(bucharest_center, radius=6000, fill=False, color='white').add_to(map_clusters)\n",
"folium.Circle(bucharest_center, radius=10000, fill=False, color='black').add_to(map_clusters) \n",
"map_clusters"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
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" 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></th>\n",
" <th>Neighborhood Count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NeighborhoodCluster</th>\n",
" <th>2nd Most Common Restaurant</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">0</th>\n",
" <th>Bakery</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coffee Shop</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lounge</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">1</th>\n",
" <th>Coffee Shop</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lounge</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Restaurant</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"14\" valign=\"top\">2</th>\n",
" <th>Bar</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bus Station</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Café</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coffee Shop</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dessert Shop</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fast Food Restaurant</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gym / Fitness Center</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pizza Place</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pub</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Romanian Restaurant</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sandwich Place</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soccer Field</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Supermarket</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>Hotel</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"7\" valign=\"top\">4</th>\n",
" <th>Burger Joint</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Café</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Clothing Store</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Italian Restaurant</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pizza Place</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Romanian Restaurant</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Supermarket</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">5</th>\n",
" <th>Bus Station</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hotel</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Plaza</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Count\n",
"NeighborhoodCluster 2nd Most Common Restaurant \n",
"0 Bakery 1\n",
" Coffee Shop 1\n",
" Lounge 2\n",
"1 Coffee Shop 2\n",
" Lounge 1\n",
" Restaurant 1\n",
"2 Bar 1\n",
" Bus Station 1\n",
" Café 2\n",
" Coffee Shop 1\n",
" Dessert Shop 1\n",
" Fast Food Restaurant 1\n",
" Gym / Fitness Center 1\n",
" Middle Eastern Restaurant 1\n",
" Pizza Place 1\n",
" Pub 1\n",
" Romanian Restaurant 1\n",
" Sandwich Place 1\n",
" Soccer Field 2\n",
" Supermarket 1\n",
"3 Hotel 4\n",
"4 Burger Joint 1\n",
" Café 1\n",
" Clothing Store 1\n",
" Italian Restaurant 1\n",
" Pizza Place 1\n",
" Romanian Restaurant 1\n",
" Supermarket 2\n",
"5 Bus Station 2\n",
" Hotel 1\n",
" Plaza 1"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#print(venues_cluster.groupby(['NeighborhoodCluster','1st Most Common Restaurant' ]).count()[['Neighborhood']].rename(columns={\"Neighborhood\": \"Neighborhood Count\"}))\n",
"\n",
"venues_cluster.groupby(['NeighborhoodCluster','2nd Most Common Restaurant' ]).count()[['Neighborhood']].rename(columns={\"Neighborhood\": \"Neighborhood Count\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Methodology\n",
"\n",
"I have used various data from wikipedia to analyze the places around Bucharest and counted number of safe places and drinking places.\n",
"analysis is the data was done by seeing the ratings and all.\n",
"Then I used Maps and got errors. \n",
"On the map the points depict the places or neighbourhoods segregated or clustered."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cyan coloured places are dry and safe for the teenagers less than 18 years old.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The places center to Bucharest are less alcoholic places. Parents can send their kids to these places.\n",
"these are safe and dry places"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#SofiaSunam"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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