Zamato Spatial analysis.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')


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df = pd.read_csv('E:\Delete\Zomato/zomato.csv')


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df.head()


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df.isna().sum()


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df.dropna(axis='index', subset=['location'], inplace=True)


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df.isna().sum()


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len(df['location'].unique())


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locations = pd.DataFrame()


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locations['Name'] = df['location'].unique()


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locations.head()


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get_ipython().system('pip install geopy')


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from geopy.geocoders import Nominatim


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geolocator = Nominatim(user_agent = 'app')


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lat = []
lon = []
for location in locations['Name']:
    location = geolocator.geocode(location)
    if location is None:
        lat.append(np.nan)
        lon.append(np.nan)
    else:
        lat.append(location.latitude)
        lon.append(location.longitude)


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locations['lat'] = lat
locations['lon'] = lon


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locations.head()


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locations.to_csv('E:\Delete\Zomato/zomato_location.csv', index=False)


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Rest_locations = df['location'].value_counts().reset_index()


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Rest_locations


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Rest_locations.columns=['Name','count']
Rest_locations


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Restaurant_locations = Rest_locations.merge(locations, on='Name', how='left').dropna()
Restaurant_locations.head()


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get_ipython().system('pip install folium')


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def generateBaseMap(default_location = [12.97, 77.59], default_zoom_start = 12):
    basemap = folium.Map(location=default_location, zoom_start = default_zoom_start)
    return basemap


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import folium
basemap = generateBaseMap()
basemap


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from folium.plugins import HeatMap


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HeatMap(Restaurant_locations[['lat','lon', 'count']], zoom=20).add_to(basemap)


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basemap


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from folium.plugins import FastMarkerCluster


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FastMarkerCluster(Restaurant_locations[['lat','lon','count']], zoom=20).add_to(basemap)


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basemap


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df['rate'].unique()


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df.dropna(axis=0,subset=['rate'], inplace=True)


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def split(x):
    return x.split('/')[0]


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df.head()


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df['rating'] = df['rate'].apply(split)


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df.head()


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df.replace('NEW',0, inplace=True)


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df.replace('-',0, inplace=True)
df['rating'].unique()


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df['rating'] = pd.to_numeric(df['rating'])


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df.groupby('location')['rating'].mean()


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df.groupby('location')['rating'].mean().sort_values()


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avg_rating = df.groupby('location')['rating'].mean().sort_values(ascending=False).values


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location = df.groupby('location')['rating'].mean().sort_values(ascending=False).index


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location


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rating = pd.DataFrame()


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lat = []
lon = []
for place in location:
    place = geolocator.geocode(place)
    if place is None:
        lat.append(np.nan)
        lon.append(np.nan)
    else:
        lat.append(place.latitude)
        lon.append(place.longitude)


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rating['location'] = location
rating['lat'] = lat
rating['long'] = lon
rating['avg_rating'] = avg_rating


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rating.head()


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rating.isna().sum()


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rating.dropna(inplace = True)


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rating[['lat','long','avg_rating']]


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HeatMap(rating[['lat','long','avg_rating']]).add_to(basemap)


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basemap


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df.head()


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filter = df['cuisines']=="North Indian"
df2 = df[filter]
df2.head()


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df.group