How Analyzing Zomato Restaurant Data Can Help You Extract the Best Cuisines Data in Local Areas?

Author: Food Spark

Zomato restaurant data scraping is among the most valuable tools for foodies looking to sample the finest cuisines from across the world while staying within their budget. This research is also helpful for people looking for low-cost eateries in various parts of the nation that serve a variety of cuisines. Additionally, restaurant data extraction responds to the demands of consumers who are looking for the best cuisine in a country and which locality in that country has the most restaurants serving that cuisine.

Dataset Details

Restaurant Id: Every restaurant's unique identifier in numerous cities throughout the world

Restaurant Name: Name of the restaurant

Country Code: Location of the Country in which Restaurant is located

Address: Address of the restaurant

Locality: City location

Locality Verbose: Detailed explanation of the location – Longitude: The location of the restaurant's longitude coordinates

Latitude: The restaurant's location's latitude coordinate

Cuisines: The restaurant's menu includes a variety of cuisines.

Average cost for two: Cost of two individuals in different currencies

Currency: Currency of the country

Has Table Booking: yes/no

Has Online Delivery: Yes/No

Is Delivering: Yes/ No

Switch to order menu: Yes/No

Price range: range of price of food

Aggregate Rating: Average rating out of 5

Rating color: Depending upon the average rating color

Rating text: text depending on basis of rating

Votes: Number of ratings casted by people.

Dataset Importing

This project can be developed on google Colab. The dataset can be downloaded as follows:

from google.colab import files files.upload()! pip install opendatasets --upgrade import opendatasets as od dataset_url = 'https://www.kaggle.com/shrutimehta/zomato-restaurants-data' od.download(dataset_url) Reading Data Using Pandasimport pandas as pd df = pd.read_csv('/content/zomato-restaurants-data/zomato.csv', engine='python') df.head(2) Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotes06317637Le Petit Souffle162Makati CityThird Floor, Century City Mall, Kalayaan Avenu...Century City Mall, Poblacion, Makati CityCentury City Mall, Poblacion, Makati City, Mak...121.02753514.565443French, Japanese, Desserts1100Botswana Pula(P)YesNoNoNo34.8Dark GreenExcellent31416304287Izakaya Kikufuji162Makati CityLittle Tokyo, 2277 Chino Roces Avenue, Legaspi...Little Tokyo, Legaspi Village, Makati CityLittle Tokyo, Legaspi Village, Makati City, Ma...121.01410114.553708Japanese1200Botswana Pula(P)YesNoNoNo34.5Dark GreenExcellent591Checking if Dataset Contains any Null## Checking if dataset contains any null nan_values = df.isna() nan_columns = nan_values.any() columns_with_nan = df.columns[nan_columns].tolist() print(columns_with_nan) ['Cuisines']

Null values appear to exist in cuisines. As a result, any additional study employing Cuisines must take the NaN values into account.

Along with this dataset, there is another file that is also available.

df1 = pd.read_excel('/content/zomato-restaurants-data/Country-Code.xlsx') df1.head()

Let's combine the two datasets. This will assist us in comprehending the dataset on a country-by-country basis.

Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotesCountry06317637Le Petit Souffle162Makati CityThird Floor, Century City Mall, Kalayaan Avenu...Century City Mall, Poblacion, Makati CityCentury City Mall, Poblacion, Makati City, Mak...121.02753514.565443French, Japanese, Desserts1100Botswana Pula(P)YesNoNoNo34.8Dark GreenExcellent314Phillipines16304287Izakaya Kikufuji162Makati CityLittle Tokyo, 2277 Chino Roces Avenue, Legaspi...Little Tokyo, Legaspi Village, Makati CityLittle Tokyo, Legaspi Village, Makati City, Ma...121.01410114.553708Japanese1200Botswana Pula(P)YesNoNoNo34.5Dark GreenExcellent591Phillipinesdf2 = pd.merge(df,df1,on='Country Code',how='left') df2.head(2) Exploratory Zomato Restaurant Analysis and Visualizations

Before we ask questions about the dataset, it's important to understand the geographical spread of the restaurants, as well as the ratings, currency, online delivery, city coverage, and so on.

List of Countries the Survey is Doneprint('List of counteris the survey is spread accross - ') for x in pd.unique(df2.Country): print(x) print() print('Total number to country', len(pd.unique(df2.Country)))

List of countries the survey is spread across –

  • Philippines
  • Brazil
  • United States
  • Australia
  • Canada
  • Singapore
  • UAE
  • India
  • Indonesia
  • New Zealand
  • United Kingdom
  • Qatar
  • South Africa
  • Sri Lanka
  • Turkey

Total number to country 15

The study appears to have been conducted in 15 nations. This demonstrates that Zomato is an international corporation with operations in all of those nations.

from plotly.offline import init_notebook_mode, plot, iplot labels = list(df2.Country.value_counts().index) values = list(df2.Country.value_counts().values) fig = { "data":[ { "labels" : labels, "values" : values, "hoverinfo" : 'label+percent', "domain": {"x": [0,.9]}, "hole" : 0.6, "type" : "pie", "rotation":120, }, ], "layout": { "title" : "Zomato's Presence around the World", "annotations": [ { "font": {"size":20}, "showarrow": True, "text": "Countries", "x":0.2, "y":0.9, }, ] } } iplot(fig)

Zomato is an Indian startup, thus it's only natural that it has the most business among Indian restaurants.

Understanding the Rating aggregate, Color, and Textdf3 = df2.groupby(['Aggregate rating','Rating color', 'Rating text']).size().reset_index().rename(columns={0:'Rating Count'}) df3 df3 Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotesCountry06317637Le Petit Souffle162Makati CityThird Floor, Century City Mall, Kalayaan Avenu...Century City Mall, Poblacion, Makati CityCentury City Mall, Poblacion, Makati City, Mak...121.02753514.565443French, Japanese, Desserts1100Botswana Pula(P)YesNoNoNo34.8Dark GreenExcellent314Phillipines16304287Izakaya Kikufuji162Makati CityLittle Tokyo, 2277 Chino Roces Avenue, Legaspi...Little Tokyo, Legaspi Village, Makati CityLittle Tokyo, Legaspi Village, Makati City, Ma...121.01410114.553708Japanese1200Botswana Pula(P)YesNoNoNo34.5Dark GreenExcellent591Phillipines

The preceding information clarifies the relationship between aggregate rating, color, and text. Finally, we assign the following hue to the ratings:

Rating 0 — White — Not rated

Rating 1.8 to 2.4 — Red — Poor

Rating 2.5 to 3.4 — Orange — Average

Rating 3.5 to 3.9 — Yellow — Good

Rating 4.0 to 4.4 — Green — Very Good

Rating 4.5 to 4.9 — Dark Green — Excellent

Let's try to figure out why eateries have such a wide range of ratings.import seaborn as sns import matplotlib import matplotlib.pyplot as plt %matplotlib inline sns.set_style('darkgrid') matplotlib.rcParams['font.size'] = 14 matplotlib.rcParams['figure.figsize'] = (9, 5) matplotlib.rcParams['figure.facecolor'] = '#00000000' plt.figure(figsize=(12,6)) # plt.xticks(rotation=75) plt.title('Rating Color') sns.barplot(x=df3['Rating color'], y=df3['Rating Count']);

Surprisingly, the majority of establishments appear to have dropped their star ratings. Let's see whether any of these restaurants are from a specific country.

No_rating = df2[df2['Rating color']=='White'].groupby('Country').size().reset_index().rename(columns={0:'Rating Count'}) No_rating CountryRating Count0Brazil51India21392United Kingdom13United States3

India appears to have the highest number of unrated restaurants. Because the habit of ordering food is still gaining traction in India, most eateries remain unrated on Zomato, as individuals may choose to visit the restaurant for a meal.

Country and CurrencyCountryCurrency0PhillipinesBotswana Pula(P)1BrazilBrazilian Real(R$)2AustraliaDollar($)3CanadaDollar($)4SingaporeDollar($)5United StatesDollar($)6UAEEmirati Diram(AED)7IndiaIndian Rupees(Rs.)8IndonesiaIndonesian Rupiah(IDR)9New ZealandNewZealand($)10United KingdomPounds(??)11QatarQatari Rial(QR)12South AfricaRand(R)13Sri LankaSri Lankan Rupee(LKR)14TurkeyTurkish Lira(TL)country_currency = df2[['Country','Currency']].groupby(['Country','Currency']).size().reset_index(name='count').drop('count', axis=1, inplace=False) country_currency.sort_values('Currency').reset_index(drop=True)

The table above shows the countries and the currencies they accept. Surprisingly, four countries appear to accept dollars as a form of payment.

Online Delivery Distributionplt.figure(figsize=(12,6)) plt.title('Online Delivery Distribution') plt.pie(df2['Has Online delivery'].value_counts()/9551*100, labels=df2['Has Online delivery'].value_counts().index, autopct='%1.1f%%', startangle=180);

Only around a quarter of eateries accept internet orders. Because the majority of the restaurants listed here are from India, this data may be skewed. Perhaps a city-by-city examination would be more useful.

Understanding the Coverage of the Cityfrom plotly.offline import init_notebook_mode, plot, iplot import plotly.graph_objs as go plt.figure(figsize=(12,6)) # import plotly.plotly as py labels = list(df2.City.value_counts().head(20).index) values = list(df2.City.value_counts().head(20).values) fig = { "data":[ { "labels" : labels, "values" : values, "hoverinfo" : 'label+percent', "domain": {"x": [0,.9]}, "hole" : 0.6, "type" : "pie", "rotation":120, }, ], "layout": { "title" : "Zomato's Presence Citywise", "annotations": [ { "font": {"size":20}, "showarrow": True, "text": "Cities", "x":0.2, "y":0.9, }, ] } } iplot(fig); Questions and Answers

Which locality has maximum hotels listed in Zomato?

Delhi = df2[(df2.City == 'New Delhi')] plt.figure(figsize=(12,6)) sns.barplot(x=Delhi.Locality.value_counts().head(10), y=Delhi.Locality.value_counts().head(10).index) plt.ylabel(None); plt.xlabel('Number of Resturants') plt.title('Resturants Listing on Zomato');

Connaught Place appears to have a large number of restaurants listed on Zomato. Let's take a look at the cuisines that the highest eateries have to offer.

2. What Kind of the Cuisines Highly Rated Restaurants Offers?

  • I achieve this by the following steps ## Fetching the resturants having 'Excellent' and 'Very Good' rating ConnaughtPlace = Delhi[(Delhi.Locality.isin(['Connaught Place'])) & (Delhi['Rating text'].isin(['Excellent','Very Good']))] ConnaughtPlace = ConnaughtPlace.Cuisines.value_counts().reset_index() ## Extracing all the cuisens in a single list cuisien = [] for x in ConnaughtPlace['index']: cuisien.append(x) # cuisien = '[%s]'%', '.join(map(str, cuisien)) cuisien ['North Indian, Chinese, Italian, Continental', 'North Indian', 'North Indian, Italian, Asian, American', 'Continental, North Indian, Chinese, Mediterranean', 'Chinese', 'Continental, American, Asian, North Indian', 'North Indian, Continental', 'Continental, Mediterranean, Italian, North Indian', 'North Indian, European, Asian, Mediterranean', 'Japanese', 'Bakery, Desserts, Fast Food', 'North Indian, Afghani, Mughlai', 'Biryani, North Indian, Hyderabadi', 'Ice Cream', 'Continental, Mexican, Burger, American, Pizza, Tex-Mex', 'North Indian, Chinese', 'North Indian, Mediterranean, Asian, Fast Food', 'North Indian, European', 'Healthy Food, Continental, Italian', 'Continental, North Indian, Italian, Asian', 'Continental, Italian, Asian, Indian', 'Biryani, Hyderabadi', 'Bakery, Fast Food, Desserts', 'Italian, Mexican, Continental, North Indian, Finger Food', 'Cafe', 'Asian, North Indian', 'South Indian', 'Modern Indian', 'North Indian, Chinese, Continental, Italian', 'North Indian, Chinese, Italian, American, Middle Eastern', 'Fast Food, American, Burger'] from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt import pandas as pd comment_words = '' stopwords = set(STOPWORDS) # iterate through the csv file for val in cuisien: # typecaste each val to string val = str(val) # split the value tokens = val.split() # Converts each token into lowercase for i in range(len(tokens)): tokens[i] = tokens[i].lower() comment_words += " ".join(tokens)+" " wordcloud = WordCloud(width = 800, height = 800, background_color ='white', stopwords = stopwords, min_font_size = 10).generate(comment_words) # plot the WordCloud image plt.figure(figsize = (8, 8), facecolor = 'b', edgecolor='g') plt.title('Resturants cuisien - Top Resturants') plt.imshow(wordcloud) plt.axis("off") plt.tight_layout(pad = 0) plt.show()
The following cuisine appears to be performing well among top-rated restaurants.

  • North Indian
  • Chinese
  • Italian
  • American

3. How Many Restaurants Accept Online Food Delivery?

top_locality = Delhi.Locality.value_counts().head(10) sns.set_theme(style="darkgrid") plt.figure(figsize=(12,6)) ax = sns.countplot(y= "Locality", hue="Has Online delivery", data=Delhi[Delhi.Locality.isin(top_locality.index)]) plt.title('Resturants Online Delivery');

Apart from Shahdara, restaurants in other parts of the city accept online orders.

In Defense colony and Malviya Nagar, online delivery appears to be on the rise.

4. Understanding Restaurants Ratings Locality

Besides Malviya Nagar and Defense colony, the rest of the neighborhoods seemed to prefer going to restaurants than buying food online.

Now let us know how these restaurants in Malviya Nagar, Defense colony, rate when it comes to online delivery.

The number of highly rated restaurants appears to be high in Defense Colony, but Malviya Nagar appears to have done better as a result of Good and Average restaurants.

As eateries with a 'Bad' or 'Not Rated' rating are far fewer than those with a 'Good', 'Very Good', or 'Excellent' rating. As a result, residents in these areas prefer to order online.

5. Rating vs. Cost of Dining

plt.figure(figsize=(12,6)) sns.scatterplot(x="Average Cost for two", y="Aggregate rating", hue='Price range', data=Delhi) plt.xlabel("Average Cost for two") plt.ylabel("Aggregate rating") plt.title('Rating vs Cost of Two');

It is found that there is no relation between pricing and rating. For example, restaurant having good rating (like 4-5) have eateries encompasses the entire X axis with a wide range of prices.

6. Location of Highly Rated Restaurants Across New Delhi

Delhi['Rating text'].value_counts() Average 2495 Not rated 1425 Good 1128 Very Good 300 Poor 97 Excellent 28 Name: Rating text, dtype: int64 import plotly.express as px Highly_rated = Delhi[Delhi['Rating text'].isin(['Excellent'])] fig = px.scatter_mapbox(Highly_rated, lat="Latitude", lon="Longitude", hover_name="City", hover_data=["Aggregate rating", "Restaurant Name"], color_discrete_sequence=["fuchsia"], zoom=10, height=300) fig.update_layout(mapbox_style="open-street-map") fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}) fig.update_layout(title='Highle rated Resturants Location', autosize=True, hovermode='closest', showlegend=False) fig.update_layout( autosize=False, width=800, height=500,) fig.show()

The four cities indicated above account for almost 65 percent of the total information in the dataset. Besides the highest rated local restaurants, it would be fascinating to learn about the well-known eateries in the area. These are the verticals over which they can be found:

  • Ice Creams
  • Shakes
  • and Desserts

7. Common Eateries

Breakfast and Coffee Locations

types = { "Breakfast and Coffee" : ["Cafe Coffee Day", "Starbucks", "Barista", "Costa Coffee", "Chaayos", "Dunkin' Donuts"], "American": ["Domino's Pizza", "McDonald's", "Burger King", "Subway", "Dunkin' Donuts", "Pizza Hut"], "Ice Creams and Shakes": ["Keventers", "Giani", "Giani's", "Starbucks", "Baskin Robbins", "Nirula's Ice Cream"] } breakfast = Delhi[Delhi['Restaurant Name'].isin(types['Breakfast and Coffee'])] american = Delhi[Delhi['Restaurant Name'].isin(types['American'])] ice_cream = Delhi[Delhi['Restaurant Name'].isin(types['Ice Creams and Shakes'])] breakfast = breakfast[['Restaurant Name','Aggregate rating']].groupby('Restaurant Name').mean().reset_index().sort_values('Aggregate rating',ascending=False) breakfast https://blog.jovian.ai/explanatory-data-analysis-of-zomato-restaurant-data-71ba8c3c7e5e import plotly.express as px df= breakfast fig = px.bar(df, y='Aggregate rating', x='Restaurant Name', text='Aggregate rating', title="Breakfast and Coffee locations") fig.update_traces(texttemplate='%{text:.3s}', textposition='outside') fig.update_layout( autosize=False, width=800, height=500,) fig.show()

The Chaayos stores are doing much better. More of those are needed in Delhi. Café Coffee Day appears to have a low average rating. They must increase the quality of their offerings.

Fast Food Restaurants

american = american[['Restaurant Name','Aggregate rating']].groupby('Restaurant Name').mean().reset_index().sort_values('Aggregate rating',ascending=False) american Restaurant NameAggregate rating0Burger King3.4777783McDonald's3.4454552Dunkin' Donuts3.3000004Pizza Hut3.1583335Subway3.0473681Domino's Pizza2.794545import plotly.express as px df= american fig = px.bar(df, y='Aggregate rating', x='Restaurant Name', text='Aggregate rating', title="Fast Food Resturants") fig.update_traces(texttemplate='%{text:.3s}', textposition='outside') fig.update_layout( autosize=False, width=800, height=500,) fig.show()

Ice Cream Parlors

ice_cream = ice_cream[['Restaurant Name','Aggregate rating']].groupby('Restaurant Name').mean().reset_index().sort_values('Aggregate rating',ascending=False) ice_cream Restaurant NameAggregate rating5Starbucks3.7500002Giani's3.0117653Keventers2.9833330Baskin Robbins2.7692311Giani2.6750004Nirula's Ice Cream2.400000import plotly.express as px df= ice_cream fig = px.bar(df, y='Aggregate rating', x='Restaurant Name', text='Aggregate rating', title="Ice Cream Parlours") fig.update_traces(texttemplate='%{text:.3s}', textposition='outside') fig.update_layout( autosize=False, width=800, height=500,) fig.show()

Local brands appear to be doing better than foreign brands.

Conclusion

Here's a quick rundown of a few:

The dataset is skewed toward India and does not include all restaurant data from around the world.

Restaurants are rated in six different categories.

  • Not a PG-13 film
  • Average
  • Good
  • Excellent
  • Excellent

Although Connaught Palace has the most restaurants listed on Zomato, it does not offer online deliveries. The situation in Defense Colony and Malviya Nagar appears to be improving.

The following cuisine appears to be receiving good ratings at the top-rated restaurants.

Italian-American Chinese-North Indian

There is no link between the price and the rating. Some of the highest-rated restaurants are also the least expensive, and vice versa.

In terms of common eateries, Indian restaurants appear to be better rated for breakfast and coffee, whereas American restaurants appear to be better rated for fast food chains and ice cream parlors.

If you want to know more about our other restaurant data scraping services or Zomato restaurant data scraping service then contact Foodspark now!!

Know more : https://www.foodspark.io/how-analyzing-zomato-restaurant-data-can-help-you-extract-the-best-cuisines-data-in-local-areas.php