Swiggy Restaurant Analysis Notebook
import pandas as pd
import numpy as np
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import squarifydf = pd.read_csv("swiggy.csv")df.head(10)
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| ID | Area | City | Restaurant | Price | Avg ratings | Total ratings | Food type | Address | Delivery time | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 211 | Koramangala | Bangalore | Tandoor Hut | 300.0 | 4.4 | 100 | Biryani,Chinese,North Indian,South Indian | 5Th Block | 59 |
| 1 | 221 | Koramangala | Bangalore | Tunday Kababi | 300.0 | 4.1 | 100 | Mughlai,Lucknowi | 5Th Block | 56 |
| 2 | 246 | Jogupalya | Bangalore | Kim Lee | 650.0 | 4.4 | 100 | Chinese | Double Road | 50 |
| 3 | 248 | Indiranagar | Bangalore | New Punjabi Hotel | 250.0 | 3.9 | 500 | North Indian,Punjabi,Tandoor,Chinese | 80 Feet Road | 57 |
| 4 | 249 | Indiranagar | Bangalore | Nh8 | 350.0 | 4.0 | 50 | Rajasthani,Gujarati,North Indian,Snacks,Desser... | 80 Feet Road | 63 |
| 5 | 254 | Indiranagar | Bangalore | Treat | 800.0 | 4.5 | 100 | Mughlai,North Indian | 100 Feet Road | 56 |
| 6 | 258 | Indiranagar | Bangalore | Chinita Real Mexican Food | 1000.0 | 4.5 | 500 | Mexican,Beverages,Salads | Double Road | 53 |
| 7 | 263 | Koramangala | Bangalore | Cupcake Noggins - Cakespastries And Desserts | 150.0 | 4.3 | 100 | Desserts,British,Bakery,Pizzas,Snacks | 4Th Block | 57 |
| 8 | 267 | Domlur | Bangalore | Tea Brew | 350.0 | 4.1 | 100 | American,Italian,Beverages,Continental,Chinese... | Double Road | 57 |
| 9 | 308 | Koramangala | Bangalore | Bangaliana | 300.0 | 4.0 | 500 | Bengali | 7Th Block | 57 |
df.describe<bound method NDFrame.describe of ID Area City Restaurant \
0 211 Koramangala Bangalore Tandoor Hut
1 221 Koramangala Bangalore Tunday Kababi
2 246 Jogupalya Bangalore Kim Lee
3 248 Indiranagar Bangalore New Punjabi Hotel
4 249 Indiranagar Bangalore Nh8
... ... ... ... ...
8675 464626 Panjarapole Cross Road Ahmedabad Malt Pizza
8676 465835 Rohini Delhi Jay Mata Ji Home Kitchen
8677 465872 Rohini Delhi Chinese Kitchen King
8678 465990 Rohini Delhi Shree Ram Paratha Wala
8679 466488 Navrangpura Ahmedabad Sassy Street
Price Avg ratings Total ratings \
0 300.0 4.4 100
1 300.0 4.1 100
2 650.0 4.4 100
3 250.0 3.9 500
4 350.0 4.0 50
... ... ... ...
8675 500.0 2.9 80
8676 200.0 2.9 80
8677 150.0 2.9 80
8678 150.0 2.9 80
8679 250.0 2.9 80
Food type Address \
0 Biryani,Chinese,North Indian,South Indian 5Th Block
1 Mughlai,Lucknowi 5Th Block
2 Chinese Double Road
3 North Indian,Punjabi,Tandoor,Chinese 80 Feet Road
4 Rajasthani,Gujarati,North Indian,Snacks,Desser... 80 Feet Road
... ... ...
8675 Pizzas Navrangpura
8676 South Indian Rohini
8677 Chinese,Snacks,Tandoor Rohini
8678 North Indian,Indian,Snacks Rohini
8679 Chaat,Snacks,Chinese Navrangpura
Delivery time
0 59
1 56
2 50
3 57
4 63
... ...
8675 40
8676 28
8677 58
8678 28
8679 44
[8680 rows x 10 columns]>
num_records = len(df)
num_records8680
df.shape(8680, 10)
price_range_counts = df['Price'].value_counts()
price_pie =px.pie(price_range_counts, names = price_range_counts.index, values= price_range_counts.values)
price_range_counts.head()Price
300.0 1776
200.0 1774
250.0 968
400.0 838
500.0 605
Name: count, dtype: int64
Data Analysis
#Correlation between Restaurant Price and Average Rating
# Correlation between restaurant price and average rating
fig = px.scatter(df, x='Price', y='Avg ratings', title='Correlation between Restaurant Price and Average Rating', color_discrete_sequence=['red'])
fig.update_layout(template="plotly_dark")
fig.show()
#Most Popular Food Types in Top 10 Cities
# Group by 'City' and 'Food type', then count occurrences
city_food_counts = df.groupby(['City', 'Food type']).size().reset_index(name='Count')
# Find the most popular food type in each city
popular_food_types = city_food_counts.loc[city_food_counts.groupby('City')['Count'].idxmax()]
# Get the top 10 cities with the most popular food types
top_10_cities = popular_food_types.nlargest(10, 'Count')
# Prepare the data for the treemap
labels = top_10_cities['City'] + ' - ' + top_10_cities['Food type']
sizes = top_10_cities['Count']
# Create the treemap
import matplotlib.pyplot as plt
import squarify
plt.figure(figsize=(12, 6))
squarify.plot(sizes=sizes, label=labels, alpha=0.8)
plt.axis('off')
plt.title('Most Popular Food Types in Top 10 Cities')
plt.show()
Top 10 Cities ordering food from Swiggy
city_count = df["City"].value_counts()
top_n_cities = city_count.nlargest(10)
vis= px.pie(names=top_n_cities.index, values= top_n_cities.values, title = f'Top 10 Cities Ordering Food from Swiggy',labels={'City': 'City'}, width = 650, height= 650)
fig.update_layout(template="plotly_dark")
vis.show()
#Heat Mapnumeric_df = df.select_dtypes(include=[np.number])
correlation_matrix = numeric_df.corr()
fig = go.Figure(data=go.Heatmap(z=correlation_matrix, x=correlation_matrix.columns, y=correlation_matrix.columns,colorscale='Viridis') )
fig.update_layout(title='Correlation Heatmap of Factors Affecting Average Rating')
fig.show()