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# Histogram for 'cost_of_the_order'
plt.figure(figsize=(8, 6))
sns.histplot(data=df, x='cost_of_the_order')
plt.title('Distribution of Cost of the Order')
plt.xlabel('Cost of the Order')
plt.ylabel('Count')
plt.show()# Write the code here
Marks: 60
The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.
The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.
The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business.
The data contains the different data related to a food order. The detailed data dictionary is given below.
# import libraries for data manipulation
import numpy as np
import pandas as pd
# import libraries for data visualization
import matplotlib.pyplot as plt
import seaborn as sns
# read the data
df = pd.read_csv('foodhub_order.csv')
# returns the first 5 rows
df.head()
| order_id | customer_id | restaurant_name | cuisine_type | cost_of_the_order | day_of_the_week | rating | food_preparation_time | delivery_time | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1477147 | 337525 | Hangawi | Korean | 30.75 | Weekend | Not given | 25 | 20 |
| 1 | 1477685 | 358141 | Blue Ribbon Sushi Izakaya | Japanese | 12.08 | Weekend | Not given | 25 | 23 |
| 2 | 1477070 | 66393 | Cafe Habana | Mexican | 12.23 | Weekday | 5 | 23 | 28 |
| 3 | 1477334 | 106968 | Blue Ribbon Fried Chicken | American | 29.20 | Weekend | 3 | 25 | 15 |
| 4 | 1478249 | 76942 | Dirty Bird to Go | American | 11.59 | Weekday | 4 | 25 | 24 |
The DataFrame has 9 columns as mentioned in the Data Dictionary. Data in each row corresponds to the order placed by a customer.
# Check the number of rows and columns
num_rows, num_cols = df.shape
# Print the results
print("Number of rows:", num_rows)
print("Number of columns:", num_cols)
Number of rows: 1898 Number of columns: 9
We have 1898 rows, 9 columns in the dataset.
# Use info() to print a concise summary of the DataFrame
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1898 entries, 0 to 1897 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 order_id 1898 non-null int64 1 customer_id 1898 non-null int64 2 restaurant_name 1898 non-null object 3 cuisine_type 1898 non-null object 4 cost_of_the_order 1898 non-null float64 5 day_of_the_week 1898 non-null object 6 rating 1898 non-null object 7 food_preparation_time 1898 non-null int64 8 delivery_time 1898 non-null int64 dtypes: float64(1), int64(4), object(4) memory usage: 133.6+ KB
We have 1 decimal, 4 integer and 4 categorical variables in the dataset.
# Check for missing values
df.isnull().sum()
order_id 0 customer_id 0 restaurant_name 0 cuisine_type 0 cost_of_the_order 0 day_of_the_week 0 rating 0 food_preparation_time 0 delivery_time 0 dtype: int64
There are 0 missing values in the dataset. No need to do an further operation.
# Calculate the minimum, average, and maximum food preparation time
min_prep_time = df['food_preparation_time'].min()
avg_prep_time = df['food_preparation_time'].mean()
max_prep_time = df['food_preparation_time'].max()
# Print the results
print("Minimum food preparation time:", min_prep_time)
print("Average food preparation time:", avg_prep_time)
print("Maximum food preparation time:", max_prep_time)
Minimum food preparation time: 20 Average food preparation time: 27.371970495258168 Maximum food preparation time: 35
Minimum food preparation time: 20 mins
Average food preparation time: 27.37 mins
Maximum food preparation time: 35 mins
# Count the number of orders that are not rated
num_unrated_orders = df[df['rating'] == 'Not given'].shape[0]
# Print the number of orders that are not rated
print("Number of orders not rated:", num_unrated_orders)# Write your code here
Number of orders not rated: 736
736 orders are not rated.
# Histogram for 'cost_of_the_order'
plt.figure(figsize=(8, 6))
sns.histplot(data=df, x='cost_of_the_order')
plt.title('Distribution of Cost of the Order')
plt.xlabel('Cost of the Order')
plt.ylabel('Count')
plt.show()# Write the code here
Most of the orders are ranged between 10-15$ range. Also distributions seems right skewed.
# Create the histogram
plt.figure(figsize=(10, 6))
plt.hist(df['food_preparation_time'], bins=10, edgecolor='black')
plt.xlabel('Food Preparation Time')
plt.ylabel('Frequency')
plt.title('Histogram of Food Preparation Time')
plt.tight_layout()
# Display the plot
plt.show()
Food preperation time distribution seems uniform and ranges between 20 - 34 minutes.
# Combine 'food_preparation_time' and 'delivery_time' into a single DataFrame
time_df = pd.DataFrame({'Food Preparation Time': df['food_preparation_time'],
'Delivery Time': df['delivery_time']})
# Box plot for 'food_preparation_time' and 'delivery_time'
plt.figure(figsize=(8, 6))
sns.boxplot(data=time_df)
plt.title('Box Plot of Food Preparation Time and Delivery Time')
plt.ylabel('Time (minutes)')
plt.show()
In terms of total delivery time, food preparation is taking more time than the delivery.
# Count plot for 'day_of_the_week'
plt.figure(figsize=(8, 6))
sns.countplot(data=df, x='day_of_the_week')
plt.title('Count of Orders by Day of the Week')
plt.xlabel('Day of the Week')
plt.ylabel('Count')
plt.show()
Even if the weekends are just 2 days, the number of orders are more than double in the weekends.
# Count plot for 'rating'
plt.figure(figsize=(8, 6))
sns.countplot(data=df, x='rating')
plt.title('Count of Orders by Rating')
plt.xlabel('Rating')
plt.ylabel('Count')
plt.show()
There is no rating below than 3 in this dataset and most of the rated orders got 5 points.
# Group the DataFrame by 'restaurant_name' and count the number of orders
restaurant_orders = df.groupby('restaurant_name')['order_id'].count()
# Sort the restaurants in descending order based on the number of orders
top_restaurants = restaurant_orders.sort_values(ascending=False).head(5)
# Print the top 5 restaurants
print("Top 5 restaurants by number of orders:")
print(top_restaurants)
Top 5 restaurants by number of orders: restaurant_name Shake Shack 219 The Meatball Shop 132 Blue Ribbon Sushi 119 Blue Ribbon Fried Chicken 96 Parm 68 Name: order_id, dtype: int64
Top 5 restaurants by number of orders:
Shake Shack
The Meatball Shop
Blue Ribbon Sushi
Blue Ribbon Fried Chicken
Parm
# Filter the data for weekends
weekend_data = df[df['day_of_the_week'].isin(['Weekend'])]
# Count the frequency of each cuisine type
cuisine_frequency = weekend_data['cuisine_type'].value_counts()
print("Cuisine frequency is:,", cuisine_frequency)
# Get the most popular cuisine
most_popular_cuisine = cuisine_frequency.idxmax()
# Print the most popular cuisine
print("The most popular cuisine on weekends is:", most_popular_cuisine)
Cuisine frequency is:, cuisine_type American 415 Japanese 335 Italian 207 Chinese 163 Mexican 53 Indian 49 Mediterranean 32 Middle Eastern 32 Thai 15 French 13 Korean 11 Southern 11 Spanish 11 Vietnamese 4 Name: count, dtype: int64 The most popular cuisine on weekends is: American
With 415 orders in the weekends, American cuisine is the most popular cuisine in the weekends.
# Number of total orders
total_orders = len(df)
print("Total orders:" , total_orders)
# Number of orders above 20$
orders_over_20 = len(df[df['cost_of_the_order'] > 20])
print("Orders over 20$:" , orders_over_20)
# Calculating the percentage
percentage_over_20 = (orders_over_20 / total_orders) * 100
print("Percentage of orders that cost more than $20:", percentage_over_20)
Total orders: 1898 Orders over 20$: 555 Percentage of orders that cost more than $20: 29.24130663856691
%29.24 of the orders are more than 20$.
# The mean order delivery time
mean_delivery_time = df['delivery_time'].mean()
print("Mean order delivery time:", mean_delivery_time)
Mean order delivery time: 24.161749209694417
Average order delivery time is 24.161
# Group the dataset by 'customer_id' and count the number of orders
customer_orders = df.groupby('customer_id')['order_id'].count()
print("Customer orders:\n", customer_orders.head(5))
print()
# Sort the customers in descending order based on the number of orders
top_customers = customer_orders.sort_values(ascending=False).head(3)
print("Top customers:\n", top_customers)
print()
# Get the customer IDs and their corresponding number of orders
customer_ids = top_customers.index.tolist()
order_counts = top_customers.values.tolist()
# Print the IDs and number of orders for the top 3 customers
print("Top 3 most frequent customers:")
for i in range(len(customer_ids)):
print("Customer ID:", customer_ids[i])
print("Number of Orders:", order_counts[i])
print()
Customer orders: customer_id 1311 1 5139 1 5444 1 5693 1 6987 2 Name: order_id, dtype: int64 Top customers: customer_id 52832 13 47440 10 83287 9 Name: order_id, dtype: int64 Top 3 most frequent customers: Customer ID: 52832 Number of Orders: 13 Customer ID: 47440 Number of Orders: 10 Customer ID: 83287 Number of Orders: 9
Top 3 most frequent customers:
Customer ID: 52832 - Number of Orders: 13
Customer ID: 47440 - Number of Orders: 10
Customer ID: 83287 - Number of Orders: 9
# Selecting the important variables for analysis
important_vars = ['cost_of_the_order', 'food_preparation_time', 'delivery_time', 'cuisine_type', 'rating']
# Creating a DataFrame with the selected variables
selected_df = df[important_vars]
# Scatter plot matrix
sns.pairplot(selected_df)
plt.show()
There is not much clear relations coming from the pair graph.
# Box plot with categorical variable
plt.figure(figsize=(10, 6))
sns.boxplot(x='cuisine_type', y='cost_of_the_order', data=df)
plt.xticks(rotation=45)
plt.title('Box Plot of Cost of Order by Cuisine Type')
plt.show()
All order costs vary between range of 5$-35$. Korean and Vietnamese cuisines seems to be the cheapest ones accross all of the cuisines. On the other side, there is no order below $10 for the French and Spanish cuisines.
# Heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(df[['cost_of_the_order', 'food_preparation_time', 'delivery_time']].corr(), annot=True)
plt.title('Correlation Heatmap')
plt.show()
There is no apparent correlation between cost of the order, preparation time or delivery times.
# Calculate the average rating_numeric for each cuisine_type
average_rating_by_cuisine = df.groupby('cuisine_type')['rating_numeric'].mean()
# Create a bar plot
plt.figure(figsize=(10, 6))
average_rating_by_cuisine.plot(kind='bar')
plt.xlabel('Cuisine Type')
plt.ylabel('Average Rating')
plt.title('Average Rating by Cuisine Type')
plt.xticks(rotation=45)
plt.ylim(3.5, plt.ylim()[1]) # Set the y-axis limits starting from 3
plt.tight_layout()
# Display the plot
plt.show()
Vietnamese and Korean cuisines gets the lowest ratings.
# Calculate the revenue for each cuisine by multiplying the count of order_id with the sum of cost_of_the_order
revenue_by_cuisine = df.groupby('cuisine_type').apply(lambda x: x['order_id'].count() * x['cost_of_the_order'].sum()).reset_index()
revenue_by_cuisine.columns = ['Cuisine Type', 'Revenue']
# Sort the DataFrame by revenue in descending order
revenue_by_cuisine = revenue_by_cuisine.sort_values('Revenue', ascending=False)
# Create a bar plot
plt.figure(figsize=(10, 6))
plt.bar(revenue_by_cuisine['Cuisine Type'], revenue_by_cuisine['Revenue'])
plt.xlabel('Cuisine Type')
plt.ylabel('Revenue')
plt.title('Revenue by Cuisine Type (Ordered)')
plt.xticks(rotation=45)
plt.tight_layout()
# Display the plot
plt.show()
# Create the box plot
plt.figure(figsize=(10, 6))
sns.boxplot(x='cuisine_type', y='food_preparation_time', data=df)
plt.xticks(rotation=45)
plt.xlabel('Cuisine Type')
plt.ylabel('Food Preparation Time')
plt.title('Box Plot of Food Preparation Time by Cuisine Type')
plt.tight_layout()
# Display the plot
plt.show()
Korean and Vietnamese food seesm to be the quickest but there is not much difference between the cuisines in averall.
# Convert 'rating' column to numeric values
df['rating_numeric'] = pd.to_numeric(df['rating'], errors='coerce')
# Filter the data based on the criteria
promotional_restaurants = df.groupby('restaurant_name').filter(lambda x: len(x) > 50 and x['rating_numeric'].mean() > 4)
promotional_restaurants
# Create a pivot table with restaurant name, average of 'rating_numeric', and count of 'order_id'
pivot_table = pd.pivot_table(df, values=['rating_numeric', 'order_id'],
index='restaurant_name',
aggfunc={'rating_numeric': 'mean',
'order_id': 'count'})
pivot_table
| order_id | rating_numeric | |
|---|---|---|
| restaurant_name | ||
| 'wichcraft | 1 | 5.000000 |
| 12 Chairs | 4 | 4.500000 |
| 5 Napkin Burger | 5 | 4.000000 |
| 67 Burger | 1 | 5.000000 |
| Alidoro | 1 | NaN |
| ... | ... | ... |
| Zero Otto Nove | 2 | 4.000000 |
| brgr | 2 | 3.000000 |
| da Umberto | 3 | 5.000000 |
| ilili Restaurant | 18 | 4.153846 |
| indikitch | 4 | 4.500000 |
178 rows × 2 columns
# Filter the pivot table based on the criteria of count of orders > 50 and average rating > 4
filtered_pivot_table = pivot_table[(pivot_table['order_id'] > 50) & (pivot_table['rating_numeric'] > 4)]
# Print the filtered pivot table
print(filtered_pivot_table)
order_id rating_numeric restaurant_name Blue Ribbon Fried Chicken 96 4.328125 Blue Ribbon Sushi 119 4.219178 Parm 68 4.128205 RedFarm Broadway 59 4.243902 RedFarm Hudson 55 4.176471 Shake Shack 219 4.278195 The Meatball Shop 132 4.511905
There are 7 restaurants fitting to the criteria given:
# Calculate the revenue for each order
df['revenue'] = 0
# Apply the charging percentages
df.loc[df['cost_of_the_order'] > 20, 'revenue'] = df['cost_of_the_order'] * 0.25
df.loc[(df['cost_of_the_order'] > 5) & (df['cost_of_the_order'] <= 20), 'revenue'] = df['cost_of_the_order'] * 0.15
# Calculate the net revenue across all orders
net_revenue = df['revenue'].sum()
# Print the net revenue
print("Net Revenue: $", net_revenue)
Net Revenue: $ 6166.303
Company's net revenue across all the orders: $ 6166.303
# Calculate the total time required for each order (food preparation time + delivery time)
df['total_time'] = df['food_preparation_time'] + df['delivery_time']
# Calculate the percentage of orders that take more than 60 minutes
percentage_above_60 = (df[df['total_time'] > 60].shape[0] / df.shape[0]) * 100
# Print the percentage
print("Percentage of orders taking more than 60 minutes: {:.2f}%".format(percentage_above_60))
Percentage of orders taking more than 60 minutes: 10.54%
Percentage of orders taking more than 60 minutes: 10.54%
# Calculate the mean delivery time for weekdays and weekends
mean_delivery_weekdays = df.loc[df['day_of_the_week'] == 'Weekday', 'delivery_time'].mean()
mean_delivery_weekends = df.loc[df['day_of_the_week'] == 'Weekend', 'delivery_time'].mean()
# Print the mean delivery time for weekdays and weekends
print("Mean Delivery Time on Weekdays: {:.2f} minutes".format(mean_delivery_weekdays))
print("Mean Delivery Time on Weekends: {:.2f} minutes".format(mean_delivery_weekends))
Mean Delivery Time on Weekdays: 28.34 minutes Mean Delivery Time on Weekends: 22.47 minutes