# Installing the libraries with the specified version.
%pip install numpy==2.0.2 pandas==2.2.2 matplotlib==3.10.0 seaborn==0.13.2 -q --user

[notice] A new release of pip is available: 25.3 -> 26.0.1
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

# 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

# uncomment and run the below code snippets if the dataset is present in the Google Drive
# from google.colab import drive
# drive.mount('/content/drive')

# Write your code here to read the data
df = pd.read_csv('./foodhub_order.csv')

# Write your code here to view the first 5 rows
df.head()

# Write your code here
df.shape

(1898, 9)

# Write your code here
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

# Write your code here
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

# Statistical summary of the data
df.describe()

# Write the code here
df[df['rating'] == 'Not given'].shape[0]

736

df['order_id'].nunique()

1898

df['order_id'].nunique()

1898

df['restaurant_name'].nunique()

178

print('Unique cuisine types:', df['cuisine_type'].nunique())
plt.figure(figsize = (15,5))
sns.countplot(data = df, x = 'cuisine_type')

Unique cuisine types: 14

<Axes: xlabel='cuisine_type', ylabel='count'>

sns.histplot(data=df,x='cost_of_the_order')
plt.show()
sns.boxplot(data=df,x='cost_of_the_order')
plt.show()

print('Days of the week', df['day_of_the_week'].nunique())
sns.countplot(data = df, x = 'day_of_the_week')

Days of the week 2

<Axes: xlabel='day_of_the_week', ylabel='count'>

print('Unique ratings:', df['rating'].nunique())
sns.countplot(data = df, x = 'rating')

Unique ratings: 4

<Axes: xlabel='rating', ylabel='count'>

sns.histplot(data=df,x='food_preparation_time')
plt.show()
sns.boxplot(data=df,x='food_preparation_time')
plt.show()

sns.histplot(data=df,x='delivery_time')
plt.show()
sns.boxplot(data=df,x='delivery_time')
plt.show()

# Write the code here
df['restaurant_name'].value_counts().head(5)

restaurant_name
Shake Shack                  219
The Meatball Shop            132
Blue Ribbon Sushi            119
Blue Ribbon Fried Chicken     96
Parm                          68
Name: count, dtype: int64

df_weekend = df[df['day_of_the_week'].isin(['Weekend'])]
df_weekend['cuisine_type'].value_counts().head(1)

cuisine_type
American    415
Name: count, dtype: int64

df_greater_than_20 = df[df['cost_of_the_order'] > 20]
print('The number of total orders that cost above 20 dollars is:', df_greater_than_20.shape[0])

percentage = (df_greater_than_20.shape[0] / df.shape[0]) * 100
print('The percentage of total orders that cost above 20 dollars is:', percentage)

The number of total orders that cost above 20 dollars is: 555
The percentage of total orders that cost above 20 dollars is: 29.24130663856691

mean_del_time = df['delivery_time'].mean()
print('The mean delivery time is', round(mean_del_time, 2), 'minutes')

The mean delivery time is 24.16 minutes

df['customer_id'].value_counts().head(3)

customer_id
52832    13
47440    10
83287     9
Name: count, dtype: int64

plt.figure(figsize=(15,7))
sns.boxplot(x = "cuisine_type", y = "cost_of_the_order", data = df, palette = 'deep', hue = "cuisine_type")
plt.xticks(rotation = 60)
plt.show()

plt.figure(figsize=(15,7))
sns.boxplot(x = "cuisine_type", y = "food_preparation_time", data = df, palette = 'deep', hue = "cuisine_type")
plt.xticks(rotation = 60)
plt.show()

plt.figure(figsize=(15,7))
sns.boxplot(x = "day_of_the_week", y = "delivery_time", data = df, palette = 'deep', hue = "day_of_the_week")
plt.xticks(rotation = 60)
plt.show()

# Cost of order by restaurant name - summary table
df_cost_by_restaurant = df.groupby('restaurant_name')['cost_of_the_order'].agg(
    Total_Revenue='sum',
    Orders='count',
    Avg_Cost_Per_Order='mean'
).sort_values(by='Total_Revenue', ascending=False)

df_cost_by_restaurant['Total_Revenue'] = df_cost_by_restaurant['Total_Revenue'].round(2)
df_cost_by_restaurant['Avg_Cost_Per_Order'] = df_cost_by_restaurant['Avg_Cost_Per_Order'].round(2)

df_cost_by_restaurant.head(14)

plt.figure(figsize=(15, 7))
sns.pointplot(x = 'rating', y = 'delivery_time', data = df)
plt.show()

plt.figure(figsize=(15,7))
sns.boxplot(x = "rating", y = "food_preparation_time", data = df, palette = 'deep', hue = "rating")
plt.xticks(rotation = 60)
plt.show()

plt.figure(figsize=(15,7))
sns.pointplot(x = 'rating', y = 'cost_of_the_order', data = df)
plt.show()

col_list = ['cost_of_the_order', 'food_preparation_time', 'delivery_time']
plt.figure(figsize=(15, 7))
sns.heatmap(df[col_list].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()

# ratings that are not Not given
df_rated = df[df['rating'] != 'Not given'].copy()
# convert rating to int
df_rated['rating'] = df_rated['rating'].astype('int')
#restaurant names with their rating counts
df_rating_count = df_rated.groupby(['restaurant_name'])['rating'].count().sort_values(ascending = False).reset_index()
df_rating_count.head()

# restaurant names that have rating counts greater than 50
rest_names = df_rating_count[df_rating_count['rating'] > 50]['restaurant_name']
# data of the restaurants that have rating counts greater than 50 and mean rating of the restaurants with rating counts greater than 50
df_data_greater_than_50 = df_rated[df_rated['restaurant_name'].isin(rest_names)].copy()
df_mean_rating = df_data_greater_than_50.groupby('restaurant_name')['rating'].mean().sort_values(ascending=False)

# restaurants with average rating greater than 4
df_avg_rating_greater_than_4 = df_mean_rating[df_mean_rating > 4].sort_values(ascending=False).reset_index()
print(df_avg_rating_greater_than_4)

             restaurant_name    rating
0          The Meatball Shop  4.511905
1  Blue Ribbon Fried Chicken  4.328125
2                Shake Shack  4.278195
3          Blue Ribbon Sushi  4.219178

def compute_rev(x):
    if x > 20:
        return x*0.25
    elif x > 5:
        return x*0.15
    else:
        return x*0

df['Revenue'] = df['cost_of_the_order'].apply(compute_rev) ## Write the apprpriate column name to compute the revenue
print('Total Revenue:', df['Revenue'].sum())

Total Revenue: 6166.303

df['total_time'] = df['food_preparation_time'] + df['delivery_time']
df_greater_than_60 = df[df['total_time'] > 60]
print('The number of total orders that took more than 60 minutes is:', df_greater_than_60.shape[0])

percentage = (df_greater_than_60.shape[0]/df.shape[0])*100
print('The percentage of total orders that took more than 60 minutes is:', round(percentage, 2),'%')

The number of total orders that took more than 60 minutes is: 200
The percentage of total orders that took more than 60 minutes is: 10.54 %

print('The mean delivery time on weekdays is around',
      round(df[df['day_of_the_week'] == 'Weekday']['delivery_time'].mean()),
     'minutes')
print('The mean delivery time on weekends is around',
      round(df[df['day_of_the_week'] == 'Weekend']['delivery_time'].mean()),
     'minutes')

The mean delivery time on weekdays is around 28 minutes
The mean delivery time on weekends is around 22 minutes

	order_id	customer_id	cost_of_the_order	food_preparation_time	delivery_time
count	1.898000e+03	1898.000000	1898.000000	1898.000000	1898.000000
mean	1.477496e+06	171168.478398	16.498851	27.371970	24.161749
std	5.480497e+02	113698.139743	7.483812	4.632481	4.972637
min	1.476547e+06	1311.000000	4.470000	20.000000	15.000000
25%	1.477021e+06	77787.750000	12.080000	23.000000	20.000000
50%	1.477496e+06	128600.000000	14.140000	27.000000	25.000000
75%	1.477970e+06	270525.000000	22.297500	31.000000	28.000000
max	1.478444e+06	405334.000000	35.410000	35.000000	33.000000

	Total_Revenue	Orders	Avg_Cost_Per_Order
restaurant_name
Shake Shack	3579.53	219	16.34
The Meatball Shop	2145.21	132	16.25
Blue Ribbon Sushi	1903.95	119	16.00
Blue Ribbon Fried Chicken	1662.29	96	17.32
Parm	1112.76	68	16.36
RedFarm Broadway	965.13	59	16.36
RedFarm Hudson	921.21	55	16.75
TAO	834.50	49	17.03
Han Dynasty	755.29	46	16.42
Blue Ribbon Sushi Bar & Grill	666.62	44	15.15
Rubirosa	660.45	37	17.85
Sushi of Gari 46	640.87	37	17.32
Nobu Next Door	623.67	42	14.85
Five Guys Burgers and Fries	506.47	29	17.46

	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

Project Python Foundations: FoodHub Data Analysis¶

Context¶

Objective¶

Data Description¶

Data Dictionary¶

Let us start by importing the required libraries¶

Understanding the structure of the data¶

Question 1: How many rows and columns are present in the data? [0.5 mark]¶

Observations:¶

Question 2: What are the datatypes of the different columns in the dataset? (The info() function can be used) [0.5 mark]¶

Observations:¶

Question 3: Are there any missing values in the data? If yes, treat them using an appropriate method. [1 mark]¶

Observations:¶

Question 4: Check the statistical summary of the data. What is the minimum, average, and maximum time it takes for food to be prepared once an order is placed? [2 marks]¶

Observations:¶

Question 5: How many orders are not rated? [1 mark]¶

Observations:¶

Exploratory Data Analysis (EDA)¶

Univariate Analysis¶

Question 6: Explore all the variables and provide observations on their distributions. (Generally, histograms, boxplots, countplots, etc. are used for univariate exploration.) [9 marks]¶

Order ID¶

Customer ID¶

Observations:¶

Restaurant Name¶

Observations¶

Cuisine Type¶

Observations¶

Cost of the order¶

Observations¶

Day of the week¶

Observations¶

Rating¶

Observations¶

Food Preparation Time¶

Observations¶

Delivery Time¶

Observations:¶

Question 7: Which are the top 5 restaurants in terms of the number of orders received? [1 mark]¶

Observations:¶

Question 8: Which is the most popular cuisine on weekends? [1 mark]¶

Observations:¶

Question 9: What percentage of the orders cost more than 20 dollars? [2 marks]¶

Observations:¶

Question 10: What is the mean order delivery time? [1 mark]¶

Observations:¶

Question 11: The company has decided to give 20% discount vouchers to the top 3 most frequent customers. Find the IDs of these customers and the number of orders they placed. [1 mark]¶

Observations:¶

Multivariate Analysis¶

Question 12: Perform a multivariate analysis to explore relationships between the important variables in the dataset. (It is a good idea to explore relations between numerical variables as well as relations between numerical and categorical variables) [10 marks]¶

Cuisine vs Cost¶

Observations¶

Cuisine vs Food Preparation Time¶

Observations¶

Days of Week vs Delivery time¶

Observations¶

Revenue¶

Observations¶

Rating vs Delivery Time¶

Observations¶

Rating vs Food preparation time¶

Observation¶

Rating vs Cost of Order¶

Observation¶

Heatmaps¶

Observations¶

Question 14: The company charges the restaurant 25% on the orders having cost greater than 20 dollars and 15% on the orders having cost greater than 5 dollars. Find the net revenue generated by the company across all orders. [3 marks]¶

Observations:¶

Question 15: The company wants to analyze the total time required to deliver the food. What percentage of orders take more than 60 minutes to get delivered from the time the order is placed? (The food has to be prepared and then delivered.) [2 marks]¶

Observations:¶

Question 16: The company wants to analyze the delivery time of the orders on weekdays and weekends. How does the mean delivery time vary during weekdays and weekends? [2 marks]¶

Observations:¶

Conclusion and Recommendations¶

Question 17: What are your conclusions from the analysis? What recommendations would you like to share to help improve the business? (You can use cuisine type and feedback ratings to drive your business recommendations.) [6 marks]¶

Conclusions:¶

Recommendations:¶