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. As a Data Scientist in this company there are some key questions that need to be answered. I have to perform a 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.
# Installing the libraries with the specified version.
# !pip install numpy==1.25.2 pandas==1.5.3 matplotlib==3.7.1 seaborn==0.13.1 -q --user
Note: After running the above cell, kindly restart the notebook kernel and run all cells sequentially from the start again.
# 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 following lines for Google Colab
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
# Write your code here to read the data
# Reading FoodHub Master Data
data = pd.read_csv('/content/drive/MyDrive/content/foodhub_order.csv')
# Write your code here to view the first 5 rows
# Reading 5 top rows
data.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 |
# Write your code here
print('Number of rows in the data:', data.shape[0])
print('Number of columns in the data:', data.shape[1])
Number of rows in the data: 1898 Number of columns in the data: 9
# Write your code here
data.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
Checking for missing values:
# Write your code here
data.isnull().sum()
| 0 | |
|---|---|
| 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 |
Using this space to convert columns to more related type for them
data[['customer_id','order_id']]=data[['customer_id','order_id']].astype(object)
# Write your code here
data.describe()
| 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 |
Preparation time: On average, it takes 27 minutes and 37 seconds to prepare an order with the time ranging between 20 minutes as minimum and goes up to 35 minutes as maximum time.# Write the code here
data.groupby('rating')['rating'].count()
| rating | |
|---|---|
| rating | |
| 3 | 188 |
| 4 | 386 |
| 5 | 588 |
| Not given | 736 |
chr = sns.countplot(data = data, x = 'cuisine_type', stat='percent',
legend=True, order=data['cuisine_type'].value_counts().index);
plt.xlabel('Type of food');
plt.ylabel('Total orders(%)');
plt.xticks(rotation=90);
plt.title('Prefered food by Customers');
chr.bar_label(chr.containers[0], fmt='%.0f%%');
plt.show()
# Write the code here
sns.histplot(data = data, x = 'cost_of_the_order', kde=True);
plt.xlabel('Cost of the order');
plt.ylabel('Count');
plt.title('Distribution of Cost per order');
plt.show()
sns.boxplot(data = data, x = 'cost_of_the_order');
plt.xlabel('Cost of the order');
plt.ylabel('Count');
plt.title('Distribution of Cost per order');
plt.show()
chr = sns.histplot(data = data, x = 'day_of_the_week', stat='percent', legend=True);
plt.xlabel('Day of the week');
plt.ylabel('Orders by day');
plt.title('Distribution of Order per day');
chr.bar_label(chr.containers[0], fmt='%.1f%%');
plt.show()
chr = sns.histplot(data = data, x = 'rating', stat='percent', legend=True);
plt.xlabel('Rating');
plt.ylabel('Value distribution');
plt.title('Rating by orders');
chr.bar_label(chr.containers[0], fmt='%.1f%%');
plt.show()
Changing Rating to float and "Not Given" values to NaN:
#Replace Not given values' with nan
data['rating'] = data['rating'].replace(['Not given'],np.nan)
#Convert the data type to a float
data['rating'] = data['rating'].astype(float)
chr = sns.histplot(data = data, x = 'food_preparation_time', legend=True, kde=True);
plt.xlabel('Preparation time');
plt.ylabel('Total Orders');
plt.show()
chr = sns.histplot(data = data, x = 'delivery_time', legend=True, kde=True);
plt.xlabel('Delivery time');
plt.ylabel('Total Orders');
plt.show()
Observations
# Write the code here
data['restaurant_name'].value_counts().nlargest(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
Top 5 restaurantes by orders received are:
# Write the code here
data.groupby('day_of_the_week')['cuisine_type'].value_counts().nlargest(1)
day_of_the_week cuisine_type Weekend American 415 Name: count, dtype: int64
The most popular food delivered on weekends is American Food
# Write the code here
df_v = [data[data['cost_of_the_order']>20].shape[0]/data.shape[0]*100,
data[data['cost_of_the_order']<21].shape[0]/data.shape[0]*100]
df_l = ['More than 20 dollars', 'Less than 20 dollars']
# Creating chart
plt.figure(figsize=(6,6))
sns.set_style("whitegrid")
sns.set_palette("pastel")
plt.pie(df_v, labels=df_l, autopct='%.0f%%')
# Add a title
plt.title("Percentage of orders that cost more than $20")
# Display the plot
plt.show()
# Write the code here
round(data['delivery_time'].mean(), 1)
24.2
# Write the code here
data['customer_id'].value_counts().nlargest(3)
customer_id 52832 13 47440 10 83287 9 Name: count, dtype: int64
Change data types for the following questions in required columns
#Convert the data type to a float
data['order_id'] = data['order_id'].astype(str)
data['customer_id'] = data['customer_id'].astype(str)
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1898 entries, 0 to 1897 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 order_id 1898 non-null object 1 customer_id 1898 non-null object 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 1162 non-null float64 7 food_preparation_time 1898 non-null int64 8 delivery_time 1898 non-null int64 9 total_delivery_time 1898 non-null int64 dtypes: float64(2), int64(3), object(5) memory usage: 148.4+ KB
# Write the code here
plt.figure(figsize=(7,3))
sns.heatmap(data=data[['cost_of_the_order', 'rating', 'food_preparation_time', 'delivery_time']].corr(), annot=True, cmap='Spectral', vmin=-1, vmax=1)
plt.show()
sns.boxplot(data = data, x = 'rating', y = 'cost_of_the_order');
plt.xlabel('Rating');
plt.ylabel('Cost of the order');
plt.show()
sns.boxplot(data = data, x = 'cuisine_type', y = 'cost_of_the_order', hue='cuisine_type');
plt.xlabel('Cuisine Type');
plt.ylabel('Cost of the order');
plt.xticks(rotation=90);
plt.show()
sns.boxplot(data = data, x = 'day_of_the_week', y = 'food_preparation_time', hue='day_of_the_week');
plt.xlabel('Day of the week');
plt.ylabel('Preparation time');
plt.show()
Creating a separate Dataframe to get restaurantes with rating mean and count
# Write the code here
df = data.groupby('restaurant_name')['rating'].agg(['mean','count'])
| mean | count | |
|---|---|---|
| restaurant_name | ||
| 'wichcraft | 5.000000 | 1 |
| 12 Chairs | 4.500000 | 2 |
| 5 Napkin Burger | 4.000000 | 2 |
| 67 Burger | 5.000000 | 1 |
| Alidoro | NaN | 0 |
| ... | ... | ... |
| Zero Otto Nove | 4.000000 | 1 |
| brgr | 3.000000 | 1 |
| da Umberto | 5.000000 | 1 |
| ilili Restaurant | 4.153846 | 13 |
| indikitch | 4.500000 | 2 |
178 rows × 2 columns
Filtering now restaurantes according to the conditions defined in the question
df = df[(df['count']>50) & (df['mean']>4)]
df
| mean | count | |
|---|---|---|
| restaurant_name | ||
| Blue Ribbon Fried Chicken | 4.328125 | 64 |
| Blue Ribbon Sushi | 4.219178 | 73 |
| Shake Shack | 4.278195 | 133 |
| The Meatball Shop | 4.511905 | 84 |
The restaurants that will get the promotional advertisement are:
Creating 2 separete datasets to get the revenue by category
fd_20 = data[data['cost_of_the_order']>20]
fd_5 = data[(data['cost_of_the_order']>5) & (data['cost_of_the_order']<=20)]
Now let's compute the revenue by category
fd_20_rv = fd_20['cost_of_the_order'].sum()*0.25
fd_5_rv = fd_5['cost_of_the_order'].sum()*0.15
Finally combining the 2 entries and pinting
print(fd_20_rv + fd_5_rv)
6166.303
We first need to add a new column adding up both preparation time and delivery time. New column name is: total_delivery_time
data['total_delivery_time'] = data['food_preparation_time'] + data['delivery_time']
Now we can work with the new column to filter values based on the criteria defined in the question and calculate the percentage of orders taken more than 1 hour
round(data[data['total_delivery_time'] > 60].shape[0]/data.shape[0]*100, 2)
10.54
# Write the code here
data.groupby('day_of_the_week')['delivery_time'].mean().round(2)
day_of_the_week Weekday 28.34 Weekend 22.47 Name: delivery_time, dtype: float64
plt.figure(figsize=(6,6))
sns.boxplot(data = data, x = 'day_of_the_week', y = 'delivery_time');
plt.xlabel('Day of the week');
plt.ylabel('Delivery time');
plt.title('Delivery time by day of the week');
plt.show()
