apply() function for custom logic in Data Analysis Python - Mini Project: Build & Apply

Using apply() Function for Custom Logic in Data Science

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You have a table of sales data and want to add a new column that classifies each day's sales as 'High' or 'Low' based on a sales threshold.

🎯 Goal: Build a program that uses the apply() function to add a new column to a sales DataFrame. This new column will label each row as 'High' or 'Low' sales depending on a threshold you set.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Define a sales threshold variable

Write a custom function to classify sales

Use the apply() function to apply this logic to the DataFrame

Print the updated DataFrame with the new classification column

💡 Why This Matters

🌍 Real World

Classifying data based on conditions is common in business to quickly understand performance, like sales levels or customer ratings.

💼 Career

Data analysts and scientists often use apply() to run custom logic on data columns for feature engineering and data cleaning.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
'Bread_Sales': [120, 80, 150, 90, 200]

Data Analysis Python

# Create the sales_data DataFrame with the given days and Bread_Sales values
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the exact keys and lists for days and sales.

2

Set the sales threshold

Create a variable called sales_threshold and set it to 100. This will be the cutoff to decide if sales are 'High' or 'Low'.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread_Sales': [120, 80, 150, 90, 200]
})

# Create sales_threshold variable and set it to 100
# Your code here

Hint

Just create a variable named sales_threshold and assign it the value 100.

3

Write a custom function and apply it

Define a function called classify_sales that takes one argument sales. It should return the string 'High' if sales is greater than or equal to sales_threshold, otherwise return 'Low'.
Then, use apply() on the Bread_Sales column of sales_data with classify_sales to create a new column called Sales_Level.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread_Sales': [120, 80, 150, 90, 200]
})

sales_threshold = 100

# Define classify_sales function
# Use apply() on Bread_Sales to create Sales_Level column
# Your code here

Hint

Write a function with an if-else to check sales against sales_threshold. Then use apply() on the Bread_Sales column to create Sales_Level.

4

Print the updated DataFrame

Write a print statement to display the sales_data DataFrame with the new Sales_Level column.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread_Sales': [120, 80, 150, 90, 200]
})

sales_threshold = 100

def classify_sales(sales):
    if sales >= sales_threshold:
        return 'High'
    else:
        return 'Low'

sales_data['Sales_Level'] = sales_data['Bread_Sales'].apply(classify_sales)

# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to show the full DataFrame with the new column.