Why custom functions matter in Pandas - See It in Action

Why custom functions matter

📖 Scenario: You work in a small store that tracks daily sales. You want to analyze sales data to find out which days had high sales and which had low sales. To do this efficiently, you will use a custom function to label each day's sales as 'High' or 'Low'.

🎯 Goal: Build a small pandas program that uses a custom function to label sales as 'High' or 'Low' based on a sales threshold.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Define a sales threshold variable

Write a custom function to label sales as 'High' or 'Low'

Apply the custom function to the DataFrame

Print the updated DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often analyze sales data to understand performance. Custom functions help automate labeling and categorizing data quickly.

💼 Career

Data analysts and scientists use custom functions in pandas to clean, transform, and analyze data efficiently in real jobs.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with two columns: 'Day' and 'Sales'. Use these exact values: Days are 'Mon', 'Tue', 'Wed', 'Thu', 'Fri'. Sales are 200, 150, 300, 100, 250 respectively.

Pandas

import pandas as pd
# Create the sales_data DataFrame with the given days and sales
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing the two columns.

2

Set the sales threshold

Create a variable called threshold and set it to 200. This will be the cutoff to decide if sales are high or low.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 150, 300, 100, 250]
})
# Create the threshold variable and set it to 200
# Your code here

Need a hint?

Just assign the number 200 to the variable threshold.

3

Write the custom function to label sales

Define a function called label_sales that takes one argument sale. The function should return the string 'High' if sale is greater than or equal to threshold, otherwise return 'Low'. Then, create a new column in sales_data called 'Label' by applying the label_sales function to the 'Sales' column.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 150, 300, 100, 250]
})
threshold = 200
# Define the label_sales function
# Apply label_sales to sales_data['Sales'] and create sales_data['Label']
# Your code here

Need a hint?

Use def to create the function. Use apply() to run it on the sales column.

4

Print the updated DataFrame

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

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 150, 300, 100, 250]
})
threshold = 200
def label_sales(sale):
    if sale >= threshold:
        return 'High'
    else:
        return 'Low'
sales_data['Label'] = sales_data['Sales'].apply(label_sales)
# Print the sales_data DataFrame
# Your code here

Need a hint?

Use print(sales_data) to show the full table.