Pipe for method chaining in Data Analysis Python - Mini Project: Build & Apply

Using Pipe for Method Chaining in Data Analysis

📖 Scenario: Imagine you work in a small bakery. You have sales data for different types of bread sold each day. You want to clean and analyze this data step-by-step in a clear and simple way.

🎯 Goal: You will create a small sales data table, set a filter value, use pipe to chain methods for filtering and calculating total sales, and finally print the result.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Create a filter threshold variable

Use pipe method chaining to filter and sum sales

Print the final total sales value

💡 Why This Matters

🌍 Real World

Data analysts often clean and transform data step-by-step. Using pipe helps keep the code readable and easy to follow.

💼 Career

Knowing how to use method chaining with pipe is useful for data scientists and analysts to write clean, maintainable data processing code.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with these exact columns and rows:
Product: 'Sourdough', 'Baguette', 'Rye', 'Focaccia'
Sales: 150, 200, 90, 120

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with Product and Sales columns
# Your code here

Hint

Use pd.DataFrame with a dictionary containing lists for 'Product' and 'Sales'.

2

Set the sales filter threshold

Create a variable called min_sales and set it to 100 to filter products with sales above this number.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Sourdough', 'Baguette', 'Rye', 'Focaccia'],
    'Sales': [150, 200, 90, 120]
})
# Create min_sales variable with value 100
# Your code here

Hint

Just assign the number 100 to the variable min_sales.

3

Use pipe to filter and sum sales

Use the pipe method on sales_data to filter rows where Sales is greater than min_sales, then calculate the sum of the Sales column. Store the result in a variable called total_sales. Use a lambda function inside pipe.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Sourdough', 'Baguette', 'Rye', 'Focaccia'],
    'Sales': [150, 200, 90, 120]
})
min_sales = 100
# Use pipe to filter sales_data and sum Sales column
# Your code here

Hint

Inside pipe, use a lambda that filters rows with Sales greater than min_sales. Then sum the Sales column.

4

Print the total sales

Print the variable total_sales to show the sum of sales for products with sales above min_sales.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Sourdough', 'Baguette', 'Rye', 'Focaccia'],
    'Sales': [150, 200, 90, 120]
})
min_sales = 100
total_sales = sales_data.pipe(lambda df: df[df['Sales'] > min_sales])['Sales'].sum()
# Print total_sales
# Your code here

Hint

Use print(total_sales) to show the result.