GroupBy with pipe for chaining in Pandas - Mini Project: Build & Apply

GroupBy with pipe for chaining

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You want to analyze the sales data to find the average and maximum sales per bread type.

🎯 Goal: Build a pandas DataFrame with sales data, then use groupby and pipe to calculate average and maximum sales per bread type in a clean, chained way.

📋 What You'll Learn

Create a pandas DataFrame with bread sales data

Create a function to calculate average and maximum sales

Use groupby on the bread type

Use pipe to apply the function after grouping

Print the final grouped summary

💡 Why This Matters

🌍 Real World

Grouping and summarizing sales data helps businesses understand product performance and make better decisions.

💼 Career

Data analysts and data scientists often use pandas groupby and pipe to write clear, readable data transformation pipelines.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Bread': ['Baguette', 'Baguette', 'Sourdough', 'Sourdough', 'Rye', 'Rye']
'Sales': [20, 30, 15, 25, 10, 12]

Pandas

# Create the sales_data DataFrame with columns 'Bread' and 'Sales'
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing the exact lists for 'Bread' and 'Sales'.

2

Define a summary function

Create a function called summary_stats that takes a DataFrame and returns a new DataFrame with two columns:
- 'Average': the mean of the 'Sales' column
- 'Max': the maximum of the 'Sales' column

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Bread': ['Baguette', 'Baguette', 'Sourdough', 'Sourdough', 'Rye', 'Rye'],
    'Sales': [20, 30, 15, 25, 10, 12]
})

# Define the summary_stats function
# Your code here

Need a hint?

Use df['Sales'].mean() and df['Sales'].max() inside the function and return a DataFrame with these values.

3

Group by bread and apply the function with pipe

Use sales_data.groupby('Bread') and then use pipe(summary_stats) to apply the summary_stats function to each group. Save the result in a variable called grouped_summary.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Bread': ['Baguette', 'Baguette', 'Sourdough', 'Sourdough', 'Rye', 'Rye'],
    'Sales': [20, 30, 15, 25, 10, 12]
})

def summary_stats(df):
    return pd.DataFrame({
        'Average': df['Sales'].mean(),
        'Max': df['Sales'].max()
    }, index=[0])

# Group sales_data by 'Bread' and apply summary_stats using pipe
# Your code here

Need a hint?

Use sales_data.groupby('Bread').pipe(lambda g: g.apply(summary_stats).reset_index(level=1, drop=True)) to apply the function to each group and keep the bread names as index.

4

Print the grouped summary

Print the variable grouped_summary to display the average and maximum sales per bread type.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Bread': ['Baguette', 'Baguette', 'Sourdough', 'Sourdough', 'Rye', 'Rye'],
    'Sales': [20, 30, 15, 25, 10, 12]
})

def summary_stats(df):
    return pd.DataFrame({
        'Average': df['Sales'].mean(),
        'Max': df['Sales'].max()
    }, index=[0])

# Group sales_data by 'Bread' and apply summary_stats using pipe
grouped_summary = sales_data.groupby('Bread').pipe(lambda g: g.apply(summary_stats).reset_index(level=1, drop=True))

# Print grouped_summary
# Your code here

Need a hint?

Use print(grouped_summary) to show the final result.