melt() for unpivoting in Pandas - Mini Project: Build & Apply

Using melt() for unpivoting data in pandas

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. The sales data is recorded in a table where each column is a bread type and each row is a day. You want to reshape this data to a long format to analyze sales more easily.

🎯 Goal: Learn how to use the pandas melt() function to unpivot a wide table into a long table with one column for bread type and one for sales.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data for three bread types.

Define the columns to keep fixed during unpivoting.

Use melt() to transform the DataFrame from wide to long format.

Print the final melted DataFrame.

💡 Why This Matters

🌍 Real World

Unpivoting data is common when you want to convert wide tables into long tables for easier analysis and visualization.

💼 Career

Data analysts and scientists often reshape data using melt() to prepare it for charts, reports, or machine learning.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Day': ['Monday', 'Tuesday', 'Wednesday']
'Sourdough': [10, 12, 9]
'Baguette': [5, 7, 6]
'Rye': [3, 4, 2]

Pandas

import pandas as pd

# Create the sales_data DataFrame with the given data
# Your code here

Need a hint?

Use pd.DataFrame() with a dictionary where keys are column names and values are lists of data.

2

Define the id_vars for melting

Create a list called id_vars containing the column 'Day' to keep fixed during melting.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday'],
    'Sourdough': [10, 12, 9],
    'Baguette': [5, 7, 6],
    'Rye': [3, 4, 2]
})

# Define id_vars list with 'Day'
# Your code here

Need a hint?

The id_vars list tells melt() which columns to keep as they are.

3

Use melt() to unpivot the DataFrame

Use pd.melt() on sales_data with id_vars=id_vars, var_name='Bread', and value_name='Sales'. Save the result in a variable called melted_data.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday'],
    'Sourdough': [10, 12, 9],
    'Baguette': [5, 7, 6],
    'Rye': [3, 4, 2]
})

id_vars = ['Day']

# Use pd.melt() to unpivot sales_data into melted_data
# Your code here

Need a hint?

The melt() function turns columns into rows. Use var_name for the new column name of the old columns, and value_name for the values.

4

Print the melted DataFrame

Print the variable melted_data to see the unpivoted sales data.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday'],
    'Sourdough': [10, 12, 9],
    'Baguette': [5, 7, 6],
    'Rye': [3, 4, 2]
})

id_vars = ['Day']

melted_data = pd.melt(sales_data, id_vars=id_vars, var_name='Bread', value_name='Sales')

# Print melted_data
# Your code here

Need a hint?

Use print(melted_data) to display the final table.