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

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

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

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

Hint

Use print(melted_data) to display the final table.

Practice

1. What does the pandas.melt() function primarily do to a DataFrame?

easy

A. Filters rows based on a condition.

B. Sorts the DataFrame rows based on column values.

C. Transforms wide format data into long format data by unpivoting columns.

D. Aggregates data by grouping columns.

Solution

1. Step 1: Understand the purpose of melt()

   The melt() function is used to convert wide data (many columns) into long data (fewer columns, more rows).

2. Step 2: Identify the transformation type

   This process is called unpivoting, where columns become rows.

3. Final Answer:

   Transforms wide format data into long format data by unpivoting columns. -> Option C

4. Quick Check:

   melt() = unpivot wide to long [OK]

Hint: melt() turns columns into rows to reshape data [OK]

Common Mistakes:

• Confusing melt() with sorting or filtering functions
• Thinking melt() aggregates data
• Assuming melt() changes row order only

2. Which of the following is the correct syntax to unpivot columns 'A' and 'B' in DataFrame df using melt() while keeping column 'ID' fixed?

easy

A. pd.melt(df, id_vars=['ID'], value_vars=['A', 'B'])

B. pd.melt(df, value_vars=['ID'], id_vars=['A', 'B'])

C. df.melt(id_vars=['A', 'B'], value_vars=['ID'])

D. df.melt(value_vars=['ID'], id_vars=['A', 'B'])

Solution

1. Step 1: Identify correct parameter usage

   id_vars are columns to keep fixed; value_vars are columns to unpivot. Here, 'ID' is fixed, 'A' and 'B' are unpivoted.

2. Step 2: Match syntax with parameters

   pd.melt(df, id_vars=['ID'], value_vars=['A', 'B']) correctly uses pd.melt(df, id_vars=['ID'], value_vars=['A', 'B']). Other options swap or misuse parameters.

3. Final Answer:

   pd.melt(df, id_vars=['ID'], value_vars=['A', 'B']) -> Option A

4. Quick Check:

   id_vars = fixed columns, value_vars = columns to unpivot [OK]

Hint: id_vars = fixed columns; value_vars = columns to unpivot [OK]

Common Mistakes:

• Swapping id_vars and value_vars
• Using DataFrame method without pd prefix incorrectly
• Passing wrong columns to parameters

3. Given the DataFrame df below:

   ID  Jan  Feb
0  1   10   20
1  2   30   40

What is the output of:

pd.melt(df, id_vars=['ID'], var_name='Month', value_name='Sales')

medium

A. DataFrame with columns: Month, Sales only

B. DataFrame with columns: ID, Month, Sales and 4 rows unpivoted from Jan and Feb

C. DataFrame with columns: ID, Jan, Feb unchanged

D. Error due to missing parameters

Solution

1. Step 1: Understand melt() parameters

   id_vars=['ID'] keeps 'ID' fixed. var_name='Month' renames the variable column. value_name='Sales' renames the value column.

2. Step 2: Predict output structure

   Jan and Feb columns become rows under 'Month' with their values under 'Sales'. Result has 4 rows (2 IDs x 2 months).

3. Final Answer:

   DataFrame with columns: ID, Month, Sales and 4 rows unpivoted from Jan and Feb -> Option B

4. Quick Check:

   melt() output = long format with renamed columns [OK]

Hint: melt() creates rows for each value_vars entry per id_vars row [OK]

Common Mistakes:

• Expecting original wide format output
• Ignoring var_name and value_name renaming
• Thinking melt() drops id_vars

4. Consider this code snippet:

df = pd.DataFrame({'ID':[1,2],'A':[10,20],'B':[30,40]})
result = pd.melt(df, id_vars=['A'], value_vars=['ID', 'B'])

What is the main issue with this code?

medium

A. The id_vars column 'A' should be a fixed identifier, but it is a value column here.

B. Syntax error due to wrong parameter names.

C. No issue; code runs correctly and melts as expected.

D. value_vars contains 'ID' which is usually an identifier and should be in id_vars.

Solution

1. Step 1: Analyze id_vars and value_vars roles

   id_vars should be columns to keep fixed (usually identifiers). value_vars are columns to unpivot.

2. Step 2: Check column roles in code

   'ID' is usually an identifier and should be in id_vars, but here it is in value_vars, which is unusual and likely a mistake.

3. Final Answer:

   value_vars contains 'ID' which is usually an identifier and should be in id_vars. -> Option D

4. Quick Check:

   Identifiers belong in id_vars, not value_vars [OK]

Hint: Keep identifiers in id_vars, not in value_vars [OK]

Common Mistakes:

• Swapping id_vars and value_vars
• Assuming melt() ignores column roles
• Thinking code has syntax error

5. You have a DataFrame with columns: ['Date', 'Store', 'Product_A', 'Product_B']. You want to reshape it so each row shows Date, Store, Product (either 'Product_A' or 'Product_B'), and Sales (the value). Which melt() call achieves this?

hard

A. pd.melt(df, id_vars=['Date', 'Store'], var_name='Product', value_name='Sales')

B. pd.melt(df, id_vars=['Product_A', 'Product_B'], var_name='Product', value_name='Sales')

C. pd.melt(df, value_vars=['Date', 'Store'], var_name='Product', value_name='Sales')

D. pd.melt(df, id_vars=['Date'], value_vars=['Store', 'Product_A', 'Product_B'])

Solution

1. Step 1: Identify fixed and unpivot columns

   'Date' and 'Store' are identifiers to keep fixed (id_vars). 'Product_A' and 'Product_B' are value columns to unpivot.

2. Step 2: Use melt() with correct parameters

   Use id_vars=['Date', 'Store'], var_name='Product' to name the unpivoted column, and value_name='Sales' for values.

3. Final Answer:

   pd.melt(df, id_vars=['Date', 'Store'], var_name='Product', value_name='Sales') -> Option A

4. Quick Check:

   Keep identifiers fixed, unpivot product columns [OK]

Hint: Keep identifiers in id_vars; unpivot product columns with var_name [OK]

Common Mistakes:

• Putting product columns in id_vars
• Unpivoting identifier columns
• Not renaming var_name or value_name for clarity