melt() for unpivoting in Pandas

Introduction

We use melt() to turn wide data into long data. This helps us see data in a simpler, tidy format.

You have a table with many columns for similar data and want to combine them into one column.

You want to prepare data for charts that need long format, like line plots.

You want to make it easier to filter or group data by a variable that is spread across columns.

You want to clean data before analysis or machine learning where long format is preferred.

Syntax

Pandas

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)

frame is your DataFrame.

id_vars are columns to keep as identifiers (not unpivoted).

Examples

Unpivot all columns into two columns: variable and value.

Pandas

pd.melt(df)

Keep 'Name' column fixed, unpivot the rest.

Pandas

pd.melt(df, id_vars=['Name'])

Rename the new columns to 'Year' and 'Score' for clarity.

Pandas

pd.melt(df, id_vars=['Name'], var_name='Year', value_name='Score')

Sample Program

This code creates a table with student names and their scores in two subjects. Then it uses melt() to turn the subject columns into one column, making the data longer and easier to analyze.

Pandas

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Math': [90, 80],
    'Science': [85, 88]
}
df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

melted = pd.melt(df, id_vars=['Name'], var_name='Subject', value_name='Score')

print('\nMelted DataFrame:')
print(melted)

Important Notes

If you don't set id_vars, all columns become variables and values, which may not be what you want.

Use var_name and value_name to give meaningful names to the new columns.

Summary

melt() changes wide data into long data.

Keep some columns fixed with id_vars.

Rename new columns with var_name and value_name for clarity.

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