How to Melt Data in Python Using pandas melt Function

To melt data in Python, use the pandas.melt() function which transforms wide-format data into long-format by unpivoting selected columns. You specify the identifier columns with id_vars and the columns to unpivot with value_vars.

Syntax

The pandas.melt() function syntax is:

• frame: The DataFrame to melt.
• id_vars: Columns to keep as identifiers (not unpivoted).
• value_vars: Columns to unpivot into rows.
• var_name: Name for the new variable column (optional).
• value_name: Name for the new value column (optional).

python

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

Example

This example shows how to convert a wide DataFrame with sales data for different years into a long format using pandas.melt().

python

import pandas as pd

data = {
    'Store': ['A', 'B', 'C'],
    '2019': [100, 150, 200],
    '2020': [110, 160, 210],
    '2021': [120, 170, 220]
}
df = pd.DataFrame(data)

melted = pd.melt(df, id_vars=['Store'], value_vars=['2019', '2020', '2021'],
                 var_name='Year', value_name='Sales')
print(melted)

Output

  Store  Year  Sales
0     A  2019    100
1     B  2019    150
2     C  2019    200
3     A  2020    110
4     B  2020    160
5     C  2020    210
6     A  2021    120
7     B  2021    170
8     C  2021    220

Common Pitfalls

Common mistakes when melting data include:

• Not specifying id_vars, which causes all columns to be unpivoted.
• Forgetting to set value_vars when you want to melt only specific columns.
• Not renaming var_name and value_name, which can make the output confusing.

Here is an example of a common mistake and the correct way:

python

import pandas as pd

data = {'Name': ['John', 'Anna'], 'Math': [90, 95], 'Science': [85, 88]}
df = pd.DataFrame(data)

# Wrong: melts all columns including 'Name'
wrong = pd.melt(df)
print('Wrong melt output:\n', wrong)

# Right: keep 'Name' as id_vars
right = pd.melt(df, id_vars=['Name'], value_vars=['Math', 'Science'],
                var_name='Subject', value_name='Score')
print('\nRight melt output:\n', right)

Output

Wrong melt output:
    variable  value
0      Name   John
1      Name   Anna
2      Math     90
3      Math     95
4   Science     85
5   Science     88

Right melt output:
    Name  Subject  Score
0   John     Math     90
1   Anna     Math     95
2   John  Science     85
3   Anna  Science     88

Quick Reference

Remember these tips when using pandas.melt():

• Use id_vars to keep columns fixed.
• Use value_vars to select columns to unpivot.
• Rename columns with var_name and value_name for clarity.
• Melting helps reshape data for easier analysis and plotting.

Key Takeaways

Use pandas.melt() to convert wide data into long format by unpivoting columns.

Always specify id_vars to keep identifier columns unchanged.

Select only the columns to melt with value_vars to avoid unwanted data changes.

Rename the output columns with var_name and value_name for better readability.

Melting data is useful for preparing data for analysis and visualization.