How to Unpivot Wide to Long with pandas melt in Python

This code demonstrates how to use pandas.melt to unpivot a wide DataFrame into a tidy long format, converting subject columns into rows.

Easy Python 3.9+ Aug 9, 2026 Data pipelines & processing 15 views 0 copies

pandas melt reshape tidy-data

Requires third-party packages — install first

pip install pandas

Python code

24 lines

Python 3.9+

import pandas as pd

# Sample wide-format data
df_wide = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie'],
    'math': [90, 85, 95],
    'science': [80, 92, 88]
})

print("Original wide DataFrame:")
print(df_wide)

# Melt: unpivot subject columns into rows
df_long = pd.melt(
    df_wide,
    id_vars=['id', 'name'],
    value_vars=['math', 'science'],
    var_name='subject',
    value_name='score'
)

print("\nLong (melted) DataFrame:")
print(df_long)

Output

stdout

Original wide DataFrame:
   id     name  math  science
0   1    Alice    90       80
1   2      Bob    85       92
2   3  Charlie    95       88

Long (melted) DataFrame:
   id     name  subject  score
0   1    Alice     math     90
1   2      Bob     math     85
2   3  Charlie     math     95
3   1    Alice  science     80
4   2      Bob  science     92
5   3  Charlie  science     88

How it works

The pd.melt function reshapes the DataFrame from wide to long format by unpivoting specified columns into two new columns: var_name for the original column names and value_name for the corresponding values. The id_vars parameter keeps the identifying columns ('id', 'name') fixed, while value_vars selects which columns to unpivot. This operation is fundamental for data cleaning and preparation, especially when transitioning to a tidy data structure where each row represents a single observation. The resulting long format is easier to analyze with group-by operations, plotting libraries, and statistical models.

Common mistakes

Forgetting to include all identifier columns in `id_vars`, causing loss of context.
Not specifying `value_vars`, which melts all remaining columns and may produce unexpected results.
Assuming `pd.melt` modifies the original DataFrame; it returns a new DataFrame instead.
Using `melt` with duplicate index values without resetting the index first.

Variations

Use `df_wide.melt(id_vars=['id', 'name'], var_name='subject', value_name='score')` as a method on the DataFrame.
Set `col_level` and `col_level` parameters when melting MultiIndex columns to control which column levels become the variable.

Real-world use cases

Preparing survey responses where each question column needs to become a row for analysis.
Transforming multi-year sales data from wide columns into a long format for time-series plotting.
Reshaping experimental results with multiple metrics into a tidy data structure for statistical testing.

How to Unpivot Wide to Long with pandas melt in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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How it works

Common mistakes

Variations

Real-world use cases

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