Reshape Data from Wide to Long Format with melt() in Pandas

Question

Given a wide financial budget DataFrame with columns `['department', '2026-01', '2026-02', '2026-03']`, unpivot it into a tidy long DataFrame with columns `['department', 'month', 'budget']`.

Accepted Answer

Optimal Solution:
import pandas as pd

long_df = pd.melt(
    wide_df,
    id_vars=['department'],
    value_vars=['2026-01', '2026-02', '2026-03'],
    var_name='month',
    value_name='budget'
)

Technical Explanation:
`pd.melt()` transforms wide columns into row pairs. `id_vars` specifies columns to retain as identifiers, `var_name` names the unpivoted headers, and `value_name` stores cell values.

department	2026-01	2026-02
Marketing	50000	60000
Engineering	120000	130000

department	month	budget
Marketing	2026-01	50000
Marketing	2026-02	60000
Engineering	2026-01	120000
Engineering	2026-02	130000