Reshaping Data: Melt, Stack & Unstack0%
DateTime Parsing, Indexing & .dt Accessor

Reshaping Data: Melt, Stack & Unstack

Beginner12 min read•Updated: Jul 11, 2026
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Reshaping Data: Melt, Stack & Unstack

Reading Time: 8 Mins | Level: Beginner to Advanced

1. Wide Format vs Long (Tidy) Format

In data analytics, data typically arrives in one of two formats:

  • Wide Format: Human-friendly tables where dates or categories are spread horizontally across multiple column headers (common in Excel financial reports).
  • Long / Tidy Format: Machine-friendly format where each variable is a column and each observation is a single row (essential for databases, Tableau, Power BI, and Seaborn).

2. Converting Wide to Long with pd.melt()

pd.melt() unpivots a DataFrame from wide format to long format:

Python
import pandas as pd
 
# Wide Excel-style report
df_wide = pd.DataFrame({
'City': ['Delhi', 'Mumbai'],
'Q1_Sales': [45000, 72000],
'Q2_Sales': [52000, 68000],
'Q3_Sales': [61000, 81000]
})
print("Wide Table:\n", df_wide)
 
# Unpivot into Tidy Long Format
df_long = pd.melt(
df_wide,
id_vars=['City'], # Identifier columns to keep fixed
value_vars=['Q1_Sales', 'Q2_Sales', 'Q3_Sales'], # Columns to unpivot into rows
var_name='Quarter', # New column name for former column headers
value_name='Revenue_INR' # New column name for the values
)
print("\nLong Tidy Table:\n", df_long)

Output:

Output
Long Tidy Table:
City Quarter Revenue_INR
0 Delhi Q1_Sales 45000
1 Mumbai Q1_Sales 72000
2 Delhi Q2_Sales 52000
3 Mumbai Q2_Sales 68000
4 Delhi Q3_Sales 61000
5 Mumbai Q3_Sales 81000

3. Reshaping with stack() & unstack()

  • stack(): Pivots the innermost column level to the innermost row index level (makes DataFrame taller and narrower).
  • unstack(): Pivots the innermost row index level to the innermost column level (makes DataFrame shorter and wider).
Python
# Stacking moves columns into a MultiIndex Series
stacked = df_wide.set_index('City').stack()
 
# Unstacking restores it back to columns
restored = stacked.unstack()
Interactive Quiz

Multiple Choice Questions

Question 1
Which function converts a wide DataFrame into a long (tidy) DataFrame by unpivoting columns into rows?
Question 2
In pd.melt(df, id_vars=['City']), what do the columns in id_vars represent?
Question 3
What does df.unstack() do?
Question 4
Why is 'tidy' (long) data preferred when exporting data to Tableau or Power BI?
Question 5
In pd.melt(), which parameters customize the names of the resulting melted columns?
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