Reshaping: pivot_table(), melt() & stack/unstack

Converting between wide and long data formats for analysis and visualization

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Pivoting and melting reshape tabular data between Wide format (columns for each variable) and Long format (tidy rows with key-value pairs).

pivot_table() vs melt()

df.pivot_table(index=..., columns=..., values=..., aggfunc=...): Spreads categorical values into separate column headers, computing aggregated summaries (identical to Excel Pivot Tables).

pd.melt(df, id_vars=[...], value_vars=[...]): Unpivots wide columns into clean long tidy rows, essential for plotting with Seaborn or ggplot.

Building a Dynamic Pivot Table

Try It Yourself »

import pandas as pd

data = {
    "year": [2023, 2023, 2024, 2024],
    "category": ["Hardware", "Software", "Hardware", "Software"],
    "sales": [45000, 32000, 58000, 41000]
}
df = pd.DataFrame(data)

# Create 2D pivot table comparing sales by year and category
pivot = df.pivot_table(index="year", columns="category", values="sales", aggfunc="sum")
print("Pivot Table Report:
", pivot)

pivot_table cross-tabulates data along multi-dimensional indices.

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Test Yourself With Exercise

+15 XP

Which Pandas function "unpivots" a wide DataFrame with multiple metric columns into a tidy long format?

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