How do you use pd.pivot_table to summarise data?

pd.pivot_table reshapes and aggregates a DataFrame simultaneously, producing a cross-tabulation — exactly like a spreadsheet pivot table. It is the go-to function for producing summary reports broken down by two categorical dimensions.

import pandas as pd

sales = pd.DataFrame({
    'region':  ['East','East','West','West','East','West','West'],
    'quarter': ['Q1',  'Q2',  'Q1',  'Q2',  'Q1',  'Q1',  'Q2'],
    'product': ['A',   'A',   'A',   'A',   'B',   'B',   'B'],
    'revenue': [100,   120,   90,    110,   80,    70,    95],
})

# Basic pivot: average revenue by region (rows) and quarter (columns)
pt = pd.pivot_table(
    sales,
    values='revenue',
    index='region',
    columns='quarter',
    aggfunc='sum',       # sum, mean, count, np.median, list, ...
    fill_value=0,        # replace NaN with 0
    margins=True,        # add row/column totals (labelled 'All')
    margins_name='Total',
)
print(pt)
# quarter  Q1   Q2  Total
# region
# East    180  120    300
# West    160  205    365
# Total   340  325    665

# Multiple values and multiple aggregations
pd.pivot_table(sales, values='revenue', index='region',
               columns='product', aggfunc=['sum', 'count'])

The inverse operation — converting a wide pivot back to long form — is pd.melt(). df.stack() and df.unstack() do similar reshape operations on the index levels directly.

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