Python Data Science Handbook, 2nd Edition

Chapter 21. Pivot Tables

We have seen how the groupby abstraction lets us explore relationships within a dataset. A pivot table is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data. The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data. The difference between pivot tables and groupby can sometimes cause confusion; it helps me to think of pivot tables as essentially a multidimensional version of groupby aggregation. That is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid.

Motivating Pivot Tables

For the examples in this section, we’ll use the database of passengers on the Titanic, available through the Seaborn library (see Chapter 36):

In [1]: import numpy as np
        import pandas as pd
        import seaborn as sns
        titanic = sns.load_dataset('titanic')

In [2]: titanic.head()
Out[2]:    survived  pclass     sex   age  sibsp  parch     fare embarked  class  \
        0         0       3    male  22.0      1      0   7.2500        S  Third
        1         1       1  female  38.0      1      0  71.2833        C  First
        2         1       3  female  26.0      0      0   7.9250        S  Third
        3         1       1  female  35.0      1      0  53.1000        S  First
        4         0       3    male  35.0      0      0   8.0500        S  Third

             who  adult_male deck  embark_town alive  alone
        0    man        True  NaN  Southampton    no  False
        1  woman       False    C    Cherbourg   yes  False
        2  woman       False  NaN  Southampton   yes   True
        3  woman       False    C  Southampton   yes  False
        4    man        True  NaN  Southampton ...