pandas (original) (raw)

From #15091 (at 028188):

from pandas import DataFrame df = DataFrame([[1, 2, 11111111111111111]], columns=['index', 'type', 'value']) df.pivot_table(index='index', columns='type', values='value') type 2 index
1 11111111111111112

When we pivot, we have to aggregate the values we group together by the index and columns parameters. When we specify mean as the aggregator, we eventually get around to calling _get_cython_function, which searches for an implemention of mean in groupby.pyx for integers. However, groupby_helper.pxi only defines them for floats, so the data is then cast to float for aggregating before being reconverted back to int in the final result, leading to the mysterious increment due to rounding.

Had there been an implementation of mean for int, then this wouldn't happen. However, implementing mean for int isn't straightforward because we can't guarantee returning int as the float implementations can't guarantee returning float without losing precision (which is the contract in the groupby_helper.pxi template).