How to Find the Most Common Value in a Pandas DataFrame After Grouping?

GroupBy pandas DataFrame and Select Most Common Value

To cleanse data with multiple string columns, group by the first two columns and select the most common value for the third column in each combination.

Problem

The provided code fails with a KeyError, and grouping only by the City column results in an AssertionError. A robust solution is required.

Solution

Post pandas v0.16, pd.Series.mode offers a versatile and efficient method for this task:

source.groupby(['Country', 'City'])['Short name'].agg(pd.Series.mode)

Addressing Multiple Modes

In the case of multiple modes within a group, Series.mode returns a list of values. For a single result, apply a lambda function:

source.groupby(['Country', 'City'])['Short name'].agg(lambda x: pd.Series.mode(x)[0])

Alternatives to Consider

scipy.stats.mode can also be used, but it raises an error when encountering multiple modes.

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