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

GroupBy pandas DataFrame and Select Most Common Value

You're tasked with cleaning a data frame with three string columns, ensuring that the third column contains the correct value for the specified combination of the first two columns. The code snippet you provided attempts to group the data frame by the first two columns and select the most common value of the third column for each combination. However, you encounter an issue when trying to execute the agg function.

Using Pandas >= 0.16

The syntax you used in your code is outdated. Instead, utilize the pd.Series.mode function, which is available in Pandas versions 0.16 and above. This function returns the most common value in a series of strings. Here's how to apply it:

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

This syntax groups the data frame by 'Country' and 'City,' applies the pd.Series.mode function to each group's 'Short name' column, and displays the results.

If you require the output as a DataFrame, use this line:

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

Handling Multiple Modes

The pd.Series.mode function also effectively handles situations where multiple modes exist. For instance, if multiple values occur with the same frequency as the most common value, they will be returned as a list of modes.

Alternatives (Not Recommended)

You could use the statistics.mode function from the Python standard library. However, this approach doesn't perform well when dealing with multiple modes. It raises a StatisticsError when there isn't a single most common value.

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