How to Fill Missing Values in Pandas DataFrames using Group Means?

Filling Missing Values in Groups with Mean

You are given a pandas DataFrame with missing values, and you aim to fill these values with the mean of each group defined by a specific column. This common task can be solved using various methods.

Using GroupBy and Transformation

An effective approach involves using the groupby() and transform() functions:

grouped = df.groupby('name')
df["value"] = grouped.transform(lambda x: x.fillna(x.mean()))

In this code, we first group the DataFrame by the 'name' column using groupby(). Then, we apply a lambda function using transform() on the 'value' column. This function examines each group and fills in missing values with the mean of that group. The final result is stored back in the original 'value' column.

By employing this technique, you can efficiently handle missing values by replacing them with meaningful values derived from each group's data.

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