Home > Article > Backend Development > Apply vs. Transform: When Should You Use Which in Pandas Groupby?
Overview:
In Pandas, the groupby() method provides two options for manipulating data grouped by a specific column: apply() and transform(). These methods differ in terms of their input, output, and behavior.
Key Differences:
Feature | Apply | Transform | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Passes DataFrame containing all columns for each group | Passes individual Series for each column in each group | ||||||||||||
Output: | Can return scalars, Series, DataFrames, or other objects | Must return a sequence (Series, array, or list) with the same length as the group | ||||||||||||
Behavior: |
Operates on the entire DataFrame within each group | Operates on a single column at a time |
When you need to apply a custom function to the entire DataFrame within each group.This allows complex row-wise processing and returns a DataFrame with the same number of rows as the input.
df.groupby('State').apply(lambda x: pd.DataFrame({'Average': x.mean()}))
Example:
When you need to apply a custom function on a column-by-column basis within each group.This allows you to manipulate specific columns without affecting the entire DataFrame.
df.groupby('State').transform(lambda x: x - x.mean())
Example:
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