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Difference between methods apply and applymap of a Pandas DataFrame
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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Both the DataFrame methods apply(~) and applymap(~) transform values in the DataFrame using the specified function.
However, the difference is as follows:
apply(~)applies the specified function to each row or column of the DataFrame.applymap(~)applies the specified function to each value of the DataFrame.
Examples
Consider the following DataFrame:
df = pd.DataFrame({"A":[3,4], "B":[5,6]}, index=["a","b"])df
A Ba 3 5b 4 6
To compute the sum of each column of df:
df.apply(np.sum)
A 7B 15dtype: int64
Since the summation is an operation involving a row or column, we must use apply(~) method - applymap(~) will not work here.
The applymap(~) comes into play when we want to apply a transformation on each value of the DataFrame:
df.applymap(lambda x : 1 if x > 3 else 0)
A Ba 0 1b 1 1
Related
Pandas DataFrame | apply method
Applies the specified function to each row or column of the DataFrame.
Pandas DataFrame | applymap method
Applies a function on each entry of the source DataFrame.
Published by Isshin Inada
Edited by 0 others
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