I'm learning data exploration in Python. While practising the pandas library, I saw two functions named df.assign() and df.apply(). The definition of both functions looked very similar. Can you please explain to me these two functions and their unique use cases?
What is the difference between pandas assign() function and apply() function?
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2 Answers
The difference concerns whether you wish to modify an existing frame, or create a new frame while maintaining the original frame as it was.
In particular, DataFrame.assign returns you a new object that has a copy of the original data with the requested changes, the original frame remains unchanged.
For example:
df = DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})
If you wish to create a new frame in which A is everywhere 1 without destroying df, you could use .assign
new_df = df.assign(A=1)
Although .apply is not intended to be used to modify a dataframe, there is no guarantee that applying the function will not change the dataframe.
assign() method assign new columns to a DataFrame, returning a new object (a copy) with the new columns added to the original ones. Existing columns that are re-assigned will be overwritten.
Apply a function along an axis of the DataFrame. apply() allow the users to pass a function and apply it on every single value of the Pandas series.
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