What is the difference between pandas assign() function and apply() function?

Question

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?

Answer 1

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.