Assign values to multiple columns using DataFrame.assign()

Question

I have a list of strings stored in a pandas dataframe df, with column name of text (i.e. df['text']). I have a function f(text: str) -> (int, int, int). Now, I want to do the following.

df['a'], df['b'], df['c'] = df['text'].apply(f)

How can I create three columns with the three returned values from the function?

The above code gives the error of

ValueError: too many values to unpack (expected 3)

I tried

df['a', 'b', 'c'] = df['text'].apply(f)

but I get one column with the name of 'a', 'b', 'c'

NB:

There is a similar question in SO, but when I use the following solution from there, I again get an error.

df[['a', 'b', 'c']] = df['text'].apply(f, axis=1, result_type='expand')

The error is

f() got an unexpected keyword argument 'axis'
f() got an unexpected keyword argument 'result_type' #(once I remove the axis=1 parameter)

Note that df has other columns as well

Thinking and I have to write - Really thank you for feedback. ;) — jezrael
– jezrael, Commented Jan 23, 2023 at 12:47

jezrael · Accepted Answer · 2023-01-18 11:27:52Z

1

For me your solutions working. But need test if correct return 3 values in tuple.

Here is alternative:

df = pd.DataFrame({'text':[1,2,3]})

def f(x):
    return((x,x+1,x-5))

df[['a', 'b', 'c']] = pd.DataFrame(df['text'].apply(f).tolist(), index=df.index)
print (df)
   text  a  b  c
0     1  1  2 -4
1     2  2  3 -3
2     3  3  4 -2

answered Jan 18, 2023 at 11:27

jezrael

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3 Comments

berinaniesh Over a year ago

It works, but 1. I get the following warning. 2. Can you give an overview of what is going on, what the tolist() and index=df.index does.

jezrael Over a year ago

@berinaniesh - First warning - before my solution i not some filtering? Like df = df[df['col'] > 10] ? If yes, for avoid it use copy like df = df[df['col'] > 10].copy()

jezrael Over a year ago

@berinaniesh - For list of tuples is used tolist() function and then for DataFrame with same index values is pass df.index. It is necessary, if original dataframe has different indices.

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