Pandas list unpacking to multiple columns

Question

I have a pandas DataFrame with one column containing lists, like:

>>> import pandas as pd
>>> d = {'A': [1, 2, 3], 'B': [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]}
>>> df = pd.DataFrame(data=d)
>>> df
   A                B
0  1  [0.1, 0.2, 0.3]
1  2  [0.4, 0.5, 0.6]
2  3  [0.7, 0.8, 0.9]

I can unpack these lists to individual columns

>>> df[['x','y','z']] = df.B.tolist()
>>> df
   A                B    x    y    z
0  1  [0.1, 0.2, 0.3]  0.1  0.2  0.3
1  2  [0.4, 0.5, 0.6]  0.4  0.5  0.6
2  3  [0.7, 0.8, 0.9]  0.7  0.8  0.9

but would like to do this with a chaining compatible command.

I thought about using .assign but here I need to define each variable explicitly and unpacking via lambdas gets a bit involved.

>>> (df.assign(q=lambda df_: df_.B.apply(lambda x: x[0]),
...            w=lambda df_: df_.B.apply(lambda x: x[1]),
...            u=lambda df_: df_.B.apply(lambda x: x[2])))
   A                B    q    w    u
0  1  [0.1, 0.2, 0.3]  0.1  0.2  0.3
1  2  [0.4, 0.5, 0.6]  0.4  0.5  0.6
2  3  [0.7, 0.8, 0.9]  0.7  0.8  0.9

Is there a better way to do this?

Asked By: MartinE

||

Source

Answer 1

pipe is always useful to chain anything:

(pd.DataFrame(d)
   .pipe(lambda d: d.join(pd.DataFrame(d['B'].to_list(),
                                       columns=['q', 'w', 'u'],
                                       index=d.index))
        )
)

Variant with pipe+assign:

df.pipe(lambda d: d.assign(**dict(zip(['q', 'w', 'u'], zip(*d['B'].to_list())))))

Output:

   A                B    q    w    u
0  1  [0.1, 0.2, 0.3]  0.1  0.2  0.3
1  2  [0.4, 0.5, 0.6]  0.4  0.5  0.6
2  3  [0.7, 0.8, 0.9]  0.7  0.8  0.9

Answered By: mozway

Answer 2

Building on the great hints from @mozway, with two simplifications:

Use zip to create a dict inside of assign:

df.assign(**dict(zip(['x', 'y', 'z'], zip(*df['B']))))

Answered By: MartinE

Pandas list unpacking to multiple columns

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