Creating two Numpy arrays in Pandas DataFrame Cells and performing multiplication between them

Question

I have a dataframe like:

import pandas as pd
import numpy as np

d = {'col1': [[1,2,3]]}
df = pd.DataFrame(d)

Now lets say I want to create two new columns, squared and cubed like:


df['squared'] = [np.array([i**2 for i in x]) for x in df['col1']]
df['cubed'] = [np.array([i**3 for i in x]) for x in df['col1']]

df
        col1   squared      cubed
0  [1, 2, 3] [1, 4, 9] [1, 8, 27]

Now I would want to do element-wise multiplication and sum with each element of the squared and cubed numpy arrays. How could I go about doing this an an efficient manner (if say I had a large amount of data?)

Chris · Accepted Answer · 2022-08-05 01:21:12Z

1

You could try matrix multiplication.

df.apply(lambda x: x['squared'] @ x['cubed'], axis=1)

answered Aug 5, 2022 at 1:21

Chris

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2 Comments

Coldchain9 Over a year ago

I don't think I've seen @ used in this context. Is this a Pandas or Numpy feature? I really like it.

Chris Over a year ago

It's available in both actually, but most people aren't aware it's been part of Python since I believe 3.5. But in this case numpy

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