Get values of pandas series from a array of index locations

Question

I have a 2-d array of an index of a pandas series. Would like to create a 2-d array of the values from the pandas series that correspond to the index.

For example:

import pandas as pd
import numpy as np
A = pd.Series(data=[1,2,3,4,5])
idx = np.array([[0,2,3],[2,3,1]])

Would like to return:

B = np.array([[1,3,4],[3,4,2]])

I know I could do this as a loop:

B = np.zeros((2,3))
for i in [0,1]:
    B[i,:] = test[idx[i]]

However, in practice need to do this repeatedly so would like to broadcast the index locations directly. Pandas is not necessary, happy to do it all in numpy if easier.

Chris · Accepted Answer · 2022-02-14 16:04:37Z

2

Something like this might work:

A[idx.flatten()].values.reshape(idx.shape)

answered Feb 14, 2022 at 16:04

Chris

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1 Comment

Adam W. Over a year ago

This works and produces what I was looking for, the flatten and then reshape is what I was missing.

hpaulj · Accepted Answer · 2022-02-14 16:26:18Z

0

A[idx] gives a Cannot index with multidimensional key error.

In [190]: A = pd.Series(data=[1,2,3,4,5])
     ...: idx = np.array([[0,2,3],[2,3,1]])

But the 1d array derived from the Series, can be indexed this way:

In [191]: A.values
Out[191]: array([1, 2, 3, 4, 5])
In [192]: A.values[idx]
Out[192]: 
array([[1, 3, 4],
       [3, 4, 2]])

numpy has no problems returning an array with a dimension that matches idx.

Indexing the Series like this returns a Series - which by definition is 1d:

In [194]: A[idx.ravel()]
Out[194]: 
0    1
2    3
3    4
2    3
3    4
1    2
dtype: int64

answered Feb 14, 2022 at 16:26

hpaulj

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