numpy ndarrays: Is it possible to access a row based on a member element?

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Say I have a 2x3 ndarray:

[[0,1,1],
 [1,1,1]]

I want to replace {any row that has 0 in the first index} with [0,0,0]:

[[0,0,0],
 [1,1,1]]

Is it possible to do this with np.where? Here's my attempt:

import numpy as np
arr = np.array([[0,1,1],[1,1,1]])
replacement = np.full(arr.shape,[0,0,0])
new = np.where(arr[:,0]==0,replacement,arr)

I'm met with the following error at the last line:

ValueError: operands could not be broadcast together with shapes (2,) (2,3) (2,3)

The error makes sense, but I don't know how to fix the code to accomplish my goal. Any advice would be greatly appreciated!

Edit: I was trying to simplify a higher-dimensional case, but turns out it might not generalize.

If I have this ndarray:

[[[0,1,1],[1,1,1],[1,1,1]],
 [[1,1,1],[1,1,1],[1,1,1]],
 [[1,1,1],[1,1,1],[1,1,1]]]

how can I replace the first triplet with [0,0,0]?

numpy python
1 Answers

Simple indexing/broadcasting will do:

a[a[:,0]==0] = [0,0,0]

output:

array([[0, 0, 0],
       [1, 1, 1]])

explanation:

# get first column
a[:,0]
# array([0, 1])

# compare to 0 creating a boolean array
a[:,0]==0
# array([ True, False])

# select rows where the boolean is True
a[a[:,0]==0]
# array([[0, 1, 1]])

# replace those rows with new array
a[a[:,0]==0] = [0,0,0]
using np.where

this is less elegant in my opinion:

a[np.where(a[:,0]==0)[0]] = [0,0,0]

Edit: generalization

input:

a = np.arange(3**3).reshape((3,3,3))

array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8]],

       [[ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17]],

       [[18, 19, 20],
        [21, 22, 23],
        [24, 25, 26]]])

transformation:

a[a[...,0]==0] = [0,0,0]

array([[[ 0,  0,  0],
        [ 3,  4,  5],
        [ 6,  7,  8]],

       [[ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17]],

       [[18, 19, 20],
        [21, 22, 23],
        [24, 25, 26]]])
user_163434640
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