Python indexing numpy array using a smaller boolean array

Question

https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html

If obj.ndim == x.ndim, x[obj] returns a 1-dimensional array filled with the elements of x corresponding to the True values of obj. The search order will be row-major, C-style. If obj has True values at entries that are outside of the bounds of x, then an index error will be raised. If obj is smaller than x it is identical to filling it with False.

I read from the numpy reference that I can index a larger array using a smaller boolean array ,and the rest entries would be automatically filled with False.

Example : From an array, select all rows which sum up to less or equal two:

>>> x = np.array([[0, 1], [1, 1], [2, 2]])
>>> rowsum = x.sum(-1)
>>> x[rowsum <= 2, :] 
array([[0, 1],[1, 1]])

But if rowsum would have two dimensions as well:

>>> rowsum = x.sum(-1, keepdims=True)
>>> rowsum.shape 
(3, 1)
>>> x[rowsum <= 2, :]    # fails 
IndexError: too many indices
>>> x[rowsum <= 2] 
array([0, 1])

The last one giving only the first elements because of the extra dimension.

But the example simply doesn't work ,it says "IndexError: boolean index did not match indexed array along dimension 1; dimension is 2 but corresponding boolean dimension is 1"

How to make it work ?I'm using python 3.6.3 and numpy 1.13.3.

Community · Accepted Answer · 2020-06-20 09:12:55Z

2

From Numpy 11, It's not compatible with the new default behaviour : (boolean-indexing-changes) :

Boolean indexing changes.

...

...

Boolean indexes must match the dimension of the axis that they index.

...

Internals have been optimized, the docs not yet ....

edited Jun 20, 2020 at 9:12

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answered Dec 6, 2017 at 13:00

B. M.

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Anake · Accepted Answer · 2017-12-06 09:19:05Z

2

I think what you are looking for is NumPy broadcasting.

import numpy as np

x = np.array([[0, 1], [1, 1], [2, 2]])
rowsum = x.sum(axis=1)
x[rowsum <= 2]

Gives:

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

The problem is that you used keepdims=True, which means the sum creates a column vector, rather than a rank one array which can be broadcasted.

answered Dec 6, 2017 at 9:19

Anake

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2 Comments

iouvxz Over a year ago

This sample code is directly from the numpy documentation ,so is the documentation wrong about this ?

Anake Over a year ago

It looks like you've found a bug in the documentation!

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