How to index 0-d array in Python?

Question

This may be a well-known question stored in some FAQ but i can't google the solution. I'm trying to write a scalar function of scalar argument but allowing for ndarray argument. The function should check its argument for domain correctness because domain violation may cause an exception. This example demonstrates what I tried to do:

import numpy as np
def f(x):
    x = np.asarray(x)
    y = np.zeros_like(x)
    y[x>0.0] = 1.0/x
    return y

print f(1.0)

On assigning y[x>0.0]=... python says 0-d arrays can't be indexed. What's the right way to solve this execution?

Tricky ;). Use y[np.asarray(x>0.0)] = ... should do the trick. — seberg
– seberg, Commented Sep 1, 2013 at 17:46
Comes down to difference between scalar arrays and (array-)scalars... And boolean indexing is only triggered with boolean arrays at this time at least. — seberg
– seberg, Commented Sep 1, 2013 at 17:59
@seberg please consider posting your suggestion as an answer — Benjamin Gruenbaum
– Benjamin Gruenbaum, Commented Jan 30, 2014 at 23:51

seberg · Accepted Answer · 2014-02-10 22:54:59Z

2

This will work fine in NumPy >= 1.9 (not released as of writing this). On previous versions you can work around by an extra np.asarray call:

x[np.asarray(x > 0)] = 0

answered Feb 10, 2014 at 22:54

seberg

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Comments

YXD · Accepted Answer · 2013-09-01 17:45:18Z

1

Could you call f([1.0]) instead?

Otherwise you can do:

x = np.asarray(x)
if x.ndim == 0:
    x = x[..., None]

answered Sep 1, 2013 at 17:45

YXD

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1 Comment

Evgeny P. Kurbatov Over a year ago

Not me call it but scipy.integration.quad does. Anyway, seberg gave the trick.

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