Why isn't np.nan_to_num() converting this (n x m) array?

Question

I would like to set all nan entries in my numpy array a to zero.

Regardless how I use np.nan_to_num(), the array is not processed at all (it still leaves np.nan in the array)

import numpy as np

a = np.empty((0, 3), dtype='object')
for runner in range(10):
    a = np.insert(a, a.shape[0], [[1, np.nan, 1]], axis=0)

These are are my unsuccessful tries:

np.nan_to_num(a)
np.nan_to_num(a,copy=True)
np.nan_to_num(a,copy=False)
a=np.nan_to_num(a)
a=np.nan_to_num(a,copy=False)
a=np.nan_to_num(a,copy=True)

Answer 1

As the nan_to_num docstring states:

If x is not inexact, then no replacements are made.

And dtype object does not count as inexact.

If for some reason one needs to use dtype object (perhaps one wants to have nans and exact ints, for example), then here is a work-around:

a[a!=a] = 0

Note that in theory there could be other objects than nan for which x!=x evaluates to True (one can of course create one's own class and fiddle with __eq__, __neq__) but in practice I can't think of any.

Only mildly contrived example:

>>> import numpy as np
>>> import math
>>> 
>>> a = np.random.randint(0, 1000, (6,)).astype(object)
>>> a[a%2==0] = np.nan
>>> 
>>> fact_exact = np.vectorize(math.factorial, 'O', 'O')
>>> 
>>> fact_exact(a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/paul/.local/lib/python3.6/site-packages/numpy/lib/function_base.py", line 1972, in __call__
    return self._vectorize_call(func=func, args=vargs)
  File "/home/paul/.local/lib/python3.6/site-packages/numpy/lib/function_base.py", line 2048, in _vectorize_call
    outputs = ufunc(*inputs)
ValueError: factorial() only accepts integral values
>>> 
>>> a[a!=a] = 0
>>> fact_exact(a)
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       1,
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       1, 1], dtype=object)