What might be the cause of 'invalid value encountered in less_equal' in numpy

Adding to the above answers another way to suppress this warning is to use numpy.less explicitly, supplying the where and out parameters:

np.less([1, 2], [2, np.nan])  

outputs: array([ True, False]) causing the runtime warning,

np.less([1, 2], [2, np.nan], where=np.isnan([2, np.nan])==False)

does not calculate result for the 2nd array element according to the docs leaving the value undefined (I got True output for both elements), while

np.less([1, 2], [2, np.nan], where=np.isnan([2, np.nan])==False, out=np.full((1, 2), False)

writes the result into an array pre-initilized to False (and so always gives False in the 2nd element).

This happens due to Nan values in dataframe, which is completely fine with DF.

In Pycharm, This worked like a charm for me:

import warnings

warnings.simplefilter(action = "ignore", category = RuntimeWarning)

That's most likely happening because of a np.nan somewhere in the inputs involved. An example of it is shown below -

In [1]: A = np.array([4, 2, 1])

In [2]: B = np.array([2, 2, np.nan])

In [3]: A<=B
RuntimeWarning: invalid value encountered in less_equal
Out[3]: array([False,  True, False], dtype=bool)

For all those comparisons involving np.nan, it would output False. Let's confirm it for a broadcasted comparison. Here's a sample -

In [1]: A = np.array([4, 2, 1])

In [2]: B = np.array([2, 2, np.nan])

In [3]: A[:,None] <= B
RuntimeWarning: invalid value encountered in less_equal
Out[3]: 
array([[False, False, False],
       [ True,  True, False],
       [ True,  True, False]], dtype=bool)

Please notice the third column in the output which corresponds to the comparison involving third element np.nan in B and that results in all False values.

As a follow-up to Divakar's answer and his comment on how to suppress the RuntimeWarning, a safer way is suppressing them only locally using with np.errstate() (docs): it is good to generally be alerted when comparisons to np.nan yield False, and ignore the warning only when this is really what is intended. Here for the OP's example:

with np.errstate(invalid='ignore'):
  center_dists[j] <= center_dists[i]

Upon exiting the with block, error handling is reset to what it was before.

Instead of invalid value encountered, one can also ignore all errors by passing all='ignore'. Interestingly, this is missing from the kwargs in the docs for np.errstate(), but not in the ones for np.seterr(). (Seems like a small bug in the np.errstate() docs.)