## Problem Formulation

You use NumPy’s `np.nanmedian()`

function in your code that is supposed to ignore `NaN`

values when computing the mean of a NumPy array.

import numpy as np a = np.array([np.NaN, np.NaN]) mean = np.nanmedian(a)

But when using it, NumPy raises a `RuntimeWarning: All-NaN slice encountered`

message:

Warning (from warnings module): File "C:\Users\xcent\AppData\Local\Programs\Python\Python37\lib\site-packages\numpy\lib\nanfunctions.py", line 1114 overwrite_input=overwrite_input) RuntimeWarning: All-NaN slice encountered

What is the reason for this warning and how to fix it?

## Solution + Explanation

The reason this warning arises is because you apply the `np.nanmedian()`

function on an empty array. The function doesn’t cause an error if the array has at least one non-NaN value:

>>> np.nanmedian([0.42, np.NaN, np.NaN]) 0.42

If at least one element is a numerical value, the mean is clearly defined: **get the median of all not NaN elements. **

However, if all elements in a NumPy array are `np.NaN`

, NumPy raises the `RuntimeWarning`

:

import numpy as np a = np.array([np.NaN, np.NaN]) mean = np.nanmedian(a) print(mean) ''' OUTPUT: Warning (from warnings module): File "C:\Users\xcent\AppData\Local\Programs\Python\Python37\lib\site-packages\numpy\lib\nanfunctions.py", line 1114 overwrite_input=overwrite_input) RuntimeWarning: All-NaN slice encountered nan '''

Yet, you can also see that it still generates the return value:* not-a-number* or `nan`

.

As this border case is properly defined and unambiguous, this has caused some programmers to ask whether it makes even sense to issue this warning.

💡 In my opinion, issuing a warning doesn’t make a lot of sense in the case of the `np.nanmedian()`

function. From Python’s Zen of Python, we know that * “explicit is better than implicit”*. So, either raise an exception and let the programmer handle it directly or just let it go through if everything is properly defined.

If, like me, you’re annoyed by this warning, you can simply suppress it:

## How to Suppress RuntimeWarning?

The context manager `warnings.catch_warnings`

suppresses the warning, but only if you indeed anticipate it coming. Otherwise, you may miss some additional `RuntimeWarning`

s you didn’t see coming.

import numpy as np import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) median = np.nanmedian([np.NaN, np.NaN]) print(median)

The output is without warning:

nan

However, you need to be sure that this is the only warning that can appear in the `with`

block environment. Otherwise, you may miss some important warnings as already discussed.

A safer way would probably be to use `warnings.filterwarnings(action='ignore', message='All-NaN slice encountered')`

to let through not anticipated `RuntimeWarning`

s.

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