You use NumPy’s
np.nanmean() 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.nanmean(a)
But when using it, NumPy raises a
RuntimeWarning: Mean of empty slice message:
Warning (from warnings module): File "C:\Users\xcent\Desktop\code.py", line 3 mean = np.nanmean(a) RuntimeWarning: Mean of empty slice
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.nanmean() function on an empty array. The function doesn’t cause an error if the array has at least one non-NaN value:
>>> np.nanmean([0.42, np.NaN, np.NaN]) 0.42
If at least one element is a numerical value, the mean is clearly defined: sum over all elements that are not
NaN and divide by the number of those elements.
However, if all elements in a NumPy array are
np.NaN, NumPy raises the
>>> np.nanmean([np.NaN, np.NaN]) Warning (from warnings module): File "C:\Users\xcent\Desktop\code.py", line 1 import numpy as np RuntimeWarning: Mean of empty slice nan
Yet, you can also see that it still generates the return value: not-a-number or
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.nanmean() 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
RuntimeWarnings you didn’t see coming.
import numpy as np import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore", category=RuntimeWarning) mean = np.nanmean([np.NaN, np.NaN]) print(mean)
The output is without warning:
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='Mean of empty slice') to let through not anticipated
Alternative Solution: Check for all-NaN Values
If you don’t like the previous solution for its lack of brevity—like me—you can also defensively check if the array contains only
NaN values. If it does, you simply hard-code the solution to be
nan without even running the
np.nanmean() function that generates the warning message.
The following code creates a custom function
numpy_nan_mean() that takes an array as input and returns the mean or
nan if all values are
import numpy as np def numpy_nan_mean(a): return np.NaN if np.all(a!=a) else np.nanmean(a) print(numpy_nan_mean([np.NaN, np.NaN, np.NaN])) # nan print(numpy_nan_mean([np.NaN, np.NaN, 1.23])) # 1.23
The code uses the observation that comparing two
np.NaN values will always return
False. Only if all values are
np.Nan will the function call
>>> np.NaN == np.NaN False
We use the ternary one-liner operator
x if y else z to return
np.NaN in that particular case instead of executing the
np.nanmean() function that would produce the warning.
RuntimeWarning – Calculating Mean From Empty Array
Interestingly, there’s another source of this warning message: if you try to calculate the
np.nanmean() of an empty NumPy array or empty list:
>>> np.nanmean() Warning (from warnings module): File "C:\Users\xcent\Desktop\code.py", line 1 import numpy as np RuntimeWarning: Mean of empty slice nan
You can fix this by first checking the array for emptiness and only calculating the mean if it is non-empty:
>>> def nanmean(a): if a.size == 0: return np.NaN else: return np.nanmean(a) >>> nanmean(np.array()) nan >>> nanmean(np.array([1, 2, 3])) 2.0
Where to Go From Here?
Enough theory, let’s get some practice!
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While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com. He’s author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.