To check if all the values in a Numpy array are NaN or not, you can use a combination of the
numpy.isnan() function and the
all() function. The idea is to check if each value in the array is nan or not using
numpy.isnan() which results in a boolean array and check if all the values in the resulting boolean array are
True or not using the
The following is the syntax –
import numpy as np # check if numpy array is all zero np.isnan(ar).all()
Alternatively, you can iterate through the array and return
False if you encounter any non-NaN value.
Let’s now look at the methods mentioned above with the help of some examples.
Example 1 – Check if the Array is all NaN using
If you compare an array with a scaler value, the resulting array would be a boolean array with
True for the array values that were equal to the scaler value and
In this method, we compare the array with
np.nan and check if all the values in the resulting boolean array are
True or not using the numpy array
Let’s look at an example
import numpy as np # create two arrays ar1 = np.array([np.nan, np.nan, np.nan, np.nan]) ar2 = np.array([np.nan, 0, 1, np.nan]) # check if array is all NaN print(np.isnan(ar1).all()) print(np.isnan(ar2).all())
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Here, we created two arrays.
ar1 with all values as nan and
ar2 with only some values as nan (not all) and then checked if these arrays are all nan or not using our method.
ar2, which is the correct result.
Example 2 – Iterate through the array
Alternatively, we can iterate through the entire array, element by element, and check if each element is nan or not using the
numpy.isnan() function. If we encounter a non-nan element, we return
import numpy as np # create two arrays ar1 = np.array([np.nan, np.nan, np.nan, np.nan]) ar2 = np.array([np.nan, 0, 1, np.nan]) # function to check if array is all nan def is_array_all_nan(ar): if len(ar) == 0: return False for val in ar: if np.isnan(val): continue else: return False return True # check if array is all zero print(is_array_all_nan(ar1)) print(is_array_all_nan(ar2))
We get the same result as above. You can think of this method as a more verbose version (and less optimized) version of method 1.
You might also be interested in –
- Numpy – Check if Array is all Zero
- Numpy – Check If Array has any Duplicates
- How to search and replace text in a file using Python?
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