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Removing rows containing NaN in a NumPy array

schedule Aug 10, 2023
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To remove rows containing NaN in a NumPy array, we can use a combination of the isnan(~) and any(~) methods.

Example

Consider the following array:

np.array([[1,2,np.nan], [4,5,6]])
array([[ 1., 2., nan],
[ 4., 5., 6.]])

To remove rows containing NaN:

a = np.array([[1,2,np.nan], [4,5,6]])
a[~np.isnan(a).any(axis=1)]
array([[4., 5., 6.]])

Explanation

In the above code snippet, first we are checking each element in array a for np.nan using isnan(~):

np.isnan(a)
array([[False, False, True],
[False, False, False]])

Next any(axis=1) returns True if at least one element in each row evaluates to True:

np.isnan(a).any(axis=1)
array([ True, False])

Finally, the bitwise NOT (~) operator inverts the True and False values:

~np.isnan(a).any(axis=1)
array([False, True])

Now we apply this boolean mask to the original array a to return only the rows not containing NaN:

a[~np.isnan(a).any(axis=1)]
array([[4., 5., 6.]])
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Published by Arthur Yanagisawa
Edited by 0 others
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