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containing `NaN` in a NumPy array, we can use a combination of the [`isnan(~)`](https://www.skytowner.com/explore/numpy_isnan_method) and [`any(~)`](https://www.skytowner.com/explore/numpy_any_method) 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.]]) ``` [](https://www.skytowner.com/profile/jo42E3w6Unbqe8vKejJonz4GTtx1) Published by [Arthur Yanagisawa](https://www.skytowner.com/profile/jo42E3w6Unbqe8vKejJonz4GTtx1) Edited by 0 others Did you find this page useful? thumb\_up thumb\_down Comment Citation Ask a question or leave a feedback... 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To remove rows containing `NaN` in a NumPy array, we can use a combination of the [`isnan(~)`](https://www.skytowner.com/explore/numpy_isnan_method) and [`any(~)`](https://www.skytowner.com/explore/numpy_any_method) methods. ## Example Consider the following array: To remove rows containing `NaN`: ## Explanation In the above code snippet, first we are checking each element in array `a` for `np.nan` using `isnan(~)`: Next `any(axis=1)` returns `True` if at least one element in each row evaluates to `True`: Finally, the bitwise NOT (`~`) operator inverts the `True` and `False` values: Now we apply this boolean mask to the original array `a` to return only the rows not containing `NaN`: