NumPy
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Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
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Removing rows containing NaN in a NumPy array
schedule Aug 10, 2023
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Tags Python●NumPy
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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:
array([[ 1., 2., nan], [ 4., 5., 6.]])
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(~):
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.]])
Published by Arthur Yanagisawa
Edited by 0 others
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