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How to mask an array where invalid values occur in NumPy
Onyejiaku Theophilus ChidaluOverview
The masked_invalid() function in NumPy is used to mask an array where invalid values occur. These invalid values could be NaNs or infs.
Syntax
ma.masked_invalid(a)
Syntax for the masked_invalid() function
Parameters
The masked_invalid() function takes a single parameter value, a, which is an array.
Return value
The masked_invalid() function returns a masked array.
Example
# A code to illustrate the masked_invalid() function # importing the necessary libraries import numpy as np import numpy.ma as ma # creating an input array my_array = np.array([1, 2, 3, np.NaN, 5, 6, np.PINF, 8, 9, np.NaN]) # printing the array print(my_array) # masking the values greater than 2 mask_array =ma.masked_invalid(my_array, 2) print(mask_array)
Implementing the masked_invalid() function
Explanation
- Line 4–5: We import the necessary library and module.
- Line 8: We create an input array,
my_array, containing some invalid values. - Line 11: We print the input array,
my_array. - Line 14: We mask the invalid values of the input array using the
masked_invalid()function. The result is assigned to a variable,mask_array. - Line 16: We print the masked array,
mask_array.
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