In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arrays that may have missing or invalid entries.
numpy.MaskedArray.masked_invalid() function is used to mask an array where invalid values occur (NaNs or infs).This function is a shortcut to masked_where, with condition = ~(numpy.isfinite(arr)).
Syntax :
numpy.ma.masked_invalid(arr, copy=True)Parameters:
arr : [ndarray] Input array which we want to mask.
copy : [bool] If True (default) make a copy of arr in the result. If False modify arr in place and return a view.Return : [ MaskedArray] The resultant array after masking.
Code #1 :
# Python program explaining # numpy.MaskedArray.masked_invalid() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array with invalid values in_arr = geek.array([1, 2, geek.nan, -1, geek.inf]) print ("Input array : ", in_arr) # applying MaskedArray.masked_invalid # methods to input array mask_arr = ma.masked_invalid(in_arr) print ("Masked array : ", mask_arr) |
Input array : [ 1. 2. nan -1. inf] Masked array : [1.0 2.0 -- -1.0 --]
Code #2 :
# Python program explaining # numpy.MaskedArray.masked_invalid() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array with invalid element in_arr = geek.array([5e8, 3e-5, geek.nan, 4e4, 5e2]) print ("Input array : ", in_arr) # applying MaskedArray.masked_invalid # methods to input array mask_arr = ma.masked_invalid(in_arr) print ("Masked array : ", mask_arr) |
Input array : [5.e+08 3.e-05 nan 4.e+04 5.e+02] Masked array : [500000000.0 3e-05 -- 40000.0 500.0]
