Numpy MaskedArray.masked_invalid() function | Python
Last Updated :
27 Sep, 2019
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 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([ 1 , 2 , geek.nan, - 1 , geek.inf])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_invalid(in_arr)
print ( "Masked array : " , mask_arr)
|
Output:
Input array : [ 1. 2. nan -1. inf]
Masked array : [1.0 2.0 -- -1.0 --]
Code #2 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([ 5e8 , 3e - 5 , geek.nan, 4e4 , 5e2 ])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_invalid(in_arr)
print ( "Masked array : " , mask_arr)
|
Output:
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]
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