Numpy MaskedArray.anom() function | Python
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.anom()
function Compute the anomalies (deviations from the arithmetic mean) along the given axis.It returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis.
Syntax : numpy.MaskedArray.anom(axis=None, dtype=None)
Parameters:
axis : [int or None] Axis over which the anomalies are taken.
dtype : [ dtype, optional] Type to use in computing the variance.
Return : [ndarray]an array of anomalies.
Code #1 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([ 1 , 2 , 3 , - 1 , 5 ])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_array(in_arr, mask = [ 0 , 0 , 1 , 0 , 0 ])
print ( "Masked array : " , mask_arr)
out_arr = mask_arr.anom()
print ( "Output anomalies array : " , out_arr)
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Output:
Input array : [ 1 2 3 -1 5]
Masked array : [1 2 -- -1 5]
Output anomalies array : [-0.75 0.25 -- -2.75 3.25]
Code #2 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([ 10 , 20 , 30 , 40 , 50 ])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_array(in_arr, mask = [ 1 , 0 , 1 , 0 , 0 ])
print ( "Masked array : " , mask_arr)
out_arr = mask_arr.anom()
print ( "Output anomalies array : " , out_arr)
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Output:
input array : [10 20 30 40 50]
Masked array : [-- 20 -- 40 50]
Output anomalies array : [-- -16.666666666666664 -- 3.3333333333333357 13.333333333333336]
Last Updated :
30 Nov, 2022
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