Numpy MaskedArray.astype() 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.astype() function returns a copy of the MaskedArray cast to given newtype.
Syntax : numpy.MaskedArray.astype(newtype)
Parameters:
newtype : Type in which we want to convert the masked array.
Return : [MaskedArray] A copy of self cast to input newtype. The returned record shape matches self.shape.
Code #1 :
Python3
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)
print (mask_arr.dtype)
out_arr = mask_arr.astype( 'float64' )
print ( "Output typecasted array : " , out_arr)
print (out_arr.dtype)
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Output:
Input array : [ 1 2 3 -1 5]
Masked array : [1 2 -- -1 5]
int32
Output typecasted array : [1.0 2.0 -- -1.0 5.0]
float64
Code #2 :
Python3
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([ 10.1 , 20.2 , 30.3 , 40.4 , 50.5 ], dtype = 'float64' )
print ( "Input array : " , in_arr)
mask_arr = ma.masked_array(in_arr, mask = [ 1 , 0 , 1 , 0 , 0 ])
print ( "Masked array : " , mask_arr)
print (mask_arr.dtype)
out_arr = mask_arr.astype( 'int32' )
print ( "Output typecasted array : " , out_arr)
print (out_arr.dtype)
|
Output:
Input array : [10.1 20.2 30.3 40.4 50.5]
Masked array : [-- 20.2 -- 40.4 50.5]
float64
Output typecasted array : [-- 20 -- 40 50]
int32
Last Updated :
16 Jun, 2021
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