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.
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Syntax : numpy.MaskedArray.astype(newtype)
Parameters:Â
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 :Â
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Python3
# Python program explaining# numpy.MaskedArray.astype() method Â
# importing numpy as geek # and numpy.ma module as maimport numpy as geekimport numpy.ma as maÂ
# creating input array in_arr = geek.array([1, 2, 3, -1, 5])print ("Input array : ", in_arr)Â
# Now we are creating a masked array of int32 # and making third entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[0, 0, 1, 0, 0])print ("Masked array : ", mask_arr)Â
# printing the data type of masked arrayprint(mask_arr.dtype) Â
# applying MaskedArray.astype methods to mask array# and converting it to float64out_arr = mask_arr.astype('float64')print ("Output typecasted array : ", out_arr)Â
# printing the data type of typecasted masked arrayprint(out_arr.dtype) |
Output:Â
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
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Code #2 :Â
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Python3
# Python program explaining# numpy.MaskedArray.astype() method Â
# importing numpy as geek # and numpy.ma module as maimport numpy as geekimport numpy.ma as maÂ
# creating input array in_arr = geek.array([10.1, 20.2, 30.3, 40.4, 50.5], dtype ='float64')print ("Input array : ", in_arr)Â
# Now we are creating a masked array by making # first and third entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[1, 0, 1, 0, 0])print ("Masked array : ", mask_arr)Â
# printing the data type of masked arrayprint(mask_arr.dtype) Â
# applying MaskedArray.astype methods to mask array# and converting it to int32out_arr = mask_arr.astype('int32')print ("Output typecasted array : ", out_arr)Â
# printing the data type of typecasted masked arrayprint(out_arr.dtype) |
Output:Â
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
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