Numpy MaskedArray.astype() function | Python Last Updated : 16 Jun, 2021 Comments Improve Suggest changes Like Article Like Report 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 # Python program explaining # numpy.MaskedArray.astype() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([1, 2, 3, -1, 5]) print ("Input array : ", in_arr) # 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) # printing the data type of masked array print(mask_arr.dtype) # applying MaskedArray.astype methods to mask array # and converting it to float64 out_arr = mask_arr.astype('float64') print ("Output typecasted array : ", out_arr) # printing the data type of typecasted masked array print(out_arr.dtype) 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 # Python program explaining # numpy.MaskedArray.astype() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([10.1, 20.2, 30.3, 40.4, 50.5], dtype ='float64') print ("Input array : ", in_arr) # 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) # printing the data type of masked array print(mask_arr.dtype) # applying MaskedArray.astype methods to mask array # and converting it to int32 out_arr = mask_arr.astype('int32') print ("Output typecasted array : ", out_arr) # printing the data type of typecasted masked array 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 Comment More infoAdvertise with us Next Article Numpy MaskedArray.flatten() function | Python J jana_sayantan Follow Improve Article Tags : Python Python-numpy Python numpy-arrayManipulation Practice Tags : python Similar Reads Numpy MaskedArray.any() 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 arra 3 min read Numpy MaskedArray.flatten() function | Python numpy.MaskedArray.flatten() function is used to return a copy of the input masked array collapsed into one dimension. Syntax : numpy.ma.flatten(order='C') Parameters: order : [‘C’, ‘F’, ‘A’, ‘K’, optional] Whether to flatten in C (row-major), Fortran (column-major) order, or preserve the C/Fortran o 2 min read Numpy MaskedArray.all() 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 arr 3 min read Numpy MaskedArray.argsort() 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 arra 3 min read Numpy MaskedArray.dot() function | Python numpy.MaskedArray.dot() function is used to calculate the dot product of two mask arrays. Syntax : numpy.ma.dot(arr1, arr2, strict=False) Parameters: arr1, arr2:[ ndarray] Inputs arrays. strict : [bool, optional] Whether masked data are propagated (True) or set to 0 (False) for the computation. Defa 3 min read 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 arra 2 min read Like