numpy.MaskedArray.cumprod() Return the cumulative product of the masked array elements over the given axis.Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations.
Syntax :
numpy.ma.cumprod(axis=None, dtype=None, out=None)Parameters:
axis :[ int, optional] Axis along which the cumulative product is computed. The default (None) is to compute the cumprod over the flattened array.
dtype : [dtype, optional] Type of the returned array, as well as of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of arr, unless arr has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.Return : [cumprod_along_axis, ndarray] A new array holding the result is returned unless out is specified, in which case a reference to out is returned.
Code #1 :
# Python program explaining # numpy.MaskedArray.cumprod() 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, -3]]) print ("Input array : ", in_arr) # Now we are creating a masked array. # by making entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]]) print ("Masked array : ", mask_arr) # applying MaskedArray.cumprod # methods to masked array out_arr = mask_arr.cumprod() print ("cumulative product of masked array along default axis : ", out_arr) |
Input array : [[ 1 2] [ 3 -1] [ 5 -3]] Masked array : [[-- 2] [-- -1] [5 -3]] cumulative sum of masked array along default axis : [-- 2 -- -2 -10 30]
Code #2 :
# Python program explaining # numpy.MaskedArray.cumprod() 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, 0, 3], [ 4, 1, 6]]) print ("Input array : ", in_arr) # Now we are creating a masked array. # by making one entry as invalid. mask_arr = ma.masked_array(in_arr, mask =[[ 0, 0, 0], [ 0, 0, 1]]) print ("Masked array : ", mask_arr) # applying MaskedArray.cumprod methods # to masked array out_arr1 = mask_arr.cumprod(axis = 0) print ("cumulative product of masked array along 0 axis : ", out_arr1) out_arr2 = mask_arr.cumprod(axis = 1) print ("cumulative product of masked array along 1 axis : ", out_arr2) |
Input array : [[1 0 3] [4 1 6]] Masked array : [[1 0 3] [4 1 --]] cumulative product of masked array along 0 axis : [[1 0 3] [4 0 --]] cumulative product of masked array along 1 axis : [[1 0 0] [4 4 --]]
