  # Numpy MaskedArray.prod () Function | python

NumPy | Python Methods and Functions

`numpy.MaskedArray.prod()` is used to calculate the product of array elements along a given axis. Here masked items are set to 1 for internal computation.

Syntax: ` numpy.ma.prod (self, axis = None, dtype = None, out = None, keepdims = False) `

Parameters:

axis: [int, optional] Axis along which the product is computed. The default (None) is to compute the product over the flattened array.
dtype: [dtype, optional] Type of the returned array, as well as of the accumulator in which the elements are multiplied.
out: [ndarray, optional] A location into which the result is stored.
- & gt; If provided, it must have a shape that the inputs broadcast to.
- & gt; If not provided or None, a freshly-allocated array is returned.
keepdims: [bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return: [product_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:

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` # Program Python explaining # numpy.MaskedArray.prod () method   # import numy as geek # and numpy.ma module as ma import numpy as geek  import numpy.ma as ma    # create input array in_arr = geek.array ([[ 1 , 2 ], [ 3 , - 1 ], [ 5 , - 3 ]]) print ( "Input array:" , in_arr)    # Now we create a masked array. # invalidating the entry. mask_arr = ma.masked_array (in_arr, mask = [[ 1 , 0 ], [ 1 , 0 ], [ 0 , 0 ]])  print ( "Masked array:" , mask_arr)    # apply MaskedArray.prod # methods of the masked array out_arr = ma.prod (mask_arr)  print ( "product of masked array along default axis:" , out_arr) `

` ` Output:

` Input array: [[1 2] [3 -1] [5 -3]] Masked array: [[- 2] [- -1] [5 -3]] product of masked array along default axis: 30 `

Code # 2:

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 ` # Python program explaining ` ` # numpy.MaskedArray.prod () method `   ` # import numy as geek ` ` # and numpy.ma module as ma ` ` import ` ` numpy as geek ` ` import ` ` numpy.ma as ma `   ` # create input array ` ` in_arr ` ` = ` ` geek.array ([[` ` 1 ` ` , ` ` 0 ` `, ` ` 3 ` `], [` ` 4 ` `, ` ` 1 ` `, ` ` 6 ` `]]) ` ` print ` ` (` ` "Input array:" ` `, in_arr) `   ` # Now we create a masked array. ` ` # invalidating one entry. ` ` mask_arr ` ` = ` ` ma.masked_array (in_arr, mask ` ` = ` ` [[` ` 0 ` `, ` ` 0 ` `, ` ` 0 ` `], [` ` 0 ` `, ` ` 0 ` `, ` ` 1 ` `]]) ` ` print ` ` (` ` "Masked array:" ` `, mask_arr ) `   ` # applying MaskedArray.prod methods ` ` # to masked array ` ` out_arr1 ` ` = ` ` ma.prod (mask_arr, axis ` ` = ` ` 0 ` `) ` ` print ` ` (` `" product of masked array along 0 axis: "` `, out_arr1) `   ` out_arr2 ` ` = ` ` ma.prod (mask_arr, axis ` ` = ` ` 1 ` `) ` ` print ` ` (` ` "product of masked array along 1 axis:" ` `, out_arr2) `
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` ` Output:

` Input array: [[1 0 3]  [4 1 6]]  Masked array: [[1 0 3]  [4 1 -]]  product of masked array along 0 axis: [4 0 3]  product of masked array along 1 axis: [0 4] `