Reduce a multi-dimensional array and multiply elements in Numpy

To reduce a multi-dimensional array, use the np.ufunc.reduce() method in Python Numpy. Here, we have used multiply.reduce() to reduce it to the multiplication of elements.

A universal function (or ufunc for short) is a function that operates on ndarrays in an element-byelement fashion, supporting array broadcasting, type casting, and several other standard features. That is, a ufunc is a "vectorized" wrapper for a function that takes a fixed number of specific inputs and produces a fixed number of specific outputs. The numpy.ufunc has functions that operate element by element on whole arrays. The ufuncs are written in C (for speed) and linked into Python with NumPy’s ufunc facility.

Steps

At first, import the required library −

import numpy as np

Create a multi-dimensional array −

arr = np.arange(27).reshape((3,3,3)) 

Display the array −

print("Array...
", arr)

Get the type of the array −

print("
Our Array type...
", arr.dtype) 

Get the dimensions of the Array −

print("
Our Array Dimensions...
",arr.ndim)

To reduce a multi-dimensional array, use the np.ufunc.reduce() method in Python Numpy. Here, we have used multiply.reduce() to reduce it to the multiplication of elements −

print("
Result (multiply)...
",np.multiply.reduce(arr)) 

Example

import numpy as np # The numpy.ufunc has functions that operate element by element on whole arrays. # ufuncs are written in C (for speed) and linked into Python with NumPy’s ufunc facility # Create a multi-dimensional array arr = np.arange(27).reshape((3,3,3)) # Display the array print("Array...
", arr) # Get the type of the array print("
Our Array type...
", arr.dtype) # Get the dimensions of the Array print("
Our Array Dimensions...
",arr.ndim) # To reduce a multi-dimensional array, use the np.ufunc.reduce() method in Python Numpy # Here, we have used multiply.reduce() to reduce it to the multiplication of elements print("
Result (multiply)...
",np.multiply.reduce(arr))

Output

Array... [[[ 0 1 2] [ 3 4 5] [ 6 7 8]] [[ 9 10 11] [12 13 14] [15 16 17]] [[18 19 20] [21 22 23] [24 25 26]]] Our Array type... int64 Our Array Dimensions... 3 Result (multiply)... [[ 0 190 440] [ 756 1144 1610] [2160 2800 3536]]