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]]