What is NumPy Array Broadcasting?
Broadcasting provides a means of vectorizing array operations, therefore eliminating the need for Python loops. This is because NumPy is implemented in C Programming, which is a very efficient language.
It does this without making needless copies of data which leads to efficient algorithm implementations. But broadcasting over multiple arrays in NumPy extension can raise cases where broadcasting is a bad idea because it leads to inefficient use of memory that slows down the computation.
The resulting array returned after broadcasting will have the same number of dimensions as the array with the greatest number of dimensions.
Array Element-wise Multiplication
In this example, the code multiplies element-wise arrays ‘a’ and ‘b’ with compatible dimensions, storing the result in array ‘c’ and printing it.
Python3
import numpy as np a = np.array([5, 7, 3, 1]) b = np.array([90, 50, 0, 30]) # array are compatible because of same Dimension c = a * b print(c) |
Output:
[450, 350, 0, 30]
NumPy Array Broadcasting
The term broadcasting refers to the ability of NumPy to treat arrays with different dimensions during arithmetic operations. This process involves certain rules that allow the smaller array to be ‘broadcast’ across the larger one, ensuring that they have compatible shapes for these operations.
Broadcasting is not limited to two arrays; it can be applied over multiple arrays as well.
In this article, we will learn about broadcast over multiple arrays in the NumPy extension in Python.
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