In this article we want to learn about Numpy Broadcasting in Python, so Python is one of the most popular library in the field of data science and scientific computing because it has a lot of libraries and packages for this purpose. among these libraries, Numpy is one of the most powerful libraries for numerical computations, and one of the key features of Numpy is broadcasting.
What is NumPy Broadcasting ?
NumPy broadcasting refers to the ability of the NumPy library to perform element-wise operations on arrays with different shapes, without the need for explicit loops. It simplifies array arithmetic by automatically adjusting the dimensions of arrays to make them compatible for element-wise operations.
Numpy Broadcasting Rules
To utilize NumPy broadcasting effectively, it is essential to understand about some rules of broadcasting. The following rules are applied sequentially until the arrays are compatible:
- Rule 1: Arrays with different dimensions are padded with ones on their left until their shapes match.
- Rule 2: Arrays with a size of 1 in a particular dimension are stretched to match the size of the corresponding dimension in the other array.
- Rule 3: Arrays with incompatible shapes result in a ValueError during the broadcasting operation.
Broadcasting Examples
Let’s create a few practical examples to illustrate the power of NumPy broadcasting
Numpy Scalar Array Broadcasting:
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import numpy as np scalar = 5 array = np.array([1, 2, 3]) result = scalar * array print(result) |
This will be the result
Numpy Array-Array Broadcasting:
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import numpy as np array1 = np.array([[1, 2, 3], [4, 5, 6]]) array2 = np.array([10, 20, 30]) result = array1 + array2 print(result) |
This will be the result
Numpy Higher-Dimensional Broadcasting:
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import numpy as np array1 = np.array([[1, 2, 3], [4, 5, 6]]) array2 = np.array([10, 20]) result = array1 + array2[:, np.newaxis] print(result) |
This will be the result
Learn More on Python Numpy
- Python Numpy for Machine Learning
- How to Install Numpy
- Working with Linear Algebra in Numpy
- Numpy vs Pandas
- Advance Python Numpy Techniques
- Numpy Array Indexing and Slicing
- Numpy Mathematical Functions
- Numpy Random Number Generation in Python
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