[Concept Entry] NumPy: Array Broadcasting

content/numpy/concepts/array-broadcasting/array-broadcasting.md

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+---
+Title: 'Array Broadcasting'
+Description: 'Refers to the process of expanding the shape of a smaller array to match the shape of a larger array during arithmetic operations.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'NumPy'
+  - 'Math'
+  - 'Methods'
+  - 'Arrays'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In NumPy, **array broadcasting** refers to the process of expanding the shape of a smaller [array](https://www.codecademy.com/resources/docs/numpy/ndarray) to match the shape of a larger array during arithmetic operations. This is helpful when there is a need to perform mathematical operations on two arrays of different shapes.
+
+## Example
+
+The following example demonstrates the usage of array broadcasting:
+
+```py
+import numpy as np
+
+# Create an array of size (1 x 4)
+arr1 = np.array([[11, 12, 13, 14]])
+
+# Create an array of size (2 x 4)
+arr2 = np.array([[21, 22, 23, 24], [25, 26, 27, 28]])
+
+# Add the arrays
+res = arr1 + arr2
+
+# Print the result
+print(res)
+```
+
+In the above example, the shape of the smaller array `arr1` (1 x 4) is expanded to the shape of the larger array `arr2` (2 x 4) during addition. After expansion, the array `arr1` looks like `[[11, 12, 13, 14], [11, 12, 13, 14]]`.
+
+The above code produces the following output:
+
+```shell
+[[32 34 36 38]
+ [36 38 40 42]]
+```
+
+## Codebyte Example
+
+The following codebyte example demonstrates the usage of array broadcasting:
+
+```codebyte/python
+import numpy as np
+
+# Create an array of size (1 x 3)
+arr1 = np.array([[31, 32, 33]])
+
+# Create an array of size (2 x 3)
+arr2 = np.array([[41, 42, 43], [44, 45, 46]])
+
+# Add the arrays
+res = arr1 + arr2
+
+# Print the result
+print(res)
+```