How to Do Boolean Indexing, Fancy Indexing and Subsetting of an Array Using NumPy
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Condition for Boolean Indexing, Fancy Indexing, and Subsetting
Description:
In NumPy, Boolean indexing, fancy indexing, and subsetting are essential techniques to manipulate data in arrays:
Boolean Indexing: Filters elements of an array based on a condition (True/False mask). Fancy Indexing: Uses an array of indices to access specific elements of an array. Subsetting: Refers to selecting specific parts of an array using slicing or advanced indexing techniques.
Step-by-Step Process
Import NumPy: Import the NumPy library using import numpy as np.
Create a NumPy Array: Create an array using np.array().
Boolean Indexing: Apply a condition on the array to filter elements, such as arr > 5.
Fancy Indexing: Select elements using an array of indices or multi-dimensional indexing.
Subsetting: Use slicing (:) to extract specific rows, columns, or subarrays from the array.
Sample Source Code
# Code for Boolean Indexing, Fancy Indexing, and Subsetting