Boolean Masks and Indexing in NumPy

NumPy allows for powerful data manipulation using boolean masks and indexing, which help in filtering and modifying arrays efficiently.

1. Importing NumPy

Before using boolean masks and indexing, import NumPy.

            import numpy as np

2. Creating Boolean Masks

A boolean mask is an array of boolean values (True or False) used to filter elements.

            arr = np.array([10, 20, 30, 40, 50])
            mask = arr > 25
            print(mask)
            # Output: [False False  True  True  True]

3. Applying Boolean Masks

Use a boolean mask to extract elements that meet a condition.

            filtered_arr = arr[arr > 25]
            print(filtered_arr)
            # Output: [30 40 50]

4. Combining Conditions

Use logical operators (& for AND, | for OR) to combine conditions.

            mask = (arr > 15) & (arr < 45)
            print(arr[mask])
            # Output: [20 30 40]

5. Modifying Elements Using Boolean Masks

Boolean masks can also be used to modify array elements.

            arr[arr > 25] = 99
            print(arr)
            # Output: [10 20 99 99 99]

6. Conclusion

Boolean masks and indexing in NumPy provide an efficient way to filter, modify, and manipulate data.




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