How does NumPy boolean masking and fancy indexing work?

Beyond basic integer indexing, NumPy supports two advanced selection mechanisms that are essential for data-cleaning and filtering tasks.

Boolean masking: A comparison on an array produces a boolean array of the same shape. Passing that boolean array back as an index selects only the True positions.

import numpy as np

scores = np.array([88, 45, 72, 91, 60, 33, 95])

# Boolean mask
mask = scores >= 70
print(mask)         # [True False True True False False True]
passing = scores[mask]
print(passing)      # [88 72 91 95]

# Compound conditions
mid_range = scores[(scores >= 60) & (scores < 90)]
print(mid_range)    # [88 72 60]  — use & | ~ not and/or

# Assign through a mask
scores[scores < 50] = 50   # clamp low scores to 50
print(scores)       # [88 50 72 91 60 50 95]

# np.where — vectorised if/else
grades = np.where(scores >= 70, 'Pass', 'Fail')
print(grades)       # ['Pass' 'Fail' 'Pass' 'Pass' 'Fail' 'Fail' 'Pass']

Fancy indexing: Pass an integer array (or list) as an index to select arbitrary elements in any order. Unlike slicing, fancy indexing always returns a copy, not a view.

data = np.array([10, 20, 30, 40, 50])
idx  = np.array([4, 1, 4, 0])          # can repeat indices
print(data[idx])   # [50 20 50 10]

# 2-D fancy indexing
m = np.arange(16).reshape(4, 4)
rows = [0, 2]; cols = [1, 3]
print(m[rows, cols])  # m[0,1] and m[2,3]: [1 11]

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