What is np.where and how is it used for conditional array creation?

np.where is NumPy's vectorised if/else for arrays. In its three-argument form it returns a new array built element-by-element: where the condition is True, use values from x; where False, use values from y. It is the correct alternative to writing a Python loop with an if-statement inside.

import numpy as np

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

# Classify into Pass / Fail without a loop
labels = np.where(scores >= 70, 'Pass', 'Fail')
# ['Pass' 'Fail' 'Pass' 'Pass' 'Fail' 'Fail' 'Pass']

# Apply a discount: over 80 gets 20% off, rest gets 5% off
prices = np.array([100.0, 200.0, 50.0, 150.0])
discounted = np.where(prices > 80, prices * 0.80, prices * 0.95)
# [95.  160.   47.5  120.]

# Chain multiple conditions using np.select
conditions = [
    scores >= 90,
    (scores >= 70) & (scores < 90),
    scores < 70,
]
choices = ['A', 'B', 'C']
grades = np.select(conditions, choices, default='F')
# ['B' 'C' 'B' 'A' 'C' 'C' 'A']

# One-argument form: returns indices where condition is True
failing_indices = np.where(scores < 70)
# (array([1, 4, 5]),)   — tuple of index arrays
failing_scores = scores[failing_indices]
# [45 60 33]

np.select generalises np.where to multiple conditions — the first matching condition wins. Use it whenever you have more than two output categories; chaining nested np.where calls quickly becomes unreadable.

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