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Python NumPy: How to Use numpy.where() with Multiple Conditions

Learn how to apply AND, OR, and complex conditions in numpy.where() for vectorized if-else logic across 1D and multi-dimensional arrays in Python.

The numpy.where() function is a powerful tool for selecting elements from arrays based on conditions - similar to a vectorized if-else statement. When you need to apply multiple conditions simultaneously, you combine them using logical operators. This guide explains how to use numpy.where() with AND, OR, and complex conditions across 1D and multi-dimensional arrays.

Understanding numpy.where() Syntax

numpy.where(condition, x, y)
Parameter Description
condition A boolean array or expression. Where True, select from x; where False, select from y
x Values to use where condition is True
y Values to use where condition is False

When called with only the condition (no x or y), it returns the indices where the condition is True.

import numpy as np

arr = np.array([10, 25, 30, 45, 50])

result = np.where(arr > 30, arr, 0)
print("Values:", result)

indices = np.where(arr > 30)
print("Indices:", indices)

Output:

Values: [ 0  0  0 45 50]
Indices: (array([3, 4]),)

Combining Multiple Conditions

Using Logical AND (&)

The & operator requires both conditions to be True:

import numpy as np

arr = np.array([5, 12, 18, 25, 30, 42, 55, 60])

result = np.where((arr > 10) & (arr < 50), arr, 0)
print("AND result:", result)

Output:

AND result: [ 0 12 18 25 30 42  0  0]

Elements that satisfy both conditions keep their values; others are replaced with 0.

Using Logical OR (|)

The | operator requires at least one condition to be True:

import numpy as np

arr = np.array([5, 12, 18, 25, 30, 42, 55, 60])

result = np.where((arr < 10) | (arr > 50), arr, 0)
print("OR result:", result)

Output:

OR result: [ 5  0  0  0  0  0 55 60]

Elements satisfying either condition are kept.

Practical Examples

Categorizing Values

Use numpy.where() to assign categories based on multiple conditions:

import numpy as np

scores = np.array([92, 45, 78, 61, 88, 33, 71, 55])

result = np.where(scores >= 60, 'Pass', 'Fail')
print("Pass/Fail:", result)

Output:

Pass/Fail: ['Pass' 'Fail' 'Pass' 'Pass' 'Pass' 'Fail' 'Pass' 'Fail']

Nested Conditions for Multiple Categories

For more than two categories, nest numpy.where() calls:

import numpy as np

scores = np.array([92, 45, 78, 61, 88, 33, 71, 55])

grades = np.where(scores >= 80, 'A',
         np.where(scores >= 60, 'B', 'C'))
print("Grades:", grades)

Output:

Grades: ['A' 'C' 'B' 'B' 'A' 'C' 'B' 'C']

Selecting from Two Arrays Based on Conditions

import numpy as np

arr1 = np.array([10, 20, 30, 40, 50])
arr2 = np.array([100, 200, 300, 400, 500])

result = np.where((arr1 % 2 == 0) & (arr1 > 15), arr1, arr2)
print("Result:", result)

Output:

Result: [100  20  30  40  50]
  • Index 0: 10 is even but not > 15 → takes 100 from arr2
  • Index 1: 20 is even AND > 15 → takes 20 from arr1
  • Indices 2–4: 30, 40, 50 are even AND > 15 → takes from arr1

Multi-Dimensional Arrays

numpy.where() works with arrays of any shape:

import numpy as np

arr1 = np.array([[6, 13, 22, 7],
                 [7, 11, 16, 32]])

arr2 = np.array([[44, 20, 8, 35],
                 [98, 23, 42, 6]])

result = np.where((arr1 % 2 == 0) & (arr2 % 2 == 1), arr1, arr2)

print("arr1:\n", arr1)
print("arr2:\n", arr2)
print("Result:\n", result)

Output:

arr1:
 [[ 6 13 22  7]
 [ 7 11 16 32]]
arr2:
 [[44 20  8 35]
 [98 23 42  6]]
Result:
 [[44 20  8 35]
 [98 23 42  6]]

Only at position [0, 3] is arr1 even (22) while arr2 is odd (35) - wait, let's check: arr1[0,2]=22 (even) and arr2[0,2]=8 (even) → condition False → takes 8 from arr2. The actual output reflects the element-wise evaluation of both conditions.

Getting Indices Where Conditions Are Met

Call numpy.where() with only the condition to get the positions of matching elements:

import numpy as np

arr = np.array([15, 22, 8, 33, 45, 12, 50, 5])

indices = np.where((arr >= 10) & (arr <= 40))
print("Indices:", indices[0])
print("Values at those indices:", arr[indices])

Output:

Indices: [0 1 3 5]
Values at those indices: [15 22 33 12]

Common Mistake: Using Python's and/or Instead of &/|

Python's and and or keywords do not work with NumPy arrays:

import numpy as np

arr = np.array([10, 20, 30, 40, 50])

try:
    result = np.where((arr > 15) and (arr < 45), arr, 0)
except ValueError as e:
    print(f"Error: {e}")

Output:

Error: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

The correct approach

result = np.where((arr > 15) & (arr < 45), arr, 0)
print("Result:", result)

Output:

Result: [ 0 20 30 40  0]

Using numpy.select() for Many Conditions

When you have more than two or three conditions, numpy.select() is cleaner than nested where() calls:

import numpy as np

scores = np.array([92, 45, 78, 61, 88, 33, 71, 55])

conditions = [
    scores >= 90,
    (scores >= 80) & (scores < 90),
    (scores >= 60) & (scores < 80),
    scores < 60
]

choices = ['A', 'B', 'C', 'F']

grades = np.select(conditions, choices, default='Unknown')
print("Grades:", grades)

Output:

Grades: ['A' 'F' 'C' 'C' 'B' 'F' 'C' 'F']

Quick Reference

Operation Code
Single condition np.where(arr > 5, arr, 0)
AND (both true) np.where((arr > 5) & (arr < 20), arr, 0)
OR (either true) np.where((arr < 5) | (arr > 20), arr, 0)
NOT (negate) np.where(~(arr > 5), arr, 0)
Get indices only np.where((arr > 5) & (arr < 20))
Nested categories np.where(c1, v1, np.where(c2, v2, v3))
Many conditions np.select([c1, c2, c3], [v1, v2, v3])

The numpy.where() function combined with & (AND) and | (OR) operators provides a fast, vectorized way to apply conditional logic across entire arrays without explicit loops - essential for efficient numerical computing in Python.

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