numpy.maximum() in Python (original) (raw)

Last Updated : 19 Dec, 2025

numpy.maximum() is a NumPy function that compares two arrays (or scalars) element-wise and returns a new array containing the maximum value at each position. If any compared element is NaN, the NaN is returned. If both elements are NaN, the first NaN is returned.

**Example: This example shows how numpy.maximum() compares two numbers and returns the larger one.

Python `

import numpy as np

a = 10 b = 21 print(np.maximum(a, b))

`

Syntax

numpy.maximum(arr1, arr2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None)

**Parameters:

• **arr1: First input array (or scalar).
• **arr2: Second input array (or scalar).
• **out ****(optional):** Array to store the result.
• **where: Boolean mask; True positions are computed.
• **dtype (optional): Data type of the output.
• **casting / order: Controls data casting and memory layout (rarely used).

**Note: / -> Parameters before / are positional-only (must be passed without argument names).
* -> Parameters after * are keyword-only (must be passed using their names).

Examples of numpy.maximum()

**Example 1: This example compares two 1D arrays and returns the element-wise maximum values.

Python `

import numpy as np

a = np.array([2, 8, 125]) b = np.array([3, 3, 15]) print(np.maximum(a, b))

`

**Explanation: np.maximum(a, b) compares each index, max(2, 3) -> 3, max(8, 3) -> 8 and max(125, 15) -> 125.

**Example 2: This example shows how numpy.maximum() behaves when the arrays contain NaN values.

Python `

import numpy as np

a = np.array([np.nan, 0, np.nan]) b = np.array([np.nan, np.nan, 0]) print(np.maximum(a, b))

`

**Explanation:

• When one element is np.nan, the result is np.nan.
• When both elements are np.nan, the first np.nan is returned.

**Example 3: This example compares two arrays of different shapes using broadcasting and returns element-wise maxima.

Python `

import numpy as np

a = np.array([[1, 4, 7], [2, 5, 8]]) b = np.array([3, 3, 3]) print(np.maximum(a, b))

`

**Explanation:

• b is broadcast across rows.
• np.maximum(a, b) compares each column with [3, 3, 3].