numpy.minimum() in Python

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

Example: This example shows how numpy.minimum() compares two numbers and returns the smaller one.

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Syntax

numpy.minimum(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 output.
• where: Boolean mask; only True positions are computed.
• dtype (optional): Data type of output.
• casting / order: Controls casting and memory layout (advanced use).

Note on / and *
/ -> parameters before it are positional-only.
* -> parameters after it are keyword-only.

Examples of numpy.minimun()

Example 1: This example compares two 1D arrays element-wise and returns the minimum of each pair.

[ 2 3 15]

Explanation: np.minimum(a1, a2), min(2, 3) -> 2, min(8, 3) -> 3 and min(125, 15) -> 15

Example 2: This example shows how numpy.minimum() behaves when NaN values are present in the arrays.

[nan nan nan]

Explanation:

• When one side is np.nan, the result becomes np.nan.
• When both are np.nan, the first np.nan is returned.

Example 3: This example compares arrays of different shapes using broadcasting and finds element-wise minimum values.

[[3 6 9]
 [1 5 8]]

Explanation:

• a2 is broadcast across each row.
• np.minimum(a1, a2) compares each column pair.