NumPy minimum() – Minimum of Two Arrays Element-wise

NumPy minimum()

The numpy.minimum() function computes the element-wise minimum of two input arrays. If one of the elements being compared is NaN, that element is returned. If both elements are NaNs, the first one is returned.

Syntax

numpy.minimum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x1, x2	array_like	Arrays containing elements to be compared. If shapes differ, they must be broadcastable.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array to store the result. If None, a new array is created.
where	array_like, optional	Boolean mask specifying where to compute the minimum. Elsewhere, the original value is retained.
casting	str, optional	Defines the casting behavior when computing the minimum function.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Defines the data type of the output array.
subok	bool, optional	Determines if subclasses of ndarray are preserved in the output.

Return Value

Returns an array or scalar containing the element-wise minimum values from x1 and x2. If both inputs are scalars, a scalar is returned.

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Examples

1. Element-wise Minimum of Two Arrays

Computing the minimum between two arrays element-wise.

import numpy as np

# Define two input arrays
x1 = np.array([3, 7, 2, 9])
x2 = np.array([4, 5, 1, 10])

# Compute the element-wise minimum
result = np.minimum(x1, x2)

# Print the result
print("Element-wise minimum:", result)

Output:

Element-wise minimum: [3 5 1 9]

2. Handling NaN Values

NaN values are preserved when comparing elements.

import numpy as np

# Define two arrays with NaN values
x1 = np.array([3, np.nan, 5])
x2 = np.array([4, 2, np.nan])

# Compute element-wise minimum
result = np.minimum(x1, x2)

# Print the result
print("Element-wise minimum with NaNs:", result)

Output:

Element-wise minimum with NaNs: [ 3. nan nan]

3. Using the out Parameter

Storing results in an existing array instead of creating a new one.

import numpy as np

# Define two input arrays
x1 = np.array([3, 8, 2])
x2 = np.array([4, 5, 1])

# Create an output array with the same shape
output_array = np.empty_like(x1)

# Compute element-wise minimum and store in output_array
np.minimum(x1, x2, out=output_array)

# Print the results
print("Stored in output array:", output_array)

Output:

Stored in output array: [3 5 1]

4. Using the where Parameter

Computing the minimum only for selected elements using a condition.

import numpy as np

# Define two input arrays
x1 = np.array([3, 8, 2, 6])
x2 = np.array([4, 5, 1, 7])

# Define a mask (compute minimum only where mask is True)
mask = np.array([True, False, True, False])

# Compute minimum where mask is True
result = np.minimum(x1, x2, where=mask)

# Print the results
print("Element-wise minimum with mask:", result)

Output:

Element-wise minimum with mask: [3 0 1 0]

The element-wise minimum is computed only where mask=True.