NumPy fmin() – Minimum of Two Arrays Element-wise Ignoring NaNs

NumPy fmin()

The numpy.fmin() function computes the element-wise minimum of two input arrays, ignoring NaNs when possible. If one of the elements is NaN, it returns the non-NaN element. If both elements are NaNs, the first NaN is returned.

Syntax

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

Parameters

Parameter	Type	Description
x1, x2	array_like	Input arrays to compare. They must be broadcastable to a common shape.
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 which elements to compute. Elements where where=False retain their original value.
casting	str, optional	Defines the casting behavior.
order	str, optional	Specifies the 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.

Return Value

Returns an array with the element-wise minimum values of 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 element-wise minimum values between two arrays.

import numpy as np

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

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

# Print the results
print("Array 1:", x1)
print("Array 2:", x2)
print("Element-wise minimum:", result)

Output:

Array 1: [2 5 8 3]
Array 2: [4 3 7 6]
Element-wise minimum: [2 3 7 3]

2. Handling NaN Values with fmin()

numpy.fmin() ignores NaN values and returns the non-NaN element if available.

import numpy as np

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

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

# Print the results
print("Array 1:", x1)
print("Array 2:", x2)
print("Element-wise minimum ignoring NaNs:", result)

Output:

Array 1: [ 3. nan  5. nan]
Array 2: [nan  4. nan  2.]
Element-wise minimum ignoring NaNs: [ 3.  4.  5.  2.]

3. Using the out Parameter

Storing the result in an output array instead of creating a new one.

import numpy as np

# Define two input arrays
x1 = np.array([1, 7, 9, 4])
x2 = np.array([2, 5, 6, 8])

# Create an output array
out_array = np.empty_like(x1)

# Compute the minimum and store it in out_array
np.fmin(x1, x2, out=out_array)

# Print the results
print("Element-wise minimum stored in output array:", out_array)

Output:

Element-wise minimum stored in output array: [1 5 6 4]

4. Using the where Parameter

Computing the element-wise minimum only for selected elements based on a condition.

import numpy as np

# Define two input arrays
x1 = np.array([5, 9, 3, 6])
x2 = np.array([7, 2, 8, 4])

# Define a condition mask
mask = np.array([True, False, True, False])

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

# Print the results
print("Computed minimum with condition:", result)

Output:

Computed minimum with condition: [5 0 3 0]

The minimum values are computed only for elements where mask=True. The other elements remain unchanged.