NumPy maximum() – Maximum of Arrays Element-wise

NumPy maximum()

The numpy.maximum() function compares two input arrays element-wise and returns a new array containing the maximum values at each index. If an element in either array is NaN, it is propagated in the output.

Syntax

numpy.maximum(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 be compared. If their shapes differ, they must be broadcastable to a common shape.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where the result is stored. If None, a new array is created.
where	array_like, optional	Boolean mask specifying where the computation should take place. Elements where where=False retain their original values.
casting	str, optional	Specifies the casting rule when performing the operation.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Specifies 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 with the element-wise maximum values of x1 and x2. If both inputs are scalars, a scalar is returned.

---

Examples

1. Element-wise Maximum of Two Arrays

Comparing two NumPy arrays element-wise to return the maximum values.

import numpy as np

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

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

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

Output:

Element-wise maximum: [2 4 5 7 5]

2. Handling NaN Values

When NaN values are present, they are propagated in the result.

import numpy as np

# Define two arrays with NaN values
x1 = np.array([1.0, np.nan, 3.0, np.nan])
x2 = np.array([2.0, 2.0, np.nan, 4.0])

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

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

Output:

Element-wise maximum with NaNs: [ 2. nan  3. nan]

3. Using the out Parameter

Using an output array to store results instead of creating a new array.

import numpy as np

# Define two input arrays
x1 = np.array([10, 20, 30])
x2 = np.array([15, 10, 35])

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

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

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

Output:

Stored in output array: [15 20 35]

4. Using the where Parameter

Applying a condition to selectively compute the maximum values.

import numpy as np

# Define two input arrays
x1 = np.array([10, 5, 20, 15])
x2 = np.array([8, 25, 18, 12])

# Define a condition mask (True means comparison will be applied)
mask = np.array([True, False, True, False])

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

# Print the results
print("Computed maximum with mask:", result)

Output:

Computed maximum with mask: [10  0 20  0]

Here, the maximum values are computed only for elements where mask=True.