December 19, 2025 - 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)) Output · 21 · numpy.maximum(arr1, arr2, /, out=None, *, where=True, ...

Here, maximum() compares the corresponding elements of array1 and array2 and returns a new array result with the maximum value at each position. import numpy as np array1 = np.array([1, 3, 5, 7, 9]) array2 = np.array([2, 4, 6, 8, 10]) # create an empty array with the same shape as array1 output ...

Notice that the initial value is used as one of the elements for which the maximum is determined, unlike for the default argument Python’s max function, which is only used for empty iterables.

The maximum value of an array along a given axis, propagates NaNs.

July 2, 2025 - If axis is specified, returns an array with maximum values along the specified axis. Note: If any element in the input array is NaN, the function will return NaN. To ignore NaN values, use np.nanmax() instead. This example demonstrates finding the maximum value in a one-dimensional array:

import numpy as np matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) max_in_columns = np.max(matrix, axis=0) print(max_in_columns) Here, `np.max(matrix, axis=0)` computes the maximum values along each column, resulting in `[7, 8, 9]`. max_in_rows = np.max(matrix, axis=1) print(max_in_rows) In this additional example, `np.max(matrix, axis=1)` computes the maximum values along each row, resulting in `[3, 6, 9]`.

Python Program to get the maximum value of Numpy Array - To get the maximum value of a Numpy Array, use numpy function numpy.max(). Example programs contain a numpy array and finding the element with maximum or largest value.

October 22, 2025 - NumPy follows the standard Python convention for ranges and returns an ndarray containing the integers 0 to 9. The second example specifies a starting value of 2, an upper limit of 3, and an increment of 0.1.

The maximum value of an array along a given axis, propagates NaNs.

If axis = 1, the maximum of the largest element in each row is returned. import numpy as np array = np.array([[10, 17, 25], [15, 11, 22]])

June 15, 2023 - We create an array using the array function from NumPy and assign it to the variable namarray. We display the elements of namarray on the screen. Next, we use the max function, passing namarray as the parameter, to find the maximum value.

November 18, 2024 - The function processes two 2x2 matrices comparing each element at identical positions. The resulting matrix, [2, 3], [6, 7], comprises the maximum values from both matrices. Using NumPy's maximum() function, effectively streamlines the process ...

Maximum of a. If axis is None, the result is a scalar value. If axis is an int, the result is an array of dimension a.ndim - 1.

September 18, 2022 - In this example, np.max(a, axis = 1, out = x) will save the maximum of each row result in x array. import numpy as np a = np.random.randint(10, 80, size = (5, 4)) print(a) x = np.arange(5) b = np.max(a, axis = 1) print(b) np.max(a, axis = 1, out = x) print('x = ', x) [[47 14 73 75] [35 60 64 ...

Notice that the initial value is used as one of the elements for which the maximum is determined, unlike for the default argument Python’s max function, which is only used for empty iterables.

The maximum value of an array along a given axis, propagates NaNs.

The maximum value of an array along a given axis, propagates NaNs.

Maximum of a. If axis is None, the result is a scalar value. If axis is an int, the result is an array of dimension a.ndim - 1.

numpy.maximum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'maximum'> In the syntax given above, x1 and x2 are non-optional parameters and the rest are optional parameters.

The maximum value of an array along a given axis, propagates NaNs.