NumPy ndarray.max() Method

NumPy ndarray.max()

The numpy.ndarray.max() method returns the maximum value in a NumPy array. It can operate across the entire array or along a specified axis.

Syntax

ndarray.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Parameters

Parameter	Type	Description
axis	None, int, or tuple of ints, optional	Axis or axes along which the maximum is computed. If None, it computes the maximum of the entire array.
out	ndarray, optional	Alternative output array for storing the result. Must have the same shape as expected output.
keepdims	bool, optional	If True, the reduced dimensions are kept as size one, allowing proper broadcasting.
initial	scalar, optional	Specifies the minimum value to start the comparison.
where	array_like of bool, optional	Specifies elements to consider when computing the maximum.

Return Value

Returns the maximum value in the array if axis=None, or an array of maximum values if an axis is specified.

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Examples

1. Finding the Maximum Value in an ndarray

In this example, we create a 2D array and find the maximum value.

import numpy as np

# Creating a 2D array
arr = np.array([[3, 5, 7],
                [2, 8, 1]])

# Finding the maximum value in the entire array
max_value = arr.max()
print("Maximum value in the array:", max_value)

Output:

Maximum value in the array: 8

2. Using the axis Parameter in ndarray.max()

Here, we compute the maximum values along different axes.

import numpy as np

# Creating a 2D array
arr = np.array([[3, 5, 7],
                [2, 8, 1]])

# Finding the maximum value along axis 0 (columns)
max_axis0 = arr.max(axis=0)
print("Maximum values along columns:", max_axis0)

# Finding the maximum value along axis 1 (rows)
max_axis1 = arr.max(axis=1)
print("Maximum values along rows:", max_axis1)

Output:

Maximum values along columns: [3 8 7]
Maximum values along rows: [7 8]

3. Keeping Dimensions with keepdims=True in ndarray.max()

In this example, we use keepdims=True to retain the reduced axis as a dimension of size one.

import numpy as np

# Creating a 2D array
arr = np.array([[3, 5, 7],
                [2, 8, 1]])

# Finding the maximum value along axis 1 and keeping dimensions
max_keepdims = arr.max(axis=1, keepdims=True)
print("Maximum values along rows with keepdims=True:\n", max_keepdims)

Output:

Maximum values along rows with keepdims=True:
 [[7]
 [8]]

4. Using the initial Parameter in ndarray.max()

The initial parameter sets a baseline value for comparison.

import numpy as np

# Creating an array
arr = np.array([3, 5, 7, 2, 8, 1])

# Finding the maximum value with an initial baseline
max_with_initial = arr.max(initial=10)
print("Maximum value with initial=10:", max_with_initial)

Output:

Maximum value with initial=10: 10

5. Using the where Parameter in ndarray.max()

The where parameter allows computing the maximum only over specified elements.

import numpy as np

# Creating an array
arr = np.array([3, 5, 7, 2, 8, 1])

# Creating a mask to specify selected elements
mask = np.array([True, False, True, False, True, False])

# Finding the maximum value considering only selected elements
max_where = arr.max(initial=0, where=mask)
print("Maximum value where mask is True:", max_where)

Output:

Maximum value where mask is True: 8