NumPy max() in python

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In NumPy, max() is a method used to find the maximum value in an array. It can work on the entire array or along a specific axis (row or column). This method is useful when you need to identify the highest number in a dataset or perform data analysis operations.

Example

import numpy as np

arr = np.array([1, 5, 8, 3])

print(np.max(arr))  # 8

What it does

np.max() scans through the elements of an array and returns the largest one. You can also use it to find the maximum value along a specific dimension (row or column) in a multi-dimensional array.

  • Overall maximum: By default, np.max() returns the highest value in the entire array.
  • Axis-specific: You can specify an axis to find the maximum along rows or columns in 2D arrays.

Examples

Example 1: Finding the maximum in a 1D array

import numpy as np

arr = np.array([3, 7, 2, 9, 5])
print(np.max(arr))  # 9

Here, np.max() scans through the array [3, 7, 2, 9, 5] and returns 9 since it’s the largest number in this 1D array.

Example 2: Finding the maximum in a 2D array

import numpy as np

matrix = np.array([[1, 5, 3],
                   [7, 2, 8],
                   [4, 6, 0]])
print(np.max(matrix))  # 8

When used on a 2D array, np.max() returns the highest value from all elements in the array. In this case, it returns 8 as it’s the largest value in the matrix.

Example 3: Finding the maximum along a specific axis

import numpy as np

matrix = np.array([[2, 9, 4],
                   [6, 1, 8],
                   [3, 5, 7]])

print(np.max(matrix, axis=0))  # [6 9 8]
print(np.max(matrix, axis=1))  # [9 8 7]
  • When specifying axis=0, np.max() finds the maximum values for each column, returning [6, 9, 8].
  • When specifying axis=1, it finds the maximum values for each row, returning [9, 8, 7].

Example 4: Finding the maximum with NaN values

import numpy as np

arr = np.array([3, np.nan, 7, 2])
print(np.nanmax(arr))  # 7

If your array contains NaN values, use np.nanmax() to find the maximum while ignoring NaN. Here, it returns 7, ignoring the NaN value.

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