np.nanmax () in Python

Michael Zippo 18.07.2021

numpy.nanmax() is used to return the maximum value of an array or along any particular mentioned axis of the array, ignoring any Nan value.

Syntax: numpy.nanmax (arr, axis = None, out = None, keepdims = no value)

Parameters:
arr: Input array.
axis: Axis along which we want the max value. Otherwise, it will consider arr to be flattened (works on all the axis) axis = 0 means along the column
and axis = 1 means working along the row.
out: Different array in which we want to place the result. The array must have same dimensions as expected output.
keepdims: If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

Return: maximum array value (a scalar value if axis is none) or array with maximum value along specified axis .

Code # 1: Work

# Python program illustrating
# numpy.nanmax () method

import numpy as np

# 1D array

arr = [ 1 , 2 , 7 , 0 , np.nan]

print ( " arr: " , arr)

print ( "max of arr:" , np .amax (arr))

# nanmax ignores NaN values.

print ( "nanmax of arr:" , np.nanmax (arr))

Output:

 arr: [1, 2, 7, 0, nan] max of arr: nan nanmax of arr: 7.0

Code # 2:

import < code class = "plain"> numpy as np

# 2D array

arr = [[np.nan, 17 , 12 , 33 , 44 ],

[ 15 , 6 , 27 , 8 , 19 ]]

print ( "arr:" , arr)

# maximum smoothed array

print ( "max of arr, axis = None:" , np.nanmax (arr))

# maximum along the first axis
# axis 0 means vertical

print ( "max of arr, axis = 0:" , np.nanmax (arr, axis = 0 ))

# maximum along the second axis
# axis 1 means horizontal

print ( "max of arr, axis = 1:" , np.nanmax (arr, axis = 1 ))

Output:

 arr: [[nan, 17, 12, 33, 44], [15, 6, 27, 8, 19]] max of arr, axis = None: 44.0 max of arr, axis = 0: [15. 17. 27. 33. 44.] max of arr, axis = 1: [44. 27.]

Code # 3:

import numpy as np

arr1 = np.arange ( 5 )

print ( "Initial arr1:" , arr1)

# using the out parameter

np.nanmax (arr, axis = 0 , out = arr1)

print ( "Changed arr1 (having results):" , arr1)

Output:

 Initial arr1: [0 1 2 3 4] Changed arr1 (having results): [15 17 27 33 44]

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np.nanmax () in Python

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