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Method argpartition
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NumPy | argmax method

schedule Aug 10, 2023
Last updated
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NumPy's argmax(~) method returns the index that corresponds to the largest element in the array.

NOTE

If your array has missing values (i.e. NaNs), then the np.argmax(~) method will consider them as the largest value. If you want to ignore missing values, then use the np.nanargmax(~) method instead.

Parameters

1. a | array_like

The input array.

2. axis | int | optional

The axis along which to compute the method. For 2D arrays, if axis=0, then the method is performed column-wise, and if axis=1 then row-wise. If no axis is provided, then NumPy will deem your array as a flattened array.

Return value

If no axis is provided, then a scalar is returned. Otherwise, a NumPy array is returned.

Examples

One-dimensional arrays

x = np.array([3,5,2,1])
np.argmax(x)
1

Here, 1 is returned because the largest value (i.e. 5) is located at index 1.

Two-dimensional arrays

Suppose we have the following 2D NumPy array:

x = np.array([[1,5],[2,3]])
x
array([[1, 5],
[2, 3]])

Max index of entire array

To obtain the index of the maximum value in the entire array, leave out the axis parameter:

np.argmax(x)
1

Max indices of every column

To obtain the index of the maximum values column-wise, set axis=0:

np.argmax(x, axis=0)
array([1, 0])

Here, we're going over each column of the matrix and computing the index of its largest value.

Max indices of every row

To obtain the index of the maximum values row-wise, set axis=1:

np.argmax(x, axis=1)
array([1, 1])

Here, we're going over each row of the matrix and computing the index of its largest value.

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Published by Isshin Inada
Edited by 0 others
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