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Method argpartition
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NumPy | argmin method

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

NOTE

If your array has missing values (i.e. NaNs), then the np.argmin(~) method will consider them as the smallest value. If you want to ignore missing values, then use the np.nanargmin(~) 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.argmin(x)
3

Here, 3 is returned because the smallest value (i.e. 1) is located at index 3.

Two-dimensional arrays

Suppose we have the following 2D NumPy array:

x = np.array([[5,4],[1,3]])
x
array([[5, 4],
[1, 3]])

Max index of entire array

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

np.argmin(x)
2

Max indices of every column

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

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

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

Max indices of every row

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

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

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

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Published by Isshin Inada
Edited by 0 others
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