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NumPy | nanargmin method

schedule Aug 10, 2023
Last updated
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Numpy's nanargmin(~) method ignores all missing values (i.e. NaNs) returns the index that corresponds to the smallest element in the array.

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([5,np.NaN,1,3])
np.nanargmin(x)
2

Here, 2 is returned because the smallest value (i.e. 1) is located at index 2. In contrast, the np.argmin(x) method would return 1 since it considers NaN to be the smallest.

Two-dimensional arrays

Suppose we have the following 2D Numpy array:

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

Min index of entire array

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

np.nanargmin(x)
2

Min indices of every column

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

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

Here, we're going over each column of the matrix and computing the index of its smallest value, while ignoring any missing values.

Min indices of every row

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

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

Here, we're going over each row of the matrix and computing the index of its smallest value, while ignoring any missing values.

robocat
Published by Isshin Inada
Edited by 0 others
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