NumPy | argmin method
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NumPy's argmin(~) method returns the index that corresponds to the smallest element in the array.
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.
