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# NumPy | argmin method

schedule Aug 10, 2023
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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. `NaN`s), 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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