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# NumPy | nanargmin method

schedule Mar 5, 2023
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Numpy's `nanargmin(~)` method ignores all missing values (i.e. `NaN`s) 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.

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