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# NumPy | nanmin method

schedule Aug 12, 2023
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Numpy's `nanmin(~)` method ignores all missing values and returns the smallest value in the Numpy array. The minimums can also be computed row-wise and column-wise.

# Parameters

1. `a` | `array_like`

The input array.

2. `axis` | `None` or `int` | `optional`

The allowed values are as follows:

Parameter value

Meaning

axis=0

Minimum computed column-wise

axis=1

Minimum computed row-wise

None

Minimum computed from entire array

By default, `axis=None`.

3. `initial` | `int` | `optional`

If the computed minimum is larger than `initial`, then `initial` will be returned instead.

4. `where` | `array-like` of `booleans` | `optional`

Instead of considering all the values, we can choose which values to consider by providing this parameter. Only values corresponding to `True` in the mask will be considered.

# Return value

A scalar is returned if the axis parameter is not supplied. Otherwise, a Numpy array is returned. The data-type is the same as that of your input array `a`.

# Examples

## Minimum of the entire array

``` np.nanmin([[2,5],[np.NaN,3]]) 2.0 ```

## Minimum of each column

``` np.nanmin([[2,np.NaN],[1,3]], axis=0) array([1., 3.]) ```

## Minimum of each row

``` np.nanmin([[2,np.NaN],[1,3]], axis=1) array([2., 1.]) ```

## Passing in initial parameter

``` np.nanmin([[2,np.NaN],[1,3]], initial=-4) -4 ```

Here, the computed minimum is 1, yet it is larger than the provided value of initial (i.e. -4), so -4 is returned instead.

## Passing in a boolean mask

Instead of considering all the values, we can choose which values to compute the minimum of by providing a mask:

``` np.nanmin([2,5,3,4], where=[False,False,True,True], initial=8) 3 ```

Here, although 2 is technically the smallest value, it is ignored since its corresponding value in the mask is `False`. Note that we need to supply the parameter `initial` here, which will be the returned value if the minimum cannot be computed (e.g. when the mask is all `False`).

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