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# NumPy | any method

schedule Aug 10, 2023
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Numpy's `any(~)` method returns `True` if at least one element in the input array evaluate to `True`. Note that missing values (`np.NaN`) would evaluate to True.

# Parameters

1. `a` | `array_like`

The input array.

2. `axis` | `int` | `optional`

For 2D arrays, the allowed values are as follows:

Axis

Description

0

Performed column-wise

1

Performed row-wise

`None`

Performed on entire DataFrame

By default, `axis=None`.

3. `out` | `Numpy array` | `optional`

Instead of creating a new array, you can place the computed result into the array specified by `out`.

4. `where` | `array` of `boolean` | `optional`

Values that are flagged as False will be ignored, that is, their original value will be uninitialized. If you specified the out parameter, the behavior is slightly different - the original value will be kept intact.

# Return value

If `axis=None`, then a boolean is returned. Otherwise, a Numpy array of booleans is returned.

# Examples

## Basic usage

``` np.any([True, False, True]) True `````` np.any([5, 0, 0]) True ```

## 2D arrays

Consider the following 2D array:

``` a = np.array([[0,np.NaN], [0,2]])a array([[ 0., nan], [ 0., 2.]]) ```

### All values

``` np.any(a) True ```

### Column-wise

``` np.any(a, axis=0) array([False, True]) ```

### Row-wise

``` np.any(a, axis=1) # remember, NaN still evaluates to True array([ True, True]) ```
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