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# NumPy | logical_and method

schedule Aug 10, 2023
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Numpy's `logical_and(~)` compares the input arrays element-wise, and for each comparison, returns True if both values evaluates to True.

# Parameters

1. `x1` | `array_like`

The first input array.

2. `x2` | `array-like`

The second input array.

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 `x1` and `x2` are scalars, then a single boolean is returned. Otherwise, a Numpy array of booleans is returned.

# Examples

## Basic usage

``` np.logical_and(False, True) False `````` np.logical_and([False, True], [True, True]) array([False, True]) ```

On a more practical note, suppose we wanted to find an even number larger than 3 from an array:

``` a = np.array([1,2,3,4,5])mask = np.logical_and(a%2==0, a>3)mask array([False, False, False, True, False]) ```

To fetch the values that fit our criteria (i.e. the one flagged as True):

``` a[mask] array([4]) ```
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