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# NumPy | abs method

Programming
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Python
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NumPy
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Documentation
schedule Mar 9, 2022
Last updated
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NumPy's `np.abs(~)` method returns a NumPy array with the absolute value applied to each of its values.

NOTE

`np.abs(~)` is shorthand for `np.absolute(~)`

# Parameter

1. `x` | `array-like`

The input array.

2. `out` | `Numpy array` | `optional`

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

3. `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 behaviour is slightly different - the original value will be kept intact.

# Return Value

A NumPy array with the absolute value applied to each of its value.

# Examples

To return a NumPy array with absolute values of array `x`:

``` x = np.array([-1,2, 3,-4])np.abs(x) array([1, 2, 3, 4]) ```

Note that the source NumPy array is left intact, that is, `x` in this example would still have negative values.

NOTE

There is a difference between `np.abs` and `np.fabs()` methods. The `f` in `fabs()` denotes `float`, which means that the return type for `fabs()` is always `float`. On the other hand, `np.abs()` returns the same data type as the input array.

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