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# NumPy | float_power method

schedule Aug 12, 2023
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NumPy's `float_power(~)` method raises each value in the input array by the specified amount.

NOTE

There is a difference between NumPy's `power(~)` and `float_power(~)`. NumPy's `power(~)` method uses the same data-type as the input array to perform the calculation; if your input array only contains integers, then the returned result will also be of type `int`. On the other hand, `float_power(~)` always uses `float64` for maximum precision.

# Parameters

1. `x1` | `array_like`

The input array.

2. `x2` | `array_like`

An array of exponents.

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

Instead of creating a new array, you can place the computed mean 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 behaviour is slightly different - the original value will be kept intact.

# Return value

A scalar is returned if `x1` and `x2` are scalars, otherwise a NumPy array is returned. Either way, the returned data-type is `float64`.

# Examples

## A common exponent

``` np.float_power([1,2,3], 2) array([1., 4., 9.]) ```

## Multiple exponents

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

Here, we are doing `1**3=1`, `2**2=4` and `3**1=3`.

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