NumPy | power method
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NumPy's power(~) method is used to compute the power of each number of the input array.
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 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 base numbers.
2. x2 | array_like
The exponents.
3. where | array_like of boolean | optional
Instead of computing the power of all the numbers, we can choose specific numbers. Values corresponding to True will be considered, while those corresponding to False values will be ignored.
Return value
A scalar if x1 is a scalar, otherwise a NumPy array is returned.
Use the ** syntax instead whenever possible. Instead of using np.power([1,2,3],2), simply use [1,2,3]**2, which offers a huge performance boost. The only case when you'd want to use this power(~) method is when you have multiple exponents.
Examples
Using a common exponent
To raise the the numbers by a common exponent, provide a scalar:
np.power([1,2,3], 2)
array([1, 4, 9])
Using multiple exponents
You can pass an array as the exponents as well:
np.power([1,2,3], [2,3,4])
array([ 1, 8, 81])
What we are doing here is computing 1^2, 2^3 and 3^4.
Using a mask
We can choose which values to take power of by providing a boolean mask, like follows:
np.power([2,3,4], 2, where=[False, True, False])
array([1, 9, 3])
Notice how only the values that are flagged as True in the boolean masks are considered (i.e. the value 3 in this case).
