Grokking Modern System Design Interview for Engineers & Managers
Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.
Python’s numpy.power()
raises the first array elements to the power of second array elements. The illustration below shows this functionality:
numpy.power()
is declared as follows:
numpy.power(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'power'>
In the syntax given above, x1
and x2
are non-optional parameters, and the rest are optional parameters.
A universal function (ufunc) is a function that operates on ndarrays in an element-by-element fashion. The
power()
method is a universal function.
The numpy.power()
method takes the following compulsory parameters:
x1
[array-like] - input array elements that act as the base.
x2
[array-like] - input array elements that acts as the exponent. If the x1
and x2
is different, they must be broadcastable to a common shape for representing the output.
The numpy.power()
method takes the following optional parameters:
Parameter | Description |
out | represents the location into which the output of the method is stored. |
where | True value indicates that a universal function should be calculated at this position. |
casting | controls the type of datacasting that should occur. The same_kind option indicates that safe casting or casting within the same kind should take place. |
order | controls the memory layout order of the output function. The option K means reading the elements in the order they occur in memory. |
dtype | represents the desired data type of the array. |
subok | decides if subclasses should be made or not. If True, subclasses will be passed through. |
numpy.power()
returns the bases in x1
raised to the exponents in x1
.
If x2
is negative, a ValueError
is raised.
If both x1
and x2
are scalars, the return type is scalar.
In the example below, the bases in array arr1
are raised to the exponents in the array arr2
:
import numpy as nparr1 = np.array([2, 3, 4])arr2 = np.array([2, 2, 2])print (np.power(arr1, arr2))
In the example below, the bases in array arr3
are raised to the exponents in the array arr4
:
import numpy as nparr3 = np.array([[1, 2, 3], [4, 5, 6]])arr4 = np.array([[1, 2, 3], [4, 5, 6]])print (np.power(arr3, arr4))
The following example shows that the ValueError
is raised when an exponent is a negative number.
import numpy as nparr5 = np.array([1, 2, 3])arr6 = np.array([-1, 3, -5])print (np.power(arr5, arr6))
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Grokking Modern System Design Interview for Engineers & Managers
Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.