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.log()
is a mathematical function that computes the natural logarithm of an input array’s elements.
The natural logarithm is the inverse of the exponential function, such that log (exp(x)) = x.
numpy.log
is declared as shown below:
numpy.log(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'log'>
In the syntax above, x
is the non-optional parameter and the rest are optional parameters.
A universal function (ufunc) is a function that operates on ndarrays in an element-by-element fashion. The
log()
method is a universal function.
The numpy.log()
method takes the following compulsory parameter:
x
[array-like] - input array.The numpy.log()
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.log()
returns the element-wise natural logarithm of values in x
. If x
is scalar, the return type is also scalar.
If x
is a real-valued data type, the return type will also be a real value. If a value cannot be written as a real value, then NaN
is returned.
If x
is a complex-valued input, the numpy.log
method has a branch cut [-inf
,0
], and it is continuous above it.
In the example below, numpy.log()
computes the natural logarithm of two numbers, a
and b
:
import numpy as npa = 0.5b = np.exp(8.9)print (np.log(a))print (np.log(b))
In the example below, numpy.log()
computes the element-wise natural logarithm of the array arr1
:
import numpy as nparr1 = np.array([1,2,0,3,4])print (np.log(arr1))
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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.