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How to compute natural, base 10, and base 2 logarithm for all elements in a given array using NumPy?
  • Difficulty Level : Easy
  • Last Updated : 29 Aug, 2020

numpy.log( ) function in Python returns natural logarithmic of the input where the natural logarithm of a number is its logarithm to the base of the mathematical constant e, where e is an irrational and transcendental number approximately equal to 2.718281828459. 

Syntax: numpy.log(arr,out)

Parameters:
arr : Input Value. Can be scalar and numpy ndim array as well.
out : A location into which the result is stored. If provided, it must have a shape that the
inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
shape must be same as input array.

If a scalar is provided to the function as input then the function is applied on the scalar and a scalar is returned. 

Example: if 3 was given as input then log(3) will be returned as output.



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import numpy
  
n = 3
print("natural logarithm of {} is".format(n), numpy.log(n))
  
n = 5
print("natural logarithm of {} is".format(n), numpy.log(n))

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Output:

natural logarithm of 3 is 1.0986122886681098
natural logarithm of 5 is 1.6094379124341003

If input is an n-dim array then function is applied element-wise. ex- np.log([1,2,3]) is equivalent to [np.log(1),np.log(2),np.log(3)]

Example:

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import numpy
  
  
arr = np.array([6, 2, 3, 4, 5])
print(numpy.log(arr))

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Output:

[1.79175947 0.69314718 1.09861229 1.38629436 1.60943791]

Functions similar to numpy.log() :

  • numpy.log2(): To calculate base 2 logarithms. Parameters of this functions are same as numpy.log(). It is also called the binary logarithm. Base 2 logarithm of y is the power to which the number 2 must be raised to obtain the value y.
  • numpy.log10(): To calculate base 10 logarithms. Parameters are the same as numpy.log(). Base 10 logarithm of y is the power to which the number 10 must be raised to obtain the value y.

Example:

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# importing numpy
import numpy
  
# natural logarithm
print("natural logarithm -")
arr = numpy.array([6, 2, 3, 4, 5])
print(numpy.log(arr))
  
# Base 2 logarithm
print("Base 2 logarithm -")
arr = numpy.array([6, 2, 3, 4, 5])
print(numpy.log2(arr))
  
# Base 10 logarithm
print("Base 10 logarithm -")
arr = numpy.array([6, 2, 3, 4, 5])
print(numpy.log10(arr))

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Output:

natural logarithm -
[1.79175947 0.69314718 1.09861229 1.38629436 1.60943791]
Base 2 logarithm -
[2.5849625  1.         1.5849625  2.         2.32192809]
Base 10 logarithm -
[0.77815125 0.30103    0.47712125 0.60205999 0.69897   ]

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