# 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.

## Python3

 `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))`

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:

## Python3

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

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:

## Python

 `# 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))`

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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