**Logarithms **are used to depict and represent large numbers. The log is an inverse of the exponent. This article will dive into the **Python log() functions**. The logarithmic functions of Python help the users to find the log of numbers in a much **easier **and **efficient **manner.

Table of Contents

## Understanding the log() functions in Python

In order to use the functionalities of Log functions, we need to **import **the `math`

module using the below statement.

import math

We all need to take note of the fact that the **Python Log functions cannot be accessed directly. **We need to use the `math`

module to access the log functions in the code.

**Syntax:**

math.log(x)

The `math.log(x)`

function is used to calculate the **natural logarithmic value** i.e. **log to the base e** (Euler’s number) which is about 2.71828, of the parameter value (**numeric expression**), passed to it.

**Example:**

import math print("Log value: ", math.log(2))

In the above snippet of code, we are requesting the logarithmic value of 2.

**Output:**

Log value: 0.6931471805599453

## Variants of Python log() Functions

The following are the variants of the basic log function in Python:

**log2(x)****log(x, Base)****log10(x)****log1p(x)**

### 1. log2(x) – log base 2

The `math.log2(x)`

function is used to calculate the **logarithmic value of a numeric expression of base 2**.

**Syntax:**

math.log2(numeric expression)

**Example:**

import math print ("Log value for base 2: ") print (math.log2(20))

**Output:**

Log value for base 2: 4.321928094887363

### 2. log(n, Base) – log base n

The `math.log(x,Base)`

function calculates the logarithmic value of x i.e. numeric expression for a **particular (desired) base value**.

**Syntax:**

math.log(numeric_expression,base_value)

This function accepts two arguments:

**numeric expression****Base value**

**Note**: If **no base value** is provided to the function, the math.log(x,(Base)) acts as a** basic log function** and calculates the log of the numeric expression to the **base e**.

**Example:**

import math print ("Log value for base 4 : ") print (math.log(20,4))

**Output:**

Log value for base 4 : 2.1609640474436813

### 3. log10(x) – log base 10

The `math.log10(x)`

function calculates the logarithmic value of the numeric expression to the **base 10**.

**Syntax:**

math.log10(numeric_expression)

**Example:**

import math print ("Log value for base 10: ") print (math.log10(15))

In the above snippet of code, the logarithmic value of **15** to the **base** **10** is calculated.

**Output:**

Log value for base 10 : 1.1760912590556813

### 4. log1p(x)

The `math.log1p(x)`

function calculates the **log(1+x)** of a particular input value i.e. **x**

Note: **math.log1p(1+x) is equivalent to math.log(x)**

**Syntax:**

math.log1p(numeric_expression)

**Example:**

import math print ("Log value(1+15) for x = 15 is: ") print (math.log1p(15))

In the above snippet of code, the log value of (1+15) for the input expression 15 is calculated.

Thus, `math.log1p(15)`

is equivalent to `math.log(16)`

.

**Output:**

Log value(1+15) for x = 15 is: 2.772588722239781

## Understanding log in Python NumPy

Python NumPy enables us to calculate the **natural logarithmic values** of the input NumPy array elements simultaneously.

In order to use the numpy.log() method, we need to **import the NumPy module** using the below statement.

import numpy

**Syntax:**

numpy.log(input_array)

The `numpy.log()`

function accepts **input array** as a parameter and returns the array with the **logarithmic value of elements** in it.

**Example:**

import numpy as np inp_arr = [10, 20, 30, 40, 50] print ("Array input elements:\n", inp_arr) res_arr = np.log(inp_arr) print ("Resultant array elements:\n", res_arr)

**Output:**

Array input elements: [10, 20, 30, 40, 50] Resultant array elements: [ 2.30258509 2.99573227 3.40119738 3.68887945 3.91202301]

## Conclusion

In this article, we have understood the working of Python Log functions and have unveiled the variants of the logarithmic function in Python.