While we believe that this content benefits our community, we have not yet thoroughly reviewed it. If you have any suggestions for improvements, please let us know by clicking the “report an issue“ button at the bottom of the tutorial.

**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.

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
```

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

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

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
```

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
```

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
```

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
```

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]
```

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

Thanks for learning with the DigitalOcean Community. Check out our offerings for compute, storage, networking, and managed databases.

Working on improving health and education, reducing inequality, and spurring economic growth? We'd like to help.

Get paid to write technical tutorials and select a tech-focused charity to receive a matching donation.