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**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.

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