Hello geeks and welcome to today’s article, we will discuss the NumPy log. We will cover syntax, parameters and look at a couple of examples along with it. Numpy.log is one of the many useful modules of NumPy. Solving a log problem can sometimes be a tricky and tedious task. Worry not.

**NumPy log is here to help you out. Now let me define it for you. Numpy log is a function that helps the user solve the Natural logarithm of x (user input). In this article, we will look at its syntax, parameters, and a couple of examples, which will help us better understand the topic.**

## Syntax Of Numpy Log

The general syntax of the function is

`numpy.log`

(x,/,out=None,*,where=True,casting='same_kind',order='K',dtype=None,subok=True[,signature,extobj])

Above we can see the general syntax, it has several parameters in it which we will see in the next section.

## Parameters Of Numpy Log

As evident from the syntax, there are a total of 7 parameters that constitute the syntax and effects the output in their ways. We will now see each one of them in detail

**X- array_like**

This parameter defines the input value for which the Natural logarithm has to be calculated.

**Out- Ndarray**

An optional parameter that defines the location at which the result is to be stored. If we define it must have a shape; otherwise, the freshly allocated array is returned. A Tuple has a length equal to the number of output.

**where: array_like**

It is an optional parameter. It is a condition if defined, applies to every input element, and states that at this location if the condition is true, the output will be set to universal function. Else, it returns the same as the input array.

**dtype: data-type**

It is an optional parameter that overrides the dtype of calculation.

**casting:**

An optional parameter that controls the type of datacasting that may occur.

**Order:**

This parameter specifies the iteration order or the memory layout of the array.

**Subok: boolean**

This parameter is set to true by default. In case it is set to false instead of subtypes, we get an array as output.

## Return Type Of Numpy Log

**Y:ndarray**

The natural logarithm of X(input array).

## Examples Of Numpy Log

Now let us look at some examples that involve the application of NumPy log()

```
import numpy as ppool
in_array = [1, 2, 8, 4**3]
print ("Input array : ", in_array)
out_array = ppool.log10(in_array)
print ("Output array : ", out_array)
```

**Output**

```
Input array : [1, 2, 8, 64]
Output array : [0.,0.30103,0.90308999,1.80617997]
```

### Explanation

In the above example, we can see that first, we have imported the NumPy module as ppool. In the following step, we have defined an input array consisting of values for which we want to find the natural logarithm. Note in the array 4**3 is used for representing the cube of 4. Then in the next step, we have defined log base 10 as we want values with respect to it. In the last step, we get our desired values.

## Numpy Log With Base 2

```
import numpy as ppool
print("\n ppool.log2(10) : ", ppool.log2(10))
print("\n ppool.log(20**2) : ", ppool.log(20**2))
```

**Output:**

```
ppool.log2(10) : 3.321928094887362
ppool.log(20**2) : 5.991464547107982
```

### Explanation

In the above example, instead of using an array, we have operated on single values separately. In the first case, the value is calculated concerning base 2. Whereas in the second case concerning e.

## Must Read

- ln in Python: Implementation and Real Life Uses
- Matplotlib Log Scale Using Various Methods in Python
- WHAT IS NUMPY DIFF? ALONG WITH EXAMPLES
- NUMPY INSERT IN PYTHON WITH EXAMPLES

## Conclusion

In this article, we covered the NumPy log. As well as, its syntax, parameter and also looked at a couple of examples. Through different examples, we tried to analyze how it works under different conditions. At last, we can conclude that the NumPy log helps us in solving the problems related to the natural logarithm. I hope this article was able to clarify all your doubts. But in case you still have unsolved doubts feel free to write them below in the comment section. Done reading this read NumPy tanh next.