# Get the Natural Log of Each Element in Numpy Array

The Numpy library in Python comes with a number of built-in functions to perform common mathematical operations on arrays. In this tutorial, we will look at one such function that helps us get the element-wise natural log of a Numpy array with the help of some examples.

You can use the `numpy.log()` function to get the natural logarithm of each element in a Numpy array. Pass the array as an argument.

The following is the syntax –

`numpy.log(ar)`

It returns an array containing the natural log value of each element in the passed array.

Let’s now look at a step-by-step example of using the `numpy.log()` function.

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### Step 1 – Create a Numpy array

First, we will create a Numpy array that we will use throughout this tutorial.

```import numpy as np

# create numpy array
e = np.exp(1)
ar = np.array([1, 2, e, e**2, 10])
# display the array
print(ar)```

Output:

`[ 1.          2.          2.71828183  7.3890561  10.        ]`

Here, we used the `numpy.array()` function to create a Numpy array containing some numbers. You can see that the array contains some integers and some lower powers of `e` (we use `numpy.exp()` to get the value of `e`).

### Step 2 – Get the natural log using `numpy.log()`

To get the natural log of each element in a Numpy array, pass the array as an argument to the `numpy.log()` function.

Let’s get the natural log for the array created above.

```# get the log of each element
np.log(ar)```

Output:

`array([0.        , 0.69314718, 1.        , 2.        , 2.30258509])`

We get a Numpy array with the natural log value of each element in the array `ar`.

The `numpy.log()` function works similarly on higher-dimensional arrays. For example, let’s apply this function to a 2D array of some numbers.

```# create 2D numpy array
e = np.exp(1)
ar = np.array([[1, e, 1],
[e**2, 10, e**2],
[4, e**4, 5]])
# get the log of each element
np.log(ar)```

Output:

```array([[0.        , 1.        , 0.        ],
[2.        , 2.30258509, 2.        ],
[1.38629436, 4.        , 1.60943791]])```

You can see that we get the natural log value of each element in the 2D array.

For more on the `numpy.log()` function, refer to its documentation.

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