# Get Log2 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 log2 (logarithm with base 2) of a Numpy array with the help of some examples.

## How to get the log2 of values in a Numpy array?

You can use the `numpy.log2()` function to get the log2 (logarithm with base 2) of each element in a Numpy array. Pass the array as an argument.

The following is the syntax –

`numpy.log2(ar)`

It returns an array containing the base 2 logarithm of each element in the passed array.

Let’s now look at a step-by-step example of using the `numpy.log2()` function.

### 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, 4, 6, 8])
# display the array
print(ar)```

Output:

`[1 2 4 6 8]`

Here, we used the `numpy.array()` function to create a Numpy array containing some numbers. You can see that the array contains some integers (note that there are some values that are powers of two – 2, 4, and 8).

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### Step 2 – Get the log2 using `numpy.log2()`

To get the base 2 log of each element in a Numpy array, pass the array as an argument to the `numpy.log2()` function.

Let’s get the base 2 log for the array created above.

```# get the log2 of each element
np.log2(ar)```

Output:

`array([0.       , 1.       , 2.       , 2.5849625, 3.       ])`

We get a Numpy array with the base 2 logarithm value of each element in the array `ar`.

The `numpy.log2()` 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, 2, 1],
[4, 10, 4],
[5, 8, 6]])
# get the log2 of each element
np.log2(ar)```

Output:

```array([[0.        , 1.        , 0.        ],
[2.        , 3.32192809, 2.        ],
[2.32192809, 3.        , 2.5849625 ]])```

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

For more on the `numpy.log2()` function, refer to its documentation.

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