 Search Introduction to NumPy Log Functions in Python

Numpy Log Functions – Before moving ahead, let’s know a bit of Python Numpy Simple Arithmetic Operators

Python Numpy Works with the Log method therefore it provides log functions to work with.

Log functions are following –

1. base 2

2. e

3. 10

## Log at Base 2

To perform log at the base 2 using the log2() function.

Example – Finding all elements of the array at base 2 of log.

`import numpy as nparray = np.arange(2, 8)print(np.log2(array))`
```Output -

[1. 1.5849625 2. 2.32192809 2.5849625 2.80735492]```

As shown above, it returned an array with all elements of the array at base 2 of log.

## Log at Base 10

To perform log at the base 10 using the log10() function.

### Example – Finding the all elements of array at base 10 of log.

`import numpy as nparray = np.arange(2, 8)print(np.log10(array))`
```Output -

[0.30103 0.47712125 0.60205999 0.69897 0.77815125 0.84509804]```

As shown above, it returned an array with all elements of the array at base 10 of log.

## Natural Log, or Log at Base e

To perform log at the base e using the log() function.

Example – Finding all elements of the array at base e of log.

`import numpy as nparray = np.arange(2, 8)print(np.log(array))`
```Output -

[0.69314718 1.09861229 1.38629436 1.60943791 1.79175947 1.94591015]```

As shown above, it returned an array with all elements of the array at the base e of log.

## Log at Any Base

NumPy doesn’t have any function that can take log at any base. We can instead use the frompyfunc() function with the built-in function math.log(), which has two input parameters and one out parameter.

Example – Using frompyfunc() and math.log() in exchange for a function that could take log at any function.

`from math import logimport numpy as npnplog = np.frompyfunc(log, 2, 1)print(nplog(75, 30))`

As shown above, it returned a number based on given log’s value.

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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