In This Article, You Will Learn About Numpy Log Functions.
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 np
array = 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 np
array = 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 np
array = 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 log
import numpy as np
nplog = 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.
Like us on
