Using numpy, how can I do the following:
ln(x)
Is it equivalent to:
np.log(x)
I apologise for such a seemingly trivial question, but my understanding of the difference between log
and ln
is that ln
is logspace e?
Using numpy, how can I do the following:
ln(x)
Is it equivalent to:
np.log(x)
I apologise for such a seemingly trivial question, but my understanding of the difference between log
and ln
is that ln
is logspace e?
You could simple just do the reverse by making the base of log to e.
import math
e = 2.718281
math.log(e, 10) = 2.302585093
ln(10) = 2.30258093
Correct, np.log(x)
is the Natural Log (base e
log) of x
.
For other bases, remember this law of logs: log-b(x) = log-k(x) / log-k(b)
where log-b
is the log in some arbitrary base b
, and log-k
is the log in base k
, e.g.
here k = e
l = np.log(x) / np.log(100)
and l
is the log-base-100 of x
I usually do like this:
from numpy import log as ln
Perhaps this can make you more comfortable.
Numpy seems to take a cue from MATLAB/Octave and uses log
to be "log base e" or ln
. Also like MATLAB/Octave, Numpy does not offer a logarithmic function for an arbitrary base.
If you find log
confusing you can create your own object ln
that refers to the numpy.log function:
>>> import numpy as np
>>> from math import e
>>> ln = np.log # assign the numpy log function to a new function called ln
>>> ln(e)
1.0
np.log
isln
, whereasnp.log10
is your standard base 10 log.