How do I get a lognormal distribution in Python with Mu and Sigma?

Question

I have been trying to get the result of a lognormal distribution using Scipy. I already have the Mu and Sigma, so I don\'t need to do any other prep work. If I need to be

Answer 1

I know this is a bit late (almost one year!) but I've been doing some research on the lognorm function in scipy.stats. A lot of folks seem confused about the input parameters, so I hope to help these people out. The example above is almost correct, but I found it strange to set the mean to the location ("loc") parameter - this signals that the cdf or pdf doesn't 'take off' until the value is greater than the mean. Also, the mean and standard deviation arguments should be in the form exp(Ln(mean)) and Ln(StdDev), respectively.

Simply put, the arguments are (x, shape, loc, scale), with the parameter definitions below:

loc - No equivalent, this gets subtracted from your data so that 0 becomes the infimum of the range of the data.

scale - exp μ, where μ is the mean of the log of the variate. (When fitting, typically you'd use the sample mean of the log of the data.)

shape - the standard deviation of the log of the variate.

I went through the same frustration as most people with this function, so I'm sharing my solution. Just be careful because the explanations aren't very clear without a compendium of resources.

For more information, I found these sources helpful:

• http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html#scipy.stats.lognorm
• https://stats.stackexchange.com/questions/33036/fitting-log-normal-distribution-in-r-vs-scipy

And here is an example, taken from @serv-inc 's answer, posted on this page here:

import math
from scipy import stats

# standard deviation of normal distribution
sigma = 0.859455801705594
# mean of normal distribution
mu = 0.418749176686875
# hopefully, total is the value where you need the cdf
total = 37

frozen_lognorm = stats.lognorm(s=sigma, scale=math.exp(mu))
frozen_lognorm.cdf(total) # use whatever function and value you need here