Fit Probability Distributions to Data (normal, lognormal, exponential, etc) using Python

Obtain valuable statistical data from different probability density functions with these simple to use python scripts. No Minitab / SPSS required :o !
All examples [synthetic data] I am showing are included in the repository; they will be available in /templates-examples after you clone the statistics rep to your computer.

[Captinofthemudslayer]

how would you do this

Say you started a YouTube channel about a year ago. You’ve done quite well so far and have collected some data. You want to know the probability of at least x visitors to your channel given some time period. The obvious choice in distributions is the Poisson distribution which depends only on one parameter, λ, which is the average number of occurrences per interval. We want to estimate this parameter using Maximum Likelihood Estimation.

Simulate 100 visits to your youtube channel, assuming that they will a Poisson distribution with a mean of 10 visits per minute. Plot the arrival time vs visitor index.

[malvis]

Hi, I want to generate random numbers using lognormal, numpy library.int(math.log(np.random.lognormal(40, 23)))) but it gives me negative numbers. I had understood that normal log does not give negative numbers. I generate 30 random numbers and so I get =[76, 19, -13, 31, 68, 36, -3, 55, 10, 26, 29, 51, 37, 57, 34, 59, 48, 32, 0, 28, 69, 18, 81, 76, 38, 42, 56, 29, 76, 29], what am I doing wrong?

[smsm]

Hello my Professor, please Sir, supposing we have a series of data, always we suppose the normal distribution?

[saurabhgoel]

Hi Andrew, where can I find the code for the same?