Python equivalent for MATLAB binofit
I think the function you suggested is good enough. I ran some test comparing Matlab binofit and Python statsmodels.stats.proportion.proportion_confint. The test was empyrical like testin 100K experiments like [phat,pci] = binofit(x,n,alpha) with min_conf,max_conf = proportion_confint(x,n,alpha=alpha,method='beta').
The RMSE bewteen confidence interval limits from Matlab and Python are below 5e-6 for values values of x and n between 0 and 10000. Tested with alpha=0.05 and 0.01.
I know this is not strict demonstrations but for my project I decided to consider the two estimates of confidence intervals as equivalent.
In MATLAB, binofit returns the maximum likelihood estimate of the success of binomial probability distribution and confidence intervals. 
statsmodels.stats.proportion.proportion_confint returns confidence intervals as well, but couldn't find a function for maximum likelihood estimate of the binomial probability distribution. Is there any function that you can suggest as a binofit function in MATLAB for python?