student t confidence interval in python

I am interested in using python to compute a confidence interval from a student t.

I am using the StudentTCI() function in Mathematica and now need to code the same function in python http://reference.wolfram.com/mathematica/HypothesisTesting/ref/StudentTCI.html

I am not quite sure how to build this function myself, but before I embark on that, is this function in python somewhere? Like numpy? (I haven’t used numpy and my advisor advised not using numpy if possible).

What would be the easiest way to solve this problem? Can I copy the source code from the StudentTCI() in numpy (if it exists) into my code as a function definition?

edit: I’m going to need to build the Student TCI using python code (if possible). Installing scipy has turned into a dead end. I am having the same problem everyone else is having, and there is no way I can require Scipy for the code I distribute if it takes this long to set up.

Anyone know how to look at the source code for the algorithm in the scipy version? I’m thinking I’ll refactor it into a python definition.

Answer

I guess you could use scipy.stats.t and its interval method:

In [1]: from scipy.stats import t
In [2]: t.interval(0.95, 10, loc=1, scale=2)  # 95% confidence interval
Out[2]: (-3.4562777039298762, 5.4562777039298762)
In [3]: t.interval(0.99, 10, loc=1, scale=2)  # 99% confidence interval
Out[3]: (-5.338545334351676, 7.338545334351676)

Sure, you can make your own function if you like. Let’s make it look like in Mathematica:

from scipy.stats import t


def StudentTCI(loc, scale, df, alpha=0.95):
    return t.interval(alpha, df, loc, scale)

print StudentTCI(1, 2, 10)
print StudentTCI(1, 2, 10, 0.99)

Result:

(-3.4562777039298762, 5.4562777039298762)
(-5.338545334351676, 7.338545334351676)