Chi-Squared test in Python

scipy.stats.chisquare expects observed and expected absolute frequencies, not ratios. You can obtain what you want with

>>> observed = np.array([20., 20., 0., 0.])>>> expected = np.array([.25, .25, .25, .25]) * np.sum(observed)>>> chisquare(observed, expected)(40.0, 1.065509033425585e-08)

Although in the case that the expected values are uniformly distributed over the classes, you can leave out the computation of the expected values:

>>> chisquare(observed)(40.0, 1.065509033425585e-08)

The first returned value is the χ² statistic, the second the p-value of the test.

Just wanted to point out that while the answer appears to be correct syntactically, you should not be using a Chi-squared distribution with your example because you have observed frequencies that are too small for an accurate Chi-square test.

"This test is invalid when the observed or expected frequencies in each category are too small. A typical rule is that all of the observed and expected frequencies should be at least 5." see:http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html#scipy.stats.chisquare

An alternative would be to call your R code from python. You can do this:

• by making an R script run as a command line tool. See this link for more information on running R scripts form the command line using Rscript. From python you can then run an R script by executing a system call using either subprocess or os.system. Any data exchange is done through text or binary files. I like this approach because it is very simple, and it is easy to debug the R script separate from the python code. The downside is that all data goes through the harddrive, which could prove to be very slow.
• by using rpy, or rpy2 to run R code directly from within python. In this way the integration is more tight, but this link also introduces its own little quirks. For example, in my experience debugging R code called through rpy is a little harder to debug.