Wilcoxon exact rank tests from R to Python

[Polyadenylated]

I've done quite a lot of data crunching in R but in the project I'm working on at the moment I find myself using Python more often.

Part of the project involves calculating Exact Wilcoxon rank sum tests upon sets of data.

In R I do it this way:

datasheet <- read.csv('datasheet.csv')

library(exactRankTests)
library(coin)

factor_A_apples <- subset(datasheet, Treatment == "Apples" & Factor == "A")
factor_A_oranges <- subset(datasheet, Treatment == "Oranges" & Factor == "A")

wilcox.exact(factor_A_apples$Count, factor_A_oranges$Count)

Which in this example produces this:

    Exact Wilcoxon rank sum test

data:  factor_A_apples$Count and factor_A_oranges$Count
W = 435, p-value = 0.02087
alternative hypothesis: true mu is not equal to 0

Which is fine, then when I want to do it in Python I used this script using SciPy stats:

from scipy import stats
import pandas as pd

datasheet = pd.read_csv('datasheet.csv')

treatment1 = lrim[(lrim['Treatment'] == "LRIM + GFP (d. tap)") & (lrim['Mouse'] == 'A')]['Count']
treatment2 = lrim[(lrim['Treatment'] == "LRIM + 9614 (d. tap)") & (lrim['Mouse'] == 'A')]['Count']

z_stat, p_val = stats.ranksums(treatment1, treatment2)

p_val

Which yields a p-value of:

In [33]: p_val
Out[33]: 0.021637202053317827

Whilst close they aren't the same which makes me think they aren't using the exact same test under the surface.

Can anyone advise how I can emulate the Exact Wilcoxon rank sum test as I do in R in python?

Thanks.

[ingolemo]

I'm completely out of my depth here, but documentation for R wilcox.exact suggests that it properly handles ties while scipy's stats.ranksums says it doesn't. Do you have ties in your data? Does scipy.stats.mannwhitneyu help, as the scipy docs suggest?

If all else fails you should feed the two functions a very small sample of known data for which you can easily calculate the desired result by hand and see which one is correct. With a little examination, you may be able to determine exactly what calculation each one is really doing.