I have two dataframe of different length (one is 16 and the other 28). I want to do a Wilcoxon test between those two using scipy.stats.wilcoxon. For this I have created a function:
def wilcoxon_test(df1, df2): list_col_1 = df1.columns list_col_2 = df2.columns for i in range(0, len(list_col_1)): name = list_col_1[i] for j in range(0, len(list_col_2)): name_check = list_col_2[j] if name_check == name: stat, pvalue = stats.wilcoxon(df1[name], df2[name_check]) print("Wilcoxon test of <> and <>: stat = <>, pvalue = <>".format(name,name_check,stat,pvalue)) if pvalue < 0.01: print("Pvalue between <>and <> < 0.01".format(name,name_check)) return None It works well when data have the same size, but I am working with DataFrames of different size, and it gives me this error: ValueError: The samples x and y must have the same length. I've seen on this post discussing this issue on R, that you can do it by passing paired: FALSE. By doing this, it's equivalent to doing a Mann-Whitney test. It's there a way to do the same on Python with scipy.stats.wilocoxon or should I directly use scipy.stats.mannwhitneyu ? Thanks