[英]Transforming a Dataframe for statsmodels t-test
I'm trying to run a t-test in pandas/statsmodels to compare differences in performance between two groups, but I'm having difficulty formatting the data in a way that statsmodels can use (in a reasonable way). 我试图在pandas / statsmodels中运行t检验以比较两组之间的性能差异,但是我很难以statsmodels可以使用的方式(以合理的方式)格式化数据。
My pandas dataframe currently looks like this: 我的熊猫数据框当前如下所示:
Treatment Performance
a 2
b 3
a 2
a 1
b 0
And it's my understanding that to perform a t-test I need the data organized by treatment, like so: 据我了解,要执行t检验,我需要按处理方式整理数据,如下所示:
TreatmentA TreatmentB
2 3
2 0
1
This code almost does the trick: 这段代码几乎可以解决问题:
cat1 = df.groupby('Treatment', as_index=False).groups['a']
cat2 = df.groupby('Treatment', as_index=False).groups['b']
print(ttest_ind(cat1, cat2))
But when I print, it looks like it's pulling the indices where that treatment occurred instead of the performance values: 但是,当我打印时,看起来好像在拉扯发生处理的索引而不是性能值:
print(cat1)
[0, 2, 4, 5, 9, 10, 11, 16, 18,...131, 133, 142, 147, 152, 153, 156, 157, 158]
It [maybe?] needs to be something more like this: [也许?]应该更像这样:
print(cat1)
[2, 2, 1, ...0, 3, 1, 1, 0, 2, 0, 0, 0]
What is the best way to convert this dataframe into a format that I can perform t-tests on? 将这个数据框转换为可以执行t检验的格式的最佳方法是什么?
I think the simplest way is to do it like this: 我认为最简单的方法是这样做:
ttest_ind(df[df['Treatment'] == 'a']['Performance'], df[df['Treatment'] == 'b']['Performance'])
Hope it helps. 希望能帮助到你。
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