Pandas 中的 T 检验 [英] T-test in Pandas

问题描述

如果我想计算 Pandas 中两个类别的均值，我可以这样做:

If I want to calculate the mean of two categories in Pandas, I can do it like this:

data = {'Category': ['cat2','cat1','cat2','cat1','cat2','cat1','cat2','cat1','cat1','cat1','cat2'],
        'values': [1,2,3,1,2,3,1,2,3,5,1]}
my_data = DataFrame(data)
my_data.groupby('Category').mean()

Category:     values:   
cat1     2.666667
cat2     1.600000

我有很多这样格式化的数据，现在我需要做一个 T-test 来看看 cat1 和 cat2 在统计上是不同的.我该怎么做?

I have a lot of data formatted this way, and now I need to do a T-test to see if the mean of cat1 and cat2 are statistically different. How can I do that?

推荐答案

这取决于你想做什么样的 t 检验(一侧或两侧依赖或独立)，但它应该像这样简单:

it depends what sort of t-test you want to do (one sided or two sided dependent or independent) but it should be as simple as:

from scipy.stats import ttest_ind

cat1 = my_data[my_data['Category']=='cat1']
cat2 = my_data[my_data['Category']=='cat2']

ttest_ind(cat1['values'], cat2['values'])
>>> (1.4927289925706944, 0.16970867501294376)

它返回一个带有 t 统计量的元组 &p值

it returns a tuple with the t-statistic & the p-value

其他 t 检验请参见此处 http://docs.scipy.org/doc/scipy/reference/stats.html

see here for other t-tests http://docs.scipy.org/doc/scipy/reference/stats.html

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