问题
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
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?
回答1:
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)
it returns a tuple with the t-statistic & the p-value
see here for other t-tests http://docs.scipy.org/doc/scipy/reference/stats.html
回答2:
EDIT: I had not realized this was about the data format. You could use
two_data = pd.DataFrame(data, index=data['Category'])
Then accessing the categories is as simple as
scipy.stats.ttest_ind(two_data.loc['cat'], two_data.loc['cat2'], equal_var=False)
The loc operator accesses rows by label.
As @G Garcia said
one sided or two sided dependent or independent
If you have two independent samples but you do not know that they have equal variance, you can use Welch's t-test. It is as simple as
scipy.stats.ttest_ind(cat1['values'], cat2['values'], equal_var=False)
For reasons to prefer Welch's test, see https://stats.stackexchange.com/questions/305/when-conducting-a-t-test-why-would-one-prefer-to-assume-or-test-for-equal-vari.
For two dependent samples, you can use
scipy.stats.ttest_rel(cat1['values'], cat2['values'])
回答3:
I simplify the code a little bit.
from scipy.stats import ttest_ind
ttest_ind(*my_data.groupby('Category')['value'].apply(lambda x:list(x)))
来源:https://stackoverflow.com/questions/13404468/t-test-in-pandas