CODEWITHSUNDEEP
pythonpandas
Lisa
Lisa

Reputation: 3526

pandas dataframe: subset by column + groupby another column

I'm new to pandas dataframes and would appreciate help with the following problem (similar to this). I have the following data:

data = {'Cat1': [2,1,2,1,2,1,2,1,1,1,2],
        'Cat2': [0,0,0,0,0,0,1,1,1,1,1],
        'values': [1,2,3,1,2,3,1,2,3,5,1]}
my_data = DataFrame(data)

I would like to perform a ttest_ind for every category in Cat2 to distinguish between categories in Cat1.

The way I see it, I could separate the data into 

cat1_1 = my_data[my_data['Cat1']==1]
cat1_2 = my_data[my_data['Cat1']==2]

And then loop through every value in Cat2 to perform a t-test:

for cat2 in [0,1]:

    subset_1 = cat1_1[cat1_1['Cat2']==cat2]
    subset_2 = cat1_2[cat1_2['Cat2']==cat2]

    t, p = ttest_ind(subset_1['values'], subset_2['values'])

But this seems really convoluted. Could there be a simpler solution, maybe with groupby? Thanks a lot!

Upvotes: 2

Views: 515

Answers (1)

jezrael
jezrael

Reputation: 863166

IIUC you can try groupby by column Cat2 and apply function f:

import pandas as pd
from scipy.stats import ttest_ind

data = {'Cat1': [2,1,2,1,2,1,2,1,1,1,2],
        'Cat2': [0,0,0,0,0,0,1,1,1,1,1],
        'values': [1,2,3,1,2,3,1,2,3,5,1]}
my_data =pd.DataFrame(data)
print my_data
    Cat1  Cat2  values
0      2     0       1
1      1     0       2
2      2     0       3
3      1     0       1
4      2     0       2
5      1     0       3
6      2     1       1
7      1     1       2
8      1     1       3
9      1     1       5
10     2     1       1

def f(x):
    #print x   
    cat1_1 = x[x['Cat1']==1]
    cat1_2 = x[x['Cat1']==2]

    t, p = ttest_ind(cat1_1['values'], cat1_2['values'])
    return pd.Series({'a':t, 'b':p})     

print my_data.groupby('Cat2').apply(f) 
            a         b
Cat2                   
0     0.00000  1.000000
1     2.04939  0.132842

Upvotes: 1

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