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What is the best way to look for patterns in a data set?

I have a data set that is about 100,000 rows long and it looks like this:
In[1]: df1 = pd.DataFrame(data={‘Name’:[‘A’,’A’,’B’,’B’,’C’,’C’],
‘Group’:[‘A’,’B’,’C’,’D’,’E’,’F’],
‘Date’:[’01/01/2012′,’01/01/2012′,’03/01/2012′,’03/01/2012′,’01/03/2012′,’01/04/2012′]})
Out[1]:
Name Group Date
0 A A 2012-01-01
1 A B 2012-01-01
2 B C 2012-03-01
3 B D 2012-03-01
4 C E 2012-01-03
5 C F 2012-01-04

I want to find a pattern where all the Cs are grouped together (2 rows) and all the As are grouped together (2 rows). Is there a function in pandas that will help me do this?

A:

Use np.groupby and custom function, because convert all values to Series:
def f(x):
for i in x.index:
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