Suppose I have the following DataFrame:
a b
0 A 1.516733
1 A 0.035646
2 A -0.942834
3 B -0.157334
4 A 2.226809
5 A 0.768516
6 B -0.015162
7 A 0.710356
8 A 0.151429
And I need to group it given the "edge B"; that means the groups will be:
a b
0 A 1.516733
1 A 0.035646
2 A -0.942834
3 B -0.157334
4 A 2.226809
5 A 0.768516
6 B -0.015162
7 A 0.710356
8 A 0.151429
That is any time I find a 'B' in the column 'a' I want to split my DataFrame.
My current solution is:
#create the dataframe
s = pd.Series(['A','A','A','B','A','A','B','A','A'])
ss = pd.Series(np.random.randn(9))
dff = pd.DataFrame({"a":s,"b":ss})
#my solution
count = 0
ls = []
for i in s:
if i=="A":
ls.append(count)
else:
ls.append(count)
count+=1
dff['grpb']=ls
and I got the dataframe:
a b grpb
0 A 1.516733 0
1 A 0.035646 0
2 A -0.942834 0
3 B -0.157334 0
4 A 2.226809 1
5 A 0.768516 1
6 B -0.015162 1
7 A 0.710356 2
8 A 0.151429 2
Which I can then split with dff.groupby('grpb').
Is there a more efficient way to do this using pandas' functions?
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