Comments (1)
Good catch! This is in fact a bug. It was happening because I was using the original dataframe's index to sort, then re-indexing with the sorted indices. When there were duplicate indices it would duplicate the rows.
Should be fixed now. I just changed to indexing using .iloc instead.
I tried the same on my machine with the new master branch:
from dfply import *
utime = pd.DataFrame({"u":1,"eventTime":["01-01-1971 01:04:00","01-01-1971 02:07:00","01-01-1971 01:09:00","01-01-1971 01:10:00"]})
print(utime >> arrange(X.eventTime))
eventTime u
0 01-01-1971 01:04:00 1
2 01-01-1971 01:09:00 1
3 01-01-1971 01:10:00 1
1 01-01-1971 02:07:00 1
utime = utime.set_index("u")
print(utime >> arrange(X.eventTime))
eventTime
u
1 01-01-1971 01:04:00
1 01-01-1971 01:09:00
1 01-01-1971 01:10:00
1 01-01-1971 02:07:00
Which is the behavior you expected. If you pull the master branch and reinstall it should work.
from dfply.
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