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 python pandas: diff between 2 dates in a groupby
#1
Hi all,

I'm trying to implement this example:

import pandas as pd
import io
df = pd.read_csv(io.StringIO('''transactionid;event;datetime;info
1;START;2017-04-01 00:00:00;
1;END;2017-04-01 00:00:02;foo1
2;START;2017-04-01 00:00:02;
3;START;2017-04-01 00:00:02;
2;END;2017-04-01 00:00:03;foo2
4;START;2017-04-01 00:00:03;
3;END;2017-04-01 00:00:03;foo3
4;END;2017-04-01 00:00:04;foo4'''), sep=';', parse_dates=['datetime'])

df.datetime = pd.to_datetime(df.datetime)

funcs = {
    'datetime':{
        'start_date':   'min',
        'end_date':     'max',
        'duration':     lambda x: x.max() - x.min(),
    },
    'info':             'last'
}

df.groupby(by='transactionid')['datetime','info'].agg(funcs).reset_index()

print(df)
The expected output should be:
Output:
transactionid start_date end_date duration info 0 1 2017-04-01 00:00:00 2017-04-01 00:00:02 00:00:02 foo1 1 2 2017-04-01 00:00:02 2017-04-01 00:00:03 00:00:01 foo2 2 3 2017-04-01 00:00:02 2017-04-01 00:00:03 00:00:01 foo3 3 4 2017-04-01 00:00:03 2017-04-01 00:00:04 00:00:01 foo4
Using python3.7 and I'm getting the following error:
Error:
python.py:24: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead. df.groupby(by='transactionid')['datetime','info'].agg(funcs).reset_index() Traceback (most recent call last): File "python.py", line 24, in <module> df.groupby(by='transactionid')['datetime','info'].agg(funcs).reset_index() File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 928, in aggregate result, how = self._aggregate(func, *args, **kwargs) File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/base.py", line 342, in _aggregate raise SpecificationError("nested renamer is not supported") pandas.core.base.SpecificationError: nested renamer is not supported
Any idea to solve this issue?

Thanks a lot
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