How to use pandas to find consecutive same data in time series

This should do it

df['value_grp'] = (df.Values.diff(1) != 0).astype('int').cumsum()

value_grp will increment by one whenever Value changes. Below, you can extract the group results

pd.DataFrame({'BeginDate' : df.groupby('value_grp').Date.first(),               'EndDate' : df.groupby('value_grp').Date.last(),              'Consecutive' : df.groupby('value_grp').size(),               'No' : df.groupby('value_grp').No.first()}).reset_index(drop=True)

Here is an alternative solution:

rslt = (df.assign(Consecutive=df.Value                                .groupby((df.Value != df.Value.shift())                                         .cumsum())                                .transform('size'))          .query('Consecutive > 1')          .groupby('Consecutive')          .agg({'No':{'No':'first'}, 'Date': {'BeginDate':'first', 'EndDate':'last'}})          .reset_index())rslt.columns = [t[1] if t[1] else t[0] for t in rslt.columns]

Demo:

In [225]: %pasterslt = (df.assign(Consecutive=df.Value                                .groupby((df.Value != df.Value.shift())                                         .cumsum())                                .transform('size'))          .query('Consecutive > 1')          .groupby('Consecutive')          .agg({'No':{'No':'first'}, 'Date': {'BeginDate':'first', 'EndDate':'last'}})          .reset_index())rslt.columns = [t[1] if t[1] else t[0] for t in rslt.columns]## -- End pasted text --In [226]: rsltOut[226]:   Consecutive  BeginDate    EndDate      No0            2 1999-11-26 1999-11-29  6000011            4 1999-11-19 1999-11-24  6000002            6 1999-11-10 1999-11-17  600000