Python pandas: How to slice dataframe using 13 digit timestamp

Question

I have the following dataframe:

              DateTime                   Seq
timestamp
1475504294990,10/03/2016 10:18:14:990000,2123847
1475504446660,10/03/2016 10:20:46:660000,2123908
1475504524410,10/03/2016 10:22:04:410000,2123953
1475504848100,10/03/2016 10:27:28:100000,2124067
1475504940530,10/03/2016 10:29:00:530000,2124126

i want to slice this dataframe using a start and end time stamp

start = 1475504446660
end = 1475504848100
print df[start:end]
              DateTime                   Seq
timestamp
1475504446660,10/03/2016 10:20:46:660000,2123908
1475504524410,10/03/2016 10:22:04:410000,2123953
1475504848100,10/03/2016 10:27:28:100000,2124067

However,I am getting this error:

IndexError: failed to coerce slice entry of type long to integer

I tried using df[int(start):int(end)], still getting same error

Answer 1

To slice you have to define the timestamp as the index and use loc to perform label indexing (else it is ambiguous between position and label indexing for integer indexes).

df = df.set_index('timestamp')
df.loc[start:end]

#                                  DateTime      Seq
# timestamp                                         
# 1475504446660  10/03/2016 10:20:46:660000  2123908
# 1475504524410  10/03/2016 10:22:04:410000  2123953
# 1475504848100  10/03/2016 10:27:28:100000  2124067

By default in the case of an integer index the indexing is made by position and not by label, see the result in this example.

df[0:2] # equivalent to df.iloc[0:2]

#                                  DateTime      Seq
# timestamp                                         
# 1475504294990  10/03/2016 10:18:14:990000  2123847
# 1475504446660  10/03/2016 10:20:46:660000  2123908

Note

If you do not want to define timestamp as the index you can use this syntax to obtain the same result.

df.query('@start <= timestamp <= @end')

#        timestamp                    DateTime      Seq
# 1  1475504446660  10/03/2016 10:20:46:660000  2123908
# 2  1475504524410  10/03/2016 10:22:04:410000  2123953
# 3  1475504848100  10/03/2016 10:27:28:100000  2124067