What is pandas equivalent of the following SQL?

OK, I have a dataframe that looks like the following:

>> df
id  trip_id segment_id  session_id  start_timestamp     lat_start   lon_start   lat_end     lon_end travelmode
563097015   563097  15  128618  2017-05-20 17:47:12+01  41.1783308  -8.5949878  41.1784478  -8.5948463  0
563097013   563097  13  128618  2017-05-20 17:45:29+01  41.1781344  -8.5951169  41.1782919  -8.5950689  0
563097011   563097  11  128618  2017-05-20 17:43:41+01  41.1781196  -8.5954075  41.1782139  -8.5950689  0
563097009   563097  9   128618  2017-05-20 17:41:48+01  41.1782497  -8.595197   41.1781101  -8.5954124  0
563097003   563097  3   128618  2017-05-20 17:10:29+01  41.1832512  -8.6081606  41.1782561  -8.5950259  0

In SQL, to filter unique segments (segment_id) by travelmode I will do:

SELECT travelmode, COUNT(DISTINCT segment_id) AS NumOfSegments
FROM df_table
GROUP BY travelmode

What is the pandas equivalent of this expression?

Answer

Maybe:

 df.groupby('travelmode').segment_id.nunique()

as suggested in this post.