inner join with group by pandas python -

i have 2 dataframes named geostat , ref, dataframes follows:

geostat:       count percent  grpno. state code 0          14.78       1         ca 1           0.00       2         ca 2           8.80       3         ca 3           9.60       4         fl 4          55.90       4         ma 5           0.00       2         fl 6           0.00       6         nc 7           0.00       5         nc 8           6.90       1         fl 9          59.00       4         ma res:     grpno.  maxofcount percent 0       1               14.78 1       2                0.00 2       3                8.80 3       4               59.00 4       5                0.00 5       6                0.00 

i want select first(res.maxofcount percent), res.grpno., , geostat.first(statecode) dataframe geostat , res inner join on columns res.maxofcount percent = geostat.count percent , res. grpno. = geostat.grpno. group res.grpno.

i want python pandas, not sure on how inner join group by.can me on this?

the output dataframe given below:

   firstofmaxofstate count percent  state pool number firstofstate code 0                            14.78                  1                ca 1                             0.00                  2                ca 2                             8.80                  3                ca 3                            59.00                  4                ma 4                             0.00                  5                nc 5                             0.00                  6                nc 

note: first(column name) access function should equivalent of in python?

edited: changed output dataframe.

use pandas.dataframe.merge()

geostat.merge(res, left_on=['count percent', 'grpno.'], right_on=['maxofcount percent', 'grpno.'],how='inner')     count percent  grpno. state code  maxofcount percent 0          14.78       1         ca               14.78 1           0.00       2         ca                0.00 2           0.00       2         fl                0.00 3           8.80       3         ca                8.80 4           0.00       6         nc                0.00 5           0.00       5         nc                0.00 6          59.00       4         ma               59.00