与熊猫python内部连接

Question

I have 2 dataframes named geostat and ref, the dataframes are as 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 to select the first(res.Maxofcount percent), res.grpno., and geostat.first(statecode) from the dataframe geostat and res inner join on columns res.Maxofcount percent = geostat.count percent AND res. grpno. = geostat.grpno. group by res.grpno.

I want to do this python pandas, I am not sure on how to do inner join with group by.Can anyone help me on this?

The output dataframe is 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) is an access function what should be equivalent of it in python?

EDITED: Changed the output dataframe.

Answer 1

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