Table:
Club	Season	League	State League	National Cup	Continental	Other	Total

Division	Apps	Goals	Apps	Goals	Apps	Goals	Apps	Goals	Apps	Goals	Apps	Goals

Remo	2012	Série D	4	0	0	0	4	0	—	8	0

Ponte Preta	2013	Série A	1	0	1	0	2	0	—	4	0

Vila Nova (loan)	2013	Série C	11	0	0	0	0	0	—	11	0

Fortaleza	2014	Série C	20	1	23	1	0	0	—	43	2

Ceará	2015	Série B	17	0	15	0	3	0	—	8	0	43	0

2016	Série B	27	1	11	1	4	0	—	8	1	50	3

2017	Série B	27	0	13	1	1	0	—	3	0	44	1

Total	71	1	39	2	8	0	—	19	1	137	4

Avaí (loan)	2018	Série B	0	0	8	0	0	0	—	8	0

Brasil de Pelotas (loan)	2018	Série B	1	0	0	0	0	0	—	1	0

Career total	108	2	71	3	14	0	0	0	19	1	212	6

Analyze the provided table and extract the contents of specified rows or columns, identified by row or column indices. Output the cell values in each row or column as JSON in the format {"row_id/column_id":"<row or column index>", "cell_list":"<a list of cells in this row/column>"}, e.g., {"column_id":"3", "cell_list": ["123.89", "News gathering", "selected tools"]}.
Target rows or columns:
the 2nd row
the 10th row
the 4th row
the 13th column
the 9th row

The cell values of target rows or columns are as follows:
For the 2nd row: {"row_id": "2", "cell_list": ['', '', 'division', 'apps', 'goals', 'apps', 'goals', 'apps', 'goals', 'apps', 'goals', 'apps', 'goals', 'apps', 'goals']}
For the 10th row: {"row_id": "10", "cell_list": ['', 'total', '', '71', '1', '39', '2', '8', '0', '-', '', '19', '1', '137', '4']}
For the 4th row: {"row_id": "4", "cell_list": ['ponte preta', '2013', 'serie a', '1', '0', '1', '0', '2', '0', '-', '', '4', '0', 'none', 'none']}
For the 13th column: {"column_id": "13", "cell_list": ['', 'goals', '0', '0', '0', '2', '0', '1', '0', '1', '0', '0', '1']}
For the 9th row: {"row_id": "9", "cell_list": ['', '2017', 'serie b', '27', '0', '13', '1', '1', '0', '-', '', '3', '0', '44', '1']}