Table:
Year

Team

Games

Passing

Rushing

Sacked

Fumbles

G
GS
Att
Comp
Pct
Yds
Avg
Long
TD
Int
Rtg
Att
Yds
Avg
TD
Sack
Yds
Fum
Lost

2004
NYG

9
7
197
95
48.2
1,043
5.3
52
6
9
55.4
6
35
5.8
0
13
83
3
1

2005
NYG

16
16
557
294
52.8
3,762
6.8
78
24
17
75.9
29
80
2.8
1
28
184
9
2

2006
NYG

16
16
522
301
57.7
3,244
6.2
55
24
18
77.0
25
21
0.8
0
25
186
9
2

2007
NYG

16
16
529
297
56.1
3,336
6.3
60
23
20
73.9
29
69
2.4
1
27
217
13
7

2008
NYG

16
16
479
289
60.3
3,238
6.8
48
21
10
86.4
20
10
0.5
1
27
174
5
2

2009
NYG

16
16
509
317
62.3
4,021
7.9
74
27
14
93.1
17
65
3.8
0
30
216
13
8

2010
NYG

16
16
539
339
62.9
4,002
7.4
92
31
25
85.3
32
70
2.2
0
16
117
7
5

2011
NYG

16
16
589
359
61.0
4,933
8.4
99
29
16
92.9
35
15
0.4
1
28
199
8
4

2012
NYG

16
16
536
321
59.9
3,948
7.4
80
26
15
87.2
20
30
1.5
0
19
136
4
1

2013
NYG

16
16
551
317
57.5
3,818
6.9
70
18
27
69.4
18
36
2.0
0
39
281
7
2

2014
NYG

16
16
601
379
63.1
4,410
7.3
80
30
14
92.1
12
31
2.6
1
28
187
7
4

2015
NYG

16
16
618
387
62.6
4,432
7.2
87
35
14
93.6
20
61
3.1
0
27
157
11
4

2016
NYG

16
16
598
377
63.0
4,027
6.7
75
26
16
86.0
21
-9
-0.4
0
21
142
7
4

2017
NYG

15
15
571
352
61.6
3,468
6.1
77
19
13
80.4
12
26
2.2
1
31
189
11
5

2018
NYG

16
16
576
380
66.0
4,299
7.5
58
21
11
92.4
15
20
1.3
1
47
358
7
4

Total
232
230
7,972
4,804
60.3
55,981
7.0
99
360
239
84.1
311
560
1.8
7
406
2,826
122
55

Examine the given table and extract the contents of in the specific rows or columns, denoted by row or column indices. Show the cells in each row or column in the JSON format {"row_id/column_id":"<row or column index>", "cell_list":"<a list of cells in this row/column>"}. For example, {"row_id":"3", "cell_list": ["A", "B", "C"]}.
Specified rows or columns:
the 19th column,
the 21th column,
the 4th column,
the 18th column,
the 9th row,

The cell values of target rows or columns are as follows:
For the 19th column: {"column_id": "19", "cell_list": ['', 'yds', '83', '184', '186', '217', '174', '216', '117', '199', '136', '281', '187', '157', '142', '189', '358', '2,826']}
For the 21th column: {"column_id": "21", "cell_list": ['', 'lost', '1', '2', '2', '7', '2', '8', '5', '4', '1', '2', '4', '4', '4', '5', '4', '55']}
For the 4th column: {"column_id": "4", "cell_list": ['', 'gs', '7', '16', '16', '16', '16', '16', '16', '16', '16', '16', '16', '16', '16', '15', '16', '230']}
For the 18th column: {"column_id": "18", "cell_list": ['sacked', 'sack', '13', '28', '25', '27', '27', '30', '16', '28', '19', '39', '28', '27', '21', '31', '47', '406']}
For the 9th row: {"row_id": "9", "cell_list": ['2010', 'nyg', '16', '16', '539', '339', '62.9', '4,002', '7.4', '92', '31', '25', '85.3', '32', '70', '2.2', '0', '16', '117', '7', '5']}