How to fill Matrix using a nested loop

[#1]

I am trying to extract a specific set of numeric data from a file (which contains both text and numbers) and put it into a separate table which is easier to analyze in JMP.  I want the script to work for any number of columns or rows where the data meeting specific criteria might be.  What I have come up with is below:

Rowlist = [];
For( i = 1, i <= N Rows( Dtraw ), i++,
	If( i == 1,
		If( Num( Column( Dtraw, 2 )[i] ) > 0 & Num( Column( Dtraw, 2 )[i] ) < 1000,
			Rowlist = Matrix( i )
		),
		If( Num( Column( Dtraw, 2 )[i] ) > 0 & Num( Column( Dtraw, 2 )[i] ) < 1000,
			Rowlist = V Concat( Rowlist, Matrix( i ) )
		)
	)
);
show(Rowlist);

cols= dtraw<<get column names();
MainData=J(nitems(rowlist),nitems(cols)-1);

For( i = 1, i <= N Items( rowlist ), i++,
	For( k = 2, k <= N items ( cols ), i++,
		MainData[i,k] =  Num( Column( dtraw, k )[rowlist[i]] );
		
		)
	);
show(MainData);

Rowlist is the matrix of rows that meet the criteria I need (that is, they are numbers and not text).  I want to use this list and "<<get column names" to define the regions of my raw data file where the data I need resides.  I want to extract that specific data into a different table.  The first For loop for Rowlist works just fine, but the nested for loop does not do what I want and I can't figue out why.  Can anyone help me with this? 

-Thanks

[#2]

Of course, the code looks 'roughly right'.

What error do you actually see, please? Can you upload a part of 'dtraw'?

[#3]

I get the error message below:

invalid subscript (must be number or list of numbers){1271} in access or evaluation of 'Subscript' , rowlist[/*###*/i]

　

In the following script, error marked by /*###*/

Show( Rowlist );

cols = dtraw << get column names();

MainData = J( N Items( rowlist ), N Items( cols ) - 1 );

For( i = 1, i <= N Items( rowlist ), i++,

For( k = 2, k <= N Items( cols ), i++,

MainData[i, k] = Num( Column( dtraw, k )[rowlist[/*###*/i]] )

)

);

[#4 · Accepted solution]

You have a couple of typing errors.

1. Your second For Loop

For( k = 2, k <= N Items( cols ), i++,

you are incrementing "i", not "k".  It should be

For( k = 2, k <= N Items( cols ), k++,

Secondly, you need to subtract 1 from k when referencing your MainData matrix, otherwise you will go beyond the range, since it is 1 less than the number of columns.

MainData[i, k - 1] = Num( Column( dtraw, k - 1 )[rowlist[i]] )

The whole section should be:

For( i = 1, i <= N Rows( rowlist ), i++,
	For( k = 2, k <= N Items( cols ), k++,
		MainData[i, k - 1] = Num( Column( dtraw, k - 1 )[rowlist[i]] )
	)
);

[#5]

Thank you this works perfectly!

[#6]

Below is a generic example of the data (showing the format):

Date/Time:	11/29/2017 20:11
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	Title	Title	Title	Title	Title	Title	Title	Title	Title	Title	Title
	1	1	1	1	1	1	1	1	1	1	1
	2	2	2	2	2	2	2	2	2	2	2
	3	3	3	3	3	3	3	3	3	3	3
	4	4	4	4	4	4	4	4	4	4	4
	5	5	5	5	5	5	5	5	5	5	5
	6	6	6	6	6	6	6	6	6	6	6
	7	7	7	7	7	7	7	7	7	7	7
	8	8	8	8	8	8	8	8	8	8	8
	9	9	9	9	9	9	9	9	9	9	9
	10	10	10	10	10	10	10	10	10	10	10
	11	11	11	11	11	11	11	11	11	11	11
	12	12	12	12	12	12	12	12	12	12	12
	13	13	13	13	13	13	13	13	13	13	13
	14	14	14	14	14	14	14	14	14	14	14
	15	15	15	15	15	15	15	15	15	15	15
	16	16	16	16	16	16	16	16	16	16	16
	17	17	17	17	17	17	17	17	17	17	17
	18	18	18	18	18	18	18	18	18	18	18
	19	19	19	19	19	19	19	19	19	19	19
	20	20	20	20	20	20	20	20	20	20	20
	21	21	21	21	21	21	21	21	21	21	21
	22	22	22	22	22	22	22	22	22	22	22
	23	23	23	23	23	23	23	23	23	23	23
	24	24	24	24	24	24	24	24	24	24	24
	25	25	25	25	25	25	25	25	25	25	25
	26	26	26	26	26	26	26	26	26	26	26
	27	27	27	27	27	27	27	27	27	27	27
	28	28	28	28	28	28	28	28	28	28	28
	29	29	29	29	29	29	29	29	29	29	29
	30	30	30	30	30	30	30	30	30	30	30
	31	31	31	31	31	31	31	31	31	31	31
	32	32	32	32	32	32	32	32	32	32	32
	33	33	33	33	33	33	33	33	33	33	33
	34	34	34	34	34	34	34	34	34	34	34
	35	35	35	35	35	35	35	35	35	35	35
	36	36	36	36	36	36	36	36	36	36	36
	37	37	37	37	37	37	37	37	37	37	37
	38	38	38	38	38	38	38	38	38	38	38
	39	39	39	39	39	39	39	39	39	39	39
	40	40	40	40	40	40	40	40	40	40	40
	41	41	41	41	41	41	41	41	41	41	41
	42	42	42	42	42	42	42	42	42	42	42
	43	43	43	43	43	43	43	43	43	43	43
	44	44	44	44	44	44	44	44	44	44	44
	45	45	45	45	45	45	45	45	45	45	45
	46	46	46	46	46	46	46	46	46	46	46
	47	47	47	47	47	47	47	47	47	47	47
	48	48	48	48	48	48	48	48	48	48	48
	49	49	49	49	49	49	49	49	49	49	49
	50	50	50	50	50	50	50	50	50	50	50
	51	51	51	51	51	51	51	51	51	51	51
	52	52	52	52	52	52	52	52	52	52	52
	53	53	53	53	53	53	53	53	53	53	53
	54	54	54	54	54	54	54	54	54	54	54
	55	55	55	55	55	55	55	55	55	55	55
	56	56	56	56	56	56	56	56	56	56	56
	57	57	57	57	57	57	57	57	57	57	57
	58	58	58	58	58	58	58	58	58	58	58
	59	59	59	59	59	59	59	59	59	59	59
	60	60	60	60	60	60	60	60	60	60	60
	61	61	61	61	61	61	61	61	61	61	61
	62	62	62	62	62	62	62	62	62	62	62
	63	63	63	63	63	63	63	63	63	63	63
	64	64	64	64	64	64	64	64	64	64	64
	65	65	65	65	65	65	65	65	65	65	65
	66	66	66	66	66	66	66	66	66	66	66
	67	67	67	67	67	67	67	67	67	67	67
	68	68	68	68	68	68	68	68	68	68	68
	69	69	69	69	69	69	69	69	69	69	69
	70	70	70	70	70	70	70	70	70	70	70
	71	71	71	71	71	71	71	71	71	71	71
	72	72	72	72	72	72	72	72	72	72	72
	73	73	73	73	73	73	73	73	73	73	73
Value label		74	74	74	74	74	74	74	74
Value label		75	75	75	75	75	75	75	75
Value label		76	76	76	76	76	76	76	76
Value label		77	77	77	77	77	77	77	77
Value label		78	78	78	78	78	78	78	78
Value label		79	79	79	79	79	79	79	79
Value label		80	80	80	80	80	80	80	80
Value label		81	81	81	81	81	81	81	81
Date/Time:	11/29/2017 20:11
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	TEXT
Title	Title	Title	Title	Title	Title	Title	Title	Title	Title	Title	Title
	1	1	1	1	1	1	1	1	1	1	1
	2	2	2	2	2	2	2	2	2	2	2
	3	3	3	3	3	3	3	3	3	3	3
	4	4	4	4	4	4	4	4	4	4	4
	5	5	5	5	5	5	5	5	5	5	5
	6	6	6	6	6	6	6	6	6	6	6
	7	7	7	7	7	7	7	7	7	7	7
	8	8	8	8	8	8	8	8	8	8	8
	9	9	9	9	9	9	9	9	9	9	9
	10	10	10	10	10	10	10	10	10	10	10
	11	11	11	11	11	11	11	11	11	11	11
	12	12	12	12	12	12	12	12	12	12	12
	13	13	13	13	13	13	13	13	13	13	13
	14	14	14	14	14	14	14	14	14	14	14
	15	15	15	15	15	15	15	15	15	15	15
	16	16	16	16	16	16	16	16	16	16	16
	17	17	17	17	17	17	17	17	17	17	17
	18	18	18	18	18	18	18	18	18	18	18
	19	19	19	19	19	19	19	19	19	19	19
	20	20	20	20	20	20	20	20	20	20	20
	21	21	21	21	21	21	21	21	21	21	21
	22	22	22	22	22	22	22	22	22	22	22
	23	23	23	23	23	23	23	23	23	23	23
	24	24	24	24	24	24	24	24	24	24	24
	25	25	25	25	25	25	25	25	25	25	25
	26	26	26	26	26	26	26	26	26	26	26
	27	27	27	27	27	27	27	27	27	27	27
	28	28	28	28	28	28	28	28	28	28	28
	29	29	29	29	29	29	29	29	29	29	29
	30	30	30	30	30	30	30	30	30	30	30
	31	31	31	31	31	31	31	31	31	31	31
	32	32	32	32	32	32	32	32	32	32	32
	33	33	33	33	33	33	33	33	33	33	33
	34	34	34	34	34	34	34	34	34	34	34
	35	35	35	35	35	35	35	35	35	35	35
	36	36	36	36	36	36	36	36	36	36	36
	37	37	37	37	37	37	37	37	37	37	37
	38	38	38	38	38	38	38	38	38	38	38
	39	39	39	39	39	39	39	39	39	39	39
	40	40	40	40	40	40	40	40	40	40	40
	41	41	41	41	41	41	41	41	41	41	41
	42	42	42	42	42	42	42	42	42	42	42
	43	43	43	43	43	43	43	43	43	43	43
	44	44	44	44	44	44	44	44	44	44	44
	45	45	45	45	45	45	45	45	45	45	45
	46	46	46	46	46	46	46	46	46	46	46
	47	47	47	47	47	47	47	47	47	47	47
	48	48	48	48	48	48	48	48	48	48	48
	49	49	49	49	49	49	49	49	49	49	49
	50	50	50	50	50	50	50	50	50	50	50
	51	51	51	51	51	51	51	51	51	51	51
	52	52	52	52	52	52	52	52	52	52	52
	53	53	53	53	53	53	53	53	53	53	53
	54	54	54	54	54	54	54	54	54	54	54
	55	55	55	55	55	55	55	55	55	55	55
	56	56	56	56	56	56	56	56	56	56	56
	57	57	57	57	57	57	57	57	57	57	57
	58	58	58	58	58	58	58	58	58	58	58
	59	59	59	59	59	59	59	59	59	59	59
	60	60	60	60	60	60	60	60	60	60	60
	61	61	61	61	61	61	61	61	61	61	61
	62	62	62	62	62	62	62	62	62	62	62
	63	63	63	63	63	63	63	63	63	63	63
	64	64	64	64	64	64	64	64	64	64	64
	65	65	65	65	65	65	65	65	65	65	65
	66	66	66	66	66	66	66	66	66	66	66
	67	67	67	67	67	67	67	67	67	67	67
	68	68	68	68	68	68	68	68	68	68	68
	69	69	69	69	69	69	69	69	69	69	69
	70	70	70	70	70	70	70	70	70	70	70
	71	71	71	71	71	71	71	71	71	71	71
	72	72	72	72	72	72	72	72	72	72	72
	73	73	73	73	73	73	73	73	73	73	73
Value label		74	74	74	74	74	74	74	74
Value label		75	75	75	75	75	75	75	75
Value label		76	76	76	76	76	76	76	76
Value label		77	77	77	77	77	77	77	77
Value label		78	78	78	78	78	78	78	78
Value label		79	79	79	79	79	79	79	79
Value label		80	80	80	80	80	80	80	80
Value label		81	81	81	81	81	81	81	81