NAME

tablify - turn a delimited text file into a text table

SYNOPSIS

tablify [options] file

Options:

-h|--help           Show help
--no-headers        Assume first line is data, not headers
-l|--list           List the fields in the file (for use with -f)
-f|--fields=f1[,f2] Show only fields in comma-separated list;
                    when used in conjunction with "no-headers"
                    the list should be field numbers (starting at 1);
                    otherwise, should be field names
-w|where=f<cmp>v    Apply the "cmp" Perl operator to restrict output 
                    where field "f" matches the value "v";  acceptable
                    operators include ==, eq, >, >=, <=, and =~
--limit=n           Limit to first "n" records
--fs=x              Use "x" as the field separator 
                    (default is tab "\t")
--rs=x              Use "x" as the record separator 
                    (default is newline "\n")
--as-html           Create an HTML table instead of plain text

DESCRIPTION

This script is essentially a quick way to parse a delimited text file and view it as a nice ASCII table. By selecting only certain fields, employing a where clause to only select records where a field matches some value, and using the limit to only see some of the output, you almost have a mini-database front-end for a simple text file.

EXAMPLES

Given a data file like this:

name,rank,serial_no,is_living,age
George,General,190293,0,64
Dwight,General,908348,0,75
Attila,Hun,,0,56
Tojo,Emporor,,0,87
Tommy,General,998110,1,54

To find the fields you can reference, use the list option:

$ tablify --fs ',' -l people.dat 
+-----------+-----------+
| Field No. | Field     |
+-----------+-----------+
| 1         | name      |
| 2         | rank      |
| 3         | serial_no |
| 4         | is_living |
| 5         | age       |
+-----------+-----------+

To extract just the name and serial numbers, use the fields option:

$ tablify --fs ',' -f name,serial_no people.dat 
+--------+-----------+
| name   | serial_no |
+--------+-----------+
| George | 190293    |
| Dwight | 908348    |
| Attila |           |
| Tojo   |           |
| Tommy  | 998110    |
+--------+-----------+
5 records returned

To extract the first through third fields and the fifth field (where field numbers start at "1" -- tip: use the list option to quickly determine field numbers), use this syntax for fields:

$ tablify --fs ',' -f 1-3,5 people.dat 
+--------+---------+-----------+------+
| name   | rank    | serial_no | age  |
+--------+---------+-----------+------+
| George | General | 190293    | 64   |
| Dwight | General | 908348    | 75   |
| Attila | Hun     |           | 56   |
| Tojo   | Emporor |           | 87   |
| Tommy  | General | 998110    | 54   |
+--------+---------+-----------+------+
5 records returned

To select only the ones with six serial numbers, use a where clause:

$ tablify --fs ',' -w 'serial_no=~/^\d{6}$/' people.dat
+--------+---------+-----------+-----------+------+
| name   | rank    | serial_no | is_living | age  |
+--------+---------+-----------+-----------+------+
| George | General | 190293    | 0         | 64   |
| Dwight | General | 908348    | 0         | 75   |
| Tommy  | General | 998110    | 1         | 54   |
+--------+---------+-----------+-----------+------+
3 records returned

To find Dwight's record, you would do this:

$ tablify --fs ',' -w 'name eq "Dwight"' people.dat
+--------+---------+-----------+-----------+------+
| name   | rank    | serial_no | is_living | age  |
+--------+---------+-----------+-----------+------+
| Dwight | General | 908348    | 0         | 75   |
+--------+---------+-----------+-----------+------+
1 record returned

To find the name of all the people with a serial number who are living:

$ tablify --fs ',' -f name -w 'is_living==1' -w 'serial_no>0' people.dat 
+-------+
| name  |
+-------+
| Tommy |
+-------+
1 record returned

To filter outside of program and simply format the results, use "-" as the last argument to force reading of STDIN (and probably assume no headers):

$ grep General people.dat | tablify --fs ',' -f 1-3 --no-headers -
+---------+--------+--------+
| Field1  | Field2 | Field3 |
+---------+--------+--------+
| General | 190293 | 0      |
| General | 908348 | 0      |
| General | 998110 | 1      |
+---------+--------+--------+
3 records returned

When dealing with data lacking field names, you can specify "no-headers" and then refer to fields by number (starting at one), e.g.:

$ tail -5 people.dat | tablify --fs ',' --no-headers -w '3 eq "General"' -
+--------+---------+--------+--------+--------+
| Field1 | Field2  | Field3 | Field4 | Field5 |
+--------+---------+--------+--------+--------+
| George | General | 190293 | 0      | 64     |
| Dwight | General | 908348 | 0      | 75     |
| Tommy  | General | 998110 | 1      | 54     |
+--------+---------+--------+--------+--------+
3 records returned

SEE ALSO

• Text::RecordParser

• Text::TabularDisplay

• DBD::CSV

  Although I don't DBD::CSV this module, the idea was much the inspiration for this. I just didn't want to have to install DBI and DBD::CSV to get this kind of functionality. I think my interface is simpler.

AUTHOR

Ken Youens-Clark <kclark@cpan.org>.

LICENSE AND COPYRIGHT

Copyright (C) 2005 Ken Youens-Clark. All rights reserved.

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; version 2.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

cpanm Text::RecordParser

perl -MCPAN -e shell
install Text::RecordParser