Parsing a comma-separated file

Ruby
1

Hi, I had a question about parsing just one line at a time beforehand
and now I’m working on a program to parse multiple items on each
line-something like the following:

name, age, gender
Bob, 32, M
Stacy, 14, F
…
…

How do I parse ‘Bob’, knowing it’s the first element on the line, ‘32’
is the second, ‘M’ is the last…I’ve been reading about regular
expressions. Is this the best way to solve this problem? And how exactly
do you use them?

Thanks!!

2

Are you looking for this?
http://fastercsv.rubyforge.org/

Ruby also has the csv standard library.

Regards,
Thomas.

3

ThoML wrote:

Are you looking for this?
http://fastercsv.rubyforge.org/

Ruby also has the csv standard library.

Regards,
Thomas.

That is great Thomas! Although, I’d like to know how to do it with the
regular expressions as well.

Thanks!

4

So is the fasterCSV the most effective way of parsing a comma-separated
file?

5

On Mon, Jun 9, 2008 at 12:46 PM, Justin To [email protected] wrote:

That is great Thomas! Although, I’d like to know how to do it with the
regular expressions as well.

I’d recommend using Sring#split. In the simplest case you could just
specify line.split(‘,’); no regular expressions needed. If you wanted
you could use a regular expression argument to #split in order to skip
whitespace:

line.split(/\s*,\s*/)

but you could just as easily trim the values after the fact too:

line.split(‘,’).map{|v| v.strip}

Regular expressions are not the best solution for parsing CSV,
especially once you start dealing with quoted values.

–
Avdi

Home: http://avdi.org
Developer Blog: Avdi Grimm, Code Cleric
Twitter: http://twitter.com/avdi
Journal: http://avdi.livejournal.com

6

My experience (at least a year ago) was that fastercsv was a great way
to go if you had very clean files without errors, odd characters,
etc. Unfortunately, I had files that were a bit more problematic and
so I ended up using a combination of either parsing it myself (split,
regexs. etc) and catching all the errors and handling them or using
the parse_line method in the standard csv library.

7

Great guys, thanks for the help!

8

On Mon, Jun 9, 2008 at 2:08 PM, Justin To [email protected] wrote:

So is the fasterCSV the most effective way of parsing a comma-separated
file?

It is the fastest and most robust way.

–
Avdi

Home: http://avdi.org
Developer Blog: Avdi Grimm, Code Cleric
Twitter: http://twitter.com/avdi
Journal: http://avdi.livejournal.com

9

name, age, gender
Bob, 32, M
Stacy, 14, F
…
How do I parse ‘Bob’, knowing it’s the first element on the line, ‘32’
is the second, ‘M’ is the last…I’ve been reading about regular
expressions. Is this the best way to solve this problem? And how exactly
do you use them?

This doesn’t handle all CSV specs, but if you know you have pure data
like you show above, these are the rudimentary steps without the
one-liner tricks, so it should be pretty straight forward to understand
each step. Arranging them as methods to a class would be good.

read the file into a var

if FileTest::exist?(file_name)
file_lines = IO.readlines(file_name)
end

normalize line endings so it doesn’t matter what they are

file_lines.strip!
file_lines.gsub!(/\r\n/,’\n’)
file_lines.gsub!(/\r/,’\n’)

normalize comma delimiters so it doesn’t matter

if you have one, two or one,two or one , two etc…

file_lines.gsub!(/\s*,\s*/, ‘,’)

split lines into a single array of lines

lines_array = file_lines.split(’\n’)

split each line into an array

final_data = []

lines_array.each do |this_line|
final_data << this_line.split(’,’)
end

final_data is now an array of arrays that looks like this:

[
[‘name’, ‘age’, ‘gender’],
[‘Bob’, ‘32’, ‘M’],
[‘Stacy’, ‘14’, ‘F’]
]

So, to get Bob, you’d have to know his line number, and index into the
record array:

final_data[1][0] # Bob
final_data[2][3] # F

– greg willits

10

On Jun 9, 2008, at 4:52 PM, Charles Walden wrote:

My experience (at least a year ago) was that fastercsv was a great
way to go if you had very clean files without errors, odd
characters, etc. Unfortunately, I had files that were a bit more
problematic and so I ended up using a combination of either parsing
it myself (split, regexs. etc) and catching all the errors and
handling them or using the parse_line method in the standard csv
library.

FasterCSV has a parse_line() method as well, just FYI.

James Edward G. II

11

final_data[1][0] # Bob
final_data[2][3] # F

should the last one be:
final_data[2][2] # F
??

Thanks! Also, is this an effective way to parse a large file. What if I
had to read a million lines with multiple columns? Would this solution
still be practical?

Thanks again!