Parsing a comma-separated file

[Justin To]

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!!

 

[ThoML]

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

Ruby also has the csv standard library.

Regards,
Thomas.

 

[Justin To]

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!

 

[Avdi Grimm]

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: http://avdi.org/devblog/
Twitter: http://twitter.com/avdi
Journal: http://avdi.livejournal.com

 

[Justin To]

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

 

[Avdi Grimm]

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: http://avdi.org/devblog/
Twitter: http://twitter.com/avdi
Journal: http://avdi.livejournal.com

 

[Charles Walden]

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.

 

[Justin To]

Great guys, thanks for the help!

 

[Greg Willits]

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

 

[James Gray]

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 Gray II

 

[Justin To]

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!