Perl as Better grep Perl as Better grep
I like Perl’s pattern matching features more than Perl as a programming language. I’d like to take advantage of the former without having to... Perl as Better grep

I like Perl’s pattern matching features more than Perl as a programming language. I’d like to take advantage of the former without having to go any deeper than necessary into the latter.

The book Minimal Perl is useful in this regard. It has chapters on Perl as a better grep, a better awk, a better sed, and a better find. While Perl is not easy to learn, it might be easier to learn a minimal subset of Perl than to learn each of the separate utilities it could potentially replace. I wrote about this a few years ago and have been thinking about it again recently.

Here I want to zoom in on Perl as a better grep. What’s the minimum Perl you need to know in order to use Perl to search files the way grep would?

By using Perl as your grep, you get to use Perl’s more extensive pattern matching features. Also, you get to use one regex syntax rather than wondering about the specifics of numerous regex dialects supported across various programs.

Let RE stand for a generic regular expression. To search a file foo.txt for lines containing the pattern RE, you could type

    perl -wln -e "/RE/ and print;" foo.txt

The Perl one-liner above requires more typing than using grep would, but you could wrap this code in a shell script if you’d like.

If you’d like to print lines that don’t match a regex, change the and to or:

    perl -wln -e "/RE/ or print;" foo.txt

By learning just a little Perl you can customize your search results. For example, if you’d like to just print the part of the line that matched the regex, not the entire line, you could modify the code above to

    perl -wln -e "/RE/ and print $&;" foo.txt

because $& is a special variable that holds the result of the latest match.

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For daily tips on regular expressions, follow @RegexTip on Twitter.


 

Original Source

John Cook

John Cook

Companies come to me for help with probability, machine learning, mathematical modeling, … anything that falls under applied math, statistics, and computing. Clients have included large companies such as Amazon, Google, Microsoft, and Amgen, as well as a number of law firms, start-ups, and smaller businesses. I help companies take advantage of their data by combining it with expert opinion, uncovering latent insights, creating mathematical models, overcoming computational difficulties, and interpreting the results.