The syntax doesn't seem noticeably clearer?
And - without testing - I presume csvkit in Python is a bit slower than the GNU coreutils in C?
From the very first example:
csvgrep -c 1 -m ILLINOIS
grep -E '^ILLINOIS,' 2009.csv
grep -E '^"?ILLINOIS"?,'
grep -E '^(["]{0,1})ILLINOIS\1,'
In all but the most trivial cases it's safer and easier to use tools designed for the job. csvkit also has the very nice property that it's callable as a Python library so when you outgrow a simple shell processing pipeline you could migrate your notes / scripts to a full Python program without having to retest everything related to basic file I/O, unicode, etc. which you would otherwise need to do when switching readers.
(Bear in mind that the author works in data journalism – the target user is not a grizzled Unix expert but someone who has a bunch of CSV files and a full time job which is not shell scripting)
> And - without testing - I presume csvkit in Python is a bit slower than the GNU coreutils in C?
Perhaps but it'd be unlikely for it to be noticeable for n less than millions on a remotely modern system – the Python regular expression engine has quite decent performance and if that became an issue, PyPy will even JIT them for you. In the very few cases I've seen where there is a noticeable difference it was always because the Python version was decoding Unicode and the shell tools were running with LC_ALL=C, which meant that corrupt data made it further before being caught and, in some cases, either failing to match all of the records or subtly quietly things by not extracting all of a combined character, etc.
For the target use-case, however, this is likely all to be many, many orders of magnitude less than the time most people would spend debugging regex patterns.
cut -d, -f1 2010.csv | grep ILLINOIS
works just fine for this data. I emphasise again - I take your overall point though.
This class of error is somewhat treacherous since it's common for people not to notice it before they start working with a full data file. Using tools which don't require constant caution to avoid data errors is simply a basic good habit.
1,2,5,"Hello"
3,4,5,"How are
you doing?"So instead of making a more complex version of tools like grep, we can make the data simple for these tools to understand. That's what https://github.com/dbro/csvquote does. It can be run in a pipeline before the grep stage, and allow grep/cut/awk/... to work with unambiguous field and record delimiters. Then it can restore the newlines and commas inside the quoted fields at the end of the pipeline.
So I personally just don't see the benefit from a less generalised version of coreutil tools
... but fair does I'm sure there are plenty who have use for a more specific tool.
Others have pointed out specific examples, but the general reason (which I don't think I've seen explicitly articulated) is that CSV isn't strictly line oriented -- for intsance, records can span multiple "physical" lines, because line breaks within quoted strings are part of the field value, not separate records -- so line-oriented tools don't work with CSV generally (they may if you happen to have CSV where nothing inconsistent with a line-oriented view happens to be done, but that's luck not something that is generally true of CSV.)
It also became super simple to extract and munge data with options for tabular data. With two tiny commands I can exclude rows, include rows, sed columns, merge tables, and export new tables, all with just csv files. So it's pretty handy.
I've been playing with these old school tools lately on medium sized tabular data files and I've been impressed by the performance and capabilities.