xsv alternatives and similar packages
Based on the "Applications written in Rust" category.
Alternatively, view xsv alternatives based on common mentions on social networks and blogs.
10.0 9.8 xsv VS tauriBuild smaller, faster, and more secure desktop applications with a web frontend.
9.7 9.9 xsv VS conduitUltralight, security-first service mesh for Kubernetes. Main repo for Linkerd 2.x.
9.4 0.0 xsv VS cityboundA work-in-progress, open-source, multi-player city simulation game.
8.5 8.9 xsv VS osoOso is a batteries-included framework for building authorization in your application.
8.3 7.7 xsv VS Parity(deprecated) The fast, light, and robust client for the Ethereum mainnet.
7.7 8.1 xsv VS dotenv-linter⚡️Lightning-fast linter for .env files. Written in Rust 🦀
Do you think we are missing an alternative of xsv or a related project?
xsv is a command line program for indexing, slicing, analyzing, splitting and joining CSV files. Commands should be simple, fast and composable:
- Simple tasks should be easy.
- Performance trade offs should be exposed in the CLI interface.
- Composition should not come at the expense of performance.
This README contains information on how to
xsv, in addition to
a quick tour of several commands.
Dual-licensed under MIT or the UNLICENSE.
- cat - Concatenate CSV files by row or by column.
- count - Count the rows in a CSV file. (Instantaneous with an index.)
- fixlengths - Force a CSV file to have same-length records by either padding or truncating them.
- flatten - A flattened view of CSV records. Useful for viewing one record
at a time. e.g.,
xsv slice -i 5 data.csv | xsv flatten.
- fmt - Reformat CSV data with different delimiters, record terminators or quoting rules. (Supports ASCII delimited data.)
- frequency - Build frequency tables of each column in CSV data. (Uses parallelism to go faster if an index is present.)
- headers - Show the headers of CSV data. Or show the intersection of all headers between many CSV files.
- index - Create an index for a CSV file. This is very quick and provides constant time indexing into the CSV file.
- input - Read CSV data with exotic quoting/escaping rules.
- join - Inner, outer and cross joins. Uses a simple hash index to make it fast.
- partition - Partition CSV data based on a column value.
- sample - Randomly draw rows from CSV data using reservoir sampling (i.e., use memory proportional to the size of the sample).
- reverse - Reverse order of rows in CSV data.
- search - Run a regex over CSV data. Applies the regex to each field individually and shows only matching rows.
- select - Select or re-order columns from CSV data.
- slice - Slice rows from any part of a CSV file. When an index is present, this only has to parse the rows in the slice (instead of all rows leading up to the start of the slice).
- sort - Sort CSV data.
- split - Split one CSV file into many CSV files of N chunks.
- stats - Show basic types and statistics of each column in the CSV file. (i.e., mean, standard deviation, median, range, etc.)
- table - Show aligned output of any CSV data using elastic tabstops.
A whirlwind tour
Let's say you're playing with some of the data from the Data Science Toolkit, which contains several CSV files. Maybe you're interested in the population counts of each city in the world. So grab the data and start examining it:
$ curl -LO https://burntsushi.net/stuff/worldcitiespop.csv $ xsv headers worldcitiespop.csv 1 Country 2 City 3 AccentCity 4 Region 5 Population 6 Latitude 7 Longitude
The next thing you might want to do is get an overview of the kind of data that
appears in each column. The
stats command will do this for you:
$ xsv stats worldcitiespop.csv --everything | xsv table field type min max min_length max_length mean stddev median mode cardinality Country Unicode ad zw 2 2 cn 234 City Unicode bab el ahmar Þykkvibaer 1 91 san jose 2351892 AccentCity Unicode Bâb el Ahmar ïn Bou Chella 1 91 San Antonio 2375760 Region Unicode 00 Z9 0 2 13 04 397 Population Integer 7 31480498 0 8 47719.570634 302885.559204 10779 28754 Latitude Float -54.933333 82.483333 1 12 27.188166 21.952614 32.497222 51.15 1038349 Longitude Float -179.983333 180 1 14 37.08886 63.22301 35.28 23.8 1167162
xsv table command takes any CSV data and formats it into aligned columns
using elastic tabstops. You'll
notice that it even gets alignment right with respect to Unicode characters.
So, this command takes about 12 seconds to run on my machine, but we can speed it up by creating an index and re-running the command:
$ xsv index worldcitiespop.csv $ xsv stats worldcitiespop.csv --everything | xsv table ...
Which cuts it down to about 8 seconds on my machine. (And creating the index takes less than 2 seconds.)
Notably, the same type of "statistics" command in another CSV command line toolkit takes about 2 minutes to produce similar statistics on the same data set.
Creating an index gives us more than just faster statistics gathering. It also makes slice operations extremely fast because only the sliced portion has to be parsed. For example, let's say you wanted to grab the last 10 records:
$ xsv count worldcitiespop.csv 3173958 $ xsv slice worldcitiespop.csv -s 3173948 | xsv table Country City AccentCity Region Population Latitude Longitude zw zibalonkwe Zibalonkwe 06 -19.8333333 27.4666667 zw zibunkululu Zibunkululu 06 -19.6666667 27.6166667 zw ziga Ziga 06 -19.2166667 27.4833333 zw zikamanas village Zikamanas Village 00 -18.2166667 27.95 zw zimbabwe Zimbabwe 07 -20.2666667 30.9166667 zw zimre park Zimre Park 04 -17.8661111 31.2136111 zw ziyakamanas Ziyakamanas 00 -18.2166667 27.95 zw zizalisari Zizalisari 04 -17.7588889 31.0105556 zw zuzumba Zuzumba 06 -20.0333333 27.9333333 zw zvishavane Zvishavane 07 79876 -20.3333333 30.0333333
These commands are instantaneous because they run in time and memory proportional to the size of the slice (which means they will scale to arbitrarily large CSV data).
Switching gears a little bit, you might not always want to see every column in the CSV data. In this case, maybe we only care about the country, city and population. So let's take a look at 10 random rows:
$ xsv select Country,AccentCity,Population worldcitiespop.csv \ | xsv sample 10 \ | xsv table Country AccentCity Population cn Guankoushang za Klipdrift ma Ouled Hammou fr Les Gravues la Ban Phadèng de Lüdenscheid 80045 qa Umm ash Shubrum bd Panditgoan us Appleton ua Lukashenkivske
Whoops! It seems some cities don't have population counts. How pervasive is that?
$ xsv frequency worldcitiespop.csv --limit 5 field,value,count Country,cn,238985 Country,ru,215938 Country,id,176546 Country,us,141989 Country,ir,123872 City,san jose,328 City,san antonio,320 City,santa rosa,296 City,santa cruz,282 City,san juan,255 AccentCity,San Antonio,317 AccentCity,Santa Rosa,296 AccentCity,Santa Cruz,281 AccentCity,San Juan,254 AccentCity,San Miguel,254 Region,04,159916 Region,02,142158 Region,07,126867 Region,03,122161 Region,05,118441 Population,(NULL),3125978 Population,2310,12 Population,3097,11 Population,983,11 Population,2684,11 Latitude,51.15,777 Latitude,51.083333,772 Latitude,50.933333,769 Latitude,51.116667,769 Latitude,51.133333,767 Longitude,23.8,484 Longitude,23.2,477 Longitude,23.05,476 Longitude,25.3,474 Longitude,23.1,459
xsv frequency command builds a frequency table for each column in the
CSV data. This one only took 5 seconds.)
So it seems that most cities do not have a population count associated with them at all. No matter—we can adjust our previous command so that it only shows rows with a population count:
$ xsv search -s Population '[0-9]' worldcitiespop.csv \ | xsv select Country,AccentCity,Population \ | xsv sample 10 \ | xsv table Country AccentCity Population es Barañáin 22264 es Puerto Real 36946 at Moosburg 4602 hu Hejobaba 1949 ru Polyarnyye Zori 15092 gr Kandíla 1245 is Ólafsvík 992 hu Decs 4210 bg Sliven 94252 gb Leatherhead 43544
Erk. Which country is
at? No clue, but the Data Science Toolkit has a CSV
countrynames.csv. Let's grab it and do a join so we can see which
countries these are:
curl -LO https://gist.githubusercontent.com/anonymous/063cb470e56e64e98cf1/raw/98e2589b801f6ca3ff900b01a87fbb7452eb35c7/countrynames.csv $ xsv headers countrynames.csv 1 Abbrev 2 Country $ xsv join --no-case Country sample.csv Abbrev countrynames.csv | xsv table Country AccentCity Population Abbrev Country es Barañáin 22264 ES Spain es Puerto Real 36946 ES Spain at Moosburg 4602 AT Austria hu Hejobaba 1949 HU Hungary ru Polyarnyye Zori 15092 RU Russian Federation | Russia gr Kandíla 1245 GR Greece is Ólafsvík 992 IS Iceland hu Decs 4210 HU Hungary bg Sliven 94252 BG Bulgaria gb Leatherhead 43544 GB Great Britain | UK | England | Scotland | Wales | Northern Ireland | United Kingdom
Whoops, now we have two columns called
Country and an
Abbrev column that we
no longer need. This is easy to fix by re-ordering columns with the
$ xsv join --no-case Country sample.csv Abbrev countrynames.csv \ | xsv select 'Country,AccentCity,Population' \ | xsv table Country AccentCity Population Spain Barañáin 22264 Spain Puerto Real 36946 Austria Moosburg 4602 Hungary Hejobaba 1949 Russian Federation | Russia Polyarnyye Zori 15092 Greece Kandíla 1245 Iceland Ólafsvík 992 Hungary Decs 4210 Bulgaria Sliven 94252 Great Britain | UK | England | Scotland | Wales | Northern Ireland | United Kingdom Leatherhead 43544
Perhaps we can do this with the original CSV data? Indeed we can—because
xsv are fast.
$ xsv join --no-case Abbrev countrynames.csv Country worldcitiespop.csv \ | xsv select '!Abbrev,Country' \ > worldcitiespop_countrynames.csv $ xsv sample 10 worldcitiespop_countrynames.csv | xsv table Country City AccentCity Region Population Latitude Longitude Sri Lanka miriswatte Miriswatte 36 7.2333333 79.9 Romania livezile Livezile 26 1985 44.512222 22.863333 Indonesia tawainalu Tawainalu 22 -4.0225 121.9273 Russian Federation | Russia otar Otar 45 56.975278 48.305278 France le breuil-bois robert le Breuil-Bois Robert A8 48.945567 1.717026 France lissac Lissac B1 45.103094 1.464927 Albania lumalasi Lumalasi 46 40.6586111 20.7363889 China motzushih Motzushih 11 27.65 111.966667 Russian Federation | Russia svakino Svakino 69 55.60211 34.559785 Romania tirgu pancesti Tirgu Pancesti 38 46.216667 27.1
!Abbrev,Country syntax means, "remove the
Abbrev column and remove
the second occurrence of the
Country column." Since we joined with
countrynames.csv first, the first
Country name (fully expanded) is now
included in the CSV data.
xsv join command takes about 7 seconds on my machine. The performance
comes from constructing a very simple hash index of one of the CSV data files
join command does an inner join by default, but it also has left,
right and full outer join support too.
Binaries for Windows, Linux and macOS are available from Github.
If you're a macOS Homebrew user, then you can install xsv from homebrew-core:
$ brew install xsv
If you're a macOS MacPorts user, then you can install xsv from the official ports:
$ sudo port install xsv
If you're a Nix/NixOS user, you can install xsv from nixpkgs:
$ nix-env -i xsv
cargo install xsv
Compiling from this repository also works similarly:
git clone git://github.com/BurntSushi/xsv cd xsv cargo build --release
Compilation will probably take a few minutes depending on your machine. The
binary will end up in
I've compiled some very rough
Here are several valid criticisms of this project:
- You shouldn't be working with CSV data because CSV is a terrible format.
- If your data is gigabytes in size, then CSV is the wrong storage type.
- Various SQL databases provide all of the operations available in
xsvwith more sophisticated indexing support. And the performance is a zillion times better.
I'm sure there are more criticisms, but the impetus for this project was a 40GB CSV file that was handed to me. I was tasked with figuring out the shape of the data inside of it and coming up with a way to integrate it into our existing system. It was then that I realized that every single CSV tool I knew about was woefully inadequate. They were just too slow or didn't provide enough flexibility. (Another project I had comprised of a few dozen CSV files. They were smaller than 40GB, but they were each supposed to represent the same kind of data. But they all had different column and unintuitive column names. Useful CSV inspection tools were critical here—and they had to be reasonably fast.)
The key ingredients for helping me with my task were indexing, random sampling, searching, slicing and selecting columns. All of these things made dealing with 40GB of CSV data a bit more manageable (or dozens of CSV files).
Getting handed a large CSV file once was enough to launch me on this quest. From conversations I've had with others, CSV data files this large don't seem to be a rare event. Therefore, I believe there is room for a tool that has a hope of dealing with data that large.
This project is unrelated to another similar project with the same name: https://mj.ucw.cz/sw/xsv/
*Note that all licence references and agreements mentioned in the xsv README section above are relevant to that project's source code only.