vnlog
miller
Our great sponsors
vnlog | miller | |
---|---|---|
24 | 63 | |
158 | 8,542 | |
- | - | |
6.7 | 9.1 | |
4 months ago | 7 days ago | |
Perl | Go | |
- | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
vnlog
- Vnlog: Process labelled tabular ASCII data using normal Unix tools
- Process tabular data with Unix tools
-
Qsv: Efficient CSV CLI Toolkit
For simple analyses (i.e. what most people do most of the time) doing this on the commandline gets you there faster. I use vnlog (https://github.com/dkogan/vnlog/). By the time you fired up your editor to write your Python code, I already have analyses and plots ready.
-
Joining CSV Data Without SQL: An IP Geolocation Use Case
Alternative very appropriate for some uses cases: `vnl-join` from the vnlog toolkit (https://github.com/dkogan/vnlog). Uses the `join` tool from coreutils (works well, has been around forever), and `vnlog` for nice column labelling
-
Miller: Like Awk, sed, cut, join, and sort for CSV, TSV, and tabular JSON
There's also https://github.com/dkogan/vnlog/ which is a wrapper around the existing coreutils, so all the options work, and there's nothing to learn
- vnlog: making awk and sort and join (and friends) smarter
-
Awk equivalents to SQL query data manipulation
And to improve the ergonomics, the vnlog wrappers are available to operate on field names, while retaining the internals of the core tools:
https://github.com/dkogan/vnlog/
- Vnlog: Making Awk, grep, sort and join smarter
-
Learn to Process Text in Linux Using Grep, Sed, and Awk
I sorta, kinda agree. Tools written in AWK (and friends) are indeed somewhat unmaintainable, but they're really close to being just right for a LOT of applications. The vnlog toolkit (https://github.com/dkogan/vnlog) adds just a little bit of syntactic sugar to the usual commandline tools to make processing scripts robust and easy to read and write. This was not my intent initially, but I now do most of my data processing with the shell and vnl-wrapped awk (and sort and join, ...) It's really nice. If you write stuff in awk, you should check it out. (Disclaimer: I'm the author)
- Extending Awk with Field Labels
miller
- Qsv: Efficient CSV CLI Toolkit
-
jq 1.7 Released
jq and miller[1] are essential parts of my toolbelt, right up there with awk and vim.
-
Perl first commit: a “replacement” for Awk and sed
> This works really well if your problem can be solved in one or two liners.
My personal comfort threshold is around the 100-line mark. It's even possible to write maintainable shell scripts up to 500 lines, but it mostly depends on the problem you're trying to solve, and the discipline of the programmer to follow best practices (use sane defaults, ShellCheck, etc.).
> It go bad very quickly when, say, you have two CSV files and want to join them the sql-way.
In that case we're talking about structured data, and, yeah, Perl or Python would be easier to work with. That said, depending on the complexity of the CSV, you can still go a long way with plain Bash with IFS/read(1) or tr(1) to split CSV columns. This wouldn't be very robust, but there are tools that handle CSV specifically[1], which can be composed in a shell script just fine.
So it's always a balancing act of being productive quickly with a shell script, or reaching out for a programming language once the tools aren't a good fit, or maintenance becomes an issue.
-
Need help on cleaning this data!!
where mlr is from https://github.com/johnkerl/miller
-
Running weekly average
if this class of problems (i.e., csv/tsv data) is your main target you may find miller (https://github.com/johnkerl/miller) much more useful in the long run
-
GQL: A new SQL like query language for .git files written in Rust
That said, you may be interested in Miller (https://github.com/johnkerl/miller) which provides similar capabilities for CSV, JSON, and XML files. It doesn't use a SQL grammar, but that's just the proverbial lipstick on the thing. I'm not the author, but I have used it and I see some parallels in use cases at the very least.
- johnkerl/miller: Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
-
Any cli utility to create ascii/org mode tables?
worth giving Miller a shot
-
I wrote this iCalendar (.ics) command-line utility to turn common calendar exports into more broadly compatible CSV files.
CSV utilities (still haven't pick a favorite one...): https://github.com/harelba/q https://github.com/BurntSushi/xsv https://github.com/wireservice/csvkit https://github.com/johnkerl/miller
- Miller: Like Awk, sed, cut, join, and sort for CSV, TSV, and tabular JSON
What are some alternatives?
ttyplot - a realtime plotting utility for terminal/console with data input from stdin
visidata - A terminal spreadsheet multitool for discovering and arranging data
matplotplusplus - Matplot++: A C++ Graphics Library for Data Visualization 📊🗾
xsv - A fast CSV command line toolkit written in Rust.
RecordStream - commandline tools for slicing and dicing JSON records.
jq - Command-line JSON processor [Moved to: https://github.com/jqlang/jq]
nvim-ipy - IPython/Jupyter plugin for Neovim
dasel - Select, put and delete data from JSON, TOML, YAML, XML and CSV files with a single tool. Supports conversion between formats and can be used as a Go package.
jupytext.vim - Vim plugin for editing Jupyter ipynb files via jupytext
csvtk - A cross-platform, efficient and practical CSV/TSV toolkit in Golang
matplotlib - C++ wrappers around python's matplotlib
yq - yq is a portable command-line YAML, JSON, XML, CSV, TOML and properties processor