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https://github.com/scicloj/tablecloth
Dataset manipulation library built on the top of tech.ml.dataset
https://github.com/scicloj/tablecloth
clojure dataframe dataset machinelearning
Last synced: 4 days ago
JSON representation
Dataset manipulation library built on the top of tech.ml.dataset
- Host: GitHub
- URL: https://github.com/scicloj/tablecloth
- Owner: scicloj
- License: mit
- Created: 2020-06-15T11:00:12.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-04-19T08:15:33.000Z (7 months ago)
- Last Synced: 2024-05-01T21:35:00.162Z (6 months ago)
- Topics: clojure, dataframe, dataset, machinelearning
- Language: HTML
- Homepage: https://scicloj.github.io/tablecloth
- Size: 28.6 MB
- Stars: 266
- Watchers: 12
- Forks: 18
- Open Issues: 30
-
Metadata Files:
- Readme: README-source.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- awesome-dataframes - tablecloth - Dataset manipulation library build on the top of tech.ml.dataset. (Libraries)
README
# Tablecloth
Dataset (data frame) manipulation API for the tech.ml.dataset library
[![](https://img.shields.io/clojars/v/scicloj/tablecloth)](https://clojars.org/scicloj/tablecloth)
[![](https://api.travis-ci.org/scicloj/tablecloth.svg?branch=master)](https://travis-ci.org/github/scicloj/tablecloth)
[![](https://img.shields.io/badge/zulip-discussion-yellowgreen)](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api)## Versions
### tech.ml.dataset 7.x (master branch)
[![](https://img.shields.io/clojars/v/scicloj/tablecloth)](https://clojars.org/scicloj/tablecloth)
### tech.ml.dataset 4.x (4.0 branch)
`[scicloj/tablecloth "4.04"]`
## Introduction
[tech.ml.dataset](https://github.com/techascent/tech.ml.dataset) is a great and fast library which brings columnar dataset to the Clojure. Chris Nuernberger has been working on this library for last year as a part of bigger `tech.ml` stack.
I've started to test the library and help to fix uncovered bugs. My main goal was to compare functionalities with the other standards from other platforms. I focused on R solutions: [dplyr](https://dplyr.tidyverse.org/), [tidyr](https://tidyr.tidyverse.org/) and [data.table](https://rdatatable.gitlab.io/data.table/).
During conversions of the examples I've come up how to reorganized existing `tech.ml.dataset` functions into simple to use API. The main goals were:
* Focus on dataset manipulation functionality, leaving other parts of `tech.ml` like pipelines, datatypes, readers, ML, etc.
* Single entry point for common operations - one function dispatching on given arguments.
* `group-by` results with special kind of dataset - a dataset containing subsets created after grouping as a column.
* Most operations recognize regular dataset and grouped dataset and process data accordingly.
* One function form to enable thread-first on dataset.Important! This library is not the replacement of `tech.ml.dataset` nor a separate library. It should be considered as a addition on the top of `tech.ml.dataset`.
If you want to know more about `tech.ml.dataset` and `dtype-next` please refer their documentation:
* [tech.ml.dataset walkthrough](https://techascent.github.io/tech.ml.dataset/walkthrough.html)
* [dtype-next overview](https://cnuernber.github.io/dtype-next/overview.html)
* [dtype-next cheatsheet](https://cnuernber.github.io/dtype-next/cheatsheet.html)Join the discussion on [Zulip](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api)
## Documentation
Please refer [detailed documentation with examples](https://scicloj.github.io/tablecloth).
The old documentation (till the end of 2023) is [here](https://scicloj.github.io/tablecloth/old).
## Usage example
```{clojure results="hide"}
(require '[tablecloth.api :as tc])
``````{clojure results="asis"}
(-> "https://raw.githubusercontent.com/techascent/tech.ml.dataset/master/test/data/stocks.csv"
(tc/dataset {:key-fn keyword})
(tc/group-by (fn [row]
{:symbol (:symbol row)
:year (tech.v3.datatype.datetime/long-temporal-field :years (:date row))}))
(tc/aggregate #(tech.v3.datatype.functional/mean (% :price)))
(tc/order-by [:symbol :year])
(tc/head 10))
```## Contributing
`Tablecloth` is open for contribution. The best way to start is discussion on [Zulip](https://clojurians.zulipchat.com/#narrow/stream/236259-tech.2Eml.2Edataset.2Edev/topic/api).
### Development tools for documentation
Documentation is written in the [Kindly](https://scicloj.github.io/kindly/) convention and is rendered using [Clay](https://scicloj.github.io/clay/) composed with [Quarto](https://quarto.org/).
The old documentation was written in RMarkdown and is kept under [docs/old/](./docs/old/).
Documentation contains around 600 code snippets which are run during build. There are three relevant source files:
* [README-source.md](./README-source.md) for README.md
* [notebooks/index.clj](./notebooks/index.clj) for the detailed documentation
* [clay.edn](./clay.edn) for some styling options of the docs(`notebooks/index.clj` was generated by [dev/conversion.clj](dev/conversion.clj) from the earlier Rmarkdown-based `index.Rmd` with asome additional manual editing. Starting at 2024, it will diverge from that source, that will no longer be maintained.)
### README generation
To generate `README.md`, run the `generate!` function at the [dev/readme_generation.clj](./dev/readme_generation.clj) script.
### Detailed documentation generation
To generate the detailed documentation, call the following. You will need the Quarto CLI [installed](https://quarto.org/docs/get-started/) in your system.
Currently (April 2024), we use Quarto's [v1.5.10 pre-release](https://github.com/quarto-dev/quarto-cli/releases/tag/v1.5.10) (specifically this version, not the later ones) due to some Quarto bugs.
```{clojure eval=FALSE}
(require '[scicloj.clay.v2.api :as clay])
(clay/make! {:format [:quarto :html]
:source-path "notebooks/index.clj"})
```### Code Generation
To build this project fully we need to perform some code generation operations. These are listed below:
1. Build the `tablecloth.api.operators` namespace
The `tablecloth.api.operators` namespace is generated by
`tablecloth.api.lift_operators`. To build that namespace, you need to
load the `tablecloth.api.lift_operators` namespace, and then execute
the code surrounded by a comment at the bottom of the file.2. Build the `tablecloth.api` (aka the Dataset API)
The `tablecloth.api` namespace is generated out of `api-template`. To
build that namespace you need to load the
`tablecloth.api.api-template` namespace, and then evaluate the code
contained in the comment section at the bottom of the file. This will
re-generate the `tablecloth.api` namespace.3. Build the `tablecloth.column.api.operators` namespace
The `tablecloth.column.api.operators` namespace is generated by
`tablecloth.column.api.lift_operators`. To build that namespace, you
need to load the `tablecloth.api.lift_operators` namespace, and then
execute the code surrounded by a comment at the bottom of the file.4. Build the `tablecloth.column.api` (aka the Column API)
The `tablecloth.column.api` namespace is generated out of
`api-template`. To build that namespace you need to load the
`tablecloth.column.api.api-template` namespace, and then evaluate the
code contained in the comment section at the bottom of the file. This
will re-generate the `tablecloth.column.api` namespace.### Guideline
1. Before commiting changes please perform tests. I ususally do: `lein do clean, check, test` and build documentation as described above (which also tests whole library).
2. Keep API as simple as possible:
- first argument should be a dataset
- if parametrizations is complex, last argument should accept a map with not obligatory function arguments
- avoid variadic associative destructuring for function arguments
- usually function should working on grouped dataset as well, accept `parallel?` argument then (if applied).
3. Follow `potemkin` pattern and import functions to the API namespace using `tech.v3.datatype.export-symbols/export-symbols` function
4. Functions which are composed out of API function to cover specific case(s) should go to `tablecloth.utils` namespace.
5. Always update `README-source.md`, `CHANGELOG.md`, `notebooks/index.clj`, tests and function docs are highly welcomed.
6. Always discuss changes and PRs first### Tests
Tests are written and run using [midje](https://github.com/marick/Midje/). To run a test, evaluate a midje form. If it passes, it will return `true`, if it fails details will be printed to the REPL.
## TODO
* elaborate on tests
* tutorials## Licence
Copyright (c) 2020 Scicloj
The MIT Licence