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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

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Dataset manipulation library built on the top of tech.ml.dataset

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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