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Munging","Libraries"],"sub_categories":["Uncategorized"],"readme":"# zebras - data analysis library for javascript\n\n\u003cimg src=\"https://travis-ci.org/nickslevine/zebras.svg?branch=master\"\u003e [![Coverage Status](https://coveralls.io/repos/github/nickslevine/zebras/badge.svg?branch=master)](https://coveralls.io/github/nickslevine/zebras?branch=master)\n\n\u003cdiv align=\"center\"\u003e\u003cimg src =\"code_example.png\" width = \"60%\" /\u003e\u003c/div\u003e\n\nZebras is a data manipulation and analysis library written in JavaScript offering the convenience of pandas or R.\n\nZebras is especially useful for JS data analysis and visualization projects in notebooks on [ObservableHQ](https://beta.observablehq.com/) or using the [JS Jupyter kernel](https://github.com/n-riesco/ijavascript).\n\nUnlike pandas, zebras is purely functional. All zebras functions accept data as an argument and return a new data structure transformed, and 'dataframes' are simply arrays of JS objects without any special methods.\n\nZebras is built on top of [Ramda](https://github.com/Ramda/ramda). Think of zebras as a collection of Ramda functions, and compositions of them, curated and repackaged for common data processing and data analysis tasks.\n\nAs in Ramda, all zebras functions are curried, making them easier to chain together. This means that, for example, `zebras.sortByCol(col, df)` is equivalent to `zebras.sortByCol(col)(df)`.\n\n**An interactive introduction to the zebras library - using S\u0026P 500 data - is available [as an Observable notebook](https://beta.observablehq.com/@nickslevine/introduction-to-zebras-a-data-analysis-library-for-javascr).**\n\n**See the project roadmap on the zebras wiki [here](https://github.com/nickslevine/zebras/wiki/Zebras---Project-Roadmap).**\n\n**A guide to using zebras in a JS Jupyter notebook can be found on the zebras wiki [here](https://github.com/nickslevine/zebras/wiki/Zebras-in-a-Jupyter-Notebook).**\n\n## To Install\n\n### Locally with Node / Jupyter\n\n```sh\nnpm install zebras --save\n```\n\n```javascript\nconst z = require(\"zebras\")\n```\n\n### Online in an Observable notebook\n\n```javascript\nz = require(\"zebras\")\n```\n\n## Example Usage\n\n```javascript\nconst z = require(\"zebras\")\nconst data = z.readCSV(\"data.csv\")\nconst parsedData = z.parseNums([\"speed\", \"acc\"], data)\nconst speed = z.getCol(\"speed\", parsedData)\nconst avgSpeed = z.mean(speed)\n```\n\n**Note: make sure numerical data imported into Zebras does not contain commas (\"2342.5\", not \"2,342.5\") - commas within numbers will throw off the parsing and lead to unexpected results.**\n\nPiping example:\n\n```javascript\nconst z = require(\"zebras\")\nconst avgSpeed = z.pipe([\n  // array of functions for input to travel through\n  z.readCSV,\n  z.parseNums([\"speed\", \"acc\"]),\n  z.getCol(\"speed\"),\n  z.mean,\n])(\"data.csv\")\n```\n\n## Documentation\n\n[See the full documentation here.](http://zebrasjs.com/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnickslevine%2Fzebras","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnickslevine%2Fzebras","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnickslevine%2Fzebras/lists"}