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https://github.com/elshor/dstools
Javascript tools and utilities for the data scientist
https://github.com/elshor/dstools
data-science javascript
Last synced: 15 days ago
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Javascript tools and utilities for the data scientist
- Host: GitHub
- URL: https://github.com/elshor/dstools
- Owner: elshor
- License: mit
- Created: 2017-12-18T10:41:04.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-03-20T07:11:00.000Z (over 6 years ago)
- Last Synced: 2024-10-08T12:11:16.583Z (about 1 month ago)
- Topics: data-science, javascript
- Language: JavaScript
- Size: 471 KB
- Stars: 51
- Watchers: 5
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
Dstools - Data Science Tools for Javascript
===========================================
dstools is a collection of tools that assist in analyzing and visualizing data using Javascript code inside Jupyter notebooks. Its main features are:
* All functions are chainable (jQuery style)
* Import csv data files from the web or file system (using `csv` package)
* Statistical analysis using jStat package
* Show data as tables within Jupyter notebooks
* Visualize data using plotly javascript library within Jupyter notebooks
Function reference and jsdoc based documentation can be found at https://elshor.github.io/dstools/
## Installing dstools
Install dstools using npm
```bash
npm install dstools
```
When using dstools from Jupyter notebooks with the IJavascript kernel, the package should be installed in the same directory as the notebook.
## Getting Started
First step in handling data is usually loading the data
```js
const Collection = require('dstools').Collection;
const data = Collection().loadCSV('data.csv');
```
Alternatively, the data can be wrapped using the Collection function:
```js
const data = Collection([{field1:2,field2:4},{field1:3,field2:5}]);
```
inside the Jupyter notebook, the data can be displayed as a table using the `show` function.
```js
data.show();
```
The data will be displayed as an HTML table.
Visualizations are possible using the various visualization functions.
```js
data.histogram('field1').show();
```
When the code is executed inside a Jupyter notebook, it will display a histogram using the plotly javascript library.
The `data` function can be used to get the underlying data
```js
console.log(data.data());
```
The html function can be used to wrap html text. Subsequent calls of the `show` function will display the html in the Jupyter notebook.
Here is a more elaborate example taken from a [medium post](https://medium.com/@elshor/learning-to-talk-about-wine-using-javascript-7b59d0e0a0f):
```js
const Collection = require('dstools').Collection;
Collection()
.loadCSV('/home/elshor/data/winemag-data-130k-v2.csv')//load the data
.terms({field:'description'}).dropStopwords('term')
.sortDesc('count').head(50)
.wordCloud('term','count')//arguments are label and measure
.show();//show the wordcloud in Jupyter notebook
```
## Function Reference
Function reference and jsdoc based documentation can be found at https://elshor.github.io/dstools/
## Additional Statistical Functions
The following jStat functions take as argument the field name and return the jStat function with the column vector as its argument. The following functions are supported: `sum,sumsqrd,sumsqerr,product,min,max,mean,meansqerr,geomean,median,cumsum,cumprod,diff,rank,range,variance,deviation,stdev,skewness,kurtosis,coeffvar,quartiles,quantiles,percentile`. Consult the [jStat](https://jstat.github.io/all.html) documentation for details.
## License
Dstools is licensed under the MIT License