semvec/pypi-packages huggingface dataset:
paramnb 2.0.4
Generate ipywidgets from Parameterized objects in the notebook
License: BSD 3-Clause License
Requires Python: >=2.7

[![Build Status](https://travis-ci.org/ioam/paramnb.svg?branch=master)](https://travis-ci.org/ioam/paramnb)

[![Build status](https://ci.appveyor.com/api/projects/status/oyx4q6tqj74ld5b9/branch/master?svg=true)](https://ci.appveyor.com/project/Ioam/paramnb/branch/master)


# ParamNB

Automatically generate ipywidgets from
[Parameterized](https://github.com/ioam/param) objects in a Jupyter
notebook.

![Auto-generated
 widgets](https://github.com/ioam/paramnb/blob/master/examples/user_guide/images/intro.gif)


## Demo notebooks

The following links go to static versions of the notebooks, so after
looking at them it's best to try them out yourself interactively!

  * Introduction: [examples/user_guide/index.ipynb](http://nbviewer.jupyter.org/urls/notebooks.anaconda.org/cball/ioam-paramnb-index/download?version=)

  * Additional Features: [examples/user_guide/AdditionalFeatures.ipynb](http://nbviewer.jupyter.org/urls/notebooks.anaconda.org/cball/ioam-paramnb-additionalfeatures/download?version=)

  * Setting parameters via JSON: [examples/user_guide/JSONInit.ipynb](http://nbviewer.jupyter.org/urls/notebooks.anaconda.org/cball/ioam-paramnb-jsoninit/download?version=)


## Installation

You can easily install paramnb using conda:

```
conda install -c pyviz paramnb
```

or pip:

```
pip install paramnb
```
parambokeh 0.2.3
ParamBokeh provides an easy way to generate a UI for param based classes in the notebook or on bokeh server.
License: BSD 3-Clause License
Requires Python: >=2.7

[![Linux Build
Status](https://travis-ci.org/ioam/parambokeh.svg?branch=master)](https://travis-ci.org/ioam/parambokeh)
[![Windows Build
status](https://ci.appveyor.com/api/projects/status/27h3ybqx51wsasu5/branch/master?svg=true)](https://ci.appveyor.com/project/Ioam/parambokeh/branch/master)

# ParamBokeh

Generate widgets from [Parameterized](https://github.com/ioam/param) objects in Jupyter or in [Bokeh](http://bokeh.pydata.org) Server. See the [documentation](https://ioam.github.io/parambokeh/) for more details.