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https://github.com/altair-viz/pdvega

Interactive plotting for Pandas using Vega-Lite
https://github.com/altair-viz/pdvega

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Interactive plotting for Pandas using Vega-Lite

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# ``pdvega``: Vega-Lite plotting for Pandas Dataframes



[![build status](http://img.shields.io/travis/altair-viz/pdvega/master.svg?style=flat)](https://travis-ci.org/altair-viz/pdvega)

[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/altair-viz/pdvega/master?filepath=examples%2Fpdvega_example.ipynb)



``pdvega`` is a library that allows you to quickly create interactive

[Vega-Lite](https://vega.github.io/vega-lite/) plots from Pandas dataframes,

using an API that is nearly identical to Pandas' built-in

[visualization tools](https://pandas.pydata.org/pandas-docs/stable/visualization.html), and designed for easy use within the [Jupyter notebook](http://jupyter.org).



- [Full Documentation](http://altair-viz.github.io/pdvega/)



Pandas currently has some basic plotting capabilities based on

[matplotlib](http://matplotlib.org). So, for example, you can create

a scatter plot this way:



```python

import numpy as np

import pandas as pd



df = pd.DataFrame({'x': np.random.randn(100), 'y': np.random.randn(100)})

df.plot.scatter(x='x', y='y')

```



![matplotlib scatter output](images/mpl-scatter.png?raw=true)



The goal of ``pdvega`` is that any time you use ``dataframe.plot``, you'll be

able to replace it with ``dataframe.vgplot`` and instead get a similar

(but prettier and more interactive) visualization output in Vega-Lite that you can easily export to share or customize:



```python

import pdvega  # import adds vgplot attribute to pandas



df.vgplot.scatter(x='x', y='y')

```



![vega-lite scatter output](images/vg-scatter.png?raw=true)



The above image is a static screenshot of the interactive output; please see the

[Documentation](http://altair-viz.github.io/pdvega/) for a full set of live

usage examples.



## Installation



You can get started with ``pdvega`` using pip:



```

$ pip install jupyter pdvega

$ jupyter nbextension install --sys-prefix --py vega3

```



The first line installs ``pdvega`` and its dependencies; the second installs

the Jupyter extensions that allows plots to be displayed in the Jupyter

notebook. For more information on installation and dependencies, see the

[Installation docs](https://altair-viz.github.io/pdvega/installation.html).



## Why Vega-Lite?

When working with data, one of the biggest challenges is ensuring reproducibility of results.

When you create a figure and export it to PNG or PDF, the data become baked-in to the rendering in a

way that is difficult or impossible for others to extract. [Vega](http://vega.github.io/vega) and

[Vega-Lite](http://vega.github.io/vega-lite) change this: instead of packaging a figure by encoding its

pixel values, they package a figure by describing, in a declarative manner, the relationship between

data values and visual encodings through a JSON specification.



This means that the Vega-Lite figures produced by ``pdvega`` are portable: you can send someone the

resulting JSON specification and they can choose whether to render it interactively online, convert it to

a PNG or EPS for static publication, or even enhance and extend the figure to learn more about the data.



``pdvega`` is a step in bringing this vision of figure portability and reproducibility to the Python world.



### Relationship to Altair



[Altair](http://altair-viz.github.io) is a project that seeks to design an intuitive declarative API for generating Vega-Lite and Vega visualizations, using Pandas dataframes as data sources.



By contrast, ``pdvega`` seeks not to design new visualization APIs, but to use the existing ``DataFrame.plot`` [visualization api](https://pandas.pydata.org/pandas-docs/stable/visualization.html) and output visualizations with Vega/Vega-Lite rather than with matplotlib.



In this respect, ``pdvega`` is quite similar in spirit to the now-defunct [mpld3](http://mpld3.github.io) project, though the scope is smaller and (hopefully) **much** more manageable.