hvplot 0.8.2a1

A high-level plotting API for the PyData ecosystem built on HoloViews.

hvPlot makes data analysis and visualization simple

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hvPlot provides a familiar, high-level API for visualization

The API is based on the familiar 🐼 Pandas .plot API and the innovative .interactive API.

hvPlot works with the tools you know and ❤️

hvPlot

• supports a wide range of data sources including Pandas, XArray, Dask, Streamz, Intake, GeoPandas and NetworkX.
• supports the plotting backends Bokeh, Matplotlib and Plotly.
• exposes the powerful tools from the HoloViz ecosystem in a familiar and convenient API.

hvPlot is the recommended entrypoint to the HoloViz ecosystem.

hvPlot can be used for exploration, reporting and data apps

Check out this blog post to see how easy it is to create an interactive dashboard with hvPlot and Panel.

Installation Instructions

hvPlot supports Python 3.6, 3.7, 3.8, 3.9 and 3.10 on Linux, Windows, or Mac and can be installed with conda:

conda install -c pyviz hvplot

or with pip:

pip install hvplot

You can download the guides and example notebooks as a .zip file by clicking this link.

Please note that for versions of jupyterlab<3.0, you must install the JupyterLab extension manually with:

jupyter labextension install @pyviz/jupyterlab_pyviz

How to use hvPlot in 3 simple steps

Work with the data source you already know and ❤️

import pandas as pd, numpy as np
idx = pd.date_range('1/1/2000', periods=1000)
df  = pd.DataFrame(np.random.randn(1000, 4), index=idx, columns=list('ABCD')).cumsum()

Import the hvplot extension for your data source and optionally set the plotting backend

import hvplot.pandas
# Optional: hvplot.extension('matplotlib') or hvplot.extension('plotly')

Use the .hvplot API as you would use the Pandas .plot API.

df.hvplot()

To get started check out the user guide or the live user guide on Binder.

How to make your data pipeline .interactive

Just add .interactive and replace your normal arguments with Ipywidgets or Panel widgets.

import panel as pn
pn.extension()

df.interactive(width=600).head(n=pn.widgets.IntSlider(start=1, end=5, value=3))

To get started check out the .interactive guide or the live .interactive guide on Binder.

How to find documentation from your notebook or editor

To see the available arguments for a specific kind of plot run

hvplot.help(kind='scatter')

In a notebook or ipython environment the usual

• help and ? will provide you with documentation.
• TAB and SHIFT+TAB completion will help you navigate.

Examples

Download | Run on Binder | View Gallery

License

hvPlot is completely free and open-source. It is licensed under the BSD 3-Clause License.

Support & Feedback

Type	Channel
🐛  Bug Reports
🎁  Feature Requests
👩  Usage Questions
👩‍💻  Developer Questions
📢  Announcements

Contributions

We would ❤️ to work with you no matter whether you want to contribute to issue management, PRs, documentation, blog posts, community support or social media communication.

To get started with the code or docs check out the Developer Guide.

Reach out on Gitter to discuss with the developers, join the weekly triaging or join the bi-weekly HoloViz call.