A high-level plotting API for the PyData ecosystem built on HoloViews.
Project description
hvPlot makes data analysis and visualization simple 
| Downloads |   |
| Build Status |  |
| Coverage |  |
| Latest dev release |   |
| Latest release |      |
| Python |  |
| Docs |   |
| Binder |  |
| Support |  |
Home | Installation instructions | Getting Started Guide | Reference Guides | Examples | License | Support
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 love
hvPlot
- supports a wide range of data sources including Pandas, Polars, 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 simplest way to benefit from the HoloViz ecosystem for data exploration.
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.
Mini getting-started
Head over to the getting started guide for more!
Install
hvPlot can be installed on Linux, Windows, or Mac with conda:
conda install hvplot
or with pip:
pip install hvplot
Please note that for versions of jupyterlab<3.0, you must install the JupyterLab extension manually with:
jupyter labextension install @pyviz/jupyterlab_pyviz
Plotting data
Work with your data source:
import numpy as np
import pandas as pd
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 or Xarray .plot API:
df.hvplot()
Interactive data apps
Just add .interactive and replace your normal arguments with Panel widgets or Ipywidgets.
import panel as pn
pn.extension()
df.interactive(width=600).head(n=pn.widgets.IntSlider(start=1, end=5, value=3))
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
helpand?will provide you with documentation.TABandSHIFT+TABcompletion will help you navigate.
License
hvPlot is completely free and open-source. It is licensed under the BSD 3-Clause License.
Support & Feedback
- Usage questions and showcases -> HoloViz Community
- Bug reports and feature requests -> Github
- Developer discussions -> Discord
For more detail check out the HoloViz Community Guide.
Contributions
We would love 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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for hvplot-0.10.0rc1-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 3b1a8597d216490a2d346d04f5f7b8a722da3f3d6e21aef12021e8dcef75a2e0 |
|
| MD5 | a6fba6e256c39bfa058937090c15dfff |
|
| BLAKE2b-256 | 2543ca3cf26b3d830487c9f442f7261569e27c7a7e33d366786a7645814a9a69 |
