A high-level plotting API for the PyData ecosystem built on HoloViews.
The PyData ecosystem has a number of core Python data containers that allow users to work with a wide array of datatypes, including:
- Pandas: DataFrame, Series (columnar/tabular data)
- Rapids cuDF: GPU DataFrame, Series (columnar/tabular data)
- Dask: DataFrame, Series (distributed/out of core arrays and columnar data)
- XArray: Dataset, DataArray (labelled multidimensional arrays)
- Streamz: DataFrame(s), Series(s) (streaming columnar data)
- Intake: DataSource (data catalogues)
- GeoPandas: GeoDataFrame (geometry data)
- NetworkX: Graph (network graphs)
Several of these libraries have the concept of a high-level plotting API that lets a user generate common plot types very easily. The native plotting APIs are generally built on Matplotlib, which provides a solid foundation, but it means that users miss out on the benefits of modern, interactive plotting libraries for the web like Bokeh and HoloViews.
hvPlot provides a high-level plotting API built on HoloViews that provides a general and consistent API for plotting data in all the abovementioned formats. hvPlot can integrate neatly with the individual libraries if an extension mechanism for the native plot APIs is offered, or it can be used as a standalone component. To get started jump straight into the Getting Started Guide and check out the current functionality in the User Guide.
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 hvplot.pandas # noqa df.hvplot()