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)

  • XArray: Dataset, DataArray (multidimensional arrays)

  • Dask: DataFrame, Series, Array (distributed/out of core arrays and columnar data)

  • Streamz: DataFrame(s), Series(s) (streaming columnar data)

  • Intake: DataSource (remote data)

Many 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 means that users miss out the benefits of modern, interactive plotting libraries for the web like Bokeh and HoloViews.

hvPlot provides a high-level plotting API built on HoloViews and Bokeh that provides a general and consistent API for plotting data in all the abovementioned formats.

As a first simple illustration of using hvPlot, let’s create a small set of random data in Pandas to explore:

import numpy as np
import pandas as pd

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

2000-01-01 0.213009 -0.976671 -0.184805 -0.106426
2000-01-02 0.671262 0.057617 -1.797504 -2.298405
2000-01-03 -0.600997 0.253840 -0.435685 -2.292387
2000-01-04 -1.231221 0.000661 0.081299 -5.120763
2000-01-05 -1.881182 0.449355 -1.413497 -5.655894

Pandas default .plot()

Pandas provides Matplotlib-based plotting by default, using the .plot() method:

%matplotlib inline


The result is a PNG image that displays easily, but is otherwise static.

Switching backends

To allow using hvPlot directly with Pandas we have to import hvplot.pandas and swap the backend with:

import hvplot.pandas  # noqa

pd.options.plotting.backend = 'holoviews'