Introduction#
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)
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 that provides a general and consistent API for plotting data in all the formats mentioned above.
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()
df.head()
| A | B | C | D | |
|---|---|---|---|---|
| 2000-01-01 | -0.412654 | 0.561403 | 1.795590 | 0.678492 |
| 2000-01-02 | 1.122788 | -0.238317 | 2.209365 | 0.862654 |
| 2000-01-03 | 0.594649 | -0.038293 | -0.185135 | -0.517982 |
| 2000-01-04 | -0.218272 | -1.104669 | -0.548176 | -0.642279 |
| 2000-01-05 | 1.694364 | -2.048766 | -0.907424 | -0.662521 |
Pandas default .plot()#
Pandas provides Matplotlib-based plotting by default, using the .plot() method:
%matplotlib inline
df.plot();
The result is a PNG image that displays easily, but is otherwise static.
Switching Pandas backend#
To allow using hvPlot directly with Pandas we have to import hvplot.pandas and swap the Pandas backend with:
import hvplot.pandas # noqa
pd.options.plotting.backend = 'holoviews'