hvplot_docs/Integrations.md

import numpy as np

np.random.seed(1)

Data sources

The .hvplot() plotting API supports a wide range of data sources. Most frequently, a special import can be executed to register the .hvplot accessor on a data type. For instance, importing hvplot.pandas registers the .hvplot accessor on Pandas DataFrame and Series objects, allowing to call df.hvplot.line().

Among the data sources introduced below, Pandas](https://pandas.pydata.org) is the only library that doesn't need to be installed separately as it is a direct dependency of hvPlot.

:::{note} Supporting so many data sources is hard work! We are aware that the support for some of them isn't as good as we would like. If you encounter any issue please report it on GitHub, we always welcome Pull Requests too! :::

Columnar/tabular

Pandas

.hvplot() supports Pandas DataFrame and Series objects.

import hvplot.pandas  # noqa
import pandas as pd

df_pandas = pd.DataFrame(np.random.randn(1000, 4), columns=list('ABCD')).cumsum()
df_pandas.head(2)

# Pandas DataFrame
df_pandas.hvplot.line(height=150)

# Pandas Series
s_pandas = df_pandas['A']
s_pandas.hvplot.line(height=150)

Dask

.hvplot() supports Dask DataFrame and Series objects.

import hvplot.dask  # noqa
import dask

df_dask = dask.dataframe.from_pandas(df_pandas, npartitions=2)
df_dask

# Dask DataFrame
df_dask.hvplot.line(height=150)

# Dask Series
s_dask = df_dask['A']
s_dask.hvplot.line(height=150)

GeoPandas

.hvplot() supports GeoPandas GeoDataFrame objects.

import hvplot.pandas  # noqa
import geopandas as gpd

p_geometry = gpd.points_from_xy(
    x=[12.45339, 12.44177, 9.51667, 6.13000],
    y=[41.90328, 43.93610, 47.13372, 49.61166],
    crs='EPSG:4326'
)
p_names = ['Vatican City', 'San Marino', 'Vaduz', 'Luxembourg']
gdf = gpd.GeoDataFrame(dict(name=p_names), geometry=p_geometry)
gdf.head(2)

# GeoPandas GeoDataFrame
gdf.hvplot.points(geo=True, tiles='CartoLight', frame_height=150, data_aspect=0.5)

Ibis

Ibis is the "portable Python dataframe library", it provides a unified interface to many data backends (e.g. DuckDB, SQLite, SnowFlake, Google BigQuery). .hvplot() supports Ibis Expr objects.

import hvplot.ibis  # noqa
import ibis

table = ibis.memtable(df_pandas.reset_index())
table

# Ibis Expr
table.hvplot.line(x='index', height=150)

Polars

:::{note} Added in version 0.9.0. :::

:::{important} While other data sources like Pandas or Dask have built-in support in HoloViews, as of version 1.17.1 this is not yet the case for Polars. You can track this issue to follow the evolution of this feature in HoloViews. Internally hvPlot simply selects the columns that contribute to the plot and casts them to a Pandas object using Polars' .to_pandas() method. :::

import hvplot.polars  # noqa 
import polars

df_polars = polars.from_pandas(df_pandas)
df_polars.head(2)

.hvplot() supports Polars DataFrame, LazyFrame and Series objects.

# Polars DataFrame
df_polars.hvplot.line(y=['A', 'B', 'C', 'D'], height=150)

# Polars LazyFrame
df_polars.lazy().hvplot.line(y=['A', 'B', 'C', 'D'], height=150)

# Polars Series
df_polars['A'].hvplot.line(height=150)

Rapids cuDF

:::{important} Rapids cuDF is a Python GPU DataFrame library. Neither hvPlot's nor HoloViews' test suites currently run on a GPU part of their CI, as of versions 0.9.0 and 1.17.1, respectively. This is due to the non availability of machines equipped with a GPU on the free CI system we rely on (Github Actions). Therefore it's possible that support for cuDF gets degraded in hvPlot without us noticing it immediately. Please report any issue you might encounter. :::

.hvplot() supports cuDF DataFrame and Series objects.

Fugue

:::{admonition} Experimental :class: caution Fugue support, added in version 0.9.0, is experimental and may change in future versions. :::

hvPlot adds the hvplot plotting extension to FugueSQL.

import hvplot.fugue  # noqa
import fugue

fugue.api.fugue_sql(
    """
    OUTPUT df_pandas USING hvplot:line(
        height=150,
    )
    """
)

Multidimensional

Xarray

.hvplot() supports XArray Dataset and DataArray labelled multidimensional objects.

import hvplot.xarray  # noqa
import xarray as xr

ds = xr.Dataset({
    'A': (['x', 'y'], np.random.randn(100, 100)),
    'B': (['x', 'y'], np.random.randn(100, 100))},
    coords={'x': np.arange(100), 'y': np.arange(100)}
)
ds

# Xarray Dataset
ds.hvplot.hist(height=150)

# Xarray DataArray
ds['A'].hvplot.image(height=150)

Catalog

Intake

.hvplot() supports Intake DataSource objects.

Streaming

Streamz

.hvplot() supports Streamz DataFrame, DataFrames, Series and Seriess objects.

Graph

NetworkX

The hvPlot NetworkX plotting API is meant as a drop-in replacement for the networkx.draw methods. The draw and other draw_<> methods are available in the hvplot.networkx module.

import hvplot.networkx as hvnx
import networkx as nx

G = nx.petersen_graph()
hvnx.draw(G, with_labels=True, height=150)

Plotting extensions

hvPlot is capable of producing plots with Bokeh (default, interactive), Matplotlib (static) and Plotly (interactive). Under the hood, hvPlot delegates plotting to HoloViews which itself calls these plotting libraries. This is why we call hvPlot a high-level plotting library!

Follow the Plotting Extensions Guide for more information.

:::{note} Similarly to having to support many data sources, supporting three plotting extensions is hard work! We are aware they are not supported equivalently, you will get best support for Bokeh, followed by Matplotlib and finally Plotly. If you encounter any issue with a specific plotting extension please report it on GitHub, we always welcome Pull Requests too! :::