hvPlot#

A familiar and high-level API for data exploration and visualization

hvPlot diagram

.hvplot() is a powerful and interactive Pandas-like .plot() API


By replacing .plot() with .hvplot() you get an interactive figure. Try it out below!

import hvplot.pandas
from bokeh.sampledata.penguins import data as df

df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')

.hvplot() can generate plots from Pandas DataFrames and many other data structures of the PyData ecosystem:

import hvplot.xarray
import xarray as xr

xr_ds = xr.tutorial.open_dataset('air_temperature').load().sel(time='2013-06-01 12:00')
xr_ds.hvplot()
Works with XArray
import hvplot.pandas
from bokeh.sampledata.autompg import autompg_clean as df

table = df.groupby(['origin', 'mfr'])['mpg'].mean().sort_values().tail(5)
table.hvplot.barh('mfr', 'mpg', by='origin', stacked=True)
Works with Pandas
import dask
import hvplot.dask

df_dask = dask.dataframe.from_pandas(df, npartitions=2)
df_dask.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
Works with Dask
import geopandas as gpd
import hvplot.pandas

gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
gdf.hvplot(global_extent=True, tiles=True)
Works with GeoPandas
import polars
import hvplot.polars

df_polars = polars.from_pandas(df)
df_polars.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
Works with Polars
import hvplot.intake
from hvplot.sample_data import catalogue as cat

cat.us_crime.hvplot.line(x='Year', y='Violent Crime rate')
Works with Intake
import hvplot.networkx as hvnx
import networkx as nx

G = nx.petersen_graph()
hvnx.draw(G, with_labels=True)
Works with Networkx
import hvplot.streamz
from streamz.dataframe import Random

df_streamz = Random(interval='200ms', freq='50ms')
df_streamz.hvplot()
Works with Streamz

.hvplot() can generate plots with Bokeh (default), Matplotlib or Plotly.

import hvplot.pandas
from bokeh.sampledata.penguins import data as df

df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
Works with Bokeh (default)
import hvplot.pandas
from bokeh.sampledata.penguins import data as df

hvplot.extension('matplotlib')

df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
Works with Matplotlib
import hvplot.pandas
from bokeh.sampledata.penguins import data as df

hvplot.extension('plotly')

df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', by='species')
Works with Plotly

.hvplot() sources its power in the HoloViz ecosystem. With HoloViews you get the ability to easily layout and overlay plots, with Panel you can get more interactive control of your plots with widgets, with DataShader you can visualize and interactively explore very large data, and with GeoViews you can create geographic plots.

import hvplot.pandas
from hvplot.sample_data import us_crime as df

plot1 = df.hvplot(x='Year', y='Violent Crime rate', width=400)
plot2 = df.hvplot(x='Year', y='Burglary rate', width=400)
plot1 + plot2
laying out plots
import hvplot.pandas
import pandas
from bokeh.sampledata.penguins import data

df = data.groupby('species')['bill_length_mm'].describe().sort_values('mean')
df.hvplot.scatter(y='mean') * dff.hvplot.errorbars(y='mean', yerr1='std')
overlaying plots
import hvplot.pandas
from bokeh.sampledata.penguins import data as df

df.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', groupby='island', widget_location='top')
more control with widgets
import hvplot.pandas
from hvplot.sample_data import catalogue as cat

df = cat.airline_flights.read()
df.hvplot.scatter(x='distance', y='airtime', rasterize=True, cnorm='eq_hist', width=500)
visualize and explore large data
import hvplot.xarray
import xarray as xr, cartopy.crs as crs

air_ds = xr.tutorial.open_dataset('air_temperature').load()
air_ds.air.sel(time='2013-06-01 12:00').hvplot.quadmesh(
    'lon', 'lat', projection=crs.Orthographic(-90, 30), project=True,
    global_extent=True, cmap='viridis', coastline=True
)
geographic plots

.interactive() to turn data pipelines into widget-based interactive applications

By starting a data pipeline with .interactive() you can then inject widgets into an extract and transform data pipeline. The pipeline output dynamically updates with widget changes, making data exploration in Jupyter notebooks in particular a lot more efficient.

import hvplot.pandas
import panel as pn
from bokeh.sampledata.penguins import data as df

w_sex = pn.widgets.MultiSelect(name='Sex', value=['MALE'], options=['MALE', 'FEMALE'])
w_body_mass = pn.widgets.FloatSlider(name='Min body mass', start=2700, end=6300, step=50)

dfi = df.interactive(loc='left')
dfi.loc[(dfi['sex'].isin(w_sex)) & (dfi['body_mass_g'] > w_body_mass)]['bill_length_mm'].describe()
interactive app from pandas
import hvplot.xarray
import panel as pn
import xarray as xr

w_time = pn.widgets.IntSlider(name='time', start=0, end=10)

da = xr.tutorial.open_dataset('air_temperature').air
da.interactive.isel(time=w_time).mean().item() - da.mean().item()
interactive app from xarray

.interactive() supports displaying the pipeline output with .hvplot(). You can even output to any other output that Panel supports using .pipe(...).

import hvplot.xarray
import panel as pn
import xarray as xr

da = xr.tutorial.open_dataset('air_temperature').air
w_quantile = pn.widgets.FloatSlider(name='quantile', start=0, end=1)
w_time = pn.widgets.IntSlider(name='time', start=0, end=10)

da.interactive(loc='left') \
.isel(time=w_time) \
.quantile(q=w_quantile, dim='lon') \
.hvplot(ylabel='Air Temperature [K]', width=500)
interactive pipeline with an hvplot output

.hvplot.explorer() to explore data in a web application

The Explorer is a Panel web application that can be displayed in a Jupyter notebook and that can be used to quickly create customized plots.

import hvplot.pandas
from bokeh.sampledata.penguins import data as df

hvexplorer = df.hvplot.explorer()
hvexplorer
explore data with the hvplot explorer