Plots

Plot

Datapane supports all major Python visualization libraries, allowing you to add interactive plots and visualizations to your app.

The dp.Plot block takes a plot object from one of the supported Python visualization libraries and renders it in your app.

Info

Datapane will automatically wrap your visualization or plot in a dp.Plot block if you pass it into your app directly.

Parameters:

Name	Type	Description	Default
data	Any	The plot object to attach	required
caption	Optional[str]	A caption to display below the plot (optional)	None
responsive	bool	Whether the plot should automatically be resized to fit, set to False if your plot looks odd (optional, default: True)	True
scale	float	Set the scaling factor for the plt (optional, default = 1.0)	1.0
name	BlockId	A unique name for the block to reference when adding text or embedding (optional)	None
label	str	A label used when displaying the block (optional)	None

Datapane currently supports the following libraries:

Library	Site / Docs
Altair	https://altair-viz.github.io/
Matplotlib / Seaborn	https://matplotlib.org/ / https://seaborn.pydata.org/
Bokeh	https://bokeh.org/
Plotly	https://plotly.com/python/
Folium	https://python-visualization.github.io/folium/

If you're using another visualization library e.g. Pyvis for networks, try saving your chart as a local HTML file and wrapping that in a dp.HTML block.

Altair

Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.

To get started using Altair to make your visualizations, begin with Altair's Documentation

import altair as alt
import pandas as pd
from vega_datasets import data as vega_data

gap = pd.read_json(vega_data.gapminder.url)

select_year = alt.selection_point(
    name="select",
    fields=["year"],
    value={"year": 1955},
    bind=alt.binding_range(min=1955, max=2005, step=5),
)

alt_chart = (
    alt.Chart(gap)
    .mark_point(filled=True)
    .encode(
        alt.X("fertility", scale=alt.Scale(zero=False)),
        alt.Y("life_expect", scale=alt.Scale(zero=False)),
        alt.Size("pop:Q"),
        alt.Color("cluster:N"),
        alt.Order("pop:Q", sort="descending"),
    )
    .add_params(select_year)
    .transform_filter(select_year)
)

dp.Plot(alt_chart)

Bokeh

Bokeh is an interactive visualization library which provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large datasets.

To get started using Bokeh to make your visualizations, begin with Bokeh's User Guide.

from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.iris import flowers

colormap = {"setosa": "red", "versicolor": "green", "virginica": "blue"}
colors = [colormap[x] for x in flowers["species"]]

bokeh_chart = figure(title="Iris Morphology")
bokeh_chart.xaxis.axis_label = "Petal Length"
bokeh_chart.yaxis.axis_label = "Petal Width"

bokeh_chart.circle(
    flowers["petal_length"],
    flowers["petal_width"],
    color=colors,
    fill_alpha=0.2,
    size=10,
)

dp.Plot(bokeh_chart)

Matplotlib

Matplotlib is the original Python visualization library, often supported and used with Jupyter Notebooks. Matplotlib plots are not interactive in Datapane apps, but are saved as SVGs so can be viewed at high fidelity.

Higher-level matplotlib libraries such as Seaborn are also supported, and can be used in a similar way to the matplotlib example below,

import matplotlib.pyplot as plt
import pandas as pd
from vega_datasets import data as vega_data

gap = pd.read_json(vega_data.gapminder.url)
fig = gap.plot.scatter(x="life_expect", y="fertility")


dp.Plot(fig)

Info

You can pass either a matplotlib Figure or Axes object to dp.Plot, you can obtain the current global figure from matplotlib by running plt.gcf()

Plotly

Plotly's Python graphing library makes interactive, publication-quality graphs.

import plotly.express as px

df = px.data.gapminder()

plotly_chart = px.scatter(
    df.query("year==2007"),
    x="gdpPercap",
    y="lifeExp",
    size="pop",
    color="continent",
    hover_name="country",
    log_x=True,
    size_max=60,
)

dp.Plot(plotly_chart)

Folium

Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map.

The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys.

Info

If your folium map consumes live data which expires after a certain time, you can automate it to refresh the map on a cadence. See dp.Dynamic.

import folium

m = folium.Map(location=[45.5236, -122.6750])

dp.Plot(m)