Plots and Visualizations
Datapane supports all major Python visualization libraries, allowing you to add interactive plots and visualizations to your report.
The dp.Plot
block takes a plot object from one of the supported Python visualisation libraries and renders it in your report.
Info
Datapane will automatically wrap your visualization or plot in a dp.Plot
block if you pass it into your report directly.
It takes the following parameters:
name
: Sets the name of the chart within the captioncaption
: Adds a caption beneath your plotresponsive
: Boolean (True
by default) which controls whether the plot fills the block. Set toFalse
if you want to manually specify the height and width within the plot object.
Datapane currently supports the following libraries:
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. Altairβs 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 datapane as dp
import pandas as pd
from vega_datasets import data as vega_data
gap = pd.read_json(vega_data.gapminder.url)
select_year = alt.selection_single(
name="select",
fields=["year"],
init={"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_selection(select_year)
.transform_filter(select_year)
)
dp.Report(dp.Plot(alt_chart)).save(path="altair-plot.html")
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
import datapane as dp
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.Report(dp.Plot(bokeh_chart)).save(path="bokeh-plot.html")
Matplotlib
Matplotlib is the original Python visualisation library, often supported and used with Jupyter Notebooks. Matplotlib plots are not interactive in Datapane Reports, 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,
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.
Folium
Folium makes it easy to visualize data thatβs 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 Automation.
Plotapi
Plotapi (or PlotAPI) is a visualization package that's beautiful by default. It enables beautiful and interactive visualizations with just a single line of code.
Check out the gallery of Plotapi examples to get started.
import datapane as dp
from plotapi import Chord
Chord.set_license(plotapi_username, plotapi_license)
matrix = [
[0, 5, 6, 4, 7, 4],
[5, 0, 5, 4, 6, 5],
[6, 5, 0, 4, 5, 5],
[4, 4, 4, 0, 5, 5],
[7, 6, 5, 5, 0, 4],
[4, 5, 5, 5, 4, 0],
]
names = ["Action", "Adventure", "Comedy", "Drama", "Fantasy", "Thriller"]
fig = Chord(matrix, names)
dp.Report(dp.Plot(fig)).save(path="plotapi-plot.html")