PyData Library Styles

This theme has built-in support and special styling for several major visualization libraries in the PyData ecosystem. This ensures that the images and output generated by these libraries looks good for both light and dark modes. Below are examples of each that we use as a benchmark for reference.

Pandas

import string

import numpy as np
import pandas as pd

rng = np.random.default_rng()
data = rng.standard_normal((100, 26))
df = pd.DataFrame(data, columns=list(string.ascii_lowercase))
df

	a	b	c	d	e	f	g	h	i	j	...	q	r	s	t	u	v	w	x	y	z
0	0.621551	-0.678011	1.361140	1.205592	0.413808	0.141885	1.444735	0.505024	-0.942470	-0.219441	...	-1.010798	-0.867651	0.557123	2.773014	0.152473	-0.273795	0.380190	0.384379	-0.295625	0.378258
1	0.766013	0.758517	-1.370940	-0.743420	-0.233156	-0.851769	0.061132	0.617713	1.919868	-0.086240	...	2.109971	0.268220	0.648520	-0.206540	-0.146194	-0.345295	-0.268368	-0.894535	0.271244	0.279200
2	1.223517	0.379797	-1.041529	1.838098	2.268853	-0.971022	-0.137972	0.933401	0.257763	1.882588	...	-1.366165	0.236098	-0.483581	-0.307996	-1.386614	-2.067426	-1.514831	-0.868818	1.558775	-0.989485
3	0.907914	-0.149069	1.221954	0.615235	3.337798	-1.064425	1.311958	-0.687309	-0.148470	1.485415	...	-0.367092	0.339240	-0.327380	-0.708208	-0.704330	2.278505	-0.454720	-1.255363	-0.840784	0.374169
4	-1.257545	-0.901319	-0.350519	-0.977022	-0.269948	0.047105	0.463823	-0.014111	-0.598746	1.042452	...	1.773795	-0.133022	0.640026	-0.875964	0.144460	1.093559	-0.683122	0.843353	-0.937060	0.046954
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	-1.826161	-0.860834	-0.658106	-0.243956	-0.014873	0.253184	0.272784	-0.838849	0.500119	-0.226580	...	0.083424	-0.426504	1.378009	-1.056087	-1.157277	-0.410715	0.583756	0.510852	0.804660	-0.051881
96	0.593977	0.267760	0.658239	-0.653321	0.646940	0.572911	0.092675	-0.251702	2.938001	0.488098	...	0.527836	-0.120622	0.049128	0.103224	-1.842633	1.119237	0.418252	1.496938	-3.026498	-0.646710
97	-1.812710	-1.777983	-1.387779	-0.352277	1.822589	1.211555	0.674116	0.016123	-0.782162	-0.055965	...	-0.327913	1.421829	-1.333076	-0.491430	0.665826	-0.313587	0.100078	0.654475	0.900451	-0.093777
98	-0.323696	-0.392730	-0.047554	0.256879	0.484209	1.361722	-0.004229	0.629316	0.347696	-1.115283	...	0.774960	2.108964	0.555739	0.061425	-1.310056	1.548465	1.078677	-0.246777	0.450686	2.976022
99	0.231995	0.472944	-0.904920	0.117966	0.744879	1.018735	-0.732656	0.714426	1.157692	-0.814945	...	0.942703	-0.785208	-1.413649	1.914664	0.340083	1.470504	0.328992	-0.779164	0.363458	-0.198955

100 rows × 26 columns

Matplotlib

Here’s a sidebar to test that the code cells behave as we’d expect when there is content to the right. The code cell should be displayed to the left and with no overlap.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.scatter(df["a"], df["b"], c=df["b"], s=3)

<matplotlib.collections.PathCollection at 0x7f3956522e50>

and with the Matplotlib plot directive:

(Source code, png, hires.png, pdf)

Plotly

The HTML below shouldn’t display, but it uses RequireJS to make sure that all works as expected. If the widgets don’t show up, RequireJS may be broken.

import plotly.io as pio
import plotly.express as px
import plotly.offline as py

pio.renderers.default = "notebook"

df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size="sepal_length")
fig

Xarray

Here we demonstrate xarray to ensure that it shows up properly.

import xarray as xr
data = xr.DataArray(
        np.random.randn(2, 3),
        dims=("x", "y"),
        coords={"x": [10, 20]}, attrs={"foo": "bar"}
      )
data

<xarray.DataArray (x: 2, y: 3)>
array([[ 1.34060541, -0.53184925, -1.49496748],
       [-0.90791695, -0.08841657, -0.78232964]])
Coordinates:
  * x        (x) int64 10 20
Dimensions without coordinates: y
Attributes:
    foo:      bar

xarray.DataArray

• x: 2
• y: 3

• 1.341 -0.5318 -1.495 -0.9079 -0.08842 -0.7823

  array([[ 1.34060541, -0.53184925, -1.49496748],
         [-0.90791695, -0.08841657, -0.78232964]])

• Coordinates: (1)
  • x
    (x)
    int64
    10 20

    array([10, 20])

• Indexes: (1)
  • x
    PandasIndex

    PandasIndex(Int64Index([10, 20], dtype='int64', name='x'))

• Attributes: (1)

  foo :

  bar

ipyleaflet

ipyleaflet is a Jupyter/Leaflet bridge enabling interactive maps in the Jupyter notebook environment. this demonstrate how you can integrate maps in your documentation.

from ipyleaflet import Map, basemaps
from IPython.display import display

# display a map centered on France
m = Map(basemap=basemaps.Esri.WorldImagery,  zoom=5, center=[46.21, 2.21])
display(m)

jupyter-sphinx

This theme has styling for jupyter-sphinx, which is often used for executing and displaying widgets with a Jupyter kernel.

import matplotlib.pyplot as plt
import numpy as np

rng = np.random.default_rng()
data = rng.standard_normal((3, 100))
fig, ax = plt.subplots()
ax.scatter(data[0], data[1], c=data[2], s=3)

<matplotlib.collections.PathCollection at 0x7f2bd64cb280>