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	0.311370	0.930760	0.734203	-0.395752	-0.386222	1.944687	-0.914829	0.005161	-0.244122	-3.068303	...	-1.235248	-0.423947	-0.567292	-0.803213	1.277616	-0.981038	-2.517454	0.150340	-0.221287	0.120588
1	1.016465	-1.309161	0.119732	-0.193135	0.712014	0.661093	-0.426137	0.341819	-0.523607	1.622394	...	-2.695233	0.617742	0.550230	0.523802	0.059442	-0.220915	-0.421106	-0.001800	-0.752967	2.759070
2	1.868318	-0.746607	-0.353970	0.379558	-1.223273	-0.165177	0.020974	-0.095447	-0.094138	0.173706	...	0.592267	-0.186192	-0.783239	-0.462316	1.416612	-0.572427	0.169302	0.702257	-2.506094	-1.552825
3	-1.280711	0.899626	-0.141591	2.274749	0.735301	0.312153	-1.339304	1.611907	-0.102241	0.312316	...	-0.496129	-0.160148	1.677369	0.903861	-1.288931	0.659441	1.748047	0.433460	-0.351234	0.737601
4	0.522139	-0.499206	-0.078972	-0.352476	0.954266	-1.256487	0.300661	-2.324879	-0.656328	-0.118312	...	1.043888	0.120722	-0.555673	-1.478972	-0.073858	-2.286886	0.391620	-1.204539	0.292303	-0.056930
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	1.427196	0.180431	0.219939	1.645893	0.076371	-1.224770	-0.127598	-0.787070	0.372666	0.378871	...	0.261914	0.079047	-0.109846	0.522169	0.252848	-1.285432	-1.454377	-0.096430	-0.729139	-0.229617
96	1.178522	-0.387039	-0.057630	-0.616969	0.767219	1.017275	-2.237077	-0.524710	2.090364	-1.114673	...	2.276190	0.120314	-0.162370	0.176529	0.253103	0.532922	1.187715	-0.153143	0.752273	-2.186103
97	-0.218750	1.231444	0.832591	0.404316	0.668349	-1.194827	0.146782	-1.196502	1.033414	-1.438118	...	-1.682289	-0.501897	1.262362	-0.057749	0.251065	1.472893	-0.786191	3.407656	0.391525	1.285357
98	-2.219993	0.759655	-0.374613	-1.397357	1.627095	-1.876407	0.266039	-0.946892	-0.422397	-0.463645	...	-0.066578	2.735865	0.595039	0.183309	0.871685	-0.293767	-0.675990	-1.077739	-0.488007	-0.929621
99	0.473812	-0.050464	1.006873	-0.102286	0.710749	0.525634	-0.455428	1.479328	-0.640018	0.087520	...	-0.180498	0.048829	-0.207480	0.760001	1.218511	0.480343	-2.205869	-0.754764	0.776454	-0.889203

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 0x7f2198a2ee50>

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