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(seed=15485863)
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	1.064993	-0.835020	1.366174	0.568616	1.062697	-1.651245	-0.591375	-0.591991	1.674089	-0.394414	...	-0.378030	1.085214	1.248008	0.847716	-2.009990	2.244824	-1.283696	-0.292551	0.049112	-0.061071
1	0.175635	-1.029685	0.608107	0.965867	-0.296914	1.511633	0.551440	-2.093487	-0.363041	-0.842695	...	-1.184430	-0.399641	1.149865	0.801796	-0.745362	-0.914683	-0.332012	0.401275	0.167847	-0.674393
2	-1.627893	-1.132004	-0.520023	1.433833	0.499023	0.609095	-1.440980	1.263088	0.282536	0.788140	...	1.330569	0.729197	0.172394	-0.311494	0.428182	0.059321	-1.093189	0.006239	0.011220	0.882787
3	0.104182	-0.119232	1.426785	0.744443	0.143632	0.342422	0.591960	0.653388	-0.221575	-0.305475	...	-0.117631	-0.705664	-0.041554	0.365820	1.054793	0.280238	-0.302220	-1.105060	-1.344887	-0.186901
4	0.404584	2.172183	-0.498760	-0.537456	-0.174159	-0.421315	-0.461453	-0.456776	-0.811049	0.470270	...	-0.970498	-0.424077	0.017638	0.764396	-0.055982	0.369587	0.566487	-0.709265	0.041741	1.427378
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	0.730475	0.638252	-1.327878	0.402921	0.300998	-2.103696	0.177649	0.226409	0.391869	-2.687118	...	-1.019080	0.449696	0.603108	1.325479	-0.354819	-0.122947	-0.555267	-0.319204	1.543449	1.219027
96	-1.563950	-0.496752	-0.135757	1.468133	-0.255600	-0.551909	1.069691	0.656629	-1.674868	-0.192483	...	-0.197569	1.751076	0.536964	0.748986	-0.631070	-0.719372	0.053761	1.282812	1.842575	-0.250804
97	0.116719	-0.877987	-0.173280	-0.226328	0.514574	1.021983	-0.869675	-1.426881	1.028629	-0.403335	...	-0.328254	1.291479	0.540613	0.876653	-0.129568	-0.756255	0.614621	0.747284	-0.416865	-1.230327
98	0.744639	1.312439	1.144209	-0.749547	0.111659	-0.153397	-0.230551	-1.585670	-0.279647	0.482702	...	1.193722	-0.229955	0.201680	-0.128116	-1.278398	-0.280277	0.109736	-1.402238	1.064833	-2.022736
99	-2.392240	-1.005938	-0.227638	-1.720300	-0.297324	-0.320110	-0.338110	-0.089035	-0.009806	1.585349	...	0.717063	0.589935	0.718870	1.777263	-0.072043	-0.490852	0.535639	-0.009055	0.045785	0.322065

100 rows × 26 columns

Matplotlib

import matplotlib.pyplot as plt

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

<matplotlib.collections.PathCollection at 0x7f487a6e6110>

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 0x7f48a8c25ed0>

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([[ 1.87370337,  0.18515996,  0.10609036],
       [-0.40062429, -0.08109975,  0.28799952]])
Coordinates:
  * x        (x) int64 10 20
Dimensions without coordinates: y
Attributes:
    foo:      bar

xarray.DataArray

• x: 2
• y: 3

• 1.874 0.1852 0.1061 -0.4006 -0.0811 0.288

  array([[ 1.87370337,  0.18515996,  0.10609036],
         [-0.40062429, -0.08109975,  0.28799952]])

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

    array([10, 20])

• Indexes: (1)
  • x
    PandasIndex

    PandasIndex(Index([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

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