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

IPyWidget

import ipywidgets as widgets
import numpy as np
import pandas as pd

from IPython.display import display


tab = widgets.Tab()

descr_str = "Hello"

title = widgets.HTML(descr_str)

# create output widgets
widget_images = widgets.Output()
widget_annotations = widgets.Output()

# render in output widgets
with widget_images:
    display(pd.DataFrame(np.random.randn(10, 10)))
with widget_annotations:
    display(pd.DataFrame(np.random.randn(10, 10)))

tab.children = [widget_images, widget_annotations]
tab.titles = ["Images", "Annotations"]

display(widgets.VBox([title, tab]))

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 0x7f9840bd6d20>

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 0x7f984074a840>

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.express as px
import plotly.io as pio


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)> Size: 48B
array([[ 0.22395899, -0.01169898,  0.10159347],
       [ 1.04640364, -0.07215996,  0.08372358]])
Coordinates:
  * x        (x) int64 16B 10 20
Dimensions without coordinates: y
Attributes:
    foo:      bar

xarray.DataArray

• x: 2
• y: 3

• 0.224 -0.0117 0.1016 1.046 -0.07216 0.08372

  array([[ 0.22395899, -0.01169898,  0.10159347],
         [ 1.04640364, -0.07215996,  0.08372358]])

• 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