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#

[1]:

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

[1]:
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#

[2]:

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#

[3]:

import matplotlib.pyplot as plt


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

[3]:

<matplotlib.collections.PathCollection at 0x76384c6fc620>
../_images/examples_pydata_6_1.png 
[4]:

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)

[4]:

<matplotlib.collections.PathCollection at 0x76384c4b6a50>
../_images/examples_pydata_7_1.png

Xarray#

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

[5]:

import xarray as xr


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

[5]:

<xarray.DataArray (x: 2, y: 3)> Size: 48B
array([[-0.17327313,  0.92437762,  0.01427325],
       [ 0.2317726 ,  1.66413402,  0.80575246]])
Coordinates:
  * x        (x) int64 16B 10 20
Dimensions without coordinates: y
Attributes:
    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.

[6]:

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

[6]: