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]:
importstringimportnumpyasnpimportpandasaspdrng=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]:
importipywidgetsaswidgetsimportnumpyasnpimportpandasaspdfromIPython.displayimportdisplaytab=widgets.Tab()descr_str="Hello"title=widgets.HTML(descr_str)# create output widgetswidget_images=widgets.Output()widget_annotations=widgets.Output()# render in output widgetswithwidget_images:display(pd.DataFrame(np.random.randn(10,10)))withwidget_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]:
importmatplotlib.pyplotaspltfig,ax=plt.subplots()ax.scatter(df["a"],df["b"],c=df["b"],s=3)
[3]:
<matplotlib.collections.PathCollection at 0x7f6cbaf82380>
[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 0x7f6cb8daef20>
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.
[5]:
importplotly.expressaspximportplotly.ioaspiopio.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 demonstratexarray to ensure that it shows up properly.
[6]:
importxarrayasxrdata=xr.DataArray(np.random.randn(2,3),dims=("x","y"),coords={"x":[10,20]},attrs={"foo":"bar"})data
[6]:
<xarray.DataArray (x: 2, y: 3)> Size: 48Barray([[ 0.07442082, -0.02011233, 0.669876 ], [-1.21410822, -0.58936142, -0.66607196]])Coordinates: * x (x) int64 16B 10 20Dimensions without coordinates: yAttributes: foo: bar
ipyleaflet#
ipyleaflet is aJupyter/Leaflet bridge enabling interactive maps in the Jupyter notebook environment. this demonstrate how you can integrate maps in your documentation.
[7]:
fromipyleafletimportMap,basemaps# display a map centered on Francem=Map(basemap=basemaps.Esri.WorldImagery,zoom=5,center=[46.21,2.21])m
[7]: