pydata.md

file_format	kernelspec
mystnb	name display_name python3 Python 3

% To test this file with nbsphinx we need to convert to ipynb. To do this: % - Run this command: jupytext docs/examples/pydata.md --to ipynb % - Temporarily delete the pydata.md file % - Uncomment nbsphinx and comment myst_nb in "extensions" in our conf.py file % - Build the docs and test that the results look OK % - Undo everything in this list to make sure we revert back to the old structure

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

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)

and with the Matplotlib plot directive:

.. plot::

   import matplotlib.pyplot as plt
   import numpy as np
   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)

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

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