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"""
========================================
Example with the plotly graphing library
========================================
Sphinx-Gallery supports examples made with the `plotly library`_.
Sphinx-Gallery is able to capture the ``_repr_html_`` of plotly figure objects
(see :ref:`capture_repr`). To display the figure, the last line in your code
block should therefore be the plotly figure object.
In order to use plotly, the ``conf.py`` of the project should include the
following lines to select the appropriate plotly renderer::
import plotly.io as pio
pio.renderers.default = 'sphinx_gallery'
**Optional**: the ``sphinx_gallery`` renderer of plotly will not generate png
thumbnails. For png thumbnails, you can use instead the ``sphinx_gallery_png``
renderer, and add ``plotly.io._sg_scraper.plotly_sg_scraper`` to the list of
:ref:`image_scrapers`. The scraper requires you to
`install the orca package <https://plotly.com/python/static-image-export/>`_.
This tutorial gives a few examples of plotly figures, starting with its
high-level API `plotly express <https://plotly.com/python/plotly-express/>`_.
.. _plotly library: https://plotly.com/python/
"""
import numpy as np
import plotly.express as px
df = px.data.tips()
fig = px.bar(
df,
x="sex",
y="total_bill",
facet_col="day",
color="smoker",
barmode="group",
template="presentation+plotly",
)
fig.update_layout(height=400)
fig
# %%
# In addition to the classical scatter or bar charts, plotly provides a large
# variety of traces, such as the sunburst hierarchical trace of the following
# example. plotly is an interactive library: click on one of the continents
# for a more detailed view of the drill-down.
df = px.data.gapminder().query("year == 2007")
fig = px.sunburst(
df,
path=["continent", "country"],
values="pop",
color="lifeExp",
hover_data=["iso_alpha"],
color_continuous_scale="RdBu",
color_continuous_midpoint=np.average(df["lifeExp"], weights=df["pop"]),
)
fig.update_layout(title_text="Life expectancy of countries and continents")
fig
# %%
# While plotly express is often the high-level entry point of the plotly
# library, complex figures mixing different types of traces can be made
# with the low-level ``graph_objects`` imperative API.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
fig = make_subplots(rows=1, cols=2, specs=[[{}, {"type": "domain"}]])
fig.add_trace(go.Bar(x=[2018, 2019, 2020], y=[3, 2, 5], showlegend=False), 1, 1)
fig.add_trace(go.Pie(labels=["A", "B", "C"], values=[1, 3, 6]), 1, 2)
fig.update_layout(height=400, template="presentation", yaxis_title_text="revenue")
fig
# sphinx_gallery_thumbnail_path = '_static/plotly_logo.png'
|
