quarto-examples/renderings/plotly-python.qmd at c89b4dbbe1201873cc25747f4724089495dcce29 · quarto-dev/quarto-examples · GitHub

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---
title: plotly-python
author: "openai gpt-4o"
date: "2024-10-23 16:52:22.554560"
format:
  html:
    code-tools:
      source: https://github.com/quarto-dev/quarto-examples/blob/main/renderings/plotly-python.qmd
---

# Question

How can I draw a violin plot of the Iris data using Plotly?

# Explanation

Plotly is an interactive plotting library that is ideal for creating detailed interactive plots, including violin plots. It is easy to create a violin plot using the `plotly.express` module, which simplifies many common plotting tasks. The Iris dataset, commonly used in machine learning, is built into the `sklearn` library. We will load this dataset, convert it into a suitable format using Pandas, and then plot it using Plotly.

# Code

```{python}
#| echo: false
from quarto import theme_brand_plotly
import plotly.io as pio
pio.templates['light_brand'] = theme_brand_plotly('light-brand.yml')
pio.templates['dark_brand'] = theme_brand_plotly('dark-brand.yml')
```

```{python}
#| echo: false
#| renderings: [light, dark]
# plotly 6.* has three outputs the first time you run it.
# without "foo" you'll get the warning
# (W) need 2 cell-output-display for renderings light,dark; got 3
# and no light/dark treatment
# it seems (?) to be safe to drop "foo"
import plotly.express as px
import pandas as pd
from sklearn.datasets import load_iris
from functools import partial

# Load the iris dataset
iris_data = load_iris()
iris_df = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)
iris_df['species'] = pd.Categorical.from_codes(iris_data.target, iris_data.target_names)

plot = px.violin(iris_df, x='species', y='sepal length (cm)', box=True, points="all",
                title='Violin Plot of Sepal Length in Iris Dataset')


plot.update_layout(template='light_brand')
display(plot)

plot.update_layout(template='dark_brand')
display(plot)
```

Again!

```{python}
#| echo: false
#| renderings: [light, dark]
plot.update_layout(template='light_brand')
display(plot)

plot.update_layout(template='dark_brand')
display(plot)
```

This code uses `plotly.express` to create a violin plot of the sepal length by species from the Iris dataset. The `sepal length (cm)` is plotted on the Y-axis, with each species on the X-axis. The violin plot provides insights into the distribution of sepal length measurements within each species, along with a box plot overlay and individual measurements as jittered points for detailed data analysis.