ridgeplot 0.3.2

Beautiful ridgeline plots in python

ridgeplot: beautiful ridgeline plots in Python

---

ridgeplot is a Python package that provides a simple interface for plotting beautiful and interactive ridgeline plots within the extensive Plotly ecosystem.

Installation

ridgeplot can be installed and updated from PyPi using pip:

pip install -U ridgeplot

For more information, see the installation guide.

Getting started

Take a look at the getting started guide, which provides a quick introduction to the ridgeplot library.

The full official documentation can be found at: https://ridgeplot.readthedocs.io/en/stable/

Basic example

For those in a hurry, here's a very basic example on how to quickly get started with ridgeplot() function.

import numpy as np
from ridgeplot import ridgeplot

my_samples = [np.random.normal(n / 1.2, size=600) for n in range(7, 0, -1)]
fig = ridgeplot(samples=my_samples)
fig.show()

Flexible configuration

In this example, we will try to replicate the first ridgeline plot in this from Data to Viz post. The example in the post was created using the "Perception of Probability Words" dataset and the popular ggridges R package. In the end, we will see how the ridgeplot Python library can be used to create a (nearly) identical plot, thanks to its extensive configuration options.

import numpy as np
from ridgeplot import ridgeplot
from ridgeplot.datasets import load_probly

# Load the probly dataset
df = load_probly()

# Let's grab the subset of columns used in the example
column_names = [
    "Almost Certainly",
    "Very Good Chance",
    "We Believe",
    "Likely",
    "About Even",
    "Little Chance",
    "Chances Are Slight",
    "Almost No Chance",
]
df = df[column_names]

# Not only does 'ridgeplot(...)' come configured with sensible defaults
# but is also fully configurable to your own style and preference!
fig = ridgeplot(
    samples=df.to_numpy().T,
    bandwidth=4,
    kde_points=np.linspace(-12.5, 112.5, 500),
    colorscale="viridis",
    colormode="row-index",
    opacity=0.6,
    labels=column_names,
    spacing=5 / 9,
)

# And you can still update and extend the final
# Plotly Figure using standard Plotly methods
fig.update_layout(
    height=560,
    width=800,
    font_size=16,
    plot_bgcolor="white",
    xaxis_tickvals=[-12.5, 0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100, 112.5],
    xaxis_ticktext=["", "0", "", "25", "", "50", "", "75", "", "100", ""],
    xaxis_gridcolor="rgba(0, 0, 0, 0.1)",
    yaxis_gridcolor="rgba(0, 0, 0, 0.1)",
    yaxis_title=dict(text="Assigned Probability (%)", font_size=13),
    showlegend=False,
)

# Show us the work!
fig.show()

More examples

For more examples, take a look at the getting started guide. For instance, this example demonstrates how you can also draw multiple traces per row in your ridgeline plot:

import numpy as np
from ridgeplot import ridgeplot
from ridgeplot.datasets import load_lincoln_weather

# Load test data
df = load_lincoln_weather()

# Transform the data into a 3D (ragged) array format of
# daily min and max temperature samples per month
months = df.index.month_name().unique()
samples = [
    [
        df[df.index.month_name() == month]["Min Temperature [F]"],
        df[df.index.month_name() == month]["Max Temperature [F]"],
    ]
    for month in months
]

# And finish by styling it up to your liking!
fig = ridgeplot(
    samples=samples,
    labels=months,
    colorscale="Inferno",
    bandwidth=4,
    kde_points=np.linspace(-40, 110, 400),
    spacing=0.3,
)
fig.update_layout(
    title="Minimum and maximum daily temperatures in Lincoln, NE (2016)",
    height=600,
    width=800,
    font_size=14,
    plot_bgcolor="rgb(245, 245, 245)",
    xaxis_gridcolor="white",
    yaxis_gridcolor="white",
    xaxis_gridwidth=2,
    yaxis_title="Month",
    xaxis_title="Temperature [F]",
    showlegend=False,
)
fig.show()