ridgeplot is a Python package that provides a simple interface for plotting beautiful and interactive ridgeline plots within the extensive Plotly ecosystem.
ridgeplot can be installed and updated from PyPi using pip:
pip install -U ridgeplot
For more information, see the installation guide.
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/
For those in a hurry, here's a very basic example on how to quickly get started with the ridgeplot() function.
import numpy as np from ridgeplot import ridgeplot my_samples = [np.random.normal(n, size=900) for n in range(6, 0, -2)] fig = ridgeplot(samples=my_samples) fig.show()
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()
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, row_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()
