The ridgeplot python library aims at providing a simple API for plotting beautiful ridgeline plots within the extensive Plotly interactive graphing environment.
- Do one thing, and do it well!
- Use sensible defaults, but allow for extensive configuration!
How to get it?
The source code is currently hosted on GitHub at: https://github.com/tpvasconcelos/ridgeplot
Install and update using pip:
pip install -U ridgeplot
- plotly - the interactive graphing backend that powers
- statsmodels - Used for Kernel Density Estimation (KDE)
- numpy - Supporting library for multi-dimensional array manipulations
How to use it?
from numpy.random import normal from ridgeplot import ridgeplot synthetic_samples = [normal(n / 1.2, size=600) for n in reversed(range(9))] fig = ridgeplot(samples=synthetic_samples) fig.show()
In this example, we will be replicating the first ridgeline plot example in this from Data to Viz post, which uses the probly dataset. You can find the plobly dataset on multiple sources like in the bokeh python interactive visualization library. I'll be using the same source used in the original post.
import numpy as np import pandas as pd from ridgeplot import ridgeplot # Get the raw data df = pd.read_csv("https://raw.githubusercontent.com/bokeh/bokeh/main/bokeh/sampledata/_data/probly.csv") # Let's grab only the subset of columns displayed 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.values.T, bandwidth=4, kde_points=np.linspace(-12.5, 112.5, 400), colorscale="viridis", colormode="index", coloralpha=0.6, labels=column_names, spacing=5 / 9, ) # Again, update the figure layout to your liking here fig.update_layout( title="What probability would you assign to the phrase <i>“Highly likely”</i>?", height=650, width=800, plot_bgcolor="rgba(255, 255, 255, 0.0)", xaxis_gridcolor="rgba(0, 0, 0, 0.1)", yaxis_gridcolor="rgba(0, 0, 0, 0.1)", yaxis_title="Assigned Probability (%)", ) fig.show()