Note
Click here to download the full example code
Ridgeline Plot¶
What it's for: A Ridgeline plot (also known as a Joyplot) visualizes the distribution of a continuous variable across multiple groups by using partially overlapping density plots.
When to use: Use this to compare how a distribution changes over time (e.g., daily profiles for each month) or across different experimental groups. It is more space-efficient and often easier to read than multiple overlapping histograms or KDE plots.
How to read: * X-axis: The value of the variable being measured. * Y-axis: Represents different categories or groups. Each group has its own Kernel Density Estimate (KDE). * Interpretation: Look for shifts in the peak (mean/mode) or the width (spread) of the distributions as you move from one group to another.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from monet_plots.plots.ridgeline import RidgelinePlot
# 1. Prepare sample data
# Grouped data for different categories
np.random.seed(42)
n_points = 200
categories = ["Group A", "Group B", "Group C", "Group D"]
data = []
for i, cat in enumerate(categories):
values = np.random.normal(i * 2, 1.0, n_points)
df = pd.DataFrame({"measurement": values, "group": cat})
data.append(df)
df_all = pd.concat(data)
# 2. Initialize and create the plot
# RidgelinePlot(data, group_dim, x=None, ...)
plot = RidgelinePlot(df_all, "group", x="measurement", figsize=(10, 8))
plot.plot(title="Ridgeline Plot of Different Groups", cmap="viridis")
plt.show()
Total running time of the script: ( 0 minutes 2.202 seconds)
Download Python source code: plot_ridgeline.py