How to Create a Ridgeline Plot using matplotlib python

by aryaman@openweaver.com Updated: May 9, 2023

Solution Kit

Matplotlib is a plotting library that uses Python programming language. It has a numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications. It will use general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK. Ridgeline plots are overlapping lines that create the impression of a mountain range. They can be useful for visualizing distribution changes over time or space.

Uses:

A ridgeline plot, a density plot, or a joy plot is a data visualization technique.

It displays data distribution over a continuous interval.
It is a useful tool in Python. It helps to visualize data distributions and compare them between groups.
Ridgeline plots are particularly helpful for presenting many datasets.

Data Types:

We can plot different data types on a ridgeline plot. It includes time series, ordinal, and categorical data.

We can plot the Time series data to visualize trends over time.
We can use the Ordinal data to rank or order data points.
We can represent the Categorical data. We can do so using colors or patterns to distinguish between categories.

Plots:

Ridgeline plots can create types of plots, including bar, line, and scatter plots.
Bar charts help to compare the frequency of data points in different categories.
Line charts can visualize trends over time. Else other continuous intervals while scattering plots. It can visualize the relationship between two variables.
Pie charts display the proportion of different categories.
Histograms display the frequency distribution of data over a continuous interval.

Colors:

We can use different colors on a ridgeline plot. It includes primary, secondary, and tertiary colors.

We can create tertiary colors by mixing secondary colors.
Primary colors include red, blue, and yellow.
We can create a secondary color by mixing primary colors.

Different axes used on a ridgeline plot include the x-axis, y-axis, and z-axis. The x-axis displays the range of values for the plotted data, while the y-axis. The z-axis can display extra information, such as the color or size of the data points. It helps display the frequency or density of the data.

Point data contains individual data points. We can use different data points on a ridgeline plot, including point, line, and area data. We can do it while line data connects data points over a continuous interval. Area data displays the density of the data over a continuous interval.

We can use different lines on a ridgeline plot, including trend, linear, and nonlinear. Trend lines display the trend in the data, while linear lines connect data points in a straight line. We can use nonlinear lines to represent complex relationships between variables.

We can use different legends on a ridgeline plot. It includes the title, data labels, and y-axis labels. We can use the title to describe the plot. We can do it while data labels label the different data points or categories. Y-axis labels describe the y-axis.

The ridgeline plots are a useful tool for data analysis and data visualization. They can compare data distributions between groups. It helps display complex relationships between variables. We can customize ridgeline plots using data points, lines, and colors. It helps meet the specific needs of a project. So, including ridgeline plots in your data analysis and visualization toolkit is important.

Code

In this solution, we use the kdeplot function of the seaborn library

Python Seaborn Ridge Plot tutorial not working

Lines of Code : 71License : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries :

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

# Create the data
rs = np.random.RandomState(2022)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
df["x"] += df["g"].map(ord)

# Initialize the FacetGrid object
pal = sns.cubehelix_palette(10, start=1, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=.5, palette=pal)

# Draw the densities in a few steps
g.map(sns.kdeplot, "x",
      bw_adjust=.5, clip_on=False,
      fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=.5)

# passing color=None to refline() uses the hue mapping
# g.refline(y=0, linewidth=2, linestyle="-", color=None, clip_on=False)
g.map(plt.axhline, y=0, linewidth=2, linestyle="-", color=None, clip_on=False)

# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
    ax = plt.gca()
    ax.text(0, .2, label, fontweight="bold", color=color,
            ha="left", va="center", transform=ax.transAxes)

g.map(label, "x")

# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.25)

# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[], xlabel="", ylabel="")
g.despine(bottom=True, left=True)
plt.show()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

flights = sns.load_dataset('flights')
pal = sns.cubehelix_palette(len(flights["year"].unique()), start=1.4, rot=-.25, light=.7, dark=.4)
g = sns.FacetGrid(flights, row="year", hue="year", aspect=20, height=.5, palette=pal)

g.map(sns.kdeplot, "passengers", bw_adjust=.6, cut=5, clip_on=False, fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "passengers", bw_adjust=.6, cut=5, clip_on=False, color="w", lw=2)
g.map(plt.axhline, y=0, linewidth=2, linestyle="-", color=None, clip_on=False)

def label(x, color, label):
    ax = plt.gca()
    ax.text(0, .1, label, fontweight="bold", color=color,
            ha="left", va="center", transform=ax.transAxes)

g.map(label, "year")
g.fig.subplots_adjust(hspace=-.7)
g.set(yticks=[], xlabel="", ylabel="", xlim=(None, 680), title="")
g.despine(bottom=True, left=True)
plt.show()

Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
Modify the values.
Run the file and check the output.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections

Dependent Libraries

matplotlibby matplotlib

Python

17559

Version:v3.7.1

License: No License (null)

matplotlib: plotting with Python

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matplotlibby matplotlib

Python 17559 Version:v3.7.1 License: No License

matplotlib: plotting with Python

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seabornby mwaskom

Python

10797

Version:v0.12.2

License: Permissive (BSD-3-Clause)

Statistical data visualization in Python

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seabornby mwaskom

Python 10797 Version:v0.12.2 License: Permissive (BSD-3-Clause)

Statistical data visualization in Python

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Reuse

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

The solution is created in Python3.11.

FAQ

What is a ridgeline plot, and how is it used in Python?

A ridgeline plot is a data visualization technique. We can use it to display the distribution of one or more variables. It consists of many overlaid density plots stacked vertically. It can create a mountain range-like appearance. In Python, we can create a ridgeline plot using the Matplotlib library. It will allow plot customization to suit the visualized data.

Can you provide an example of a ridgeline plot?

We can write an example for visualizing the temperature distribution data in Sydney yearly. The y-axis represents the temperature density values, and the x-axis represents the months. We can do it by creating a series of overlapping density plots.

How can I use Visualize Data Distributions when creating a ridgeline plot in Python?

Visualize Data Distributions to explore and understand data distribution before creating a plot. We can import the NumPy and Matplotlib libraries using their functions. It can help load and manipulate the data and then use Matplotlib to create the ridgeline plot.

Is Seaborn useful for creating ridgeline plots in Python?

Seaborn can create ridgeline plots, among other data visualizations.
Seaborn offers a high-level interface for creating aesthetic and informative data visualizations.

What is Bokeh Python Interactive Visualization Library, and what features does it have? There are useful for plotting ridgelines.

Bokeh is an interactive visualization library that allows for creating complex data visualizations. It will allow you to zoom, pan, and hover over individual data points to reveal information. We can use the Bokeh to create interactive ridgeline plots.

How do joy plots differ from traditional line graphs, and how can we use them with a ridgeline plot?

Joy plots are ridgeline plots representing data distribution as smooth histograms. They differ from line graphs that display the data distribution to the trend. We can combine the Joy plots with a ridgeline plot to compare many distributions.

How should I prepare my data to create a ridgeline plot in Python if I work with data frames?

Using the Pandas library, we can load and manipulate the data for working with data frames. We can organize the data so that each row represents a single observation. Also, every column represents a variable. We can filter and plot the data before as a ridgeline plot.

What Plotly dataset functions are available for creating ridgeline plots in Python?

Plotly offers several dataset functions that can create ridgeline plots. It includes the density trace, which creates a density plot of a single variable. Then it includes the violin trace, which creates a violin plot of a single variable. We can combine the functions to create a ridgeline plot that displays many variables.

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