Sometimes it is useful to add an extra dimension to a plot, especially to see whether there are some clusters forming.

To do so, you will first have to transform the categories into numbers and then set the color as the category number.

Here is the example

# For our DataFrame
import pandas as pd

# In order to plot
import matplotlib.pyplot as plt

# We get our sample data from github
df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')

# We transform text categorical variables into numerical variables
df["species_codes"] = pd.Categorical(df["species"]).codes

# We setup our figure
fig, axes = plt.subplots(1,1, figsize=(6,5))

# We plot the scatter with c = our categorical variables 
axes.scatter(data=df,
            x="petal_length",
            y="sepal_length",
            c="species_codes")
            
# We plot the grid            
axes.grid()

# We add better labels
axes.set_xlabel("Petal Length")
axes.set_ylabel("Sepal Length")

# We set the title
axes.set_title("How to plot categorical variable as color with Matplotlib")

# We tidy things up
plt.tight_layout()

# We plot our data
plt.show()

The code

Here is the result

More on plots

If you want to know more about how to add labels, plot different types of plots, etc... check out the other articles I wrote on the topic, just here :

Matplotlib - The Python You Need

We gathered the only Python essentials that you will probably ever need.

The Python You Need

How to plot categorical variables as color with Matplotlib and Pandas

Land Your First Data Science Job

Here is the result

More on plots

Land Your First Data Science Job

Related Articles

Master Data Science in Days, Not Months 🚀

Related Articles

Getting Started
3 min read
How long does it take to learn Python for Data Science in 2023
Learn how long it takes to learn Python for Data Science in 2021. What does it take to be a Data Scientist in 2021?
4/18/2023Read More

Matplotlib
2 min read
How to make AMAZING graphs in Python that everyone will understand
Data visualization helps to turn complex data into actionable insights. To make effective and self-explanatory graphs, keep it simple, write labels and legends according to your reader, and choose the right library. Clear data visualization is a valuable tool for data analysis and communication.
4/7/2023Read More

Data Visualization
1 min read
How to plot in Python using Matplotlib
Create graphs in Python using matplotlib, a library providing functions for creating various charts and plots. Example: plt.plot(x, y); plt.xlabel("X Axis"); plt.ylabel("Y Axis"); plt.title("Line Plot"); plt.show() to create a simple line plot."
3/23/2023Read More