Matplotlib: Change Scatter Plot Marker Size

Introduction

Matplotlib is one of the most widely used data visualization libraries in Python. Much of Matplotlib's popularity comes from its customization options - you can tweak just about any element from its hierarchy of objects.

In this tutorial, we'll take a look at how to change the marker size in a Matplotlib scatter plot.

Import Data

We'll use the World Happiness dataset, and compare the Happiness Score against varying features to see what influences perceived happiness in the world:

import pandas as pd

df = pd.read_csv('worldHappiness2019.csv')

Then, we can easily manipulate the size of the markers used to represent entries in this dataset.

Change Marker Size in Matplotlib Scatter Plot

Let's start off by plotting the generosity score against the GDP per capita:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('worldHappiness2019.csv')

fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(x = df['GDP per capita'], y = df['Generosity'])
plt.xlabel("GDP per Capita")
plt.ylabel("Generosity Score")

plt.show()

This results in:

matplotlib scatter plot

Now, let's say we'd like to increase the size of each marker, based on the perceived happiness of the inhabitants of that country. The happiness score is a list, coming straight from the df, so this can work with other lists as well.

To change the size of the markers, we use the s argument, for the scatter() function. This will be the markersize argument for the plot() function:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('worldHappiness2019.csv')

fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(x = df['GDP per capita'], y = df['Generosity'], s = df['Score']*10)
plt.xlabel("GDP per Capita")
plt.ylabel("Generosity Score")

plt.show()

We've also multiplied the value of each element in the list by an arbitrary number of 25, because they're ranked from 0..1. This will produce really small markers if we use them in their original values.

This now results in:

matplotlib change scatter plot marker size

Or better yet, instead of crudely multiplying everything by 25, since the values are similar anyway, we can do something like this:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('worldHappiness2019.csv')

size = df['Score'].to_numpy()
s = [3*s**2 for s in size]

fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(x = df['GDP per capita'], y = df['Generosity'], s = s)
plt.xlabel("GDP per Capita")
plt.ylabel("Generosity Score")

plt.show()

It's important to have the s list the same length as x and y, as each value from s now gets applied to them. If the list is shorter or longer, the code will break.

Here, we've extracted the values from the Score column, scaled them and applied the size back to the scatter plot:

scaled marker size scatter plot matplotlib

Set Global Marker Size in Matplotlib Scatter Plot

If you'd like to detach the marker size from some variable, and would just like to set a standard, global size of markers in the scatter plot, you can simply pass in a single value for s:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('worldHappiness2019.csv')

fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(x = df['GDP per capita'], y = df['Generosity'], s = 100)
plt.xlabel("GDP per Capita")
plt.ylabel("Generosity Score")

plt.show()

This now results in:

global marker size matplotlib scatter plot

Conclusion

In this tutorial, we've gone over how to change the marker size in a Matplotlib Scatter Plot.

If you're interested in Data Visualization and don't know where to start, make sure to check out our book on Data Visualization in Python.

Data Visualization in Python, a book for beginner to intermediate Python developers, will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair.