Scatterplot
A scatter plot displays the relationship between 2 numeric variables, one being displayed on the X axis (horizontal) and the other on the Y axis (vertical). Each data point is represented as a circle.
Several tools allow to build scatterplots in python. Seaborn is probably the most straightforward library for the job, but matplotlib allows a greater level of customization. If you are looking for an interactive chart, plotly is definitely the way to go.
This page provides many examples of scatterplots made with those Python tools, going from simple examples to highly customized versions.
⏱ Quick start (Seaborn)
The scatterplot() function of the Seaborn library is definitely the best way to build a scatterplot in seconds. 🔥
Simply pass a numeric column of a data frame to both the x and y variable and the function will handle the rest.
# library & dataset
import seaborn as sns
df = sns.load_dataset('iris')
# use the function scatterplot() to make a scatterplot
sns.scatterplot(x=df["sepal_length"], y=df["sepal_width"])⚠️ Scatterplot and overplotting
The main danger with scatterplots is overplotting. When the sample size gets big, circles tend to overlap, making the figure unreadable.
Several workarounds exist to fix the issue, like using opacity or switching to another chart type:
Basic boxplot. You can quickly read the median, quartiles and outliers of each group.
If you add individual points with jitter, a bimodal distribution appears for group B
If you have a very large dataset, the violin plot is a better alternative than jittering
Scatterplots with Seaborn
Seaborn is a python library allowing to make better charts easily. The regplot() function should get you started in minutes. The first example below explains how to build the most basic scatterplot with python. Then, several types of customization are described: adding a regression line, tweaking markers and axis, adding labels and more.
Most basic scatterplot with seaborn
Customize markers: color, size, opacity, type and more
Color dots according to a group
Control X and Y axes limits
Control the marker's color according to their coordinates
Add labels manually on your scatterplot
A nice way to add info and highlight trend in a scatter plot is to add a regression line on top of the dots. Thanks to its regplot() and lmplot() function, it's quite easy!
The main difference between those 2 functions are that:
regplot()is used for simple scatter plot with a trend linelmplot()can also be used for facetting: e.g creating multiple scatter plots
🔎 scatterplot() function parameters → see full doc
→ Description
The scatterplot() function of seaborn creates a scatter plot to visualize the relationship between two continuous variables. It displays each observation as a point on a two-dimensional plane.
→ Arguments
Description
Dataframe-like (pandas, numpy, polars...) with the columns we want to plot.
Possible values → dataframe
It just has to be a pandas.DataFrame (columns are variables), numpy.ndarray (rows/columns are variables), or any mapping/sequence (dictionaries/lists)
Supports both long-form (each variable in its own column) and wide-form (variables in separate columns; reshaped internally).
Code Example
# Library & Dataset
import seaborn as sns
df = sns.load_dataset('iris')
# Plot
sns.scatterplot(
data=df,
x='sepal_length',
y='sepal_width'
)
plt.show()💡 Other charts involving scatterplots and Seaborn
If you are interested in scatterplots, some other chart could be useful to you.
A scatterplot with marginal distribution allows to check the distribution of both the x and y variables. A correlogram allows to check the relationship between each pair of numeric variables in a dataset.
⏱ Quick start (Matplotlib)
Matplotlib also requires only a few lines of code to draw a scatterplot thanks to its plot() function. The resulting chart is not as good-looking, but the function probably offers more flexibility in term of customization.
# libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Create a dataset:
df=pd.DataFrame({'x_values': range(1,101), 'y_values': np.random.randn(100)*15+range(1,101) })
# plot
plt.plot( 'x_values', 'y_values', data=df, linestyle='none', marker='o')
plt.show()
Scatterplots with Matplotlib
Matplotlib is another great alternative to build scatterplots with python. As often, it takes a bit more lines of code to get a decent chart, but allows more customization.
The examples below should get you covered for all the most common problems: adding markers, adding labels, changing shapes and more.
Most basic scatterplot with Matplotlib
Customize marker features: size, color, stroke, type and more
Display a math equation on your matplotlib chart
Matplotlib: add horizontal and vertical ablines
Highlight a specific area of the chart with Matplotlib
Add an annotation to highlight a specific marker
How to build a Manhattan plot with Python and Matplotlib
Scatterplot with grouping and legend
Legend customization: how to control location, add a background, customize labels and markers and more
How to use a log scale for the X or the Y axis.
How to compute and add a linear regression to a scatterplot with Python and matplotlib
Linear regression with statistics on top of a scatterplot
Customized linear regression with statistics on top of a scatterplot
Subplot, title, and margin customization
How to add an img into a matplotlib chart
Link title and scatter plot with custom annotations
Scatterplots with Plotly
If you are looking for an interactive scatterplot, plotly is definitely the way to go. Try hovering over the graph below!
Interactivity is a real plus for scatterplots. It is very useful to have a tooltip associated to every markers to get some additional information about it. Zooming on a specific area of the scatterplot is also very valuable sometimes.
The examples below should help you get started quickly with the plotly API:
Scatterplots with Pandas
Pandas, a data analysis library, also offers functions to build scatterplots. It uses matplotlib under the hood, but the syntax is more concise.
The main difference is that we have to work with Pandas objects such as Series and DataFrame.
The examples below should help you get started quickly with the pandas API:
Scatterplots with Plotnine
Plotnine is a python library allowing to make charts using the grammar of graphics principles. The geom_point() function should get you started in minutes.
The examples below should help you get started quickly with the plotnine API:
Best python scatterplot examples
The web is full of astonishing charts made by awesome bloggers, (often using R). The Python graph gallery tries to display (or translate from R) some of the best creations and explain how their source code works.
The first example below demos how to add clean labels on a scatterplot, automatically avoiding overlapping. It also explains how to control background, fonts, titles and more.
If you want to display your work here, please drop me a word or even better, submit a Pull Request!
Learn how to use the adjustText library to add many labels with no overlaps.
Scatterplot with regression fit, group colors and non-overlapping labels.
Dark mode scatterplot with custom annotations, title and explanations. All of that with a very good appearance!
A scatter plot with images inside each marker to provide additional context
A scatter plot with custom annotations and colors, with some markers being circled
A scatter plot with custom annotations and specific highlights
Related chart types
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