Seaborn is a powerful data visualization library in Python that allows you to create informative and aesthetically pleasing plots. One of its most useful features is the ability to create subplots – multiple plots organized in a grid.
In this comprehensive guide, we will explore the ins and outs of seaborn subplots.
What are Subplots?
Subplots refer to multiple plots arranged in a grid within a single figure. They allow visualization of different variables together, making it easier to compare relationships.
For example, you may want to visualize the distribution of height and weight in your dataset using histograms. Or explore if there is a correlation between math scores and reading scores. Subplots make this possible in one go!
Why Use Seaborn Subplots?
Here are some key reasons to use seaborn subplots:
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Compare different variables: Easily visualize and compare distributions, correlations etc. between multiple variables.
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Save space: Display multiple plots in limited space by organizing into grids rather than separate figures.
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Enhanced control: Tweak subplot parameters like share x/y axes, width/height ratios etc.
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Uniform style: Apply consistent styles and themes to all subplots effortlessly.
In short, subplots boost efficiency of exploratory analysis and make conveying key insights easier.
How to Create Seaborn Subplots
There are two primary ways to create seaborn subplots:
- Using
plt.subplots()function - Using
sns.FacetGrid()
Let‘s look at them in detail with examples. For both approaches, we will use the in-built tips dataset.
import seaborn as sns
tips = sns.load_dataset("tips") 1. plt.subplots()
The plt.subplots() function from Matplotlib can create figure and axes objects that serve as subplots.
fig, ax = plt.subplots(nrows=2, ncols=2)This code creates a figure fig with a 2×2 grid of axes ax. We can then plot on these axes individually.
sns.scatterplot(data=tips, x="total_bill", y="tip", ax=ax[0,0])
sns.boxplot(data=tips, x="time", y="tip", ax=ax[0,1])We can customize the layout with parameters like sharex, sharey, squeeze etc.
2. sns.FacetGrid()
The seaborn.FacetGrid class is tailored for subplots. It takes care of much of the subplot machinery.
We specify row/column variables to create subplots based on their values.
g = sns.FacetGrid(tips, col="time", row="smoker")
g.map(sns.scatterplot, "total_bill", "tip")This generates a 2×2 grid with bill vs tip plots split by time and smoker attributes. The map() method applies a plot across all subplots.
Customizing, Styling and Enhancing Seaborn Subplots
While the defaults work well, we can customize seaborn subplots further:
- Set non-uniform row/column sizes using
height_ratios,width_ratiosarguments - Share axes between subplots with
sharex=True,sharey=True - Add subplot titles and labels clearly indicating the split variable
- Apply consistent styles, color palettes, themes using seaborn tools
- Use Matplotlib tools like
plt.supxlabel(),plt.supylabel()to add overall labels - Save with ample whitespace using
plt.tight_layout()
Small tweaks like these can hugely boost subplot clarity and visual appeal.
When to Use Seaborn Subplots (and When Not to)
Subplots shine when you need to:
- Visualize relationships between multiple pairs of variables
- Compare distribution, spread etc. across segmented data
- Showcase multivariate nature of data succinctly
However, don‘t complicate simple relationships with unnecessary subplots. And avoid cluttering subplots when insights can be presented more clearly individually.
The key is relevance – use subplots only if they aid communication of insights directly related to your analysis goals.
Next Steps and Resources
I hope this guide gives you a 360 degree perspective on seaborn subplots! Some helpful next steps:
- Try out the code examples with your own dataset
- Refer the detailed subplots documentation and other recipes
- Explore advanced customization options using Matplotlib
- Learn about other multi-plot solutions like small multiples
That wraps up this comprehensive overview. Happy subplotting!