Seaborn is a fantastic library for creating visually appealing plots in Python, but sometimes its built-in styling can be a bit too opinionated. If you’re used to Matplotlib’s default style and want your Seaborn bar plots to match it, you might run into some challenges. Simply toggling a few settings often isn’t enough, as Seaborn overrides many default behaviors. This guide will walk you through how to fully revert Seaborn’s theme to Matplotlib’s default styling, ensuring consistency in your visualizations.
Why Seaborn Alters Matplotlib Defaults
Seaborn is built on top of Matplotlib and automatically modifies its styling to offer a more modern and aesthetically pleasing look. This includes changes in color palettes, figure backgrounds, axis styles, and even font settings. While this makes plots look good out of the box, it can cause inconsistencies when switching between Matplotlib and Seaborn in the same project.
For those who rely on traditional Matplotlib styles for documentation, printing, or personal preference, reverting Seaborn’s settings is necessary.
How Seaborn Overrides Matplotlib Defaults
When Seaborn is imported and used, it applies a default theme using the sns.set_theme() function. This imparts specific colors, grid styles, and background settings that override Matplotlib’s standard appearance. Seaborn also provides different themes like 'darkgrid', 'whitegrid', 'dark', 'white', and 'ticks'—all of which modify Matplotlib’s visuals significantly.
A bar plot using Seaborn’s default settings, for instance, will have:
- A gray background (instead of Matplotlib’s white)
- Grid lines with different thickness and opacity
- Bolded axis labels and different font choices
This is great for presentations but may not always be ideal for reports or publications where consistency with Matplotlib’s look is required.
Reverting Seaborn Themes to Matplotlib Default
To restore Matplotlib’s styling, three primary methods can be used:
1. Using sns.set_style()
Seaborn provides an option to manually adjust the theme to a simpler style. Setting it to 'white' gets rid of the default gray background and returns the plot closer to Matplotlib’s generic appearance.
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("white") # Removes background styling
# Sample Bar Plot
data = {'Category': ['A', 'B', 'C'], 'Values': [10, 15, 7]}
sns.barplot(x=data['Category'], y=data['Values'])
plt.show()
2. Resetting Seaborn Parameters with sns.set()
If the goal is to revert all changes Seaborn has made (not just the background), sns.set() is a better alternative. This effectively resets Seaborn to behave as if it weren’t modifying anything.
sns.set() # Resets Seaborn to default settings (similar to Matplotlib)
3. Using sns.reset_defaults()
For a more complete reset, this method returns Seaborn settings to their initial default state, as if the library had never overridden Matplotlib in the first place.
sns.reset_defaults()
This ensures that even more subtle changes, like font sizes and axis label styles, are reverted properly.
Testing the Changes
Let’s compare how the bar plot looks before and after reverting Seaborn’s theme.
import seaborn as sns
import matplotlib.pyplot as plt
# Sample Data
data = {'Category': ['A', 'B', 'C'], 'Values': [10, 15, 7]}
# Plot with Seaborn’s Default Theme
sns.set_theme() # This applies Seaborn's default theme
plt.figure(figsize=(6,4))
sns.barplot(x=data['Category'], y=data['Values'])
plt.title("Seaborn Default Style")
plt.show()
# Reverting to Matplotlib Default
sns.reset_defaults()
plt.figure(figsize=(6,4))
sns.barplot(x=data['Category'], y=data['Values'])
plt.title("Matplotlib Default Style")
plt.show()
After running this, you’ll notice that the second plot has a plain white background, thinner grid lines, and a more Matplotlib-like appearance.
Why Revert to Matplotlib’s Default Style?
Using Matplotlib’s default style has several advantages:
- Consistency: If the rest of your project follows Matplotlib’s style, having uniform visuals is beneficial.
- Print and Publication Readiness: Some journals and reports require the classic Matplotlib design to maintain visual clarity.
- Customization: While Seaborn locks in its aesthetic choices, Matplotlib offers more flexibility for fine-tuned styling.
That said, if your goal is enhanced aesthetics without much customization effort, sticking with Seaborn’s built-in themes isn’t a bad choice either.
Common Issues When Reverting Seaborn Styling
Sometimes, simply resetting Seaborn defaults may not be enough, especially if you’ve applied additional styling manually. Here are a few things to watch out for:
1. Seaborn Styles Persist Even After Reset
If resetting doesn’t seem to work, explicitly set Matplotlib’s default parameters:
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
2. Fonts and Labels Look Different
Seaborn modifies font styles, so resetting may not return everything exactly as before. Set fonts explicitly:
mpl.rcParams['font.family'] = 'sans-serif'
3. Overlapping Seaborn and Matplotlib Imports
Even after resetting, if you import Seaborn again later in your script and use sns.set_theme(), the effects will be reapplied. Always reset styles after all Seaborn imports.
Wrapping Up
Reverting Seaborn’s theme to Matplotlib’s default styling is straightforward with sns.reset_defaults(), sns.set(), or sns.set_style(). If you’re aiming for consistency in your visualizations, particularly for professional reports or research papers, knowing how to control these styling changes is beneficial.
Do you prefer Matplotlib’s classic style, or do you stick with Seaborn’s modern themes? Let me know in the comments! and make it publish-ready.
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