🎨 Seaborn Styling¶

Theming (changing the style) is a powerful feature in Seaborn that allows users to control the aesthetics of plots easily. Here's a tutorial that dives into Seaborn theming: Seaborn provides options to control the style and palette of your plots. With theming, you can ensure that your visualizations align with specific aesthetics, whether it's a publication, a presentation, or for personal preference.

First, let's import the libraries we'll be using:

In [10]:

import seaborn as sns
import matplotlib.pyplot as plt

import seaborn as sns import matplotlib.pyplot as plt

1. Setting Plot Styles¶

Seaborn has a set of predefined styles: darkgrid, whitegrid, dark, white, and ticks. These styles adjust the background and the grid lines of your plot.

Usage:

In [1]:

# Set the style
sns.set_style("whitegrid")

# Sample plot
tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

# Set the style sns.set_style("whitegrid") # Sample plot tips = sns.load_dataset("tips") sns.boxplot(x="day", y="total_bill", data=tips) plt.show()

2. Removing Axis Spines¶

If you want a cleaner look, especially with the white and ticks styles, you can remove the top and right axis spines:

In [11]:

sns.set_style("ticks")
sns.despine()

# Sample plot
sns.boxplot(x="day", y="total_bill", data=tips)

sns.set_style("ticks") sns.despine() # Sample plot sns.boxplot(x="day", y="total_bill", data=tips)

Out[11]:

<Axes: xlabel='day', ylabel='total_bill'>

3. Context Functions¶

Seaborn allows you to set contexts which control the scale of the plot. Available contexts are: paper, notebook, talk, and poster. The notebook context is the default.

Usage:

In [3]:

sns.set_context("talk")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

sns.set_context("talk") sns.boxplot(x="day", y="total_bill", data=tips) plt.show()

4. Color Palettes¶

Seaborn provides a variety of color palettes, which are essential especially for distinguishing between multiple categories or values.

• Sequential Palettes: Colors go from light to dark shades. Useful for data that has a consistent range from low to high.

In [4]:

sns.color_palette("Blues")

sns.color_palette("Blues")

Out[4]:

• Diverging Palettes: Colors diverge from a central color to two different colors. Useful for data that has a notable midpoint.

In [5]:

sns.color_palette("RdBu_r")

sns.color_palette("RdBu_r")

Out[5]:

• Categorical Palettes: Distinct colors useful for categorical data.

In [6]:

sns.color_palette("husl", 8)

sns.color_palette("husl", 8)

Out[6]:

Setting the Palette:

In [7]:

sns.set_palette("husl")

sns.set_palette("husl")

5. Customizing with set()¶

The set() function in Seaborn allows you to customize various aspects in one go:

In [8]:

sns.set(style="darkgrid", palette="muted", context="talk")

sns.set(style="darkgrid", palette="muted", context="talk")

6. Temporary Styling:¶

If you want to change the style for just one particular plot without altering the global settings, use the axes_style() and color_palette() functions in a with block:

In [9]:

with sns.axes_style("white"), sns.color_palette("husl"):
    sns.boxplot(x="day", y="total_bill", data=tips)
    plt.show()

with sns.axes_style("white"), sns.color_palette("husl"): sns.boxplot(x="day", y="total_bill", data=tips) plt.show()

Conclusion:

Seaborn's theming capabilities allow you to have more control over the aesthetics of your visualizations. A well-styled plot not only looks good but can also make your data stories clearer and more compelling.