Changing plot style and color

Introduction to Data Visualization with Seaborn

Erin Case

Data Scientist

Why customize?

Reasons to change style:

Personal preference
Improve readability
Guide interpretation

Changing the figure style

Figure "style" includes background and axes
Preset options: "white", "dark", "whitegrid", "darkgrid", "ticks"
sns.set_style()

Default figure style ("white")

sns.catplot(x="age", 
            y="masculinity_important",
            data=masculinity_data,
            hue="feel_masculine",
            kind="point")

plt.show()

Point plot of masculinity survey

Figure style: "whitegrid"

sns.set_style("whitegrid")

sns.catplot(x="age", 
            y="masculinity_important",
            data=masculinity_data,
            hue="feel_masculine",
            kind="point")

plt.show()

Point plot with white grid background

Other styles

sns.set_style("ticks")

sns.catplot(x="age", 
            y="masculinity_important",
            data=masculinity_data,
            hue="feel_masculine",
            kind="point")

plt.show()

Point plot with ticks

Other styles

sns.set_style("dark")

sns.catplot(x="age", 
            y="masculinity_important",
            data=masculinity_data,
            hue="feel_masculine",
            kind="point")

plt.show()

Point plot with dark background

Other styles

sns.set_style("darkgrid")

sns.catplot(x="age", 
            y="masculinity_important",
            data=masculinity_data,
            hue="feel_masculine",
            kind="point")

plt.show()

Point plot with dark grid background

Changing the palette

Figure "palette" changes the color of the main elements of the plot
sns.set_palette()
Use preset palettes or create a custom palette

Diverging palettes

Four example diverging palettes

Example (default palette)

category_order = ["No answer", 
                  "Not at all",
                  "Not very", 
                  "Somewhat", 
                  "Very"]

sns.catplot(x="how_masculine",
            data=masculinity_data,
            kind="count",
            order=category_order)

plt.show()

Count plot of survey responses

Example (diverging palette)

sns.set_palette("RdBu")

category_order = ["No answer", 
                  "Not at all",
                  "Not very", 
                  "Somewhat", 
                  "Very"]

sns.catplot(x="how_masculine",
            data=masculinity_data,
            kind="count",
            order=category_order)

plt.show()

Count plot with diverging palette

Sequential palettes

Four example sequential palettes

Sequential palette example

Scatter plot of horsepower vs. mpg with sequential palette

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

Custom palettes

custom_palette = ["red", "green", "orange", "blue",
                  "yellow", "purple"]

sns.set_palette(custom_palette)

Custom color name palette

Custom palettes

custom_palette = ['#FBB4AE', '#B3CDE3', '#CCEBC5', 
                  '#DECBE4', '#FED9A6', '#FFFFCC', 
                  '#E5D8BD', '#FDDAEC', '#F2F2F2']

sns.set_palette(custom_palette)

Custom hex code palette

Changing the scale

Figure "context" changes the scale of the plot elements and labels
sns.set_context()
Smallest to largest: "paper", "notebook", "talk", "poster"

Default context: "paper"

sns.catplot(x="age", 
            y="masculinity_important",
            data=masculinity_data,
            hue="feel_masculine",
            kind="point")

plt.show()

Point plot with notebook context

Larger context: "talk"

sns.set_context("talk")

sns.catplot(x="age", 
            y="masculinity_important",
            data=masculinity_data,
            hue="feel_masculine",
            kind="point")

plt.show()

Point plot with larger context

Let's practice!

Introduction to Data Visualization with Seaborn