A Comprehensive Guide to Named Colors in Matplotlib

Introduction

Matplotlib, a popular plotting library in Python, offers multiple ways to specify colors in plots, with named colors being a convenient option. However, many users are only familiar with a basic set of colors, overlooking the extensive collections available. This article provides an in-depth analysis of named colors in Matplotlib, covering everything from fundamental sets to expanded options, and includes code examples to help readers fully utilize these resources.

Overview of Color Sets

Matplotlib supports several named color collections, each with specific uses and origins:

• BASE_COLORS: A small set of basic colors represented by single-letter abbreviations such as 'b' for blue and 'g' for green, commonly used for quick plotting.
• CSS4_COLORS: A comprehensive set based on the CSS4 specification, including names like 'aliceblue' and 'antiquewhite', offering a wide range of color choices.
• XKCD_COLORS: Colors from the xkcd color survey, accessible via the 'xkcd:' prefix, e.g., 'xkcd:sky blue', with nearly 1000 colors suitable for diverse applications.
• TABLEAU_COLORS: Default colors from Tableau software, available with the 'tab:' prefix, such as 'tab:green', consisting of 10 distinct colors often used in business visualizations.

Additionally, colors can be specified using HTML hex codes, e.g., '#FF0000' for red, allowing for unlimited customization.

Code Examples for Accessing Named Colors

To retrieve the list of named colors in Matplotlib, use the matplotlib.colors module. The following code demonstrates how to print all color names and their hexadecimal values from the CSS4 set:

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors

for name, hex in mcolors.CSS4_COLORS.items():
    print(name, hex)

This code iterates through the CSS4_COLORS dictionary and outputs each color name and hex value, enabling users to quickly review available options for their plots.

Visualizing Named Colors with a Color Table

For a better understanding of color sets, visualizing them through a color table is effective. The code below defines a function plot_colortable that sorts colors by HSV values and generates a chart with color samples and names:

import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
import math

def plot_colortable(colors, ncols=4, sort_colors=True):
    cell_width = 212
    cell_height = 22
    swatch_width = 48
    margin = 12

    if sort_colors:
        by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
                        for name, color in colors.items())
        sorted_names = [name for hsv, name in by_hsv]
    else:
        sorted_names = list(colors.keys())

    n = len(sorted_names)
    nrows = math.ceil(n / ncols)
    width = cell_width * ncols + 2 * margin
    height = cell_height * nrows + 2 * margin
    dpi = 72

    fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)
    fig.subplots_adjust(left=margin/width, bottom=margin/height,
                        right=(width-margin)/width, top=(height-margin)/height)
    ax.set_xlim(0, cell_width * ncols)
    ax.set_ylim(cell_height * (nrows - 0.5), -cell_height / 2.0)
    ax.set_axis_off()

    for i, name in enumerate(sorted_names):
        row = i % nrows
        col = i // nrows
        y = row * cell_height
        swatch_start_x = cell_width * col
        swatch_end_x = swatch_start_x + swatch_width
        text_pos_x = swatch_start_x + swatch_width + 7

        ax.text(text_pos_x, y, name, fontsize=14,
                horizontalalignment='left', verticalalignment='center')
        ax.hlines(y, swatch_start_x, swatch_end_x,
                  color=colors[name], linewidth=18)

    return fig

# Example: Plot the CSS4 color set
fig = plot_colortable(mcolors.CSS4_COLORS)
plt.show()

This function creates a table with color samples and names, grouped by similarity for easy browsing. Users can adjust parameters like the number of columns or sorting method to suit their visualization needs.

Using Additional Color Sets

Beyond standard sets, Matplotlib allows access to extra colors via prefixes. For example, using XKCD colors:

plt.plot([1, 2], lw=4, c='xkcd:baby poop green')

Using Tableau colors:

plt.plot([1, 2], lw=4, c='tab:green')

And using HTML hex codes:

plt.plot([1, 2], lw=4, c='#8f9805')

These methods enhance color flexibility, enabling precise control over plot aesthetics. In practice, combining these options can lead to more engaging and informative visualizations.

Conclusion

Matplotlib provides a rich array of named colors through various collections and specification methods, from basic sets to expanded options like XKCD and Tableau colors, as well as custom hex codes. This guide's code examples and explanations aim to help users efficiently leverage these resources. It is recommended to experiment with different color combinations in practice to optimize visual impact. For more details, refer to the official Matplotlib documentation and source code.