Matplotlib Named Colors

Matplotlib Named Colors

Matplotlib is a popular Python library used for creating visualizations, and it provides a wide range of named colors that you can use in your plots. Using named colors can make your plots more visually appealing and easier to read. In this article, we will explore some of the named colors available in Matplotlib and show you how to use them in your plots.

Overview of Named Colors

Matplotlib provides a dictionary called matplotlib.colors.CSS4_COLORS that contains a large set of named colors. Each named color is associated with a specific RGB value. You can access these named colors using their respective keys.

Example Code:

import matplotlib.pyplot as plt

# Print the first 10 named colors
print(list(plt.rcParams['axes.prop_cycle'].by_key()['color'])[:10])

Using Named Colors in Plots

You can use named colors in Matplotlib plots by passing the color’s name as a string to the color parameter in plotting functions like plot() or scatter().

Example Code:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [1, 4, 9, 16], color='aqua')
plt.show()

Output:

Matplotlib Named Colors

Specifying Named Colors Using Hex Values

If you prefer using hex values to specify colors, you can easily convert a named color to its corresponding hex value using the matplotlib.colors.to_hex() function.

Example Code:

import matplotlib.colors as mcolors

color_hex = mcolors.to_hex('deepskyblue')
print(color_hex)

Output:

Matplotlib Named Colors

Creating Custom Color Maps with Named Colors

You can also create custom color maps using a combination of named colors. This can be useful for creating color gradients in your plots.

Example Code:

import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

colors = ['blue', 'green', 'red']
cmap = LinearSegmentedColormap.from_list('custom_cmap', colors, N=100)

plt.imshow([[0, 1], [0, 1]], cmap=cmap)
plt.colorbar()
plt.show()

Output:

Matplotlib Named Colors

Using Named Colors in Stylesheets

Matplotlib also provides predefined stylesheets that allow you to easily change the overall look of your plots. These stylesheets include presets for colors, fonts, and other visual elements.

Example Code:

import matplotlib.pyplot as plt

plt.style.use('ggplot')
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.show()

Output:

Matplotlib Named Colors

Customizing Named Colors

If you want to customize the existing named colors or create new ones, you can do so by modifying the matplotlib.colors.CSS4_COLORS dictionary.

Example Code:

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

# Add a new named color
mcolors.CSS4_COLORS['purple'] = '#800080'

plt.plot([1, 2, 3, 4], [1, 4, 9, 16], color='purple')
plt.show()

Output:

Matplotlib Named Colors

Conclusion

In this article, we explored the use of named colors in Matplotlib and how you can leverage them to enhance the visual appeal of your plots. By using named colors, you can create more visually appealing and easily customizable plots for your data visualizations. Experiment with different named colors and color combinations to find the style that best suits your needs.

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