Matplotlib Customization

Colors, styles, labels — making charts look professional.

Matplotlib Customization

A default matplotlib plot works, but a customized one communicates. Let's learn how to make your charts look professional — with colors, labels, legends, and styles that actually tell a story.

Colors and Styles

Matplotlib comes with built-in styles that instantly improve the look of your charts. You can also customize colors manually.


import matplotlib.pyplot as plt

plt.style.use("seaborn-v0_8-whitegrid")

x = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
y2 = [1, 3, 5, 7, 9]

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(x, y1, color="#e74c3c", linewidth=2, label="Product A")
ax.plot(x, y2, color="#3498db", linewidth=2, linestyle="--", label="Product B")
ax.legend()
plt.show()

Use plt.style.available to see all available styles. The color parameter accepts hex codes, named colors, and RGB tuples.

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Subplots

Sometimes you need multiple charts side by side. Subplots let you create a grid of plots in one figure.


import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(1, 3, figsize=(15, 5))
x = np.linspace(0, 10, 50)

axes[0].plot(x, np.sin(x), color="red")
axes[0].set_title("Sine")

axes[1].plot(x, np.cos(x), color="blue")
axes[1].set_title("Cosine")

axes[2].plot(x, np.tan(x), color="green")
axes[2].set_title("Tangent")
axes[2].set_ylim(-5, 5)

plt.tight_layout()
plt.show()

plt.tight_layout() prevents overlapping labels. Always use it when you have multiple subplots.

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Annotations and Highlights

Annotations draw attention to specific data points. They turn a good chart into a great one by guiding the viewer's eye.


import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 24, 36, 40, 50]

fig, ax = plt.subplots()
ax.plot(x, y, marker="o")
ax.annotate("Peak", xy=(4, 40), xytext=(2, 45),
            arrowprops=dict(arrowstyle="->", color="red"))
ax.set_title("Sales with Annotation")
plt.show()

The arrowprops dictionary controls the arrow's appearance. Use annotations to highlight key insights in your data.

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Saving Plots

Once your plot looks perfect, save it as a file. Matplotlib supports PNG, SVG, PDF, and more.


import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.bar(["A", "B", "C"], [10, 20, 15])
ax.set_title("Sales Report")

fig.savefig("sales_chart.png", dpi=300, bbox_inches="tight")
plt.show()

The dpi parameter controls resolution — 300 is print quality. bbox_inches="tight" removes excess whitespace around the chart.

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Key Takeaways

Built-in styles like "seaborn-v0_8-whitegrid" instantly improve appearance
Subplots let you create multiple charts in one figure
Annotations guide the viewer to important data points
savefig() exports charts with configurable resolution

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