Customizing Charts and Using Multiple Plots

Customization makes charts easier to understand and more suitable for reports, dashboards, and presentations.

Customizing Labels, Colors, and Markers

months = ["Jan", "Feb", "Mar", "Apr", "May"]

revenue = [12000, 15000, 13500, 18000, 22000]

plt.figure(figsize=(8, 4))

plt.plot(

months,

revenue,

color="green",

marker="o",

linestyle="--",

linewidth=2,

label="Revenue"

)

plt.title("Monthly Revenue Trend")

plt.xlabel("Month")

plt.ylabel("Revenue")

plt.legend()

plt.grid(True, alpha=0.3)

plt.tight_layout()

plt.show()

Here, figsize sets the figure dimensions, label defines the legend entry, and tight_layout() helps organize the chart elements.

Creating Multiple Subplots

Subplots allow multiple visualizations to appear within one figure. This is useful when comparing different measures from the same dataset.

fig, axes = plt.subplots(1, 2, figsize=(10, 4))

axes[0].bar(["A", "B", "C"], [100, 150, 120])

axes[0].set_title("Units Sold")

axes[1].plot(["Jan", "Feb", "Mar"], [12000, 15000, 18000], marker="o")

axes[1].set_title("Monthly Revenue")

plt.tight_layout()

plt.show()

The subplots() function returns the figure and axes objects. Each axis can be customized independently.

For larger figures, use a layout that keeps labels readable. Too many subplots can make comparisons difficult.