Matplotlib Axes Configuration

📏 Limits

ax.set_xlim(0, 100)
ax.set_ylim(-10, 10)
ax.set_xlim(left=0)          # only set one bound
ax.autoscale()                  # let matplotlib auto-fit to data

🎯 Ticks

ax.set_xticks([0, 25, 50, 75, 100])
ax.set_xticklabels(["A", "B", "C", "D", "E"])
ax.set_xticks([0, 25, 50, 75, 100], ["A","B","C","D","E"])   # combined, newer syntax
 
ax.tick_params(axis="x", rotation=45)             # rotate labels
ax.tick_params(axis="both", labelsize=10)
ax.tick_params(axis="y", direction="in")             # tick marks pointing inward

📐 Scale

ax.set_xscale("log")
ax.set_yscale("log")
ax.set_yscale("symlog")      # log scale that also handles negative/zero values

Use log scale when data spans multiple orders of magnitude — otherwise small values get visually crushed near zero.

🖼️ Spines (the box border)

ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_position(("outward", 10))   # offset a spine
ax.spines["bottom"].set_color("gray")

Removing top/right spines is a common, clean styling choice ("open" look).

🪟 Twin axes — two y-scales sharing one x-axis

fig, ax1 = plt.subplots()
ax2 = ax1.twinx()          # shares x-axis, independent y-axis
 
ax1.plot(x, temperature, color="red")
ax1.set_ylabel("Temperature", color="red")
 
ax2.plot(x, humidity, color="blue")
ax2.set_ylabel("Humidity", color="blue")

Twin axes with two different scales can mislead viewers into false correlation. Label both axes clearly and color-code the lines to match.

twiny() = same idea but shared y, independent x.

🔳 Aspect ratio

ax.set_aspect("equal")       # 1 unit x = 1 unit y (important for maps, circles)
ax.set_aspect("auto")           # default, stretches to fit figure

📍 Origin / invert axis

ax.invert_yaxis()      # useful for e.g. depth or ranking plots (1st at top)
ax.invert_xaxis()

🔤 Formatting tick labels

from matplotlib.ticker import FuncFormatter, PercentFormatter
 
ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f"${x:,.0f}"))
ax.yaxis.set_major_formatter(PercentFormatter(xmax=1))

🔗 Next

Matplotlib Annotations & Text · Matplotlib Colors & Colormaps