Matplotlib Axis Ticks and Formatters: Make Scales Readable

Name

Amber de Ligt

Updated on 11/21/2025

Crowded ticks, overlapping dates, and unrounded numbers make plots look noisy and mislead readers. When ticks land randomly, context disappears and trends get buried. The cure is simple: explicitly choose locators (where ticks go) and formatters (how ticks look) so every axis tells a clear story.

Locator vs formatter at a glance

Numeric axes: control spacing and labels

import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FuncFormatter
import numpy as np
 
x = np.linspace(0, 50, 200)
y = np.log1p(x) * 3.2
 
fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(x, y, color="tab:blue")
 
# Place ticks every 10 units on x, every 1.5 on y
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.yaxis.set_major_locator(MultipleLocator(1.5))
 
# Custom label with units
ax.yaxis.set_major_formatter(FuncFormatter(lambda val, _: f"{val:.1f} dB"))
 
ax.set_xlabel("Samples")
ax.set_ylabel("Signal (dB)")
ax.grid(True, axis="both", linestyle="--", alpha=0.3)
plt.tight_layout()
plt.show()

Tips:

• Use MaxNLocator(nbins=6) when data ranges vary; it keeps the axis neat across plots.
• Pair formatter and locator: formatters do not change tick positions, only text.
• Keep units in labels or formatters, not both, to avoid duplication.

Dates: prevent overlap and keep context

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
 
dates = pd.date_range("2024-01-01", periods=120, freq="D")
values = pd.Series(range(len(dates))).rolling(7, min_periods=1).mean()
 
fig, ax = plt.subplots(figsize=(8, 4))
ax.plot(dates, values, color="tab:green")
 
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
ax.xaxis.set_minor_locator(mdates.WeekdayLocator(byweekday=mdates.MO, interval=2))
 
plt.setp(ax.get_xticklabels(), rotation=30, ha="right")
ax.set_ylabel("7-day avg")
ax.grid(True, which="both", axis="x", linestyle=":", alpha=0.4)
plt.tight_layout()
plt.show()

Tips:

• Use minor locators (e.g., weekly) for subtle grid context without label clutter.
• Rotate date labels 30–45° with ha="right" to prevent overlap on dense ranges.
• For mixed time spans, switch to AutoDateLocator + ConciseDateFormatter to let Matplotlib adapt.

Dual axes: keep ticks aligned

When using twinx/secondary_yaxis, apply locators and formatters separately to each axis so colors, ticks, and units stay consistent. Align legends as shown in the secondary axis guide.

Quick debugging checklist

• Random tick jumps? Set a deterministic locator (e.g., MultipleLocator).
• Labels clipped? Enable constrained_layout=True or add plt.tight_layout().
• Too many ticks? Drop to MaxNLocator(nbins=5) or hide minors with ax.tick_params(which="minor", length=0).