How do you add annotations and text to Matplotlib charts?

Annotations turn a chart into a story — highlighting a key data point, marking a threshold, or labelling significant events on a timeline. Matplotlib provides ax.annotate() for arrow-and-text annotations and ax.text() for free-form text placement.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 200)
y = np.sin(x) * np.exp(-x / 5)

fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(x, y, color='steelblue', linewidth=2)

# Find and annotate the maximum
peak_idx = np.argmax(y)
px, py   = x[peak_idx], y[peak_idx]

ax.annotate(
    f'Peak: ({px:.2f}, {py:.2f})',
    xy=(px, py),              # point to annotate
    xytext=(px + 1.5, py),    # where the text goes
    arrowprops=dict(
        arrowstyle='->',
        color='darkred',
        lw=1.5,
    ),
    fontsize=11,
    color='darkred',
)

# Free-form text label
ax.text(0.5, 0.9, 'Damped oscillation',
        transform=ax.transAxes,   # axes-relative coords (0–1)
        fontsize=12, ha='center',
        bbox=dict(boxstyle='round,pad=0.3', fc='lightyellow', ec='grey'))

# Threshold line with label
ax.axhline(y=0.5, color='orange', linestyle='--', linewidth=1)
ax.text(9.5, 0.52, 'threshold=0.5', color='orange', ha='right', fontsize=9)

ax.set(title='Annotated Damped Sine', xlabel='x', ylabel='y')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout(); plt.show()

The two coordinate systems matter: xy in annotate uses data coordinates by default (values from your actual data range). Passing transform=ax.transAxes to ax.text() switches to axes-fraction coordinates (0,0 = bottom-left, 1,1 = top-right) — useful for fixed-position labels that stay put when the data range changes.

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