Grasp frequency at a glance with a barcode timeline

Last updated 2020-08-26 Read time 1 min

When only the event dates matter, a barcode timeline is a compact way to show density with vertical lines. It makes period bias and bursts visible at a glance.

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import pandas as pd
import matplotlib.pyplot as plt

dates = pd.to_datetime(
    [
        "2024-01-05",
        "2024-01-08",
        "2024-01-12",
        "2024-01-20",
        "2024-02-02",
        "2024-02-07",
        "2024-02-08",
        "2024-02-17",
        "2024-03-01",
        "2024-03-09",
        "2024-03-10",
        "2024-03-24",
        "2024-04-02",
        "2024-04-18",
        "2024-05-01",
    ]
)

fig, ax = plt.subplots(figsize=(6.4, 1.8))
ax.vlines(dates, ymin=0, ymax=1, color="#0f172a", linewidth=2)
ax.set_ylim(0, 1)
ax.set_yticks([])
ax.set_title("Barcode timeline of critical alert dates")
ax.set_xlabel("Date")
ax.set_xlim(dates.min() - pd.Timedelta(days=3), dates.max() + pd.Timedelta(days=3))

ax.tick_params(axis="x", rotation=45)
ax.spines[["left", "top", "right"]].set_visible(False)

fig.tight_layout()

plt.show()

Period bias and bursts are easy to spot.

Reading tips #

Denser lines indicate periods of concentrated events. It helps communicate recent congestion or peaks quickly.
Varying line height or color lets you encode event types or weights at the same time.
For long timelines, split by month or enable scrolling to keep it readable.

Gantt Chart — List task start-end periods with horizontal bars
Rug Plot — Display individual data points as short lines along the axis
Calendar Heatmap — Overview daily metrics across a year by weekday × week