The Tool Desk
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Plot timestamps directly
Matplotlib’s built-in units converter handles Python datetime sequences and NumPy datetime64 arrays, adding date-appropriate tick behavior to the axis. See the Matplotlib guides to plotting dates and strings and the matplotlib.dates API.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(times, values)
ax.set_xlabel("Time")
ax.set_ylabel("Value")
plt.show()Here, times contains date-like values and values contains the corresponding y-values. Keep them aligned: each timestamp should describe the value at the same position. You generally do not need to convert the dates to numbers yourself.
Choose date tick locations and labels
Automatic date ticks are a good starting point. When the default spacing or labels do not suit the chart, use locators to choose tick positions and formatters to choose their text. The appropriate interval depends on the displayed range: seconds for a short event, days for a few weeks, or months or years for longer spans.
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Set a predictable day interval
This example places ticks on the 1st and 15th of each month and labels them with abbreviated month names and day numbers:
import matplotlib.dates as mdates
ax.xaxis.set_major_locator(mdates.DayLocator(bymonthday=[1, 15]))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))Use a locator suited to the intended cadence, such as MonthLocator or DayLocator. For an adaptive choice, AutoDateLocator and AutoDateFormatter select date ticks and formats based on the range and scale. ConciseDateFormatter can reduce repetition when nearby labels share a year or month. The available date tools are documented in Matplotlib’s date API.
Prevent crowded labels
If labels overlap, first reduce the number of ticks by changing the locator interval. You can also rotate the tick labels; Matplotlib’s date-tick text guide demonstrates rotation and date formatting. Rotation helps fit labels, but it does not replace choosing a sensible tick density.
Control the timezone shown on the axis
Matplotlib’s date conversion, locators, and formatters are timezone-aware. The default timezone is rcParams['timezone'], which is UTC by default. If the chart must show another zone, specify that timezone in the relevant date conversion or tick-formatting tools rather than assuming the display will follow the machine’s local timezone. The date API documentation describes the timezone options.
Understand timestamp precision and the date epoch
Matplotlib represents dates as floating-point numbers of days relative to an epoch. The default epoch is 1970-01-01 UTC. Because floating-point resolution depends on distance from that origin, precision decreases for dates farther away from it.
- For dates approximately within 70 years on either side of the default epoch, Matplotlib documents achievable microsecond precision.
- Elsewhere in its supported date range, years 0001–9999, the documentation describes precision of approximately 20 microseconds.
- If a plot needs sub-microsecond resolution, the Matplotlib documentation recommends plotting floating-point seconds instead of datetime-like values.
These are properties of the date-coordinate representation, not guarantees that the input data itself is accurate to those intervals. See Matplotlib’s guide to date precision and epochs.
When fine precision is needed far from 1970
If datetime-like values must retain microsecond precision for dates far from the default epoch, set a closer epoch before any date conversion takes place. If your use case needs still finer resolution, compare that approach with plotting floating-point seconds. Choose the representation based on the time range and resolution the data actually requires; a visually detailed axis cannot restore precision lost in the coordinates.
Quick Recap
A practical configuration checklist
- Input: Use Python
datetimeor NumPydatetime64values and plot them directly. - Range: Match the tick interval to the span being shown, from short intervals to months or years.
- Labels: Choose a formatter that gives enough context without repeating information unnecessarily.
- Timezone: Set the intended display zone explicitly when it matters; otherwise the documented default is UTC.
- Precision: Check distance from the epoch for microsecond work, and use floating-point seconds for sub-microsecond plots.
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