Pass your date or time values as x and their matching measurements as y to Axes.scatter(). Matplotlib recognizes Python datetime and NumPy datetime64 values and handles date-axis conversion automatically; add a date locator and formatter when you want more control over the ticks.
Plot dates against measurements
Each x value must stay paired with the measurement recorded at that time. The two sequences should have compatible lengths and represent one-dimensional coordinates. Matplotlib can flatten multidimensional inputs, but explicit one-dimensional sequences make the pairing easier to verify.
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# times: datetime.datetime objects or NumPy datetime64 values
# values: one numeric measurement for each time
fig, ax = plt.subplots(layout="constrained")
ax.scatter(times, values)
ax.set_xlabel("Date")
ax.set_ylabel("Value")
# Optional: use date-aware ticks with concise labels
locator = mdates.AutoDateLocator()
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator))
plt.show()Replace times and values with your data. The example assumes each time is a Python datetime or NumPy datetime64 value and each measurement is numeric. With supported date inputs, Matplotlib supplies date-axis handling by default, so the explicit locator and formatter are optional. See Matplotlib’s date and string plotting documentation.
Choose scatter or plot
Axes.scatter(x, y) draws individual points and lets you vary marker size with s or color with c, for example to encode another measurement. If every point uses the same size and color, Matplotlib notes that plot can be faster for scatterplots with uniform markers. Choose based on whether per-point styling is useful and whether speed matters for your data. See the Axes.scatter API reference.
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Make date ticks readable
AutoDateLocator selects tick positions suited to the visible date range, while ConciseDateFormatter avoids repeating date components such as a year or month when they are already clear from nearby labels. This is a good starting point for charts spanning irregular or changing ranges.
For a fixed calendar interval or a specific label style, choose a locator such as MonthLocator and pair it with DateFormatter. Rotating labels can help if a manual format is long, although concise labels may make rotation unnecessary. Matplotlib’s date tick labels example demonstrates locator, formatter, and rotation options; its ConciseDateFormatter example shows concise date labels.
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Common issues and precision limits
- Mismatched dates and values: Check that each date corresponds to the value at the same position and that the sequences have compatible lengths.
- Unexpected numeric x-axis: Use actual date objects or deliberately convert values with Matplotlib’s date API. When the date converter is active, ordinary numeric x values are interpreted as days from the date epoch; they are not automatically treated as timestamps in an arbitrary unit.
- Markers appear larger than expected: Marker edges affect apparent size. Set
linewidths=0oredgecolors="none"if you want to remove the edges. - Very fine timing or distant dates: Matplotlib represents dates internally as floating-point days from its default 1970-01-01 UTC epoch. Its dates documentation says microsecond precision is achievable for dates approximately 70 years on either side of that epoch, and around 20 microseconds elsewhere in the supported date range. This is a limit of date representation, not a measure of the accuracy of your source data. See the
matplotlib.datesAPI.
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