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Pass your dates as the x-values and their matching measurements as the y-values to ax.scatter(). Matplotlib recognizes Python datetime and NumPy datetime64 values, so it can format the horizontal axis as dates without manual conversion. Add a concise date formatter when the labels become crowded.
Build a scatter plot from paired dates and measurements
Each x-value must stay paired with the measurement recorded at that time. The two sequences should have compatible lengths. This example uses Python date-time objects or a NumPy datetime64 array for times, and one numeric value per time in values:
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# times: datetime.datetime objects or a NumPy datetime64 array
# 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")
ax.xaxis.set_major_locator(mdates.AutoDateLocator())
ax.xaxis.set_major_formatter(
mdates.ConciseDateFormatter(ax.xaxis.get_major_locator())
)
plt.show()The locator and formatter in the example are optional. Matplotlib’s date conversion handles supported date inputs and supplies date-aware tick handling by default. Matplotlib’s guide to plotting dates and strings explains the conversion, while the Axes.scatter API reference documents the plotting call.
Choose between scatter and plot
Use scatter when individual points need different sizes or colors: its optional s and c arguments control marker size and color. If every marker is uniform, Matplotlib notes that plot can be faster for scatterplots where markers do not vary in size or color. The API reference describes these options and the speed distinction.
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Make date-axis labels readable
AutoDateLocator selects tick positions appropriate to the displayed range; ConciseDateFormatter avoids repeating date components such as the year or month when they are already clear from neighboring labels. Together, they suit many plots without requiring a fixed interval.
If the chart needs a particular calendar interval or label convention, use a locator such as MonthLocator and a DateFormatter with the desired format. Rotating labels is another option when a manual format is long, though concise labels may make rotation unnecessary. Matplotlib’s date tick labels example demonstrates explicit formatting and rotation; its ConciseDateFormatter example shows concise date labels.
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Check the data and marker appearance
- Keep coordinates aligned. The first date must correspond to the first measurement, the second to the second, and so on.
scatterworks with one-dimensional data and flattens multidimensional inputs, so check the shapes if the points do not represent the intended pairs. - Use dates, not ambiguous numbers. If x-values are ordinary floats while Matplotlib’s date converter is active, they are interpreted as days from the date epoch. Pass date objects or deliberately convert numeric values with Matplotlib’s date API rather than assuming numbers mean timestamps.
- Account for marker edges. A visible edge can change a marker’s apparent size. To remove edges, set
linewidths=0oredgecolors="none"in the scatter call.
When date precision matters
Matplotlib represents dates internally as floating-point days from its default 1970-01-01 UTC epoch. Most plotting does not require manual conversion, but this representation can constrain precision: Matplotlib’s matplotlib.dates documentation says microsecond precision is achievable for dates approximately 70 years on either side of the epoch, and precision is around 20 microseconds elsewhere in its supported date range. This is a limitation of date representation, not a statement about the accuracy of the measurements in your data.
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