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plot_date is no longer available in current Matplotlib: it was removed in Matplotlib 3.11. Use plot with your date values instead. Matplotlib recognizes datetime.datetime and numpy.datetime64 values automatically, so you can create marker-only scatter charts and multiple date-based lines without manually converting dates.
Replace plot_date with plot
Matplotlib discouraged plot_date starting in 3.5, deprecated it in 3.9, and removed it in 3.11. The migration is direct: change ax.plot_date(dates, values, ...) to ax.plot(dates, values, ...), and specify marker and line styling explicitly. Matplotlib’s 3.11 migration notes say that “datetime-like data should directly be plotted using plot.” Matplotlib 3.11 API changes and the 3.9 deprecation notice document the change.
Make a scatter chart with dates
For a scatter-like chart, use a marker and disable the connecting line with linestyle='none'. The following example uses a NumPy array of dates:
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import matplotlib.pyplot as plt
import numpy as np
dates = np.array(['2025-01-01', '2025-02-01', '2025-03-01'], dtype='datetime64[D]')
values = [4, 7, 5]
fig, ax = plt.subplots()
ax.plot(dates, values, marker='o', linestyle='none', label='Observations')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.legend()
plt.show()You can use datetime.datetime values in place of the NumPy dates. Matplotlib’s date unit conversion handles both types automatically and applies date-aware tick handling; explicit conversion is normally unnecessary. The date and string plotting guide explains the conversion behavior.
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Plot multiple lines against the same dates
Call plot once per series, using the same date values and a distinct label for each line. Markers are optional; omit them if you want lines only.
fig, ax = plt.subplots()
ax.plot(dates, series_a, marker='o', label='Series A')
ax.plot(dates, series_b, marker='s', label='Series B')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.legend()
plt.show()The legend uses each series’ label. Matplotlib’s plot function also supports multiple x/y pairs in one call; separate calls are often easier to read when each series needs its own styling or label. See the plot API for supported call forms.
Choose how to configure date axes and ticks
| Approach | When to use it | What it controls |
|---|---|---|
plot with datetime-like values |
Ordinary charts whose x or y data are datetime.datetime or numpy.datetime64. |
Matplotlib converts the values and selects date-aware ticks and labels automatically. |
axis_date before plotting |
Input consists of numeric values that represent dates, or you need to configure the axis timezone. | Marks the selected axis as a date axis. Use ax.xaxis.axis_date or ax.yaxis.axis_date before calling plot. |
| Explicit date locators and formatters | Automatic tick positions or labels do not suit the chart. | Use date locators such as MonthLocator or YearLocator and formatters such as DateFormatter. ConciseDateFormatter can reduce repeated date components. |
Matplotlib’s defaults, AutoDateLocator and AutoDateFormatter, are a useful starting point. For examples of setting date ticks and labels, consult the date tick labels gallery. Axis limits can be supplied as datetime-like values; numeric limits must use Matplotlib’s date-day coordinates. More detail is in the dates API documentation.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAccount for date precision when plotting fine-grained data
Matplotlib represents dates internally as floating-point days from the default epoch, 1970-01-01 UTC. Its documentation says microsecond accuracy is achievable for dates approximately 70 years on either side of that epoch; precision declines farther away. For sub-microsecond resolution, use floating-point seconds rather than datetime-like values. If you must retain datetime-like values at microsecond precision for dates far from the default epoch, set a closer epoch before converting dates. See Matplotlib’s dates API and date unit documentation.
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Common migration errors
- Calling
plot_datein Matplotlib 3.11 or later: replace it withplot; the former API was removed. - Getting connected points instead of a scatter-like chart: specify
marker='o'andlinestyle='none'. - Getting an unhelpful legend: give each series a distinct
labeland callax.legend(). - Passing numeric date coordinates without a date axis: call
ax.xaxis.axis_date()(or the corresponding y-axis method) before plotting, and use Matplotlib date-day coordinates for numeric axis limits.
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