To compare multiple series on one Matplotlib chart, call ax.plot(x, y, label="Series name") for each one, then call ax.legend(). For time series, use datetime values on the x-axis; Matplotlib formats them as dates automatically. Sort observations by time first if the lines should progress chronologically.
Plot multiple lines on one chart
Use the same x-values for each series when they share a horizontal axis. A separate plot call for each line makes its label and styling easy to set independently.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(layout="constrained")
ax.plot(x, series_a, label="Series A")
ax.plot(x, series_b, label="Series B")
ax.set_xlabel("Time")
ax.set_ylabel("Value")
ax.legend()
plt.show()Replace x with your numeric or date values and series_a and series_b with the corresponding y-values. Give each line a descriptive label; ax.legend() displays those labels so readers can tell the lines apart. The plot method returns Line2D objects and accepts styling options such as color, line style, and markers.
Use a single call for shared styling
Matplotlib also accepts multiple x/y pairs in one plot call. This is compact when the series share formatting; keyword arguments supplied to that call apply to all its lines. Use repeated calls when you want to style or label each series separately. See the Matplotlib plot API.
#1 Best Overall
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Plot a time series with date-aware axes
Pass Python datetime values or NumPy datetime64 values as x. Matplotlib converts these date values and applies date-aware tick placement and labels by default. Avoid turning timestamps into arbitrary strings: date values let Matplotlib treat the axis as time. See the Matplotlib units documentation.
Matplotlib connects points in the order they appear in your arrays; it does not automatically sort them by timestamp. If your intended line should move forward in time, sort the observations by date before plotting. Otherwise, connecting segments can move back and forth along the time axis.
Rank #2
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Choose whether gaps represent elapsed time
Plotting actual dates gives horizontal distance to elapsed calendar time. A weekend or a missing observation therefore creates a wider gap than an ordinary interval. That is useful when the duration between observations matters.
For daily records such as trading data, you may instead want every observed day to have equal spacing, so weekends and other non-observation days do not take up chart width. Plot the values at successive integer positions and format those positions as dates. The Matplotlib time-series example with a custom date formatter demonstrates this approach. Choose it only when equal spacing between observations better communicates the data than elapsed calendar time.
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Rank #3
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Control date tick density and labels
For dense plots or long date ranges, use tools from matplotlib.dates to set tick cadence and formatting. Options include AutoDateLocator and AutoDateFormatter for automatic choices, ConciseDateFormatter for compact labels, and MonthLocator or DateFormatter for more specific control. See the Matplotlib dates API.
When date precision matters
Matplotlib represents dates internally as floating-point days from its default epoch, 1970-01-01 UTC. The dates API says microsecond precision is achievable within approximately 70 years of that epoch, with lower precision farther away. For sub-microsecond time plots, it recommends plotting floating-point seconds instead. This precision limit is generally not a concern for daily or monthly charts. Consult the dates API documentation for details.
Quick Recap
Best Value
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