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For multiple series measured at the same reporting periods, use grouped bars on one axes to compare values side by side. If the actual gaps between dates matter, use date positions instead of equally spaced category positions. The examples below use Matplotlib’s object-oriented workflow and show when to choose each layout.
Choose how time should appear on the x-axis
First decide whether your periods are categories or real points on a calendar. Labels such as Jan, Feb, and Mar are often treated as equally spaced categories; this makes every period the same distance apart. If observations occur on irregular dates and the elapsed gaps matter, use the dates themselves as x positions so the spacing reflects time.
- Use categories when each reporting period should have equal visual spacing, even if its date interval differs.
- Use date positions when the distance between observations should be represented on the chart.
Plot aligned series as grouped bars
A grouped chart places each series beside the others at every reporting period. It works when the datasets share the same categories and the goal is to compare their values within each period. The explicit-position approach below uses Matplotlib’s bar method, making bar placement and width clear.
import numpy as np
import matplotlib.pyplot as plt
periods = ["Jan", "Feb", "Mar", "Apr"]
series_a = [12, 15, 11, 18]
series_b = [10, 13, 14, 16]
x = np.arange(len(periods))
width = 0.38
fig, ax = plt.subplots(figsize=(8, 4.5), layout="constrained")
ax.bar(x - width / 2, series_a, width, label="Series A")
ax.bar(x + width / 2, series_b, width, label="Series B")
ax.set_xticks(x, periods)
ax.set_xlabel("Period")
ax.set_ylabel("Value")
ax.set_title("Values by period")
ax.legend()
plt.show()The x positions are centered on each period: the first series is shifted left by half the bar width, and the second right by half. Add another ax.bar call for each additional series, shifting each series to a distinct position within the group. Keep each series’ value at the same index aligned with the corresponding period.
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About Matplotlib’s grouped-bar convenience API
Matplotlib documents Axes.grouped_bar for categorical datasets that share common categories. The API page identifies it as added in Matplotlib 3.11 and provisional. Check the installed Matplotlib version and the API’s status before depending on it; explicit positions with bar avoid reliance on that version-specific convenience method.
Plot observations at actual dates
For genuinely date-spaced observations, pass date values as the x coordinates to bar. Then configure date tick locators and formatters so the calendar labels are legible. Choose bar widths in date units that suit the intervals in your data; a width appropriate for monthly observations may not fit daily or irregular observations. Matplotlib’s gallery includes examples involving dates, date tick locators and formatters, and timelines.
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Do not substitute equally spaced category positions when irregular elapsed time is part of the message: doing so makes unequal time gaps look equal. Conversely, category positions are often simpler when the reporting periods are intended to be compared uniformly.
Use separate panels when series need room or different scales
If a single grouped chart is crowded or the series need separate y scales, give each series its own axes and share the x axis. This keeps dates aligned while allowing each panel its own scale.
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import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 1, sharex=True, layout="constrained")
axs[0].bar(dates, series_a)
axs[0].set_ylabel("Series A")
axs[1].bar(dates, series_b)
axs[1].set_ylabel("Series B")
axs[1].set_xlabel("Date")With sharex=True, the panels use a common horizontal scale; in a shared column, Matplotlib displays x tick labels on the bottom axes. The subplot examples show shared-axis layouts. Prefer grouped bars when direct within-period comparison is the priority; prefer panels when each series needs its own visual space or scale.
Build and format the chart with axes
For both layouts, create a figure and axes with fig, ax = plt.subplots() (or the plural axs when creating multiple panels), then add chart elements to those axes. This object-oriented pattern makes it straightforward to keep each series, label, and formatting choice associated with the intended plot. Matplotlib’s lifecycle and interface documentation describes this approach.
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- Give each series a meaningful label and display a legend when multiple series share an axes.
- Label the time axis and the value axis, including units where applicable.
- Keep period-to-value alignment consistent across datasets.
- Format date ticks for readability when using actual timestamps.
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