Use plt.subplots() to create a figure with multiple plots: it returns the Figure that contains the layout and one or more Axes where you draw each plot. For a regular grid, give it the number of rows and columns, then plot on each Axes. Use shared axes when panels should be compared on the same scale; use GridSpec or subplot_mosaic() when you need a custom layout.
Make a regular grid with plt.subplots()
A Matplotlib Figure is the overall container; its Axes are the individual plotting areas, including their data, labels, titles, and annotations. plt.subplots() creates both in one call, making it the simplest way to put several related graphs in one figure. See Matplotlib’s Axes and subplots guide and subplots API.
import matplotlib.pyplot as plt
# Assume x, y1, y2, categories, values, and samples are already defined.
fig, axs = plt.subplots(2, 2, figsize=(8, 6), layout="constrained")
axs[0, 0].plot(x, y1)
axs[0, 0].set_title("Line plot")
axs[0, 1].scatter(x, y2)
axs[0, 1].set_title("Scatter plot")
axs[1, 0].bar(categories, values)
axs[1, 0].set_title("Bar chart")
axs[1, 1].hist(samples)
axs[1, 1].set_title("Histogram")
fig.suptitle("Four related views")
plt.show()The first argument is the number of rows and the second is the number of columns. In this example, axs[0, 0] is the top-left Axes and axs[1, 1] is the bottom-right. Each Axes has its own plotting methods, so you can mix chart types in the same figure. layout="constrained" asks Matplotlib to arrange spacing so labels and titles are less likely to overlap.
Choose the right way to access Axes
For a small, fixed number of panels, unpack the returned Axes directly. For a grid, keep them in the plural axs variable and index by row and column:
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
ax1.plot(x, y1)
ax2.plot(x, y2)
# For a grid, use axs[row, column]:
fig, axs = plt.subplots(2, 2)The shape of axs depends on the requested grid and the squeeze setting. A 2-by-2 grid returns a two-dimensional array by default; a single row or column commonly returns a one-dimensional array, and a single subplot returns one Axes object. If code should always use two indices, even for a one-row or one-column layout, set squeeze=False:
fig, axs = plt.subplots(1, 2, squeeze=False)
axs[0, 0].plot(x, y1)
axs[0, 1].plot(x, y2)Matplotlib’s naming convention is ax for one Axes and axs for multiple Axes.
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Share an axis when the panels should be directly compared
Sharing an axis links its scale and limits across the relevant Axes. This is useful for aligned time series or side-by-side panels whose values need to be compared. It is not a good choice when panels use different units or meaningfully different ranges.
Vertically stacked time series
Use sharex=True when several panels use the same x values, such as dates. The panels share the x-axis, and redundant interior tick labels are hidden by default.
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fig, axs = plt.subplots(2, 1, sharex=True, layout="constrained")
axs[0].plot(dates, temperature)
axs[0].set_ylabel("Temperature")
axs[1].plot(dates, rainfall)
axs[1].set_ylabel("Rainfall")
axs[1].set_xlabel("Date")Side-by-side comparisons
Use sharey=True when panels should use a common vertical scale. For more targeted sharing, sharex and sharey accept 'all', 'row', 'col', or 'none'; for example, sharey='row' shares the y-axis within each row.
If you want shared scales but need to show tick labels that sharing suppresses, restore them on the Axes you choose. For example, ax.tick_params(labelbottom=True) enables bottom tick labels for that Axes. The Matplotlib multiple-subplots example illustrates shared axes and outer labels.
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Adjust spacing and panel proportions
For a straightforward grid, begin with plt.subplots(). It accepts width_ratios and height_ratios when some columns or rows need more space than others. For example, plt.subplots(2, 2, width_ratios=[2, 1], height_ratios=[1, 2]) gives the left column and bottom row more room than their counterparts.
For finer control over grid spacing or proportions, create a GridSpec and make Axes from it. The Matplotlib example below uses zero vertical gap and labels only the outside edges of a shared grid:
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fig = plt.figure(layout="constrained")
gs = fig.add_gridspec(2, 1, hspace=0)
axs = gs.subplots(sharex=True)
axs[0].plot(x, y1)
axs[1].plot(x, y2)
for ax in axs:
ax.label_outer()
plt.show()label_outer() helps reduce duplicate labels by keeping labels on the outside of the grid. GridSpec is useful when a regular arrangement needs more deliberate spacing or relative cell sizes; Matplotlib’s Figure API documents figure-level composition tools.
Use subplot_mosaic() for an irregular layout
When one chart should span several grid cells, or panel names make a layout easier to understand, use fig.subplot_mosaic(). The arrangement is written as rows of labels; repeated labels place one Axes across those cells.
fig, axd = plt.subplot_mosaic([
["main", "side"],
["main", "bottom"],
], layout="constrained")
axd["main"].plot(x, y1)
axd["side"].scatter(x, y2)
axd["bottom"].bar(categories, values)
axd["main"].set_title("Main view")
axd["side"].set_title("Side view")
axd["bottom"].set_title("Bottom view")
plt.show()The repeated "main" label makes that Axes occupy both cells in the first column. The returned dictionary lets you refer to Axes by meaningful names instead of numeric positions. Use this approach when a labeled, uneven composition is clearer than a uniform grid. See Matplotlib’s subplot mosaic guide.
Quick Recap
Which layout method should you choose?
- Use
plt.subplots()for the usual evenly arranged rows and columns. - Use shared axes when the panels need a common scale or aligned x values; keep axes independent when units or ranges differ.
- Use GridSpec when you need to control spacing or the relative size of rows and columns.
- Use
subplot_mosaic()when the composition is irregular or named panels make the code clearer. - Unpack Axes for a few known panels; use
axs[row, column]for a grid, withsqueeze=Falseif consistent two-dimensional indexing matters.
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