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Matplotlib turns Python data into charts, from a quick line plot to carefully arranged figures for reports and applications. Start with plt.subplots(), draw through its Axes, and add labels; as plots grow, the Figure-and-Axes model makes code easier to organize and reuse.
Install Matplotlib and make your first plot
Install Matplotlib in the environment where you run Python. For pip, use:
python -m pip install -U matplotlibThe official getting-started guide also documents installation with conda, pixi, and uv. Consult the installation guide for current package-manager instructions and compatibility details.
This complete example plots a small set of numeric values:
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import matplotlib.pyplot as plt
x = [0, 1, 2, 3]
y = [0, 1, 4, 9]
fig, ax = plt.subplots()
ax.plot(x, y, label="y = x squared")
ax.set_title("A simple line plot")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.legend()
plt.show()plt.subplots() creates a Figure and an Axes. ax.plot() draws the data, and the label, title, axis labels, and legend make the chart interpretable. plt.show() requests display; whether a window appears depends on the Python environment and its Matplotlib backend.
Understand Figure, Axes, Axis, and Artist
Matplotlib’s object model gives each part of a figure a distinct role. The names are similar, but an Axes is not the same thing as an Axis.
- Figure: the overall canvas or container. A Figure can hold one or more Axes.
- Axes: the plotting area where data is drawn and plot elements are configured. A chart usually has an x-axis and y-axis within its Axes.
- Axis: an object that controls a dimension’s scale and ticks, including their placement and labels.
- Artist: the general term for visible elements in a figure, such as lines, text, and patches.
In the example, fig refers to the Figure and ax to its Axes. The plotting method creates visible Artists, while methods such as set_xlabel() configure text associated with the plot. This structure is explained in the Matplotlib quick-start guide.
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Choose the interface that fits the task
Matplotlib offers a convenient state-based interface through pyplot and an explicit interface that works directly with Figure and Axes objects. The choice affects how clearly plotting code communicates which chart it changes.
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|---|---|---|---|---|
pyplot state-based calls |
Implicit: calls act on the current figure or Axes. | Convenient for short interactive work. | Can become harder to track as figures grow. | Less direct, because the target plot depends on current state. |
| Explicit Figure/Axes methods | Explicit: code names the object being changed. | Works, though it requires keeping the objects. | Well suited to complex plots, scripts, and multiple Axes. | Convenient: pass an Axes into a function. |
For example, a helper function can draw on whichever Axes it receives:
def add_series(ax, x, y, label):
ax.plot(x, y, label=label)
fig, ax = plt.subplots()
add_series(ax, [0, 1, 2], [0, 1, 4], "observations")
ax.legend()
plt.show()This avoids relying on an implicit “current” plot, which is especially useful when a script creates several charts or panels. A pyplot-only version is still handy for a quick experiment:
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plt.plot([0, 1, 2], [0, 1, 4])
plt.title("Quick check")
plt.show()Prefer the explicit style as plotting logic becomes reusable or more involved. Avoid old examples built around pylab; the current quick-start documentation describes that approach as strongly deprecated.
Make charts clear and readable
A chart should make its data and comparisons easy to understand. Add descriptive titles and axis labels, use a legend when multiple series need identification, and choose scales and ticks that suit the values rather than accepting defaults without inspection.
- Labels and titles: name the quantities and units where applicable. A title can state what the plot shows; labels explain each dimension.
- Legends: assign meaningful labels to plotted series and call
ax.legend()when readers need to distinguish them. - Scales and ticks: use a scale that matches the data and make tick positions and labels readable. The Axis objects manage scales and ticks.
- Color and annotation: use color deliberately to distinguish series or encode values, and add annotations when a particular point or region needs explanation.
- Categorical values: strings can be interpreted as categorical positions. A long sequence of unique strings may produce an excessive number of ticks; use categories only when they are meaningful and manageable.
- Multiple views: place related plots on separate Axes when the comparison benefits from distinct scales or chart types.
For two related panels, create multiple Axes with plt.subplots() and label each one directly:
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fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot([0, 1, 2], [0, 1, 4])
ax1.set_title("Series A")
ax1.set_xlabel("x")
ax1.set_ylabel("value")
ax2.plot([0, 1, 2], [0, 2, 3])
ax2.set_title("Series B")
ax2.set_xlabel("x")
plt.show()Each Axes can be configured independently while both belong to the same Figure. For more layout approaches, see the quick-start guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Display a plot or save it to a file
Showing a chart and writing one to disk are different tasks. Interactive display requires a suitable GUI backend and environment; file output can use non-interactive backends such as Agg, ps, pdf, or svg. Availability depends on the system and installed bindings or optional packages.
| Goal | Typical Matplotlib operation | What to check |
|---|---|---|
| Display a plot interactively | plt.show() |
The configured backend and environment must support display. GUI frameworks may require additional system bindings or packages. |
| Save a plot | fig.savefig("chart.png") |
savefig can write image or vector output; use a suitable filename extension and check format-specific requirements. |
For example, save the Figure before or after requesting display:
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fig, ax = plt.subplots()
ax.plot([0, 1, 2], [0, 1, 4])
ax.set_xlabel("x")
ax.set_ylabel("y")
fig.savefig("chart.png")If plt.show() does not open a window, check the official installation and troubleshooting guidance for backend requirements in your environment. LaTeX rendering and animation workflows can also require optional dependencies, so consult the relevant documentation before relying on them.
Move from basics to advanced Matplotlib
Once the Figure-and-Axes workflow is comfortable, explore features according to the problem you need to solve. The official tutorials cover these topics in greater depth.
- Styles and rcParams: apply consistent visual defaults across plots or configure individual settings.
- Layout: control how multiple Axes, labels, and legends fit within a Figure.
- Legends: customize placement and presentation when basic legends are not enough.
- Animation: update plotted elements over time; confirm the required output workflow and dependencies for your target environment.
- Transforms and paths: position elements using coordinate systems or build more specialized graphical shapes.
- Rendering performance: techniques such as blitting can help optimize certain animation workflows, but are not prerequisites for ordinary plotting.
Matplotlib supports static, animated, and interactive visualizations. The official documentation is the best place to check current APIs and environment-specific requirements as your needs expand.
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