# Usage Guide¶

This tutorial covers some basic usage patterns and best practices to help you get started with Matplotlib.

```import matplotlib.pyplot as plt
import numpy as np
```

## A simple example¶

Matplotlib graphs your data on `Figure`s (e.g., windows, Jupyter widgets, etc.), each of which can contain one or more `Axes`, an area where points can be specified in terms of x-y coordinates, or theta-r in a polar plot, x-y-z in a 3D plot, etc. The simplest way of creating a figure with an axes is using `pyplot.subplots`. We can then use `Axes.plot` to draw some data on the axes:

```fig, ax = plt.subplots()  # Create a figure containing a single axes.
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the axes.
```

Out:

```[<matplotlib.lines.Line2D object at 0x7f01e3994400>]
```

Many other plotting libraries or languages do not require you to explicitly create an axes. For example, in MATLAB, one can just do

```plot([1, 2, 3, 4], [1, 4, 2, 3])  % MATLAB plot.
```

and get the desired graph.

In fact, you can do the same in Matplotlib: for each `Axes` graphing method, there is a corresponding function in the `matplotlib.pyplot` module that performs that plot on the "current" axes, creating that axes (and its parent figure) if they don't exist yet. So, the previous example can be written more shortly as

```plt.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Matplotlib plot.
```

Out:

```[<matplotlib.lines.Line2D object at 0x7f01d54117c0>]
```

## Parts of a Figure¶

Here is a more detailed layout of the components of a Matplotlib figure.

### `Figure`¶

The whole figure. The figure keeps track of all the child `Axes`, a group of 'special' artists (titles, figure legends, etc), and the canvas. (The canvas is not the primary focus. It is crucial as it is the object that actually does the drawing to get you your plot, but as the user, it is mostly invisible to you). A figure can contain any number of `Axes`, but will typically have at least one.

The easiest way to create a new figure is with pyplot:

```fig = plt.figure()  # an empty figure with no Axes
fig, ax = plt.subplots()  # a figure with a single Axes
fig, axs = plt.subplots(2, 2)  # a figure with a 2x2 grid of Axes
```

It's convenient to create the axes together with the figure, but you can also add axes later on, allowing for more complex axes layouts.

### `Axes`¶

This is what you think of as 'a plot'. It is the region of the image with the data space. A given figure can contain many Axes, but a given `Axes` object can only be in one `Figure`. The Axes contains two (or three in the case of 3D) `Axis` objects (be aware of the difference between Axes and Axis) which take care of the data limits (the data limits can also be controlled via the `axes.Axes.set_xlim()` and `axes.Axes.set_ylim()` methods). Each `Axes` has a title (set via `set_title()`), an x-label (set via `set_xlabel()`), and a y-label set via `set_ylabel()`).

The `Axes` class and its member functions are the primary entry point to working with the OO interface.

### `Axis`¶

These are the objects most similar to a number line. They set graph limits and generate ticks (the marks on the axis) and ticklabels (strings labeling the ticks). The location of the ticks is determined by a `Locator` object and the ticklabel strings are formatted by a `Formatter`. The combination of the correct `Locator` and `Formatter` gives very fine control over the tick locations and labels.

### `Artist`¶

Basically, everything visible on the figure is an artist (even `Figure`, `Axes`, and `Axis` objects). This includes `Text` objects, `Line2D` objects, `collections` objects, `Patch` objects, etc... When the figure is rendered, all of the artists are drawn to the canvas. Most Artists are tied to an Axes; such an Artist cannot be shared by multiple Axes, or moved from one to another.

## Types of inputs to plotting functions¶

All of plotting functions expect `numpy.array` or `numpy.ma.masked_array` as input. Classes that are similar to arrays ('array-like') such as `pandas` data objects and `numpy.matrix` may not work as intended. Common convention is to convert these to `numpy.array` objects prior to plotting.

For example, to convert a `pandas.DataFrame`

```a = pandas.DataFrame(np.random.rand(4, 5), columns = list('abcde'))
a_asarray = a.values
```

and to convert a `numpy.matrix`

```b = np.matrix([[1, 2], [3, 4]])
b_asarray = np.asarray(b)
```

## The object-oriented interface and the pyplot interface¶

As noted above, there are essentially two ways to use Matplotlib:

• Explicitly create figures and axes, and call methods on them (the "object-oriented (OO) style").

• Rely on pyplot to automatically create and manage the figures and axes, and use pyplot functions for plotting.

So one can do (OO-style)

```x = np.linspace(0, 2, 100)  # Sample data.

# Note that even in the OO-style, we use `.pyplot.figure` to create the figure.
fig, ax = plt.subplots()  # Create a figure and an axes.
ax.plot(x, x, label='linear')  # Plot some data on the axes.
ax.plot(x, x**2, label='quadratic')  # Plot more data on the axes...
ax.plot(x, x**3, label='cubic')  # ... and some more.
ax.set_xlabel('x label')  # Add an x-label to the axes.
ax.set_ylabel('y label')  # Add a y-label to the axes.
ax.set_title("Simple Plot")  # Add a title to the axes.
```

Out:

```<matplotlib.legend.Legend object at 0x7f01e3abf670>
```

or (pyplot-style)

```x = np.linspace(0, 2, 100)  # Sample data.

plt.plot(x, x, label='linear')  # Plot some data on the (implicit) axes.
plt.plot(x, x**3, label='cubic')
plt.xlabel('x label')
plt.ylabel('y label')
plt.title("Simple Plot")
plt.legend()
```

Out:

```<matplotlib.legend.Legend object at 0x7f01e3ba6460>
```

In addition, there is a third approach, for the case when embedding Matplotlib in a GUI application, which completely drops pyplot, even for figure creation. We won't discuss it here; see the corresponding section in the gallery for more info (Embedding Matplotlib in graphical user interfaces).

Matplotlib's documentation and examples use both the OO and the pyplot approaches (which are equally powerful), and you should feel free to use either (however, it is preferable pick one of them and stick to it, instead of mixing them). In general, we suggest to restrict pyplot to interactive plotting (e.g., in a Jupyter notebook), and to prefer the OO-style for non-interactive plotting (in functions and scripts that are intended to be reused as part of a larger project).

Note

In older examples, you may find examples that instead used the so-called `pylab` interface, via `from pylab import *`. This star-import imports everything both from pyplot and from `numpy`, so that one could do

```x = linspace(0, 2, 100)
plot(x, x, label='linear')
...
```

for an even more MATLAB-like style. This approach is strongly discouraged nowadays and deprecated. It is only mentioned here because you may still encounter it in the wild.

If you need to make the same plots over and over again with different data sets, use the recommended signature function below.

```def my_plotter(ax, data1, data2, param_dict):
"""
A helper function to make a graph

Parameters
----------
ax : Axes
The axes to draw to

data1 : array
The x data

data2 : array
The y data

param_dict : dict
Dictionary of keyword arguments to pass to ax.plot

Returns
-------
out : list
"""
out = ax.plot(data1, data2, **param_dict)
return out
```

which you would then use as:

```data1, data2, data3, data4 = np.random.randn(4, 100)
fig, ax = plt.subplots(1, 1)
my_plotter(ax, data1, data2, {'marker': 'x'})
```

Out:

```[<matplotlib.lines.Line2D object at 0x7f01fa42b8b0>]
```

or if you wanted to have two sub-plots:

```fig, (ax1, ax2) = plt.subplots(1, 2)
my_plotter(ax1, data1, data2, {'marker': 'x'})
my_plotter(ax2, data3, data4, {'marker': 'o'})
```

Out:

```[<matplotlib.lines.Line2D object at 0x7f01d3303fd0>]
```

These examples provide convenience for more complex graphs.

## Backends¶

### What is a backend?¶

A lot of documentation on the website and in the mailing lists refers to the "backend" and many new users are confused by this term. Matplotlib targets many different use cases and output formats. Some people use Matplotlib interactively from the Python shell and have plotting windows pop up when they type commands. Some people run Jupyter notebooks and draw inline plots for quick data analysis. Others embed Matplotlib into graphical user interfaces like PyQt or PyGObject to build rich applications. Some people use Matplotlib in batch scripts to generate postscript images from numerical simulations, and still others run web application servers to dynamically serve up graphs.

To support all of these use cases, Matplotlib can target different outputs, and each of these capabilities is called a backend; the "frontend" is the user facing code, i.e., the plotting code, whereas the "backend" does all the hard work behind-the-scenes to make the figure. There are two types of backends: user interface backends (for use in PyQt/PySide, PyGObject, Tkinter, wxPython, or macOS/Cocoa); also referred to as "interactive backends") and hardcopy backends to make image files (PNG, SVG, PDF, PS; also referred to as "non-interactive backends").

### Selecting a backend¶

There are three ways to configure your backend:

Below is a more detailed description.

If there is more than one configuration present, the last one from the list takes precedence; e.g. calling `matplotlib.use()` will override the setting in your `matplotlibrc`.

Without a backend explicitly set, Matplotlib automatically detects a usable backend based on what is available on your system and on whether a GUI event loop is already running. The first usable backend in the following list is selected: MacOSX, QtAgg, GTK4Agg, Gtk3Agg, TkAgg, WxAgg, Agg. The last, Agg, is a non-interactive backend that can only write to files. It is used on Linux, if Matplotlib cannot connect to either an X display or a Wayland display.

Here is a detailed description of the configuration methods:

1. Setting `rcParams["backend"]` in your `matplotlibrc` file:

```backend : qtagg   # use pyqt with antigrain (agg) rendering
```
2. Setting the `MPLBACKEND` environment variable:

You can set the environment variable either for your current shell or for a single script.

On Unix:

```> export MPLBACKEND=qtagg
> python simple_plot.py

> MPLBACKEND=qtagg python simple_plot.py
```

On Windows, only the former is possible:

```> set MPLBACKEND=qtagg
> python simple_plot.py
```

Setting this environment variable will override the `backend` parameter in any `matplotlibrc`, even if there is a `matplotlibrc` in your current working directory. Therefore, setting `MPLBACKEND` globally, e.g. in your `.bashrc` or `.profile`, is discouraged as it might lead to counter-intuitive behavior.

3. If your script depends on a specific backend you can use the function `matplotlib.use()`:

```import matplotlib
matplotlib.use('qtagg')
```

This should be done before any figure is created, otherwise Matplotlib may fail to switch the backend and raise an ImportError.

Using `use` will require changes in your code if users want to use a different backend. Therefore, you should avoid explicitly calling `use` unless absolutely necessary.

### The builtin backends¶

By default, Matplotlib should automatically select a default backend which allows both interactive work and plotting from scripts, with output to the screen and/or to a file, so at least initially, you will not need to worry about the backend. The most common exception is if your Python distribution comes without `tkinter` and you have no other GUI toolkit installed. This happens on certain Linux distributions, where you need to install a Linux package named `python-tk` (or similar).

If, however, you want to write graphical user interfaces, or a web application server (Embedding in a web application server (Flask)), or need a better understanding of what is going on, read on. To make things easily more customizable for graphical user interfaces, Matplotlib separates the concept of the renderer (the thing that actually does the drawing) from the canvas (the place where the drawing goes). The canonical renderer for user interfaces is `Agg` which uses the Anti-Grain Geometry C++ library to make a raster (pixel) image of the figure; it is used by the `QtAgg`, `GTK4Agg`, `GTK3Agg`, `wxAgg`, `TkAgg`, and `macosx` backends. An alternative renderer is based on the Cairo library, used by `QtCairo`, etc.

For the rendering engines, users can also distinguish between vector or raster renderers. Vector graphics languages issue drawing commands like "draw a line from this point to this point" and hence are scale free. Raster backends generate a pixel representation of the line whose accuracy depends on a DPI setting.

Here is a summary of the Matplotlib renderers (there is an eponymous backend for each; these are non-interactive backends, capable of writing to a file):

Renderer

Filetypes

Description

AGG

png

raster graphics -- high quality images using the Anti-Grain Geometry engine

PDF

pdf

vector graphics -- Portable Document Format

PS

ps, eps

vector graphics -- Postscript output

SVG

svg

vector graphics -- Scalable Vector Graphics

PGF

pgf, pdf

vector graphics -- using the pgf package

Cairo

png, ps, pdf, svg

raster or vector graphics -- using the Cairo library

To save plots using the non-interactive backends, use the `matplotlib.pyplot.savefig('filename')` method.

These are the user interfaces and renderer combinations supported; these are interactive backends, capable of displaying to the screen and using appropriate renderers from the table above to write to a file:

Backend

Description

QtAgg

Agg rendering in a Qt canvas (requires PyQt or Qt for Python, a.k.a. PySide). This backend can be activated in IPython with `%matplotlib qt`.

ipympl

Agg rendering embedded in a Jupyter widget. (requires ipympl). This backend can be enabled in a Jupyter notebook with `%matplotlib ipympl`.

GTK3Agg

Agg rendering to a GTK 3.x canvas (requires PyGObject, and pycairo or cairocffi). This backend can be activated in IPython with `%matplotlib gtk3`.

GTK4Agg

Agg rendering to a GTK 4.x canvas (requires PyGObject, and pycairo or cairocffi). This backend can be activated in IPython with `%matplotlib gtk4`.

macosx

Agg rendering into a Cocoa canvas in OSX. This backend can be activated in IPython with `%matplotlib osx`.

TkAgg

Agg rendering to a Tk canvas (requires TkInter). This backend can be activated in IPython with `%matplotlib tk`.

nbAgg

Embed an interactive figure in a Jupyter classic notebook. This backend can be enabled in Jupyter notebooks via `%matplotlib notebook`.

WebAgg

On `show()` will start a tornado server with an interactive figure.

GTK3Cairo

Cairo rendering to a GTK 3.x canvas (requires PyGObject, and pycairo or cairocffi).

GTK4Cairo

Cairo rendering to a GTK 4.x canvas (requires PyGObject, and pycairo or cairocffi).

wxAgg

Agg rendering to a wxWidgets canvas (requires wxPython 4). This backend can be activated in IPython with `%matplotlib wx`.

Note

The names of builtin backends case-insensitive; e.g., 'QtAgg' and 'qtagg' are equivalent.

#### ipympl¶

The Jupyter widget ecosystem is moving too fast to support directly in Matplotlib. To install ipympl:

```pip install ipympl
jupyter nbextension enable --py --sys-prefix ipympl
```

or

```conda install ipympl -c conda-forge
```

See jupyter-matplotlib for more details.

#### How do I select PyQt5 or PySide2?¶

The `QT_API` environment variable can be set to either `pyqt5` or `pyside2` to use `PyQt5` or `PySide2`, respectively.

Since the default value for the bindings to be used is `PyQt5`, Matplotlib first tries to import it. If the import fails, it tries to import `PySide2`.

### Using non-builtin backends¶

More generally, any importable backend can be selected by using any of the methods above. If `name.of.the.backend` is the module containing the backend, use `module://name.of.the.backend` as the backend name, e.g. `matplotlib.use('module://name.of.the.backend')`.

## What is interactive mode?¶

Use of an interactive backend (see What is a backend?) permits--but does not by itself require or ensure--plotting to the screen. Whether and when plotting to the screen occurs, and whether a script or shell session continues after a plot is drawn on the screen, depends on the functions and methods that are called, and on a state variable that determines whether Matplotlib is in "interactive mode." The default Boolean value is set by the `matplotlibrc` file, and may be customized like any other configuration parameter (see Customizing Matplotlib with style sheets and rcParams). It may also be set via `matplotlib.interactive()`, and its value may be queried via `matplotlib.is_interactive()`. Turning interactive mode on and off in the middle of a stream of plotting commands, whether in a script or in a shell, is rarely needed and potentially confusing. In the following, we will assume all plotting is done with interactive mode either on or off.

Note

Major changes related to interactivity, and in particular the role and behavior of `show()`, were made in the transition to Matplotlib version 1.0, and bugs were fixed in 1.0.1. Here we describe the version 1.0.1 behavior for the primary interactive backends, with the partial exception of macosx.

Interactive mode may also be turned on via `matplotlib.pyplot.ion()`, and turned off via `matplotlib.pyplot.ioff()`.

Note

Interactive mode works with suitable backends in ipython and in the ordinary Python shell, but it does not work in the IDLE IDE. If the default backend does not support interactivity, an interactive backend can be explicitly activated using any of the methods discussed in What is a backend?.

### Interactive example¶

From an ordinary Python prompt, or after invoking ipython with no options, try this:

```import matplotlib.pyplot as plt
plt.ion()
plt.plot([1.6, 2.7])
```

This will pop up a plot window. Your terminal prompt will remain active, so that you can type additional commands such as:

```plt.title("interactive test")
plt.xlabel("index")
```

On most interactive backends, the figure window will also be updated if you change it via the object-oriented interface. That is, get a reference to the `Axes` instance, and call a method of that instance:

```ax = plt.gca()
ax.plot([3.1, 2.2])
```

If you are using certain backends (like `macosx`), or an older version of Matplotlib, you may not see the new line added to the plot immediately. In this case, you need to explicitly call `draw()` in order to update the plot:

```plt.draw()
```

### Non-interactive example¶

Start a new session as per the previous example, but now turn interactive mode off:

```import matplotlib.pyplot as plt
plt.ioff()
plt.plot([1.6, 2.7])
```

Nothing happened--or at least nothing has shown up on the screen (unless you are using macosx backend, which is anomalous). To make the plot appear, you need to do this:

```plt.show()
```

Now you see the plot, but your terminal command line is unresponsive; `pyplot.show()` blocks the input of additional commands until you manually close the plot window.

Using a blocking function has benefits to users. Suppose a user needs a script that plots the contents of a file to the screen. The user may want to look at that plot, and then end the script. Without a blocking command such as `show()`, the script would flash up the plot and then end immediately, leaving nothing on the screen.

In addition, non-interactive mode delays all drawing until `show()` is called. This is more efficient than redrawing the plot each time a line in the script adds a new feature.

Prior to version 1.0, `show()` generally could not be called more than once in a single script (although sometimes one could get away with it). For version 1.0.1 and above, this restriction is lifted, so one can write a script like this:

```import numpy as np
import matplotlib.pyplot as plt

plt.ioff()
for i in range(3):
plt.plot(np.random.rand(10))
plt.show()
```

This makes three plots, one at a time. That is, the second plot will show up once the first plot is closed.

### Summary¶

In interactive mode, pyplot functions automatically draw to the screen.

When plotting interactively, if using object method calls in addition to pyplot functions, then call `draw()` whenever you want to refresh the plot.

Use non-interactive mode in scripts in which you want to generate one or more figures and display them before ending or generating a new set of figures. In that case, use `show()` to display the figure(s) and to block execution until you have manually destroyed them.

## Performance¶

Whether exploring data in interactive mode or programmatically saving lots of plots, rendering performance can be a challenging bottleneck in your pipeline. Matplotlib provides multiple ways to greatly reduce rendering time at the cost of a slight change (to a settable tolerance) in your plot's appearance. The methods available to reduce rendering time depend on the type of plot that is being created.

### Line segment simplification¶

For plots that have line segments (e.g. typical line plots, outlines of polygons, etc.), rendering performance can be controlled by `rcParams["path.simplify"]` (default: `True`) and `rcParams["path.simplify_threshold"]` (default: `0.111111111111`), which can be defined e.g. in the `matplotlibrc` file (see Customizing Matplotlib with style sheets and rcParams for more information about the `matplotlibrc` file). `rcParams["path.simplify"]` (default: `True`) is a Boolean indicating whether or not line segments are simplified at all. `rcParams["path.simplify_threshold"]` (default: `0.111111111111`) controls how much line segments are simplified; higher thresholds result in quicker rendering.

The following script will first display the data without any simplification, and then display the same data with simplification. Try interacting with both of them:

```import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

# Setup, and create the data to plot
y = np.random.rand(100000)
y[50000:] *= 2
y[np.geomspace(10, 50000, 400).astype(int)] = -1
mpl.rcParams['path.simplify'] = True

mpl.rcParams['path.simplify_threshold'] = 0.0
plt.plot(y)
plt.show()

mpl.rcParams['path.simplify_threshold'] = 1.0
plt.plot(y)
plt.show()
```

Matplotlib currently defaults to a conservative simplification threshold of `1/9`. To change default settings to use a different value, change the `matplotlibrc` file. Alternatively, users can create a new style for interactive plotting (with maximal simplification) and another style for publication quality plotting (with minimal simplification) and activate them as necessary. See Customizing Matplotlib with style sheets and rcParams for instructions on how to perform these actions.

The simplification works by iteratively merging line segments into a single vector until the next line segment's perpendicular distance to the vector (measured in display-coordinate space) is greater than the `path.simplify_threshold` parameter.

Note

Changes related to how line segments are simplified were made in version 2.1. Rendering time will still be improved by these parameters prior to 2.1, but rendering time for some kinds of data will be vastly improved in versions 2.1 and greater.

### Marker simplification¶

Markers can also be simplified, albeit less robustly than line segments. Marker simplification is only available to `Line2D` objects (through the `markevery` property). Wherever `Line2D` construction parameters are passed through, such as `matplotlib.pyplot.plot()` and `matplotlib.axes.Axes.plot()`, the `markevery` parameter can be used:

```plt.plot(x, y, markevery=10)
```

The `markevery` argument allows for naive subsampling, or an attempt at evenly spaced (along the x axis) sampling. See the Markevery Demo for more information.

### Splitting lines into smaller chunks¶

If you are using the Agg backend (see What is a backend?), then you can make use of `rcParams["agg.path.chunksize"]` (default: `0`) This allows users to specify a chunk size, and any lines with greater than that many vertices will be split into multiple lines, each of which has no more than `agg.path.chunksize` many vertices. (Unless `agg.path.chunksize` is zero, in which case there is no chunking.) For some kind of data, chunking the line up into reasonable sizes can greatly decrease rendering time.

The following script will first display the data without any chunk size restriction, and then display the same data with a chunk size of 10,000. The difference can best be seen when the figures are large, try maximizing the GUI and then interacting with them:

```import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['path.simplify_threshold'] = 1.0

# Setup, and create the data to plot
y = np.random.rand(100000)
y[50000:] *= 2
y[np.geomspace(10, 50000, 400).astype(int)] = -1
mpl.rcParams['path.simplify'] = True

mpl.rcParams['agg.path.chunksize'] = 0
plt.plot(y)
plt.show()

mpl.rcParams['agg.path.chunksize'] = 10000
plt.plot(y)
plt.show()
```

### Legends¶

The default legend behavior for axes attempts to find the location that covers the fewest data points (`loc='best'`). This can be a very expensive computation if there are lots of data points. In this case, you may want to provide a specific location.

### Using the fast style¶

The fast style can be used to automatically set simplification and chunking parameters to reasonable settings to speed up plotting large amounts of data. The following code runs it:

```import matplotlib.style as mplstyle
mplstyle.use('fast')
```

It is very lightweight, so it works well with other styles. Be sure the fast style is applied last so that other styles do not overwrite the settings:

```mplstyle.use(['dark_background', 'ggplot', 'fast'])
```

Total running time of the script: ( 0 minutes 2.536 seconds)

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