What is a backend?#

Backends are used for displaying Matplotlib figures (see Introduction to Figures), on the screen, or for writing to files. 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:

• The rcParams["backend"] parameter in your matplotlibrc file

• The MPLBACKEND environment variable

• The function matplotlib.use()

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
   

   See also Customizing Matplotlib with style sheets and rcParams.

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 with 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 (Embed 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.

conda install ipympl -c conda-forge

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').

Information for backend implementers is available at Writing a backend -- the pyplot interface.

Backend API versions#

Matplotlib aims to maintain backward compatibility on backends. Nevertheless, we want to be able to evolve the backend API to support new features. Defining backend API versions will help to communicate which API is supported by a given version of Matplotlib.

The following backend API versions exist

There is currently no plan to remove support for older API versions.

Debugging the figure windows not showing#

Sometimes things do not work as expected, usually during an install.

If you are using a Notebook or integrated development environment (see Notebooks and IDEs), please consult their documentation for debugging figures not working in their environments.

If you are using one of Matplotlib's graphics backends (see Standalone scripts and interactive use), make sure you know which one is being used:

import matplotlib

print(matplotlib.get_backend())

Try a simple plot to see if the GUI opens:

import matplotlib
import matplotlib.pyplot as plt

print(matplotlib.get_backend())
plt.plot((1, 4, 6))
plt.show()

If it does not, you perhaps have an installation problem. A good step at this point is to ensure that your GUI toolkit is installed properly, taking Matplotlib out of the testing. Almost all GUI toolkits have a small test program that can be run to test basic functionality. If this test fails, try re-installing.

python3 -c "from PyQt6.QtWidgets import *; app = QApplication([]); win = QMainWindow(); win.show(); app.exec()"

python3 -c "from tkinter import Tk; Tk().mainloop()"

python3 -c "from gi.repository import Gtk; win = Gtk.Window(); win.connect('destroy', Gtk.main_quit); win.show(); Gtk.main()"

python3 -c "import wx; app = wx.App(); frame = wx.Frame(None); frame.Show(); app.MainLoop()"