# How-to¶

## Check whether a figure is empty¶

Empty can actually mean different things. Does the figure contain any artists? Does a figure with an empty Axes still count as empty? Is the figure empty if it was rendered pure white (there may be artists present, but they could be outside the drawing area or transparent)?

For the purpose here, we define empty as: "The figure does not contain any artists except it's background patch." The exception for the background is necessary, because by default every figure contains a Rectangle as it's background patch. This definition could be checked via:

def is_empty(figure):
"""
Return whether the figure contains no Artists (other than the default
background patch).
"""
contained_artists = figure.get_children()
return len(contained_artists) <= 1


We've decided not to include this as a figure method because this is only one way of defining empty, and checking the above is only rarely necessary. Usually the user or program handling the figure know if they have added something to the figure.

Checking whether a figure would render empty cannot be reliably checked except by actually rendering the figure and investigating the rendered result.

## Find all objects in a figure of a certain type¶

Every Matplotlib artist (see Artist tutorial) has a method called findobj() that can be used to recursively search the artist for any artists it may contain that meet some criteria (e.g., match all Line2D instances or match some arbitrary filter function). For example, the following snippet finds every object in the figure which has a set_color property and makes the object blue:

def myfunc(x):
return hasattr(x, 'set_color')

for o in fig.findobj(myfunc):
o.set_color('blue')


You can also filter on class instances:

import matplotlib.text as text
for o in fig.findobj(text.Text):
o.set_fontstyle('italic')


## Prevent ticklabels from having an offset¶

The default formatter will use an offset to reduce the length of the ticklabels. To turn this feature off on a per-axis basis:

ax.get_xaxis().get_major_formatter().set_useOffset(False)


set rcParams["axes.formatter.useoffset"] (default: True), or use a different formatter. See ticker for details.

## Save transparent figures¶

The savefig() command has a keyword argument transparent which, if 'True', will make the figure and axes backgrounds transparent when saving, but will not affect the displayed image on the screen.

If you need finer grained control, e.g., you do not want full transparency or you want to affect the screen displayed version as well, you can set the alpha properties directly. The figure has a Rectangle instance called patch and the axes has a Rectangle instance called patch. You can set any property on them directly (facecolor, edgecolor, linewidth, linestyle, alpha). e.g.:

fig = plt.figure()
fig.patch.set_alpha(0.5)
ax.patch.set_alpha(0.5)


If you need all the figure elements to be transparent, there is currently no global alpha setting, but you can set the alpha channel on individual elements, e.g.:

ax.plot(x, y, alpha=0.5)
ax.set_xlabel('volts', alpha=0.5)


## Save multiple plots to one pdf file¶

Many image file formats can only have one image per file, but some formats support multi-page files. Currently only the pdf backend has support for this. To make a multi-page pdf file, first initialize the file:

from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('multipage.pdf')


You can give the PdfPages object to savefig(), but you have to specify the format:

plt.savefig(pp, format='pdf')


An easier way is to call PdfPages.savefig:

pp.savefig()


Finally, the multipage pdf object has to be closed:

pp.close()


The same can be done using the pgf backend:

from matplotlib.backends.backend_pgf import PdfPages


## Move the edge of an axes to make room for tick labels¶

For subplots, you can control the default spacing on the left, right, bottom, and top as well as the horizontal and vertical spacing between multiple rows and columns using the matplotlib.figure.Figure.subplots_adjust() method (in pyplot it is subplots_adjust()). For example, to move the bottom of the subplots up to make room for some rotated x tick labels:

fig = plt.figure()


You can control the defaults for these parameters in your matplotlibrc file; see Customizing Matplotlib with style sheets and rcParams. For example, to make the above setting permanent, you would set:

figure.subplot.bottom : 0.2   # the bottom of the subplots of the figure


The other parameters you can configure are, with their defaults

left = 0.125
the left side of the subplots of the figure
right = 0.9
the right side of the subplots of the figure
bottom = 0.1
the bottom of the subplots of the figure
top = 0.9
the top of the subplots of the figure
wspace = 0.2
the amount of width reserved for space between subplots, expressed as a fraction of the average axis width
hspace = 0.2
the amount of height reserved for space between subplots, expressed as a fraction of the average axis height

If you want additional control, you can create an Axes using the axes() command (or equivalently the figure add_axes() method), which allows you to specify the location explicitly:

ax = fig.add_axes([left, bottom, width, height])


where all values are in fractional (0 to 1) coordinates. See Axes Demo for an example of placing axes manually.

## Automatically make room for tick labels¶

Note

This is now easier to handle than ever before. Calling tight_layout() or alternatively using constrained_layout=True argument in subplots() can fix many common layout issues. See the Tight Layout guide and Constrained Layout Guide for more details.

The information below is kept here in case it is useful for other purposes.

In most use cases, it is enough to simply change the subplots adjust parameters as described in Move the edge of an axes to make room for tick labels. But in some cases, you don't know ahead of time what your tick labels will be, or how large they will be (data and labels outside your control may be being fed into your graphing application), and you may need to automatically adjust your subplot parameters based on the size of the tick labels. Any Text instance can report its extent in window coordinates (a negative x coordinate is outside the window), but there is a rub.

The RendererBase instance, which is used to calculate the text size, is not known until the figure is drawn (draw()). After the window is drawn and the text instance knows its renderer, you can call get_window_extent(). One way to solve this chicken and egg problem is to wait until the figure is draw by connecting (mpl_connect()) to the "on_draw" signal (DrawEvent) and get the window extent there, and then do something with it, e.g., move the left of the canvas over; see Event handling and picking.

Here is an example that gets a bounding box in relative figure coordinates (0..1) of each of the labels and uses it to move the left of the subplots over so that the tick labels fit in the figure:

## Align my ylabels across multiple subplots¶

If you have multiple subplots over one another, and the y data have different scales, you can often get ylabels that do not align vertically across the multiple subplots, which can be unattractive. By default, Matplotlib positions the x location of the ylabel so that it does not overlap any of the y ticks. You can override this default behavior by specifying the coordinates of the label. The example below shows the default behavior in the left subplots, and the manual setting in the right subplots.

Align Ylabels

## Control the draw order of plot elements¶

The draw order of plot elements, and thus which elements will be on top, is determined by the set_zorder property. See Zorder Demo for a detailed description.

## Make the aspect ratio for plots equal¶

The Axes property set_aspect() controls the aspect ratio of the axes. You can set it to be 'auto', 'equal', or some ratio which controls the ratio:

ax = fig.add_subplot(111, aspect='equal')


See Equal axis aspect ratio for a complete example.

## Draw multiple y-axis scales¶

A frequent request is to have two scales for the left and right y-axis, which is possible using twinx() (more than two scales are not currently supported, though it is on the wish list). This works pretty well, though there are some quirks when you are trying to interactively pan and zoom, because both scales do not get the signals.

The approach uses twinx() (and its sister twiny()) to use 2 different axes, turning the axes rectangular frame off on the 2nd axes to keep it from obscuring the first, and manually setting the tick locs and labels as desired. You can use separate matplotlib.ticker formatters and locators as desired because the two axes are independent.

See Plots with different scales for a complete example.

## Generate images without having a window appear¶

Simply do not call show, and directly save the figure to the desired format:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3])
plt.savefig('myfig.png')