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matplotlib.pyplot.show

matplotlib.pyplot.show(*, block=None)[source]

Display all open figures.

Parameters

blockbool, optional

Whether to wait for all figures to be closed before returning.

If True block and run the GUI main loop until all figure windows are closed.

If False ensure that all figure windows are displayed and return immediately. In this case, you are responsible for ensuring that the event loop is running to have responsive figures.

Defaults to True in non-interactive mode and to False in interactive mode (see pyplot.isinteractive).

Notes

Saving figures to file and showing a window at the same time

If you want an image file as well as a user interface window, use pyplot.savefig before pyplot.show. At the end of (a blocking) show() the figure is closed and thus unregistered from pyplot. Calling pyplot.savefig afterwards would save a new and thus empty figure. This limitation of command order does not apply if the show is non-blocking or if you keep a reference to the figure and use Figure.savefig.

Auto-show in jupyter notebooks

The jupyter backends (activated via %matplotlib inline, %matplotlib notebook, or %matplotlib widget), call show() at the end of every cell by default. Thus, you usually don't have to call it explicitly there.

Examples using `matplotlib.pyplot.show`

Discrete distribution as horizontal bar chart

prop_cycle property markevery in rcParams

Creating a timeline with lines, dates, and text

Contouring the solution space of optimizations

Blend transparency with color in 2D images

Controlling view limits using margins and sticky_edges

Figure labels: suptitle, supxlabel, supylabel

Combining two subplots using subplots and GridSpec

Using Gridspec to make multi-column/row subplot layouts

Creating multiple subplots using ``plt.subplots`` — Creating multiple subplots using plt.subplots

Plot a confidence ellipse of a two-dimensional dataset

Including upper and lower limits in error bars

Creating boxes from error bars using PatchCollection

Using histograms to plot a cumulative distribution

Some features of the histogram (hist) function

Demo of the histogram function's different ``histtype`` settings — Demo of the histogram function's different histtype settings

The histogram (hist) function with multiple data sets

Producing multiple histograms side by side

The difference between \\dfrac and \\frac — The difference between \dfrac and \frac

Labeling ticks using engineering notation

Controlling style of text and labels using a dictionary

Programmatically controlling subplot adjustment

Creating a colormap from a list of colors

Line, Poly and RegularPoly Collection with autoscaling

Bayesian Methods for Hackers style sheet

Colorbar with `.AxesDivider` — Colorbar with AxesDivider

Controlling the position and size of colorbars with Inset Axes

Setting a fixed aspect on ImageGrid cells

:mod:`mpl_toolkits.axisartist.floating_axes` features — mpl_toolkits.axisartist.floating_axes features

Changing colors of lines intersecting a box

Building histograms using Rectangles and PolyCollections

Matplotlib logo

Create 2D bar graphs in different planes

Demonstrates plotting contour (level) curves in 3D

Demonstrates plotting contour (level) curves in 3D using the extend3d option

Projecting contour profiles onto a graph

2D and 3D *Axes* in same *Figure* — 2D and 3D Axes in same Figure

Generate polygons to fill under 3D line graph

3D voxel plot of the numpy logo

3D voxel / volumetric plot with rgb colors

3D voxel / volumetric plot with cylindrical coordinates

SkewT-logP diagram: using transforms and custom projections

Formatting date ticks using ConciseDateFormatter

Placing date ticks using recurrence rules

Set default y-axis tick labels on the right

Setting tick labels from a list of values

Set default x-axis tick labels on the top

Interactive Adjustment of Colormap Range

Customizing Matplotlib with style sheets and rcParams

*origin* and *extent* in `~.Axes.imshow` — origin and extent in imshow

matplotlib.pyplot.show

Notes

Examples using matplotlib.pyplot.show

Examples using `matplotlib.pyplot.show`