Below we describe several common approaches to plotting with Matplotlib.

The Pyplot API

The matplotlib.pyplot module contains functions that allow you to generate many kinds of plots quickly. For examples that showcase the use of the matplotlib.pyplot module, see the Pyplot tutorial or the Pyplot. We also recommend that you look into the object-oriented approach to plotting, described below.

matplotlib.pyplot.plotting() [source]

Function	Description
`acorr`	Plot the autocorrelation of x.
`angle_spectrum`	Plot the angle spectrum.
`annotate`	Annotate the point `xy` with text `s`.
`arrow`	Add an arrow to the axes.
`autoscale`	Autoscale the axis view to the data (toggle).
`axes`	Add an axes to the current figure and make it the current axes.
`axhline`	Add a horizontal line across the axis.
`axhspan`	Add a horizontal span (rectangle) across the axis.
`axis`	Convenience method to get or set axis properties.
`axvline`	Add a vertical line across the axes.
`axvspan`	Add a vertical span (rectangle) across the axes.
`bar`	Make a bar plot.
`barbs`	Plot a 2-D field of barbs.
`barh`	Make a horizontal bar plot.
`box`	Turn the axes box on or off on the current axes.
`boxplot`	Make a box and whisker plot.
`broken_barh`	Plot a horizontal sequence of rectangles.
`cla`	Clear the current axes.
`clabel`	Label a contour plot.
`clf`	Clear the current figure.
`clim`	Set the color limits of the current image.
`close`	Close a figure window.
`cohere`	Plot the coherence between x and y.
`colorbar`	Add a colorbar to a plot.
`contour`	Plot contours.
`contourf`	Plot contours.
`csd`	Plot the cross-spectral density.
`delaxes`	Remove the given `Axes` ax from the current figure.
`draw`	Redraw the current figure.
`errorbar`	Plot y versus x as lines and/or markers with attached errorbars.
`eventplot`	Plot identical parallel lines at the given positions.
`figimage`	Add a non-resampled image to the figure.
`figlegend`	Place a legend in the figure.
`fignum_exists`
`figtext`	Add text to figure.
`figure`	Creates a new figure.
`fill`	Plot filled polygons.
`fill_between`	Fill the area between two horizontal curves.
`fill_betweenx`	Fill the area between two vertical curves.
`findobj`	Find artist objects.
`gca`	Get the current `Axes` instance on the current figure matching the given keyword args, or create one.
`gcf`	Get a reference to the current figure.
`gci`	Get the current colorable artist.
`get_figlabels`	Return a list of existing figure labels.
`get_fignums`	Return a list of existing figure numbers.
`grid`	Turn the axes grids on or off.
`hexbin`	Make a hexagonal binning plot.
`hist`	Plot a histogram.
`hist2d`	Make a 2D histogram plot.
`hlines`	Plot horizontal lines at each y from xmin to xmax.
`hold`
`imread`	Read an image from a file into an array.
`imsave`	Save an array as in image file.
`imshow`	Display an image on the axes.
`install_repl_displayhook`	Install a repl display hook so that any stale figure are automatically redrawn when control is returned to the repl.
`ioff`	Turn interactive mode off.
`ion`	Turn interactive mode on.
`ishold`
`isinteractive`	Return status of interactive mode.
`legend`	Places a legend on the axes.
`locator_params`	Control behavior of tick locators.
`loglog`	Make a plot with log scaling on both the x and y axis.
`magnitude_spectrum`	Plot the magnitude spectrum.
`margins`	Set or retrieve autoscaling margins.
`matshow`	Display an array as a matrix in a new figure window.
`minorticks_off`	Remove minor ticks from the current plot.
`minorticks_on`	Display minor ticks on the current plot.
`over`
`pause`	Pause for interval seconds.
`pcolor`	Create a pseudocolor plot with a non-regular rectangular grid.
`pcolormesh`	Create a pseudocolor plot with a non-regular rectangular grid.
`phase_spectrum`	Plot the phase spectrum.
`pie`	Plot a pie chart.
`plot`	Plot y versus x as lines and/or markers.
`plot_date`	Plot data that contains dates.
`plotfile`	Plot the data in a file.
`polar`	Make a polar plot.
`psd`	Plot the power spectral density.
`quiver`	Plot a 2-D field of arrows.
`quiverkey`	Add a key to a quiver plot.
`rc`	Set the current rc params.
`rc_context`	Return a context manager for managing rc settings.
`rcdefaults`	Restore the rc params from Matplotlib's internal defaults.
`rgrids`	Get or set the radial gridlines on a polar plot.
`savefig`	Save the current figure.
`sca`	Set the current Axes instance to ax.
`scatter`	A scatter plot of y vs x with varying marker size and/or color.
`sci`	Set the current image.
`semilogx`	Make a plot with log scaling on the x axis.
`semilogy`	Make a plot with log scaling on the y axis.
`set_cmap`	Set the default colormap.
`setp`	Set a property on an artist object.
`show`	Display a figure.
`specgram`	Plot a spectrogram.
`spectral`	Set the colormap to "spectral".
`spy`	Plot the sparsity pattern on a 2-D array.
`stackplot`	Draws a stacked area plot.
`stem`	Create a stem plot.
`step`	Make a step plot.
`streamplot`	Draws streamlines of a vector flow.
`subplot`	Return a subplot axes at the given grid position.
`subplot2grid`	Create an axis at specific location inside a regular grid.
`subplot_tool`	Launch a subplot tool window for a figure.
`subplots`	Create a figure and a set of subplots This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
`subplots_adjust`	Tune the subplot layout.
`suptitle`	Add a centered title to the figure.
`switch_backend`	Switch the default backend.
`table`	Add a table to the current axes.
`text`	Add text to the axes.
`thetagrids`	Get or set the theta locations of the gridlines in a polar plot.
`tick_params`	Change the appearance of ticks, tick labels, and gridlines.
`ticklabel_format`	Change the `ScalarFormatter` used by default for linear axes.
`tight_layout`	Automatically adjust subplot parameters to give specified padding.
`title`	Set a title of the current axes.
`tricontour`	Draw contours on an unstructured triangular grid.
`tricontourf`	Draw contours on an unstructured triangular grid.
`tripcolor`	Create a pseudocolor plot of an unstructured triangular grid.
`triplot`	Draw a unstructured triangular grid as lines and/or markers.
`twinx`	Make a second axes that shares the x-axis.
`twiny`	Make a second axes that shares the y-axis.
`uninstall_repl_displayhook`	Uninstalls the matplotlib display hook.
`violinplot`	Make a violin plot.
`vlines`	Plot vertical lines.
`xcorr`	Plot the cross correlation between x and y.
`xkcd`	Turns on xkcd sketch-style drawing mode.
`xlabel`	Set the x-axis label of the current axes.
`xlim`	Get or set the x limits of the current axes.
`xscale`	Set the scaling of the x-axis.
`xticks`	Get or set the current tick locations and labels of the x-axis.
`ylabel`	Set the y-axis label of the current axes.
`ylim`	Get or set the y-limits of the current axes.
`yscale`	Set the scaling of the y-axis.
`yticks`	Get or set the current tick locations and labels of the y-axis.

The Object-Oriented API

Most of these functions also exist as methods in the matplotlib.axes.Axes class. You can use them with the "Object Oriented" approach to Matplotlib.

While it is easy to quickly generate plots with the matplotlib.pyplot module, we recommend using the object-oriented approach for more control and customization of your plots. See the methods in the matplotlib.axes.Axes() class for many of the same plotting functions. For examples of the OO approach to Matplotlib, see the API Examples.

Colors in Matplotlib

There are many colormaps you can use to map data onto color values. Below we list several ways in which color can be utilized in Matplotlib.

For a more in-depth look at colormaps, see the Colormaps in Matplotlib tutorial.

matplotlib.pyplot.colormaps() [source]

Matplotlib provides a number of colormaps, and others can be added using register_cmap(). This function documents the built-in colormaps, and will also return a list of all registered colormaps if called.

You can set the colormap for an image, pcolor, scatter, etc, using a keyword argument:

imshow(X, cmap=cm.hot)

or using the set_cmap() function:

imshow(X)
pyplot.set_cmap('hot')
pyplot.set_cmap('jet')

In interactive mode, set_cmap() will update the colormap post-hoc, allowing you to see which one works best for your data.

All built-in colormaps can be reversed by appending _r: For instance, gray_r is the reverse of gray.

There are several common color schemes used in visualization:

Sequential schemes: for unipolar data that progresses from low to high
Diverging schemes: for bipolar data that emphasizes positive or negative deviations from a central value
Cyclic schemes: meant for plotting values that wrap around at the endpoints, such as phase angle, wind direction, or time of day
Qualitative schemes: for nominal data that has no inherent ordering, where color is used only to distinguish categories

Matplotlib ships with 4 perceptually uniform color maps which are the recommended color maps for sequential data:

Colormap	Description
inferno	perceptually uniform shades of black-red-yellow
magma	perceptually uniform shades of black-red-white
plasma	perceptually uniform shades of blue-red-yellow
viridis	perceptually uniform shades of blue-green-yellow

The following colormaps are based on the ColorBrewer color specifications and designs developed by Cynthia Brewer:

ColorBrewer Diverging (luminance is highest at the midpoint, and decreases towards differently-colored endpoints):

Colormap	Description
BrBG	brown, white, blue-green
PiYG	pink, white, yellow-green
PRGn	purple, white, green
PuOr	orange, white, purple
RdBu	red, white, blue
RdGy	red, white, gray
RdYlBu	red, yellow, blue
RdYlGn	red, yellow, green
Spectral	red, orange, yellow, green, blue

ColorBrewer Sequential (luminance decreases monotonically):

Colormap	Description
Blues	white to dark blue
BuGn	white, light blue, dark green
BuPu	white, light blue, dark purple
GnBu	white, light green, dark blue
Greens	white to dark green
Greys	white to black (not linear)
Oranges	white, orange, dark brown
OrRd	white, orange, dark red
PuBu	white, light purple, dark blue
PuBuGn	white, light purple, dark green
PuRd	white, light purple, dark red
Purples	white to dark purple
RdPu	white, pink, dark purple
Reds	white to dark red
YlGn	light yellow, dark green
YlGnBu	light yellow, light green, dark blue
YlOrBr	light yellow, orange, dark brown
YlOrRd	light yellow, orange, dark red

ColorBrewer Qualitative:

(For plotting nominal data, ListedColormap is used, not LinearSegmentedColormap. Different sets of colors are recommended for different numbers of categories.)

Accent
Dark2
Paired
Pastel1
Pastel2
Set1
Set2
Set3

A set of colormaps derived from those of the same name provided with Matlab are also included:

Colormap	Description
autumn	sequential linearly-increasing shades of red-orange-yellow
bone	sequential increasing black-white color map with a tinge of blue, to emulate X-ray film
cool	linearly-decreasing shades of cyan-magenta
copper	sequential increasing shades of black-copper
flag	repetitive red-white-blue-black pattern (not cyclic at endpoints)
gray	sequential linearly-increasing black-to-white grayscale
hot	sequential black-red-yellow-white, to emulate blackbody radiation from an object at increasing temperatures
hsv	cyclic red-yellow-green-cyan-blue-magenta-red, formed by changing the hue component in the HSV color space
jet	a spectral map with dark endpoints, blue-cyan-yellow-red; based on a fluid-jet simulation by NCSA [1]
pink	sequential increasing pastel black-pink-white, meant for sepia tone colorization of photographs
prism	repetitive red-yellow-green-blue-purple-...-green pattern (not cyclic at endpoints)
spring	linearly-increasing shades of magenta-yellow
summer	sequential linearly-increasing shades of green-yellow
winter	linearly-increasing shades of blue-green

A set of palettes from the Yorick scientific visualisation package, an evolution of the GIST package, both by David H. Munro are included:

Colormap	Description
gist_earth	mapmaker's colors from dark blue deep ocean to green lowlands to brown highlands to white mountains
gist_heat	sequential increasing black-red-orange-white, to emulate blackbody radiation from an iron bar as it grows hotter
gist_ncar	pseudo-spectral black-blue-green-yellow-red-purple-white colormap from National Center for Atmospheric Research [2]
gist_rainbow	runs through the colors in spectral order from red to violet at full saturation (like hsv but not cyclic)
gist_stern	"Stern special" color table from Interactive Data Language software

Other miscellaneous schemes:

Colormap	Description
afmhot	sequential black-orange-yellow-white blackbody spectrum, commonly used in atomic force microscopy
brg	blue-red-green
bwr	diverging blue-white-red
coolwarm	diverging blue-gray-red, meant to avoid issues with 3D shading, color blindness, and ordering of colors [3]
CMRmap	"Default colormaps on color images often reproduce to confusing grayscale images. The proposed colormap maintains an aesthetically pleasing color image that automatically reproduces to a monotonic grayscale with discrete, quantifiable saturation levels." [4]
cubehelix	Unlike most other color schemes cubehelix was designed by D.A. Green to be monotonically increasing in terms of perceived brightness. Also, when printed on a black and white postscript printer, the scheme results in a greyscale with monotonically increasing brightness. This color scheme is named cubehelix because the r,g,b values produced can be visualised as a squashed helix around the diagonal in the r,g,b color cube.
gnuplot	gnuplot's traditional pm3d scheme (black-blue-red-yellow)
gnuplot2	sequential color printable as gray (black-blue-violet-yellow-white)
ocean	green-blue-white
rainbow	spectral purple-blue-green-yellow-orange-red colormap with diverging luminance
seismic	diverging blue-white-red
nipy_spectral	black-purple-blue-green-yellow-red-white spectrum, originally from the Neuroimaging in Python project
terrain	mapmaker's colors, blue-green-yellow-brown-white, originally from IGOR Pro

The following colormaps are redundant and may be removed in future versions. It's recommended to use the names in the descriptions instead, which produce identical output:

Colormap	Description
gist_gray	identical to gray
gist_yarg	identical to gray_r
binary	identical to gray_r
spectral	identical to nipy_spectral [5]

Footnotes

[1]	Rainbow colormaps, `jet` in particular, are considered a poor choice for scientific visualization by many researchers: Rainbow Color Map (Still) Considered Harmful

[2]	Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command Language. See Color Table Gallery

[3]	See Diverging Color Maps for Scientific Visualization by Kenneth Moreland.

[4]	See A Color Map for Effective Black-and-White Rendering of Color-Scale Images by Carey Rappaport

[5]	Changed to distinguish from ColorBrewer's Spectral map. `spectral()` still works, but `set_cmap('nipy_spectral')` is recommended for clarity.