### Related Topics

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
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)
summer sequential linearly-increasing shades of green-yellow

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.