Below we describe several common approaches to plotting with Matplotlib.
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. |
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.
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:
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.)
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. |