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Matplotlib tutorial

Matplotlib tutorial

Nicolas P. Rougier

https://zenodo.org/badge/doi/10.5281/zenodo.28747.svg

Table of Contents

Introduction

Simple plot

Figures, Subplots, Axes and Ticks

Animation

Other Types of Plots

Beyond this tutorial

Quick references

Sources are available from

github

All code and material is licensed under a Creative Commons

Attribution-ShareAlike 4.0.

You can test your installation before the tutorial using the check-installation.py script.

See also:

From Python to Numpy

100 Numpy exercices

Ten simple rules for better figures

Introduction

matplotlib is probably the single most used Python package for 2D-graphics. It

provides both a very quick way to visualize data from Python and

publication-quality figures in many formats.  We are going to explore

matplotlib in interactive mode covering most common cases.

IPython

IPython is an enhanced interactive Python shell that

has lots of interesting features including named inputs and outputs, access to

shell commands, improved debugging and much more. It allows

interactive matplotlib sessions that have Matlab/Mathematica-like functionality.

pyplot

pyplot provides a convenient interface to the matplotlib object-oriented

plotting library. It is modeled closely after Matlab(TM). Therefore, the

majority of plotting commands in pyplot have Matlab(TM) analogs with similar

arguments. Important commands are explained with interactive examples.

Simple plot

In this section, we want to draw the cosine and sine functions on the same

plot. Starting from the default settings, we'll enrich the figure step by step

to make it nicer.

The first step is to get the data for the sine and cosine functions:

import numpy as np

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)

C, S = np.cos(X), np.sin(X)

X is now a NumPy array with 256 values ranging from -π to +π (included). C is

the cosine (256 values) and S is the sine (256 values).

To run the example, you can download each of the examples and run it using:

$ python exercice_1.py

You can get source for each step by clicking on the corresponding figure.

Using defaults

Documentation

plot tutorial

plot() command

Matplotlib comes with a set of default settings that allow customizing all

kinds of properties. You can control the defaults of almost every property in

matplotlib: figure size and dpi, line width, color and style, axes, axis and

grid properties, text and font properties and so on. While matplotlib defaults

are rather good in most cases, you may want to modify some properties for

specific cases.

import numpy as np

import matplotlib.pyplot as plt

X = np.linspace(-np.pi, np.pi, 256, endpoint=True)

C,S = np.cos(X), np.sin(X)

plt.plot(X,C)

plt.plot(X,S)

plt.show()

Instantiating defaults

Documentation

Customizing matplotlib

In the script below, we've instantiated (and commented) all the figure settings

that influence the appearance of the plot. The settings have been explicitly

set to their default values, but now you can interactively play with the values

to explore their affect (see Line properties and Line styles below).

# Imports

import numpy as np

import matplotlib.pyplot as plt

# Create a new figure of size 8x6 points, using 100 dots per inch

plt.figure(figsize=(8,6), dpi=100)

# Create a new subplot from a grid of 1x1

plt.subplot(111)

X = np.linspace(-np.pi, np.pi, 256,endpoint=True)

C,S = np.cos(X), np.sin(X)

# Plot cosine using blue color with a continuous line of width 1 (pixels)

plt.plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# Plot sine using green color with a continuous line of width 1 (pixels)

plt.plot(X, S, color="green", linewidth=1.0, linestyle="-")

# Set x limits

plt.xlim(-4.0,4.0)

# Set x ticks

plt.xticks(np.linspace(-4,4,9,endpoint=True))

# Set y limits

plt.ylim(-1.0,1.0)

# Set y ticks

plt.yticks(np.linspace(-1,1,5,endpoint=True))

# Save figure using 72 dots per inch

# savefig("../figures/exercice_2.png",dpi=72)

# Show result on screen

plt.show()

Changing colors and line widths

Documentation

Controlling line properties

Line API

As a first step, we want to have the cosine in blue and the sine in red and a

slightly thicker line for both of them. We'll also slightly alter the figure

size to make it more horizontal.

...

plt.figure(figsize=(10,6), dpi=80)

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")

plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-")

...

Setting limits

Documentation

xlim() command

ylim() command

Current limits of the figure are a bit too tight and we want to make some space

in order to clearly see all data points.

...

plt.xlim(X.min()*1.1, X.max()*1.1)

plt.ylim(C.min()*1.1, C.max()*1.1)

...

Setting ticks

Documentation

xticks() command

yticks() command

Tick container

Tick locating and formatting

Current ticks are not ideal because they do not show the interesting values

(+/-π,+/-π/2) for sine and cosine. We'll change them such that they show only

these values.

...

plt.xticks( [-np.pi, -np.pi/2, 0, np.pi/2, np.pi])

plt.yticks([-1, 0, +1])

...

Setting tick labels

Documentation

Working with text

xticks() command

yticks() command

set_xticklabels()

set_yticklabels()

Ticks are now properly placed but their label is not very explicit. We could

guess that 3.142 is π but it would be better to make it explicit. When we set

tick values, we can also provide a corresponding label in the second argument

list. Note that we'll use latex to allow for nice rendering of the label.

...

plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],

       [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.yticks([-1, 0, +1],

       [r'$-1$', r'$0$', r'$+1$'])

...

Moving spines

Documentation

Spines

Axis container

Transformations tutorial

Spines are the lines connecting the axis tick marks and noting the boundaries

of the data area. They can be placed at arbitrary positions and until now, they

were on the border of the axis. We'll change that since we want to have them in

the middle. Since there are four of them (top/bottom/left/right), we'll discard

the top and right by setting their color to none and we'll move the bottom and

left ones to coordinate 0 in data space coordinates.

...

ax = plt.gca()

ax.spines['right'].set_color('none')

ax.spines['top'].set_color('none')

ax.xaxis.set_ticks_position('bottom')

ax.spines['bottom'].set_position(('data',0))

ax.yaxis.set_ticks_position('left')

ax.spines['left'].set_position(('data',0))

...

Adding a legend

Documentation

Legend guide

legend() command

Legend API

Let's add a legend in the upper left corner. This only requires adding the

keyword argument label (that will be used in the legend box) to the plot

commands.

...

plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")

plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-", label="sine")

plt.legend(loc='upper left', frameon=False)

...

Annotate some points

Documentation

Annotating axis

annotate() command

Let's annotate some interesting points using the annotate command. We choose the

2π/3 value and we want to annotate both the sine and the cosine. We'll first

draw a marker on the curve as well as a straight dotted line. Then, we'll use

the annotate command to display some text with an arrow.

...

t = 2*np.pi/3

plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=1.5, linestyle="--")

plt.scatter([t,],[np.cos(t),], 50, color ='blue')

plt.annotate(r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',

             xy=(t, np.sin(t)), xycoords='data',

             xytext=(+10, +30), textcoords='offset points', fontsize=16,

             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=1.5, linestyle="--")

plt.scatter([t,],[np.sin(t),], 50, color ='red')

plt.annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',

             xy=(t, np.cos(t)), xycoords='data',

             xytext=(-90, -50), textcoords='offset points', fontsize=16,

             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

...

Devil is in the details

Documentation

Artists

BBox

The tick labels are now hardly visible because of the blue and red lines. We can

make them bigger and we can also adjust their properties such that they'll be

rendered on a semi-transparent white background. This will allow us to see both

the data and the labels.

...

for label in ax.get_xticklabels() + ax.get_yticklabels():

    label.set_fontsize(16)

    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65 ))

...

Figures, Subplots, Axes and Ticks

So far we have used implicit figure and axes creation. This is handy for fast

plots. We can have more control over the display using figure, subplot, and

axes explicitly. A figure in matplotlib means the whole window in the user

interface. Within this figure there can be subplots. While subplot positions

the plots in a regular grid, axes allows free placement within the figure. Both

can be useful depending on your intention. We've already worked with figures

and subplots without explicitly calling them. When we call plot, matplotlib

calls gca() to get the current axes and gca in turn calls gcf() to get the

current figure. If there is none it calls figure() to make one, strictly

speaking, to make a subplot(111). Let's look at the details.

Figures

A figure is the windows in the GUI that has "Figure #" as title. Figures

are numbered starting from 1 as opposed to the normal Python way starting

from 0. This is clearly MATLAB-style.  There are several parameters that

determine what the figure looks like:

Argument

Default

Description

num

1

number of figure

figsize

figure.figsize

figure size in in inches (width, height)

dpi

figure.dpi

resolution in dots per inch

facecolor

figure.facecolor

color of the drawing background

edgecolor

figure.edgecolor

color of edge around the drawing background

frameon

True

draw figure frame or not

The defaults can be specified in the resource file and will be used most of the

time. Only the number of the figure is frequently changed.

When you work with the GUI you can close a figure by clicking on the x in the

upper right corner. You can also close a figure programmatically by calling

close. Depending on the argument it closes (1) the current figure (no

argument), (2) a specific figure (figure number or figure instance as

argument), or (3) all figures (all as argument).

As with other objects, you can set figure properties with the set_something methods.

Subplots

With subplot you can arrange plots in a regular grid. You need to specify the

number of rows and columns and the number of the plot. Note that the gridspec command is a more

powerful alternative.

Axes

Axes are very similar to subplots but allow placement of plots at any location

in the figure. So if we want to put a smaller plot inside a bigger one we do

so with axes.

Ticks

Well formatted ticks are an important part of publishing-ready

figures. Matplotlib provides a totally configurable system for ticks. There are

tick locators to specify where ticks should appear and tick formatters to give

ticks the appearance you want. Major and minor ticks can be located and

formatted independently from each other. By default minor ticks are not shown,

i.e. there is only an empty list for them because it is as NullLocator (see

below).

Tick Locators

There are several locators for different kind of requirements:

Class

Description

NullLocator

No ticks.

IndexLocator

Place a tick on every multiple of some base number of points plotted.

FixedLocator

Tick locations are fixed.

LinearLocator

Determine the tick locations.

MultipleLocator

Set a tick on every integer that is multiple of some base.

AutoLocator

Select no more than n intervals at nice locations.

LogLocator

Determine the tick locations for log axes.

All of these locators derive from the base class matplotlib.ticker.Locator.

You can make your own locator deriving from it. Handling dates as ticks can be

especially tricky. Therefore, matplotlib provides special locators in

matplotlib.dates.

Animation

For quite a long time, animation in matplotlib was not an easy task and was

done mainly through clever hacks. However, things have started to change since

version 1.1 and the introduction of tools for creating animation very

intuitively, with the possibility to save them in all kind of formats (but don't

expect to be able to run very complex animations at 60 fps though).

Documentation

See Animation

The most easy way to make an animation in matplotlib is to declare a

FuncAnimation object that specifies to matplotlib what is the figure to

update, what is the update function and what is the delay between frames.

Drip drop

A very simple rain effect can be obtained by having small growing rings

randomly positioned over a figure. Of course, they won't grow forever since the

wave is supposed to damp with time. To simulate that, we can use a more and

more transparent color as the ring is growing, up to the point where it is no

more visible. At this point, we remove the ring and create a new one.

First step is to create a blank figure:

# New figure with white background

fig = plt.figure(figsize=(6,6), facecolor='white')

# New axis over the whole figure, no frame and a 1:1 aspect ratio

ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1)

Next, we need to create several rings. For this, we can use the scatter plot

object that is generally used to visualize points cloud, but we can also use it

to draw rings by specifying we don't have a facecolor. We also have to take

care of initial size and color for each ring such that we have all sizes between

a minimum and a maximum size. In addition, we need to make sure the largest ring

is almost transparent.

# Number of ring

n = 50

size_min = 50

size_max = 50*50

# Ring position

P = np.random.uniform(0,1,(n,2))

# Ring colors

C = np.ones((n,4)) * (0,0,0,1)

# Alpha color channel goes from 0 (transparent) to 1 (opaque)

C[:,3] = np.linspace(0,1,n)

# Ring sizes

S = np.linspace(size_min, size_max, n)

# Scatter plot

scat = ax.scatter(P[:,0], P[:,1], s=S, lw = 0.5,

                  edgecolors = C, facecolors='None')

# Ensure limits are [0,1] and remove ticks

ax.set_xlim(0,1), ax.set_xticks([])

ax.set_ylim(0,1), ax.set_yticks([])

Now, we need to write the update function for our animation. We know that at

each time step each ring should grow and become more transparent while the

largest ring should be totally transparent and thus removed. Of course, we won't

actually remove the largest ring but re-use it to set a new ring at a new random

position, with nominal size and color. Hence, we keep the number of rings

constant.

def update(frame):

    global P, C, S

    # Every ring is made more transparent

    C[:,3] = np.maximum(0, C[:,3] - 1.0/n)

    # Each ring is made larger

    S += (size_max - size_min) / n

    # Reset ring specific ring (relative to frame number)

    i = frame % 50

    P[i] = np.random.uniform(0,1,2)

    S[i] = size_min

    C[i,3] = 1

    # Update scatter object

    scat.set_edgecolors(C)

    scat.set_sizes(S)

    scat.set_offsets(P)

    # Return the modified object

    return scat,

Last step is to tell matplotlib to use this function as an update function for

the animation and display the result or save it as a movie:

animation = FuncAnimation(fig, update, interval=10, blit=True, frames=200)

# animation.save('rain.gif', writer='imagemagick', fps=30, dpi=40)

plt.show()

If you use IPython, you'll have to render the animation into an html video

in order to show it in the Jupyter notebook:

from IPython.display import HTML

HTML(animation.to_html5_video())

Earthquakes

We'll now use the rain animation to visualize earthquakes on the planet from

the last 30 days. The USGS Earthquake Hazards Program is part of the National

Earthquake Hazards Reduction Program (NEHRP) and provides several data on their

website. Those data are sorted according to

earthquakes magnitude, ranging from significant only down to all earthquakes,

major or minor. You would be surprised by the number of minor earthquakes

happening every hour on the planet. Since this would represent too much data

for us, we'll stick to earthquakes with magnitude > 4.5. At the time of writing,

this already represent more than 300 earthquakes in the last 30 days.

First step is to read and convert data. We'll use the urllib library that

allows us to open and read remote data. Data on the website use the CSV format

whose content is given by the first line:

time,latitude,longitude,depth,mag,magType,nst,gap,dmin,rms,net,id,updated,place,type

2015-08-17T13:49:17.320Z,37.8365,-122.2321667,4.82,4.01,mw,...

2015-08-15T07:47:06.640Z,-10.9045,163.8766,6.35,6.6,mwp,...

We are only interested in latitude, longitude and magnitude and we won't parse

time of event (ok, that's bad, feel free to send me a PR).

import urllib

# -> https://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php

feed = "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"

# Significant earthquakes in the last 30 days

# url = urllib.request.urlopen(feed + "significant_month.csv")

# Magnitude > 4.5

url = urllib.request.urlopen(feed + "4.5_month.csv")

# Magnitude > 2.5

# url = urllib.request.urlopen(feed + "2.5_month.csv")

# Magnitude > 1.0

# url = urllib.request.urlopen(feed + "1.0_month.csv")

# Reading and storage of data

data = url.read()

data = data.split(b'\n')[+1:-1]

E = np.zeros(len(data), dtype=[('position',  float, 2),

                               ('magnitude', float)])

for i in range(len(data)):

    row = data[i].split(b',')

    E['position'][i] = float(row[2]),float(row[1])

    E['magnitude'][i] = float(row[4])

Now, we need to draw the earth on a figure to show precisely where the earthquake

center is and to translate latitude/longitude in some coordinates matplotlib

can handle. Fortunately, there is the basemap project (which is now deprecated in favor

of the cartopy project) that is really

simple to install and to use. First step is to define a projection to draw the

earth onto a screen (there exists many different projections) and we'll stick

to the mill projection which is rather standard for non-specialist like me.

from mpl_toolkits.basemap import Basemap

fig = plt.figure(figsize=(14,10))

ax = plt.subplot(1,1,1)

map = Basemap(projection='mill')

Next, we request to draw coastline and fill continents:

map.drawcoastlines(color='0.50', linewidth=0.25)

map.fillcontinents(color='0.95')

For cartopy, the steps are quite similar:

import cartopy

ax = plt.axes(projection=cartopy.crs.Miller())

ax.coastlines(color='0.50', linewidth=0.25)

ax.add_feature(cartopy.feature.LAND, color='0.95')

ax.set_global()

trans = cartopy.crs.PlateCarree()

We are almost finished. Last step is to adapt the rain code and

put some eye candy. For basemap we use the map object to

transform the coordinates whereas for cartopy we use the transform_point

function of the chosen Miller projection:

P = np.zeros(50, dtype=[('position', float, 2),

                         ('size',    float),

                         ('growth',  float),

                         ('color',   float, 4)])

scat = ax.scatter(P['position'][:,0], P['position'][:,1], P['size'], lw=0.5,

                  edgecolors = P['color'], facecolors='None', zorder=10)

def update(frame):

    current = frame % len(E)

    i = frame % len(P)

    P['color'][:,3] = np.maximum(0, P['color'][:,3] - 1.0/len(P))

    P['size'] += P['growth']

    magnitude = E['magnitude'][current]

    P['position'][i] = map(*E['position'][current]) if use_basemap else \

        cartopy.crs.Miller().transform_point(*E['position'][current], cartopy.crs.PlateCarree())

    P['size'][i] = 5

    P['growth'][i]= np.exp(magnitude) * 0.1

    if magnitude < 6:

        P['color'][i]    = 0,0,1,1

    else:

        P['color'][i]    = 1,0,0,1

    scat.set_edgecolors(P['color'])

    scat.set_facecolors(P['color']*(1,1,1,0.25))

    scat.set_sizes(P['size'])

    scat.set_offsets(P['position'])

    return scat,

animation = FuncAnimation(fig, update, interval=10, blit=True)

plt.show()

If everything went well, you should obtain something like this (with animation):

Other Types of Plots

Regular Plots

Hints

You need to use the fill_between

command.

Starting from the code below, try to reproduce the graphic on the right taking

care of filled areas.

import numpy as np

import matplotlib.pyplot as plt

n = 256

X = np.linspace(-np.pi,np.pi,n,endpoint=True)

Y = np.sin(2*X)

plt.plot (X, Y+1, color='blue', alpha=1.00)

plt.plot (X, Y-1, color='blue', alpha=1.00)

plt.show()

Click on figure for solution.

Scatter Plots

Hints

Color is given by angle of (X,Y).

Starting from the code below, try to reproduce the graphic on the right taking

care of marker size, color and transparency.

import numpy as np

import matplotlib.pyplot as plt

n = 1024

X = np.random.normal(0,1,n)

Y = np.random.normal(0,1,n)

plt.scatter(X,Y)

plt.show()

Click on figure for solution.

Bar Plots

Hints

You need to take care of text alignment.

Starting from the code below, try to reproduce the graphic on the right by

adding labels for red bars.

import numpy as np

import matplotlib.pyplot as plt

n = 12

X = np.arange(n)

Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)

Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)

plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')

plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x,y in zip(X,Y1):

    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')

plt.ylim(-1.25,+1.25)

plt.show()

Click on figure for solution.

Contour Plots

Hints

You need to use the clabel

command.

Starting from the code below, try to reproduce the graphic on the right taking

care of the colormap (see Colormaps below).

import numpy as np

import matplotlib.pyplot as plt

def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 256

x = np.linspace(-3,3,n)

y = np.linspace(-3,3,n)

X,Y = np.meshgrid(x,y)

plt.contourf(X, Y, f(X,Y), 8, alpha=.75, cmap='jet')

C = plt.contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)

plt.show()

Click on figure for solution.

Imshow

Hints

You need to take care of the origin of the image in the imshow command and

use a colorbar.

Starting from the code below, try to reproduce the graphic on the right taking

care of colormap, image interpolation and origin.

import numpy as np

import matplotlib.pyplot as plt

def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 10

x = np.linspace(-3,3,4*n)

y = np.linspace(-3,3,3*n)

X,Y = np.meshgrid(x,y)

plt.imshow(f(X,Y))

plt.show()

Click on figure for solution.

Pie Charts

Hints

You need to modify Z.

Starting from the code below, try to reproduce the graphic on the right taking

care of colors and slices size.

import numpy as np

import matplotlib.pyplot as plt

n = 20

Z = np.random.uniform(0,1,n)

plt.pie(Z)

plt.show()

Click on figure for solution.

Quiver Plots

Hints

You need to draw arrows twice.

Starting from the code above, try to reproduce the graphic on the right taking

care of colors and orientations.

import numpy as np

import matplotlib.pyplot as plt

n = 8

X,Y = np.mgrid[0:n,0:n]

plt.quiver(X,Y)

plt.show()

Click on figure for solution.

Grids

Starting from the code below, try to reproduce the graphic on the right taking

care of line styles.

import numpy as np

import matplotlib.pyplot as plt

axes = gca()

axes.set_xlim(0,4)

axes.set_ylim(0,3)

axes.set_xticklabels([])

axes.set_yticklabels([])

plt.show()

Click on figure for solution.

Multi Plots

Hints

You can use several subplots with different partition.

Starting from the code below, try to reproduce the graphic on the right.

import numpy as np

import matplotlib.pyplot as plt

plt.subplot(2,2,1)

plt.subplot(2,2,3)

plt.subplot(2,2,4)

plt.show()

Click on figure for solution.

Polar Axis

Hints

You only need to modify the axes line.

Starting from the code below, try to reproduce the graphic on the right.

import numpy as np

import matplotlib.pyplot as plt

plt.axes([0,0,1,1])

N = 20

theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)

radii = 10*np.random.rand(N)

width = np.pi/4*np.random.rand(N)

bars = plt.bar(theta, radii, width=width, bottom=0.0)

for r,bar in zip(radii, bars):

    bar.set_facecolor( cm.jet(r/10.))

    bar.set_alpha(0.5)

plt.show()

Click on figure for solution.

3D Plots

Hints

You need to use contourf.

Starting from the code below, try to reproduce the graphic on the right.

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

ax = Axes3D(fig)

X = np.arange(-4, 4, 0.25)

Y = np.arange(-4, 4, 0.25)

X, Y = np.meshgrid(X, Y)

R = np.sqrt(X**2 + Y**2)

Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')

plt.show()

Click on figure for solution.

Text

Hints

Have a look at the matplotlib logo.

Try to do the same from scratch!

Click on figure for solution.

Beyond this tutorial

Matplotlib benefits from extensive documentation as well as a large

community of users and developpers. Here are some links of interest:

Tutorials

Pyplot tutorial

Introduction

Controlling line properties

Working with multiple figures and axes

Working with text

Image tutorial

Startup commands

Importing image data into Numpy arrays

Plotting numpy arrays as images

Text tutorial

Text introduction

Basic text commands

Text properties and layout

Writing mathematical expressions

Text rendering With LaTeX

Annotating text

Artist tutorial

Introduction

Customizing your objects

Object containers

Figure container

Axes container

Axis containers

Tick containers

Path tutorial

Introduction

Bézier example

Compound paths

Transforms tutorial

Introduction

Data coordinates

Axes coordinates

Blended transformations

Using offset transforms to create a shadow effect

The transformation pipeline

Matplotlib documentation

User guide

FAQ

Installation

Usage

How-To

Troubleshooting

Environment Variables

Screenshots

Code documentation

The code is fairly well documented and you can quickly access a specific

command from within a python session:

>>> import matplotlib.pyplot as plt

>>> help(plt)

Help on function plot in module matplotlib.pyplot:

plot(*args, **kwargs)

   Plot lines and/or markers to the

   :class:`~matplotlib.axes.Axes`.  *args* is a variable length

   argument, allowing for multiple *x*, *y* pairs with an

   optional format string.  For example, each of the following is

   legal::

       plot(x, y)         # plot x and y using default line style and color

       plot(x, y, 'bo')   # plot x and y using blue circle markers

       plot(y)            # plot y using x as index array 0..N-1

       plot(y, 'r+')      # ditto, but with red plusses

   If *x* and/or *y* is 2-dimensional, then the corresponding columns

   will be plotted.

   ...

Galleries

The matplotlib gallery is

also incredibly useful when you search how to render a given graphic. Each

example comes with its source.

Mailing lists

Finally, there is a user mailing list where you can

ask for help and a developers mailing list that is more

technical.

Quick references

Here is a set of tables that show main properties and styles.

Line properties

Property

Description

Appearance

alpha (or a)

alpha transparency on 0-1 scale

antialiased

True or False - use antialised rendering

color (or c)

matplotlib color arg

linestyle (or ls)

see Line properties

linewidth (or lw)

float, the line width in points

solid_capstyle

Cap style for solid lines

solid_joinstyle

Join style for solid lines

dash_capstyle

Cap style for dashes

dash_joinstyle

Join style for dashes

marker

see Markers

markeredgewidth (mew)

line width around the marker symbol

markeredgecolor (mec)

edge color if a marker is used

markerfacecolor (mfc)

face color if a marker is used

markersize (ms)

size of the marker in points

Line styles

Symbol

Description

Appearance

-

solid line

--

dashed line

-.

dash-dot line

:

dotted line

.

points

,

pixels

o

circle

^

triangle up

v

triangle down

<

triangle left

>

triangle right

s

square

+

plus

x

cross

D

diamond

d

thin diamond

1

tripod down

2

tripod up

3

tripod left

4

tripod right

h

hexagon

H

rotated hexagon

p

pentagon

|

vertical line

_

horizontal line

Markers

Symbol

Description

Appearance

0

tick left

1

tick right

2

tick up

3

tick down

4

caret left

5

caret right

6

caret up

7

caret down

o

circle

D

diamond

h

hexagon 1

H

hexagon 2

_

horizontal line

1

tripod down

2

tripod up

3

tripod left

4

tripod right

8

octagon

p

pentagon

^

triangle up

v

triangle down

<

triangle left

>

triangle right

d

thin diamond

,

pixel

+

plus

.

point

s

square

*

star

|

vertical line

x

cross

r'$\sqrt{2}$'

any latex expression

Colormaps

All colormaps can be reversed by appending _r. For instance, gray_r is

the reverse of gray.

If you want to know more about colormaps, see Documenting the matplotlib

colormaps.

Base

Name

Appearance

autumn

bone

cool

copper

flag

gray

hot

hsv

jet

pink

prism

spectral

spring

summer

winter

GIST

Name

Appearance

gist_earth

gist_gray

gist_heat

gist_ncar

gist_rainbow

gist_stern

gist_yarg

Diverging

Name

Appearance

BrBG

PiYG

PRGn

PuOr

RdBu

RdGy

RdYlBu

RdYlGn

Spectral

Sequential

Name

Appearance

Blues

BuGn

BuPu

GnBu

Greens

Greys

Oranges

OrRd

PuBu

PuBuGn

PuRd

Purples

RdPu

Reds

YlGn

YlGnBu

YlOrBr

YlOrRd

Qualitative

Name

Appearance

Accent

Dark2

Paired

Pastel1

Pastel2

Set1

Set2

Set3

Miscellaneous

Name

Appearance

afmhot

binary

brg

bwr

coolwarm

CMRmap

cubehelix

gnuplot

gnuplot2

ocean

rainbow

seismic

terrain

https://github.com/rougier/matplotlib-tutorial

Awesome Lists containing this project

Matplotlib tutorial

Nicolas P. Rougier

IPython

pyplot

Using defaults

Instantiating defaults

Changing colors and line widths

Setting limits

Setting ticks

Setting tick labels

Moving spines

Adding a legend

Annotate some points

Devil is in the details

Figures

Subplots

Axes

Ticks

Tick Locators

Drip drop

Earthquakes

Regular Plots

Scatter Plots

Bar Plots

Contour Plots

Imshow

Pie Charts

Quiver Plots

Grids

Multi Plots

Polar Axis

3D Plots

Text

Tutorials

Matplotlib documentation

Code documentation

Galleries

Mailing lists

Line properties

Line styles

Markers

Colormaps

Base

GIST

Diverging

Sequential

Qualitative

Miscellaneous