Scales
Basic plots
Version 3.5.0
API
API
ax.set_[xy]scale(scale,…)
linear
log
+ any values
0
+ values > 0
API
-
Tick locators
API
756
0.0
0.0
432
1
2.5
2.510102101 0logit
2 0 2 symlog
Quick start
765 1234567 X,scatter(X,Y,…)
0.0
0.0
Y, [s]izes, [c]olors, marker, cmap
423
2.5 1000100any values2.5 1 0 < values < 1
import numpy as np
1
import matplotlib as mpl
2
import matplotlib.pyplot as plt
756 1234567 x,bar[h](x,height,…)
Projections
height, width, bottom, align, color
432
subplot(…,projection=p)
1
p=’polar’
X = np.linspace(0, 2*np.pi, 100)
p=’3d’
765 1234567 imshow(Z,…)
Y = np.cos(X)
Z, cmap, interpolation, extent, origin
3421
fig, ax = plt.subplots()
p=Orthographic()
ax.plot(X, Y, color=’green’)
from cartopy.crs import Cartographic
6754 1234567 contour[f]([X],[Y],Z,…)
X, Y, Z, levels, colors, extent, origin
fig.savefig(“figure.pdf”)
3
21
fig.show()
32 1234567 pcolormesh([X],[Y],Z,…)
10
X, Y, Z, vmin, vmax, cmap
Anatomy of a figure
Lines
12
Anatomy of a figure
linestyle or ls
7653 3210123 quiver([X],[Y],U,V,…)
X, Y, U, V, C, units, angles
432
capstyle or dash_capstyle
1
pie(X,…)
765 1234567 Z, explode, labels, colors, radius
432
Markers
1
765 1234567T x,text(x,y,text,…)
432 TEX y, text, va, ha, size, weight, transform
1
756 1234567 X,fill[_between][x](…)
Y1, Y2, color, where
432
1
markevery
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10
[0, -1]
(25, 5)
[0, 25, -1]
Advanced plots
step(X,Y,[fmt],…)
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Colors
X, Y, fmt, color, marker, where
432
1
Subplots layout
765 1234567 X,boxplot(X,…)
notch, sym, bootstrap, widths
subplot[s](rows,cols,…)
42
fig, axs = plt.subplots(3, 3) 3
1
756 246 X,errorbar(X,Y,xerr,yerr,…)
Y, xerr, yerr, fmt
G = gridspec(rows,cols,…)
432
Colormaps
ax = G[0,:]
1
bins, …)
plt.get_cmap(name)
71
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61
bins, range, density, weights
51
ax.inset_axes(extent)
41
Uniform
31
21
111
756 1234567 violinplot(D,…)
D, positions, widths, vert
d=make_axes_locatable(ax)
42
Sequential
ax = d.new_horizontal(’10%’) 3
1
U, V, …)
765 1234567 barbs([X],[Y],
X, Y, U, V, C, length, pivot, sizes
4
Diverging
231
Getting help
eventplot(positions,…)
Å matplotlib.org
6754 1234567 positions, orientation, lineoffsets
H github.com/matplotlib/matplotlib/issues
321
ď discourse.matplotlib.org
Qualitative
W stackoverflow.com/questions/tagged/matplotlib
hexbin(X,Y,C,…)
7
1234567
654
Ż gitter.im/matplotlib
X, Y, C, gridsize, bins
F twitter.com/matplotlib
Cyclic
321
a Matplotlib users mailing list
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Cheat sheet
plot([X],Y,[fmt],…)
X, Y, fmt, color, marker, linestyle
+
0
1
API
API
API
API
API
4
Title
Major tick
Blue signal
Red signal
Legend
Minor tick
API
"-"
Major tick label
"--"
"butt"
API
3
":"
"-."
"round"
(0,(0.01,2))
"projecting"
Grid
Y axis label
Line
(line plot)
API
API
2
Y axis label
Markers
(scatter plot)
API
1
Figure
0
'.'
'o'
's'
'P'
'X'
'*'
'p'
'D'
'<'
'>'
'^'
'v'
'1'
'2'
'3'
'4'
'+'
'x'
'|'
'_'
4
5
6
7
'$ $''$ $''$ $''$ $''$ $''$ $''$ $''$ $''$ $''$ $''$ $''$ $'
Axes
Line
(line plot)
Spines
0 0.25 0.50 0.75 1 1.25 1.50 1.75 2 2.25 2.50 2.75 3 3.25 3.50 3.75 4
X axis label
Minor tick label
X axis label
API
API
API
API
API
API
API
T = np.linspace(0, 2*np.pi, 100)
S = np.sin(T)
line, = plt.plot(T, S)
def animate(i):
line.set_ydata(np.sin(T+i/50))
anim = mpla.FuncAnimation(
plt.gcf(), animate, interval=5)
plt.show()
ticker.MultipleLocator(0.5)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
ticker.FixedLocator([0, 1, 5])
0
1
5
ticker.LinearLocator(numticks=3)
0.0
2.5
5.0
ticker.IndexLocator(base=0.5, offset=0.25)
0.25
0.75
1.25
1.75
2.25
2.75
3.25
ticker.AutoLocator()
0
1
2
ticker.MaxNLocator(n=4)
0.0
3.75
3
1.5
4.25
4.75
4
5
3.0
Styles
4.5
104
105
107
106
108
API
viridis
magma
plasma
API
Greys
YlOrBr
Wistia
API
API
API
Spectral
coolwarm
RdGy
tab10
tab20
twilight
API
plt.style.use(style)
ticker.LogLocator(base=10, numticks=15)
103
109
1010
default
1.0
Tick formatters
API
0.5
0.0
0.0
0.5
0.5
0
1
2
3
4
5
6
ggplot
1.0
])
2
3
4
5
6
bmh
0.5
0.0
0.5
0.5
four
[4.00]
[5.00]
>3<
>4<
>5<
3
4
5
1.0
6
7
0
1
2
3
4
5
6
1.0
1.0
0
1
2
3
4
5
6
4
5
6
fast
1.0
0.5
0.0
0.5
1.0
1
2
3
4
5
6
Solarize_Light2
1.0
0.0
three
[3.00]
5
seaborn
0
0.5
two
[2.00]
4
1.0
five
one
[1.00]
3
0.0
1
ticker.FuncFormatter(lambda x, pos: "[%.2f]" % x)
zero
2
0.5
0
1.0
[0.00]
1
0.5
0.5
ticker.FixedFormatter(['zero', 'one', 'two',
0.5
0
1.0
0.5
1.0
1.0
grayscale
1.0
0.5
0.0
0.0
ticker.NullFormatter()
classic
1.0
0.5
1.0
from matplotlib import ticker
ax.[xy]axis.set_[minor|major]_formatter(formatter)
0
1
2
3
seaborn-notebook
1.0
0.5
0.0
0.5
1.0
0
1
2
3
4
5
6
0
1
2
3
4
5
6
ticker.FormatStrFormatter('>%d<')
>0<
>1<
>2<
ticker.ScalarFormatter()
1
0
2
Quick reminder
ticker.StrMethodFormatter('{x}')
0.0
1.0
2.0
3.0
4.0
5.0
80%
100%
ticker.PercentFormatter(xmax=5)
0%
20%
40%
60%
Ornaments
ax.legend(…)
handles, labels, loc, title, frameon
API
Legend
label
handletextpad
handlelength
handle
markerfacecolor (mfc)
Label 1
Label 3
labelspacing
markeredgecolor (mec)
Label 2
borderpad
Label 4
Keyboard shortcuts
numpoints or scatterpoints
columnspacing
borderaxespad
ax.colorbar(…)
mappable, ax, cax, orientation
0.0
0.1
0.2
0.3
0.4
0.5
0.6
API
text
ctrl + s Save
r
f
0.7
0.8
0.9
1.0
ax.annotate(…)
API
text, xy, xytext, xycoords, textcoords, arrowprops
xytext
textcoords
ax.grid()
ax.set_[xy]lim(vmin, vmax)
ax.set_[xy]label(label)
ax.set_[xy]ticks(ticks, [labels])
ax.set_[xy]ticklabels(labels)
ax.set_title(title)
ax.tick_params(width=10, …)
ax.set_axis_[on|off]()
fig.suptitle(title)
fig.tight_layout()
plt.gcf(), plt.gca()
mpl.rc(’axes’, linewidth=1, …)
[fig|ax].patch.set_alpha(0)
text=r’$\frac{-e^{i\pi}}{2^n}$’
title
API
API
API
ticker.NullLocator()
API
1 C0 C1 C2 C3 C4 C5 C6 C7 C8 C9
’Cn’
0
0 b 2 g 4 r 6 c 8 m 10 y 12 k 14 w 16 ’x’
1 DarkRed Firebrick Crimson IndianRed Salmon
’name’
10 (1,0,0) (1,0,0,0.75) (1,0,0,0.5) (1,0,0,0.25)
0
2
4
6
8
10 12 14 16 (R,G,B[,A])
10 #FF0000
0
2
4 #FF0000BB
6
8 #FF000088
10 12 #FF000044
14 16 ’#RRGGBB[AA]’
10 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0
2
4
6
8
10 12 14 16 ’x.y’
0
0
2
4
6
8
10 12 14 16
Animation
import matplotlib.animation as mpla
API
API
API
from matplotlib import ticker
ax.[xy]axis.set_[minor|major]_locator(locator)
xy
xycoords
Event handling
fig, ax = plt.subplots()
def on_click(event):
print(event)
fig.canvas.mpl_connect(
’button_press_event’, on_click)
API
p
x
g
l
Reset view
View forward
Pan view
X pan/zoom
Minor grid 0/1
X axis log/linear
API
ctrl + w Close plot
f
Fullscreen 0/1
b View back
o Zoom to rect
y Y pan/zoom
G Major grid 0/1
L Y axis log/linear
Ten simple rules
1. Know your audience
2. Identify your message
3. Adapt the figure
4. Captions are not optional
5. Do not trust the defaults
6. Use color effectively
7. Do not mislead the reader
8. Avoid “chartjunk”
9. Message trumps beauty
10. Get the right tool
READ
Axes adjustments
API
Color names
Uniform colormaps
plt.subplots_adjust( … )
viridis
plasma
inferno
magma
top
axes width
cividis
axes height
figure height
Sequential colormaps
hspace
Greys
Purples
Blues
Greens
bottom
left
wspace
right
Oranges
Reds
YlOrBr
figure width
YlOrRd
OrRd
Extent & origin
PuRd
API
RdPu
ax.imshow( extent=…, origin=… )
5
(0,0)
origin="upper"
BuPu
GnBu
origin="upper"
PuBu
(0,0)
YlGnBu
PuBuGn
0
extent=[0,10,0,5]
origin="lower"
5
(0,0)
0
(4,4)
(4,4)
(4,4)
(4,4)
extent=[0,10,0,5]
BuGn
origin="lower"
10
darkturquoise
cadetblue
powderblue
lightblue
deepskyblue
skyblue
lightskyblue
steelblue
aliceblue
dodgerblue
lightslategray
lightslategrey
slategray
slategrey
lightsteelblue
cornflowerblue
royalblue
ghostwhite
lavender
midnightblue
navy
darkblue
mediumblue
b
blue
slateblue
darkslateblue
mediumslateblue
mediumpurple
rebeccapurple
blueviolet
indigo
darkorchid
darkviolet
mediumorchid
thistle
plum
violet
purple
darkmagenta
m
fuchsia
magenta
orchid
mediumvioletred
deeppink
hotpink
lavenderblush
palevioletred
crimson
pink
lightpink
Image interpolation
API
PiYG
(0,0)
0
BrBG
Text alignments
API
Matplotlib
center
How do I …
L
A 2
K
9
J
1
B 6
10
7 H
C 3
D
8
E
4 G
F
I
ax.legend(loc=”string”, bbox_to_anchor=(x,y))
2: upper left
6: center left
3: lower left
9: upper center
10: center
8: lower center
A: upper right / (-0.1,0.9)
1: upper right
7: center right
4: lower right
B: center right / (-0.1,0.5)
C: lower right / (-0.1,0.1)
D: upper left / (0.1,-0.1)
E: upper center / (0.5,-0.1)
F: upper right / (0.9,-0.1)
G: lower left / (1.1,0.1)
H: center left / (1.1,0.5)
I: upper left / (1.1,0.9)
J: lower right / (0.9,1.1)
K: lower center / (0.5,1.1)
L: lower left / (0.1,1.1)
None
none
nearest
bilinear
bicubic
spline16
arc3,
rad=0
arc3,
rad=0.3
angle3,
angleA=0,
angleB=90
angle,
angleA=-90,
angleB=180,
rad=0
angle,
angleA=-90,
angleB=180,
rad=25
arc,
angleA=-90,
angleB=0,
armA=0,
armB=40,
rad=0
API
RdGy
RdBu
ax.text( …, ha=… , va=…, …)
(1,1)
Legend placement
Annotation connection styles
PRGn
10
PuOr
(0,0)
left
API
floralwhite
darkgoldenrod
goldenrod
cornsilk
gold
lemonchiffon
khaki
palegoldenrod
darkkhaki
ivory
beige
lightyellow
lightgoldenrodyellow
olive
y
yellow
olivedrab
yellowgreen
darkolivegreen
greenyellow
chartreuse
lawngreen
honeydew
darkseagreen
palegreen
lightgreen
forestgreen
limegreen
darkgreen
g
green
lime
seagreen
mediumseagreen
springgreen
mintcream
mediumspringgreen
mediumaquamarine
aquamarine
turquoise
lightseagreen
mediumturquoise
azure
lightcyan
paleturquoise
darkslategray
darkslategrey
teal
darkcyan
c
aqua
cyan
Diverging colormaps
extent=[10,0,0,5]
0
YlGn
extent=[10,0,0,5]
black
k
dimgray
dimgrey
gray
grey
darkgray
darkgrey
silver
lightgray
lightgrey
gainsboro
whitesmoke
w
white
snow
rosybrown
lightcoral
indianred
brown
firebrick
maroon
darkred
r
red
mistyrose
salmon
tomato
darksalmon
coral
orangered
lightsalmon
sienna
seashell
chocolate
saddlebrown
sandybrown
peachpuff
peru
linen
bisque
darkorange
burlywood
antiquewhite
tan
navajowhite
blanchedalmond
papayawhip
moccasin
orange
wheat
oldlace
RdYlBu
RdYlGn
top
Spectral
coolwarm
center
baseline
bottom
bwr
seismic
Performance tips
right
Qualitative colormaps
Text parameters
API
Pastel1
ax.text(…, family=…, size=…, weight=…)
ax.text(…, fontproperties=…)
The quick brown fox
The quick brown fox
The quick brown fox
The quick brown fox
The quick brown fox
The quick brown fox
The quick brown fox
The quick brown fox jumps over the lazy dog
The quick brown fox jumps over the lazy dog
The quick brown fox jumps over the lazy dog
The quick brown fox jumps over the lazy dog
The quick brown fox jumps over the lazy dog
black (900)
bold (700)
semibold (600)
normal (400)
ultralight (100)
italic
normal
The quick brown fox jumps over the lazy dog
bar,
fraction=-0.3
bar,
angle=180,
fraction=-0.2
Set1
small-caps
normal
Set2
Set3
hermite
kaiser
quadric
tab10
tab20
tab20b
Annotation arrow styles
API
-
<-
->
<->
<|-
-|>
<|-|>
]-
-[
]-[
|-|
]->
<-[
simple
fancy
wedge
tab20c
catrom
Miscellaneous colormaps
gaussian
bessel
terrain
ocean
cubehelix
rainbow
twilight
scatter(X, Y)
plot(X, Y, marker=”o”, ls=””)
slow
fast
for i in range(n): plot(X[i])
plot(sum([x+[None] for x in X],[]))
slow
fast
cla(), imshow(…), canvas.draw()
im.set_data(…), canvas.draw()
slow
fast
Beyond Matplotlib
Dark2
large (1.20)
medium (1.00)
small (0.83)
x-small (0.69)
xx-small (0.58)
The quick brown fox jumps over the lazy dog
The quick brown fox jumps over the lazy dog
The quick brown fox jumps over the lazy dog
bar,
fraction=0.3
Accent
x-large (1.44)
The quick brown fox jumps over the lazy dog
The quick brown fox jumps over the lazy dog
hamming
Paired
monospace
serif
sans
cursive
The quick brown fox jumps over the lazy dog
hanning
Pastel2
xx-large (1.73)
The quick brown fox jumps over the lazy dog
spline36
… resize a figure?
→ fig.set_size_inches(w, h)
… save a figure?
→ fig.savefig(”figure.pdf”)
… save a transparent figure?
→ fig.savefig(”figure.pdf”, transparent=True)
… clear a figure/an axes?
→ fig.clear() → ax.clear()
… close all figures?
→ plt.close(”all”)
… remove ticks?
→ ax.set_[xy]ticks([])
… remove tick labels ?
→ ax.set_[xy]ticklabels([])
… rotate tick labels ?
→ ax.tick_params(axis=”x”, rotation=90)
… hide top spine?
→ ax.spines[’top’].set_visible(False)
… hide legend border?
→ ax.legend(frameon=False)
… show error as shaded region?
→ ax.fill_between(X, Y+error, Y‐error)
… draw a rectangle?
→ ax.add_patch(plt.Rectangle((0, 0), 1, 1)
… draw a vertical line?
→ ax.axvline(x=0.5)
… draw outside frame?
→ ax.plot(…, clip_on=False)
… use transparency?
→ ax.plot(…, alpha=0.25)
… convert an RGB image into a gray image?
→ gray = 0.2989*R + 0.5870*G + 0.1140*B
… set figure background color?
→ fig.patch.set_facecolor(“grey”)
… get a reversed colormap?
→ plt.get_cmap(“viridis_r”)
… get a discrete colormap?
→ plt.get_cmap(“viridis”, 10)
… show a figure for one second?
→ fig.show(block=False), time.sleep(1)
mitchell
sinc
lanczos
Seaborn: Statistical data visualization
Cartopy: Geospatial data processing
yt: Volumetric data visualization
mpld3: Bringing Matplotlib to the browser
Datashader: Large data processing pipeline
plotnine: A grammar of graphics for Python
Matplotlib Cheatsheets
Copyright (c) 2021 Matplotlib Development Team
Released under a CC‐BY 4.0 International License
Matplotlib for beginners
Matplotlib is a library for making 2D plots in Python. It is
designed with the philosophy that you should be able to
create simple plots with just a few commands:
1 Initialize
ax.pie(Z)
Z = np.random.normal(0, 1, 100)
2 Prepare
X = np.linspace(0, 4*np.pi, 1000)
Y = np.sin(X)
3 Render
fig, ax = plt.subplots()
ax.plot(X, Y)
fig.show()
X = np.linspace(0, 10, 100)
Y1, Y2 = np.sin(X), np.cos(X)
ax.plot(X, Y1, X, Y2)
ax.hist(Z)
fig, (ax1, ax2) = plt.subplots(2,1)
ax1.plot(X, Y1, color=”C1”)
ax2.plot(X, Y2, color=”C0”)
X = np.arange(5)
Y = np.random.uniform(0, 1, 5)
ax.errorbar(X, Y, Y∕4)
fig, (ax1, ax2) = plt.subplots(1,2)
ax1.plot(Y1, X, color=”C1”)
ax2.plot(Y2, X, color=”C0”)
Z = np.random.normal(0, 1, (100,3))
ax.boxplot(Z)
4 Observe
0
ax.contourf(Z)
Z = np.random.uniform(0, 1, 4)
import numpy as np
import matplotlib.pyplot as plt
1.0
0.5
0.0
0.5
1.0
Organize
765
You can plot several data on the the same figure, but you
432
can also split a figure in several subplots (named Axes):
1
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1
71
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51
41
31
21
111
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Label (everything)
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1
A Sine wave
246
543
Tweak
21
You can modify pretty much anything in a plot, including lim1234567
its, colors, markers, line width and styles, ticks and ticks laZ = np.random.uniform(0, 1, (8,8))
ax.plot(X, Y)
fig.suptitle(None)
ax.set_title(”A Sine wave”)
5
10
15
20
25
30
Choose
Matplotlib offers several kind of plots (see Gallery):
X = np.random.uniform(0, 1, 100)
Y = np.random.uniform(0, 1, 100)
ax.scatter(X, Y)
X = np.arange(10)
Y = np.random.uniform(1, 10, 10)
ax.bar(X, Y)
Z = np.random.uniform(0, 1, (8,8))
ax.imshow(Z)
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1
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bels, titles, etc.
X = np.linspace(0, 10, 100)
Y = np.sin(X)
ax.plot(X, Y, color=”black”)
X = np.linspace(0, 10, 100)
Y = np.sin(X)
ax.plot(X, Y, linestyle=”--”)
X = np.linspace(0, 10, 100)
Y = np.sin(X)
ax.plot(X, Y, linewidth=5)
X = np.linspace(0, 10, 100)
Y = np.sin(X)
ax.plot(X, Y, marker=”o”)
ax.plot(X, Y)
ax.set_ylabel(None)
ax.set_xlabel(”Time”)
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Explore
432
1
Figures are shown with a graphical user interface that alto zoom and pan the figure, to navigate between the
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different views and to show the value under the mouse.
432
1
765 1234567 Save (bitmap or vector format)
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1
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1
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Time
fig.savefig(”my-first-figure.png”, dpi=300)
fig.savefig(”my-first-figure.pdf”)
Matplotlib 3.5.0 handout for beginners. Copyright (c) 2021 Matplotlib Development
Team. Released under a CC-BY 4.0 International License. Supported by NumFOCUS.
Matplotlib for intermediate users
Ticks & labels
Legend
from mpl.ticker import MultipleLocator as ML
from mpl.ticker import ScalarFormatter as SF
ax.xaxis.set_minor_locator(ML(0.2))
ax.xaxis.set_minor_formatter(SF())
ax.tick_params(axis=’x’,which=’minor’,rotation=90)
ax.plot(X, np.sin(X), ”C0”, label=”Sine”)
ax.plot(X, np.cos(X), ”C1”, label=”Cosine”)
ax.legend(bbox_to_anchor=(0,1,1,.1),ncol=2,
mode=”expand”, loc=”lower left”)
Anatomy of a figure
Blue signal
Red signal
Legend
2
3
4
4.2
4.4
4.6
4.8
1
3.2
3.4
3.6
3.8
0
Minor tick
Sine and Cosine
Sine
2.2
2.4
2.6
2.8
Title
Major tick
1.2
1.4
1.6
1.8
4
0.2
0.4
0.6
0.8
A matplotlib figure is composed of a hierarchy of elements
that forms the actual figure. Each element can be modified.
Cosine
5
3
Major tick label
Lines & markers
Grid
Y axis label
Line
(line plot)
X = np.linspace(0.1, 10*np.pi, 1000)
Y = np.sin(X)
ax.plot(X, Y, ”C1o:”, markevery=25, mec=”1.0”)
2
Y axis label
Markers
(scatter plot)
1
1
0
1
0
Figure
0
Axes
Line
(line plot)
fig, axs = plt.subplots(3,3)
axs[0,0].set_facecolor(”#ddddff”)
axs[2,2].set_facecolor(”#ffffdd”)
gs = fig.add_gridspec(3, 3)
ax = fig.add_subplot(gs[0, :])
ax.set_facecolor(”#ddddff”)
fig, ax = plt.subplots()
ax.spines[”top”].set_color(”None”)
ax.spines[”right”].set_color(”None”)
10
15
20
25
30
Spines
0 0.25 0.50 0.75 1 1.25 1.50 1.75 2 2.25 2.50 2.75 3 3.25 3.50 3.75 4
X axis label
Minor tick label
X axis label
Figure, axes & spines
5
fig, ax = plt.subplots()
ax.set_xscale(”log”)
ax.plot(X, Y, ”C1o-”, markevery=25, mec=”1.0”)
1
0
1
101
100
10
15
20
25
10
15
20
25
30
Colors
color can be used, but Matplotlib offers sets of colors:
1 Any
C0
C1
C2
C3
C4
C5
C6
C7
C8
C9
10 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0
2
4
6
8
10 12 14 16
0
0
2
4
6
8
10 12 14 16
fig = plt.figure(figsize=(3.15,3.15), dpi=50)
plt.savefig(”figure.pdf”, dpi=600)
Period
5
A
5
Consider a square figure to be included in a two-columns A4
paper with 2cm margins on each side and a column separation of 1cm. The width of a figure is (21 - 2*2 - 1)/2 = 8cm.
One inch being 2.54cm, figure size should be 3.15×3.15 in.
ax.fill_betweenx([-1,1],[0],[2*np.pi])
ax.text(0, -1, r” Period $\Phi$”)
0
1
0
1
Size & DPI
Text & ornaments
1
0
1
ax.annotate(”A”, (X[250],Y[250]),(X[250],-1),
ha=”center”, va=”center”,arrowprops =
{”arrowstyle” : ”->”, ”color”: ”C1”})
0
Scales & projections
10 1
Annotation
30
Matplotlib 3.5.0 handout for intermediate users. Copyright (c) 2021 Matplotlib Development Team. Released under a CC-BY 4.0 International License. Supported by
NumFOCUS.
Matplotlib tips & tricks
Transparency
Text outline
Scatter plots can be enhanced by using transparency (al- Use text outline to make text more visible.
pha) in order to show area with higher density. Multiple scatimport matplotlib.patheffects as fx
ter plots can be used to delineate a frontier.
text = ax.text(0.5, 0.1, ”Label”)
X = np.random.normal(-1, 1, 500)
Y = np.random.normal(-1, 1, 500)
ax.scatter(X, Y, 50, ”0.0”, lw=2) # optional
ax.scatter(X, Y, 50, ”1.0”, lw=0) # optional
ax.scatter(X, Y, 40, ”C1”, lw=0, alpha=0.1)
Rasterization
If your figure has many graphical elements, such as a huge
scatter, you can rasterize them to save memory and keep
other elements in vector format.
text.set_path_effects([
fx.Stroke(linewidth=3, foreground=’1.0’),
fx.Normal()])
Use the Agg backend to render a figure directly in an array.
from matplotlib.backends.backend_agg import FigureCanvas
canvas = FigureCanvas(Figure()))
... # draw some stuff
canvas.draw()
Z = np.array(canvas.renderer.buffer_rgba())
You can adjust a colorbar’s size when adding it.
im = ax.imshow(Z)
cb = plt.colorbar(im,
fraction=0.046, pad=0.04)
cb.set_ticks([])
Multiline plot
Taking advantage of typography
You can plot several lines at once using None as separator.
You can use a condensed font such as Roboto Condensed
to save space on tick labels.
X,Y = [], []
for x in np.linspace(0, 10*np.pi, 100):
X.extend([x, x, None]), Y.extend([0, sin(x), None])
ax.plot(X, Y, ”black”)
for tick in ax.get_xticklabels(which=’both’):
tick.set_fontname(”Roboto Condensed”)
X = np.random.normal(-1, 1, 10_000)
Y = np.random.normal(-1, 1, 10_000)
ax.scatter(X, Y, rasterized=True)
fig.savefig(”rasterized-figure.pdf”, dpi=600)
Offline rendering
Colorbar adjustment
Getting rid of margins
Dotted lines
Once your figure is finished, you can call tight_layout()
to remove white margins. If there are remaining margins,
you can use the pdfcrop utility (comes with TeX live).
To have rounded dotted lines, use a custom linestyle and
modify dash_capstyle.
Hatching
ax.plot([0,1], [0,0], ”C1”,
linestyle = (0, (0.01, 1)), dash_capstyle=”round”)
ax.plot([0,1], [1,1], ”C1”,
linestyle = (0, (0.01, 2)), dash_capstyle=”round”)
You can achieve a nice visual effect with thick hatch patterns.
cmap = plt.get_cmap(”Oranges”)
plt.rcParams[’hatch.color’] = cmap(0.2)
plt.rcParams[’hatch.linewidth’] = 8
ax.bar(X, Y, color=cmap(0.6), hatch=”∕” )
38%
27%
2018
Range of continuous colors
Combining axes
You can use colormap to pick from a range of continuous
You can use overlaid axes with different projections.
colors.
X = np.random.randn(1000, 4)
cmap = plt.get_cmap(”Oranges”)
colors = cmap([0.2, 0.4, 0.6, 0.8])
ax.hist(X, 2, histtype=’bar’, color=colors)
ax1 = fig.add_axes([0,0,1,1],
label=”cartesian”)
ax2 = fig.add_axes([0,0,1,1],
label=”polar”,
projection=”polar”)
59%
53%
2019
Read the documentation
Matplotlib comes with an extensive documentation explaining the details of each command and is generally accompanied by examples. Together with the huge online gallery,
this documentation is a gold-mine.
Matplotlib 3.5.0 handout for tips & tricks. Copyright (c) 2021 Matplotlib Development
Team. Released under a CC-BY 4.0 International License. Supported by NumFOCUS.