back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
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%load_ext watermark
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%watermark -u -v -d -p matplotlib,numpy
[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension
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%matplotlib inline
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import numpy as np
import matplotlib.pyplot as plt
x = range(10)
y = range(10)
fig, ax = plt.subplots(2)
for sp in ax:
sp.plot(x, y)
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import matplotlib.pyplot as plt
x = range(10)
y = range(10)
fig, ax = plt.subplots(nrows=2,ncols=2)
for row in ax:
for col in row:
col.plot(x, y)
plt.show()
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fig, ax = plt.subplots(nrows=2,ncols=2)
plt.subplot(2,2,1)
plt.plot(x, y)
plt.subplot(2,2,2)
plt.plot(x, y)
plt.subplot(2,2,3)
plt.plot(x, y)
plt.subplot(2,2,4)
plt.plot(x, y)
plt.show()
In [32]:
import matplotlib.pyplot as plt
import numpy as np
fig, axes = plt.subplots(nrows=3, ncols=3,
sharex=True, sharey=True,
figsize=(8,8)
)
x = range(5)
y = range(5)
for row in axes:
for col in row:
col.plot(x, y)
for ax, col in zip(axes[0,:], ['1', '2', '3']):
ax.set_title(col, size=20)
for ax, row in zip(axes[:,0], ['A', 'B', 'C']):
ax.set_ylabel(row, size=20, rotation=0, labelpad=15)
plt.show()
In [14]:
import matplotlib.pyplot as plt
x = range(10)
y = range(10)
fig, ax = plt.subplots(nrows=2,ncols=2, sharex=True, sharey=True)
for row in ax:
for col in row:
col.plot(x, y)
plt.show()
In [20]:
import matplotlib.pyplot as plt
x = range(10)
y = range(10)
fig, ax = plt.subplots(nrows=2,ncols=2)
for row in ax:
for col in row:
col.plot(x, y)
col.set_title('title')
col.set_xlabel('x-axis')
col.set_ylabel('x-axis')
fig.tight_layout()
plt.show()
Sometimes we create more subplots for a rectangular layout (here: 3x3) than we actually need. Here is how we hide those redundant subplots. Let's assume that we only want to show the first 7 subplots:
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import matplotlib.pyplot as plt
x = range(10)
y = range(10)
fig, axes = plt.subplots(nrows=3,ncols=3)
for cnt, ax in enumerate(axes.ravel()):
if cnt < 7:
ax.plot(x, y)
else:
ax.axis('off') # hide subplot
plt.show()
Matplotlib supports 3 different ways to encode colors, e.g, if we want to use the color blue, we can define colors as
(0.0, 0.0, 1.0)'blue' or 'b''#0000FF'
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import matplotlib.pyplot as plt
samples = range(1,4)
for i, col in zip(samples, [(0.0, 0.0, 1.0), 'blue', '#0000FF']):
plt.plot([0, 10], [0, i], lw=3, color=col)
plt.legend(['RGB values: (0.0, 0.0, 1.0)',
"matplotlib names: 'blue'",
"HTML hex values: '#0000FF'"],
loc='upper left')
plt.title('3 alternatives to define the color blue')
plt.show()
The color names that are supported by matplotlib are
b: blue
g: green
r: red
c: cyan
m: magenta
y: yellow
k: black
w: white
where the first letter represents the shortcut version.
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import matplotlib.pyplot as plt
cols = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'white']
samples = range(1, len(cols)+1)
for i, col in zip(samples, cols):
plt.plot([0, 10], [0, i], label=col, lw=3, color=col)
plt.legend(loc='upper left')
plt.title('matplotlib color names')
plt.show()
More color maps are available at http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
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import numpy as np
import matplotlib.pyplot as plt
fig, (ax0, ax1) = plt.subplots(1,2, figsize=(14, 7))
samples = range(1,16)
# Default Color Cycle
for i in samples:
ax0.plot([0, 10], [0, i], label=i, lw=3)
# Colormap
colormap = plt.cm.Paired
plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, len(samples))])
for i in samples:
ax1.plot([0, 10], [0, i], label=i, lw=3)
# Annotation
ax0.set_title('Default color cycle')
ax1.set_title('plt.cm.Paired colormap')
ax0.legend(loc='upper left')
ax1.legend(loc='upper left')
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(8,6))
samples = np.arange(0, 1.1, 0.1)
for i in samples:
plt.plot([0, 10], [0, i], label='gray-level %s'%i, lw=3,
color=str(i)) # ! gray level has to be parsed as string
plt.legend(loc='upper left')
plt.title('gray-levels')
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10,10))
samples = np.random.randn(30,2)
ax[0][0].scatter(samples[:,0], samples[:,1],
color='red',
label='color="red"')
ax[1][0].scatter(samples[:,0], samples[:,1],
c='red',
label='c="red"')
ax[0][1].scatter(samples[:,0], samples[:,1],
edgecolor='white',
c='red',
label='c="red", edgecolor="white"')
ax[1][1].scatter(samples[:,0], samples[:,1],
edgecolor='0',
c='1',
label='color="1.0", edgecolor="0"')
for row in ax:
for col in row:
col.legend(loc='upper left')
plt.show()
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import matplotlib.pyplot as plt
import matplotlib.colors as col
import matplotlib.cm as cm
import numpy as np
# input data
mean_values = np.random.randint(1, 101, 100)
x_pos = range(len(mean_values))
fig = plt.figure(figsize=(20,5))
# create colormap
cmap = cm.ScalarMappable(col.Normalize(min(mean_values),
max(mean_values),
cm.hot))
# plot bars
plt.subplot(131)
plt.bar(x_pos, mean_values, align='center', alpha=0.5,
color=cmap.to_rgba(mean_values))
plt.ylim(0, max(mean_values) * 1.1)
plt.subplot(132)
plt.bar(x_pos, np.sort(mean_values), align='center', alpha=0.5,
color=cmap.to_rgba(mean_values))
plt.ylim(0, max(mean_values) * 1.1)
plt.subplot(133)
plt.bar(x_pos, np.sort(mean_values), align='center', alpha=0.5,
color=cmap.to_rgba(np.sort(mean_values)))
plt.ylim(0, max(mean_values) * 1.1)
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
markers = [
'.', # point
',', # pixel
'o', # circle
'v', # triangle down
'^', # triangle up
'<', # triangle_left
'>', # triangle_right
'1', # tri_down
'2', # tri_up
'3', # tri_left
'4', # tri_right
'8', # octagon
's', # square
'p', # pentagon
'*', # star
'h', # hexagon1
'H', # hexagon2
'+', # plus
'x', # x
'D', # diamond
'd', # thin_diamond
'|', # vline
]
plt.figure(figsize=(13, 10))
samples = range(len(markers))
for i in samples:
plt.plot([i-1, i, i+1], [i, i, i], label=markers[i], marker=markers[i], markersize=10)
# Annotation
plt.title('Matplotlib Marker styles', fontsize=20)
plt.ylim([-1, len(markers)+1])
plt.legend(loc='lower right')
plt.show()
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import numpy as np
import matplotlib.pyplot as plt
linestyles = ['-.', '--', 'None', '-', ':']
plt.figure(figsize=(8, 5))
samples = range(len(linestyles))
for i in samples:
plt.plot([i-1, i, i+1], [i, i, i],
label='"%s"' %linestyles[i],
linestyle=linestyles[i],
lw=4
)
# Annotation
plt.title('Matplotlib line styles', fontsize=20)
plt.ylim([-1, len(linestyles)+1])
plt.legend(loc='lower right')
plt.show()
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import numpy as np
import matplotlib.pyplot as plt
X1 = np.random.randn(100,2)
X2 = np.random.randn(100,2)
X3 = np.random.randn(100,2)
R1 = (X1**2).sum(axis=1)
R2 = (X2**2).sum(axis=1)
R3 = (X3**2).sum(axis=1)
plt.scatter(X1[:,0], X1[:,1],
c='blue',
marker='o',
s=32. * R1,
edgecolor='black',
label='Dataset X1',
alpha=0.7)
plt.scatter(X2[:,0], X2[:,1],
c='gray',
marker='s',
s=32. * R2,
edgecolor='black',
label='Dataset X2',
alpha=0.7)
plt.scatter(X2[:,0], X3[:,1],
c='green',
marker='^',
s=32. * R3,
edgecolor='black',
label='Dataset X3',
alpha=0.7)
plt.xlim([-3,3])
plt.ylim([-3,3])
leg = plt.legend(loc='upper left', fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.show()
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import numpy as np
import matplotlib.pyplot as plt
x = range(10)
y = range(10)
fig = plt.gca()
plt.plot(x, y)
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.show()
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import numpy as np
import matplotlib.pyplot as plt
x = range(10)
y = range(10)
fig = plt.gca()
plt.plot(x, y)
# removing frame
fig.spines["top"].set_visible(False)
fig.spines["bottom"].set_visible(False)
fig.spines["right"].set_visible(False)
fig.spines["left"].set_visible(False)
# removing ticks
plt.tick_params(axis="both", which="both", bottom="off", top="off",
labelbottom="on", left="off", right="off", labelleft="on")
plt.show()
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import numpy as np
import math
import matplotlib.pyplot as plt
X = np.random.normal(loc=0.0, scale=1.0, size=300)
width = 0.5
bins = np.arange(math.floor(X.min())-width,
math.ceil(X.max())+width,
width) # fixed bin size
ax = plt.subplot(111)
# remove axis at the top and to the right
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
# hide axis ticks
plt.tick_params(axis="both", which="both", bottom="off", top="off",
labelbottom="on", left="off", right="off", labelleft="on")
plt.hist(X, alpha=0.5, bins=bins)
plt.grid()
plt.xlabel('x label')
plt.ylabel('y label')
plt.title('title')
plt.show()
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import matplotlib.pyplot as plt
x = range(10)
y = range(10)
labels = ['super long axis label' for i in range(10)]
fig, ax = plt.subplots()
plt.plot(x, y)
# set custom tick labels
ax.set_xticklabels(labels, rotation=45, horizontalalignment='right')
plt.show()
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import matplotlib.pyplot as plt
CONST = 10
x = range(10)
y = range(10)
labels = [i+CONST for i in x]
fig, ax = plt.subplots()
plt.plot(x, y)
plt.xlabel('x-value + 10')
# set custom tick labels
ax.set_xticklabels(labels)
plt.show()
Everyone has a different perception of "style", and usually, we would make some little adjustments to matplotlib's default visuals here and there. After customization, it would be tedious to repeat the same code over and over again every time we produce a new plot.
However we have multiple options to apply the changes globally.
Here, we are only interested in the settings for the current session. In this case, one way to customize matplotlibs defaults would be the 'rcParams' attribute (in the next session, you will see a handy reference for all the different matplotlib settings). E.g., if we want to make the font size of our titles larger for all plots that follow in the active session, we could type the following:
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import matplotlib as mpl
mpl.rcParams['axes.titlesize'] = '20'
Let's see how it looks like:
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from matplotlib import pyplot as plt
x = range(10)
y = range(10)
plt.plot(x, y)
plt.title('larger title')
plt.show()
And if we want to revert it back to the default settings, we can use the command:
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mpl.rcdefaults()
Note that we have to re-execute the matplotlib inline magic function afterwards:
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%matplotlib inline
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plt.plot(x, y)
plt.title('default title size')
plt.show()
Let's assume that we decided to always prefer a particular setting over matplotlib's default (e.g., a larger font size for the title like in the previous section), we can make a change to the matplotlibrc file: This way we'd avoid to change the setting every time we start a new session or produce a new plot.
The matplotlibrc file can reside in different places depending on your sytem. An convenient way to find out the location is to use the matplotlib_fname() function.
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import matplotlib
matplotlib.matplotlib_fname()
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If we open this file in an editor, we will see an overview of all the different matplotlib default settings and their default values. We can use this list either as a reference to apply changes dynamically (see the previous section), or we can un-comment this line here and change its default value. E.g., we want to change the title size again, we could change the following line:
axes.titlesize : 20
One thing to keep in mind is that this file becomes overwritten if we install a new version of matplotlib, and it is always recommended to keep backups as you change the settings (the file with its original default settings can be found here).
Sometimes, we even want to keep and use multiple matplotlibrc files, e.g., if we are writing articles for different journals and every journal has its own requirement for figure formats.
In this case, we can load our own rc files from a different local directory:
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from matplotlib import rc_file
rc_file('/path/to/matplotlibrc_journalX')
import matplotlib.pyplot as plt