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 matplotlib.pyplot as plt
import numpy as np
all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]
fig = plt.figure(figsize=(8,6))
plt.boxplot(all_data,
notch=False, # box instead of notch shape
sym='rs', # red squares for outliers
vert=True) # vertical box aligmnent
plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')
t = plt.title('Box plot')
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]
fig = plt.figure(figsize=(8,6))
bplot = plt.boxplot(all_data,
notch=False, # box instead of notch shape
sym='rs', # red squares for outliers
vert=True) # vertical box aligmnent
plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')
for components in bplot.keys():
for line in bplot[components]:
line.set_color('black') # black lines
t = plt.title('Black and white box plot')
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]
fig = plt.figure(figsize=(8,6))
plt.boxplot(all_data,
notch=False, # box instead of notch shape
sym='rs', # red squares for outliers
vert=False) # horizontal box aligmnent
plt.yticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.ylabel('measurement x')
t = plt.title('Horizontal Box plot')
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]
fig = plt.figure(figsize=(8,6))
plt.boxplot(all_data,
notch=True, # notch shape
sym='bs', # blue squares for outliers
vert=True, # vertical box aligmnent
patch_artist=True) # fill with color
plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')
t = plt.title('Box plot')
plt.show()
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import matplotlib.pyplot as plt
import numpy as np
all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]
fig = plt.figure(figsize=(8,6))
bplot = plt.boxplot(all_data,
notch=False, # notch shape
vert=True, # vertical box aligmnent
patch_artist=True) # fill with color
colors = ['pink', 'lightblue', 'lightgreen']
for patch, color in zip(bplot['boxes'], colors):
patch.set_facecolor(color)
plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')
t = plt.title('Box plot')
plt.show()
Violin plots are closely related to Tukey's (1977) box plots but add useful information such as the distribution of the sample data (density trace).
Violin plots were added in matplotlib 1.4.
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import matplotlib.pyplot as plt
import numpy as np
fig, axes = plt.subplots(nrows=1,ncols=2, figsize=(12,5))
all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]
#fig = plt.figure(figsize=(8,6))
axes[0].violinplot(all_data,
showmeans=False,
showmedians=True
)
axes[0].set_title('violin plot')
axes[1].boxplot(all_data,
)
axes[1].set_title('box plot')
# adding horizontal grid lines
for ax in axes:
ax.yaxis.grid(True)
ax.set_xticks([y+1 for y in range(len(all_data))], )
ax.set_xlabel('xlabel')
ax.set_ylabel('ylabel')
plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],
xticklabels=['x1', 'x2', 'x3', 'x4'],
)
plt.show()
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