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DOC: Boxplot color demo update #7324

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Merged
merged 7 commits into from
Oct 21, 2016
Merged

DOC: Boxplot color demo update #7324

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ed4d7e7
correction for doc string and PEP8
aneda May 27, 2016
a3f2362
Cleanup : doc string and pep8
aneda May 27, 2016
9fafd50
Added initial description
aneda May 27, 2016
39ffd6c
update docstring
fariza Oct 4, 2016
49085a9
DOC: clarify boxplot color demo
phobson Oct 21, 2016
8291567
fix typos, add axes titles
ivanov Oct 21, 2016
11d9ec3
fix sample size in boxplot demo
phobson Oct 21, 2016
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8 changes: 8 additions & 0 deletions examples/showcase/bachelors_degrees_by_gender.py
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@@ -1,3 +1,11 @@
"""
A graph of multiple time series which demonstrates extensive custom
styling of plot frame, tick lines and labels, and line graph properties.

Also demonstrates the custom placement of text labels along the right edge
as an alternative to a conventional legend.
"""

import matplotlib.pyplot as plt
from matplotlib.mlab import csv2rec
from matplotlib.cbook import get_sample_data
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33 changes: 20 additions & 13 deletions examples/statistics/boxplot_color_demo.py
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@@ -1,24 +1,36 @@
# Box plots with custom fill colors
"""Box plots with custom fill colors.

This plot illustrates how to create two types of box plots
(rectangular and notched), and how to fill them with custom
colors by accessing the properties of the artists of the
box plots. Additionally, the ``labels`` parameter is used to
provide x-tick labels for each sample.
"""

import matplotlib.pyplot as plt
import numpy as np

# Random test data
np.random.seed(123)
all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]
all_data = [np.random.normal(0, std, size=100) for std in range(1, 4)]
labels = ['x1', 'x2', 'x3']

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))

# rectangular box plot
bplot1 = axes[0].boxplot(all_data,
vert=True, # vertical box aligmnent
patch_artist=True) # fill with color
vert=True, # vertical box alignment
patch_artist=True, # fill with color
labels=labels) # will be used to label x-ticks
axes[0].set_title('Rectangular box plot')

# notch shape box plot
bplot2 = axes[1].boxplot(all_data,
notch=True, # notch shape
vert=True, # vertical box aligmnent
patch_artist=True) # fill with color
vert=True, # vertical box alignment
patch_artist=True, # fill with color
labels=labels) # will be used to label x-ticks
axes[1].set_title('Notched box plot')

# fill with colors
colors = ['pink', 'lightblue', 'lightgreen']
Expand All @@ -29,12 +41,7 @@
# 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')

# add x-tick labels
plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],
xticklabels=['x1', 'x2', 'x3', 'x4'])
ax.set_xlabel('Three separate samples')
ax.set_ylabel('Observed values')

plt.show()
32 changes: 17 additions & 15 deletions examples/statistics/boxplot_vs_violin_demo.py
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@@ -1,13 +1,15 @@
# Box plot - violin plot comparison
#
# Note that although violin plots are closely related to Tukey's (1977) box plots,
# they add useful information such as the distribution of the sample data (density trace).
#
# By default, box plots show data points outside 1.5 x the inter-quartile range as outliers
# above or below the whiskers wheras violin plots show the whole range of the data.
#
# Violin plots require matplotlib >= 1.4.
"""Box plot - violin plot comparison.

Note that although violin plots are closely related to Tukey's (1977) box
plots, they add useful information such as the distribution of the sample
data (density trace).

By default, box plots show data points outside 1.5 x the inter-quartile range
as outliers above or below the whiskers whereas violin plots show the whole
range of the data.

Violin plots require matplotlib >= 1.4.
"""
import matplotlib.pyplot as plt
import numpy as np

Expand All @@ -20,20 +22,20 @@
axes[0].violinplot(all_data,
showmeans=False,
showmedians=True)
axes[0].set_title('violin plot')
axes[0].set_title('Violin plot')

# plot box plot
axes[1].boxplot(all_data)
axes[1].set_title('box plot')
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')
ax.set_xticks([y + 1 for y in range(len(all_data))])
ax.set_xlabel('Four separate samples')
ax.set_ylabel('Observed values')

# add x-tick labels
plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],
plt.setp(axes, xticks=[y + 1 for y in range(len(all_data))],
xticklabels=['x1', 'x2', 'x3', 'x4'])
plt.show()