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cleaned up 3 examples [MEP12] #6488

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cleaned up 3 examples [MEP12] #6488

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9c666a0
correction for doc string and PEP8
aneda May 27, 2016
1f9176d
Cleanup : doc string and pep8
aneda May 27, 2016
f2e3153
Added initial description
aneda May 27, 2016
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8 changes: 8 additions & 0 deletions examples/showcase/bachelors_degrees_by_gender.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,11 @@
"""Timeline graph comparison with aligned colored annotation.

This example shows lines tracking change over time. It features dashed
lines at fixed increments on the Y axis.
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being a bit pedantic here, but this paragraph could be used to describe just about any plot. I would just leave it out, or add a some info that is more specific to this example. In particular, with MEP12, we are looking for descriptions that not only describe the figure, but also describe what feature of matplotlib is being showcased.

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This one is a particularly flexibility testing example.

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What about something like...
"""
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.
"""

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Perfect!


It adds a text label at the right end of every line in the corresponding color.
"""

import matplotlib.pyplot as plt
from matplotlib.mlab import csv2rec
from matplotlib.cbook import get_sample_data
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6 changes: 3 additions & 3 deletions examples/statistics/boxplot_color_demo.py
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# Box plots with custom fill colors
"""Box plots with custom fill colors."""
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It would be worth having a small description here as well.

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@NelleV Any suggestions?

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You are making me work :)

Something like:
"This plot illustrates how to create two types of boxplots (rectangular and notch), and how to fill them with custom colors by accessing some properties of the artists of the boxplots."
If we'd go with this description, I'd replace the title with something shorter: "Colored box plots"

But with this description, I'd remove the horizontal grid lines and replace the setp with ax.set_xticklabels.

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I'm not convinced, @WeatherGod what do you think?


import matplotlib.pyplot as plt
import numpy as np
Expand Down Expand Up @@ -29,12 +29,12 @@
# 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_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))],
plt.setp(axes, xticks=[y + 1 for y in range(len(all_data))],
xticklabels=['x1', 'x2', 'x3', 'x4'])

plt.show()
24 changes: 13 additions & 11 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 wheras 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 @@ -29,11 +31,11 @@
# 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_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))],
plt.setp(axes, xticks=[y + 1 for y in range(len(all_data))],
xticklabels=['x1', 'x2', 'x3', 'x4'])
plt.show()