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statistical_example.py
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77 lines (58 loc) · 2.42 KB
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"""
Matplotlib has a handful of specalized statistical plotting methods.
For many statistical plots, you may find that a specalized statistical plotting
package such as Seaborn (which uses matplotlib behind-the-scenes) is a better
fit to your needs.
"""
import numpy as np
import matplotlib.pyplot as plt
import example_utils
def main():
colors = ['cyan', 'red', 'blue', 'green', 'purple']
dists = generate_data()
fig, axes = example_utils.setup_axes()
hist(axes[0], dists, colors)
boxplot(axes[1], dists, colors)
violinplot(axes[2], dists, colors)
example_utils.title(fig, 'hist/boxplot/violinplot: Statistical plotting',
y=0.9)
fig.savefig('statistical_example.png', facecolor='none')
plt.show()
def generate_data():
means = [0, -1, 2.5, 4.3, -3.6]
sigmas = [1.2, 5, 3, 1.5, 2]
# Each distribution has a different number of samples.
nums = [150, 1000, 100, 200, 500]
dists = [np.random.normal(*args) for args in zip(means, sigmas, nums)]
return dists
def hist(ax, dists, colors):
# We could call "ax.hist(dists, ...)" and skip the loop, but we'll plot
# each distribution separately so they'll overlap and turn on transparency
ax.set_color_cycle(colors)
for dist in dists:
ax.hist(dist, bins=20, density=True, edgecolor='none', alpha=0.5)
ax.margins(y=0.05)
ax.set_ylim(bottom=0)
example_utils.label(ax, 'ax.hist(dists)')
def boxplot(ax, dists, colors):
result = ax.boxplot(dists, patch_artist=True, notch=True, vert=False)
for box, color in zip(result['boxes'], colors):
box.set(facecolor=color, alpha=0.5)
for item in ['whiskers', 'caps', 'medians']:
plt.setp(result[item], color='gray', linewidth=1.5)
plt.setp(result['fliers'], markeredgecolor='gray', markeredgewidth=1.5)
plt.setp(result['medians'], color='black')
ax.margins(0.05)
ax.set(yticks=[], ylim=[0, 6])
example_utils.label(ax, 'ax.boxplot(dists)')
def violinplot(ax, dists, colors):
result = ax.violinplot(dists, vert=False, showmedians=True)
for body, color in zip(result['bodies'], colors):
body.set(facecolor=color, alpha=0.5)
for item in ['cbars', 'cmaxes', 'cmins', 'cmedians']:
plt.setp(result[item], edgecolor='gray', linewidth=1.5)
plt.setp(result['cmedians'], edgecolor='black')
ax.margins(0.05)
ax.set(ylim=[0, 6])
example_utils.label(ax, 'ax.violinplot(dists)')
main()