# yellowbrick.anscombe
# Plots Anscombe's Quartet as an illustration of the importance of visualization.
#
# Author: Benjamin Bengfort <bbengfort@districtdatalabs.com>
# Created: Wed May 18 11:38:25 2016 -0400
#
# Copyright (C) 2016 District Data Labs
# For license information, see LICENSE.txt
#
# ID: anscombe.py [0bfa366] benjamin@bengfort.com $
"""
Plots Anscombe's Quartet as an illustration of the importance of visualization.
"""
##########################################################################
## Imports
##########################################################################
import numpy as np
import matplotlib.pyplot as plt
from yellowbrick.bestfit import draw_best_fit
from yellowbrick.style import get_color_cycle
##########################################################################
## Anscombe Data Arrays
##########################################################################
ANSCOMBE = [
np.array([
[10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0],
[8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]
]),
np.array([
[10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0],
[9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74]
]),
np.array([
[10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0],
[7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]
]),
np.array([
[8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 19.0, 8.0, 8.0, 8.0],
[6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]
])
]
[belgeler]def anscombe():
"""
Creates 2x2 grid plot of the 4 anscombe datasets for illustration.
"""
fig, ((axa, axb), (axc, axd)) = plt.subplots(2, 2, sharex='col', sharey='row')
colors = get_color_cycle()
for arr, ax, color in zip(ANSCOMBE, (axa, axb, axc, axd), colors):
x = arr[0]
y = arr[1]
# Set the X and Y limits
ax.set_xlim(0, 15)
ax.set_ylim(0, 15)
# Draw the points in the scatter plot
ax.scatter(x, y, c=color)
# Draw the linear best fit line on the plot
draw_best_fit(x, y, ax, c=color)
return (axa, axb, axc, axd)
if __name__ == '__main__':
anscombe()
plt.show()