*This notebook contains an excerpt from the [Whirlwind Tour of Python](http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/WhirlwindTourOfPython).*

The text and code are released under the CC0 license; see also the companion project, the Python Data Science Handbook.

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Appendix: Figure Code¶

This section contains code used to generate figures that appear in this report.

In [1]:

%matplotlib inline
import matplotlib.pyplot as plt

In [2]:

import os
if not os.path.exists('fig'):
    os.makedirs('fig')

Section 6: List Indexing¶

This figure helps visualize how Python's indexing works.

In [3]:

L = [2, 3, 5, 7, 11]

fig = plt.figure(figsize=(10, 4))
ax = fig.add_axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False,
                  aspect='equal')

for i in range(5):
    ax.add_patch(plt.Rectangle([i - 0.5, -0.5], 1, 1, fc='none', ec='black'))
    ax.text(i, -0.05, L[i], size=100,
            ha='center', va='center', family='monospace')
    
for i in range(6):
    ax.text(i - 0.5, 0.55, str(i), size=20,
            ha='center', va='bottom', family='monospace')
    
for i in range(5):
    ax.text(i - 0.5, -0.58, str(-5 + i), size=20,
            ha='center', va='top', family='monospace')
    
ax.axis([-0.7, 4.7, -0.7, 0.7]);

fig.savefig('fig/list-indexing.png');

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