PythonTour/17-Figures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"fig/cover-small.jpg\">\n",
    "*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).*\n",
    "\n",
    "*The text and code are released under the [CC0](https://github.com/jakevdp/WhirlwindTourOfPython/blob/master/LICENSE) license; see also the companion project, the [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook).*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Resources for Further Learning](16-Further-Resources.ipynb) | [Contents](Index.ipynb) |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix: Figure Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section contains code used to generate figures that appear in this report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists('fig'):\n",
    "    os.makedirs('fig')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 6: List Indexing\n",
    "\n",
    "This figure helps visualize how Python's indexing works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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H1/vcbpehRmFmTXYoK2/MO7VdfgT4ZraIzMaEE+8RVQwTeCn9jYKw85DDseaY18c2ovco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\n28zMzMwsg/8D+aRsnfmT2tkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8a4438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = [2, 3, 5, 7, 11]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "ax = fig.add_axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False,\n",
    "                  aspect='equal')\n",
    "\n",
    "for i in range(5):\n",
    "    ax.add_patch(plt.Rectangle([i - 0.5, -0.5], 1, 1, fc='none', ec='black'))\n",
    "    ax.text(i, -0.05, L[i], size=100,\n",
    "            ha='center', va='center', family='monospace')\n",
    "    \n",
    "for i in range(6):\n",
    "    ax.text(i - 0.5, 0.55, str(i), size=20,\n",
    "            ha='center', va='bottom', family='monospace')\n",
    "    \n",
    "for i in range(5):\n",
    "    ax.text(i - 0.5, -0.58, str(-5 + i), size=20,\n",
    "            ha='center', va='top', family='monospace')\n",
    "    \n",
    "ax.axis([-0.7, 4.7, -0.7, 0.7]);\n",
    "\n",
    "fig.savefig('fig/list-indexing.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Resources for Further Learning](16-Further-Resources.ipynb) | [Contents](Index.ipynb) |"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}