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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Visualization with Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Matplotlib\n",
    "\n",
    "Just as we use the `np` shorthand for NumPy and the `pd` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `plt` interface is what we will use most often, as you shall see throughout this part of the book."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Styles\n",
    "\n",
    "We will use the `plt.style` directive to choose appropriate aesthetic styles for our figures.\n",
    "Here we will set the `classic` style, which ensures that the plots we create use the classic Matplotlib style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
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   "source": [
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-')\n",
    "plt.plot(x, np.cos(x), '--');"
   ]
  }
 ],
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