data-science-ipython-notebooks/matplotlib/04.01-Simple-Line-Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\n",
    "\n",
    "*No changes were made to the contents of this notebook from the original.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Line Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\n",
    "Here we will take a first look at creating a simple plot of this type.\n",
    "As with all the following sections, we'll start by setting up the notebook for plotting and  importing the packages we will use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all Matplotlib plots, we start by creating a figure and an axes.\n",
    "In their simplest form, a figure and axes can be created as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x102e12390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n",
    "The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\n",
    "Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances.\n",
    "\n",
    "Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/aSkJERERCAxMRGJiYn
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107527f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "x = np.linspace(0, 10, 1000)\n",
    "ax.plot(x, np.sin(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\n",
    "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/aSkJERERCAxMRGJiYn
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107066518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTx+gSZPa2a+ZOaMHgOk
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1076d5cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all there is to plotting simple functions in Matplotlib!\n",
    "We'll now dive into some more details about how to control the appearance of the axes and lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting the Plot: Line Colors and Styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first adjustment you might wish to make to a plot is to control the line colors and styles.\n",
    "The ``plt.plot()`` function takes additional arguments that can be used to specify these.\n",
    "To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color.\n",
    "The color can be specified in a variety of ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxikiO+VNj3aqarCKVZb
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077b3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x - 0), color='blue')        # specify color by name\n",
    "plt.plot(x, np.sin(x - 1), color='g')           # short color code (rgbcmyk)\n",
    "plt.plot(x, np.sin(x - 2), color='0.75')        # Grayscale between 0 and 1\n",
    "plt.plot(x, np.sin(x - 3), color='#FFDD44')     # Hex code (RRGGBB from 00 to FF)\n",
    "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n",
    "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\n",
    "\n",
    "Similarly, the line style can be adjusted using the ``linestyle`` keyword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD/CAYAAADsfV27AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VFX6xz8zmfQeWgiBEEpEWgSSACpSfqI0C6BIj33t\nuurawLWg4tp3XXARECZ0KdIEURFRipQAgdARAiHJDCSkTHpm5v7+uDPpCITMmQmcz/PkMffemTmv\nl8l3zpzzft9XoyiKgkQikUgaLFpnByCRSCSSq0MKuUQikTRwpJBLJBJJA0cKuUQikTRwpJBLJBJJ\nA0cKuUQikTRwLkvIk5KSmDBhAgCHDx/mgQceYNy4cUyaNMmhwUkkEonk0lxSyGfNmsXkyZMpKysD\nYNq0aTzzzDMsWLCAkpISfv31V0fHKJFIJJK/4JJCHhERwbRp08qPb7zxRrKzs1EUhYKCAnQ6nUMD\nlEgkEslfc0khHzhwIG5ubuXHrVu35v3332fo0KFcuHCBuLg4hwYokUgkkr/mijc733//fRYuXMi6\ndeu4++67+fDDDx0Rl0QikUgukyteFwkKCsLPzw+AZs2asXfv3lofl5iYeHWRSSQSyXVKjx49rujx\nVyzkU6ZM4YUXXkCn0+Hh4cGUKVPqLZhrlfT0dMLCwpwdhksg70UF8l5UIO9FBXWZBF+WkLdo0YLF\nixcDqjgvWrToigeSSCQSiWOQhiCJRCJp4Eghl0gkkgaOFHKJRCJp4Eghl0gkkgaOFHKJRCJp4Egh\nl0gkkgaOFHKJRCJp4Eghl0gkkgaOFHKJRCJxETZvrtvzpJBLJBKJE7FaK37/8ce6vYYUcolEInES\nn38OH3xQcfz++3V7HdkVQiKRSARx4gT8/DM88YR6/PDD4Ot79a8rZ+QSiUTiQE6erPjdzw98fCqO\nAwOhPpqsSSGXSCQSB1FcDCNHQlGRehwaChMn1v84UsglEomkHnnqKdizR/3dywv27gVvb8eOKdfI\nJRKJ5CrYv1/NPLnpJvX4b3+Ddu3ExiBn5BKJRHKFlJRU/H78OJw6VXEcHV0/G5hXwmUJeVJSEhMm\nTADgwoULPPXUU0yYMIGxY8eSmprq0AAlEonEldi+He65p+J45EgYPtx58cBlLK3MmjWLVatW4Wv7\niPn444+5++67GTRoEDt27ODkyZO0bNnS4YFKJBKJMyguhtdeg88+A60W4uJg1SpnR1WVS87IIyIi\nmDZtWvnxnj17MBgMPPTQQ6xdu5aePXs6NECJRCIRzdGjUFio/u7lpS6XlJWpx25u4OnpvNhq45JC\nPnDgQNzc3MqP09LSCAoKYs6cOYSGhvL11187NECJRCIRzYcfwpEjFccPPeR64l2ZK85aCQoKon//\n/gAMGDCAL7744qKPTU9Pr3tk1xAmk0neCxvyXlQg70UFzr4XCQk+lJRoeOyxAqDCKt9Q/nmuWMh7\n9OjB5s2bufvuu9m1axft/iLPJiws7KqCu1ZIT0+X98KGvBcVyHtRgeh7cfYs7N4N996rHk+cqOZ6\nBwcHCovBZDKxbNkywsPDGThwYPn5jIyMK36tK04/fPXVV1m5ciVjxoxhy5YtPGEvGiCRSCQujMlU\n8XtpqVr3xE5YGAQHi4tlzZo1tGzZkpUrV+Ll5XXVr6dRFEWph7hqkJiYSI8ePRzx0g0OOfOqQN6L\nCuS9qMDR96KoCG68UV33rgfdvGqys7MpKyujadOmNa7VRTulIUgikVyTTJ4Mx46pv3t7q8Yd0SJe\nXFzM4MGDKS0trXI+ODi4VhGvK1LIJRLJNUFqalWHZb9+VZdL3N2Fh4SXlxdvvvlmlcw/RyCFXCKR\nNFgqLwyvXw9btlQc3347NGkiIgaFvXv38sILL7Bp06Ya12+++WYp5BKJRFIbO3bAffdVHD/+ONgq\niQjl448/Zvjw4QQEBNC2bVvxASCrH0okkgZCURFMnw4vvggajVptcPp0sTEoioJGo6ly7umnn+bl\nl19Gq3XevFjOyCUSicuSnV1hjffygoICNXUQVKdls2biYklNTSUuLo7qiX6+vr5OFXGQQi6RSFyY\nceNg3z71d40G/vlP51nlw8PDWbVqVY0ZuSsghVwikbgM8+erP3bWroXYWHHjm0wm5syZQ//+/dm1\na1eVaxqNxmXz/uUauUQicRpZWapJJzJSPY6JAQ+PiuuiVyxeeeUV0tPTefbZZ+natavYwa8CKeQS\niUQoVmuFQKenq7PuZ59Vjzt0EBdHfn4+fn5+Vc5Nnz5d6NKJxVJEZuZKvL3bERBQ968ecmlFIpEI\no7AQoqIqWqV16QJTp4qPY+vWrTzwwAM1zosQcUVRyMn5naNHH2P79hYYDHqs1pJLP/EvkDNyiUTi\nUP79bzXfu0UL8PGBP/4Qu2FpsVhqGHJ69+7NKie0+cnPTyI5eQRarRehofHExh7A07PFVb+unJFL\nJJJ6JT8fzp+vOG7SRF1OsdO4sZg49u3bxwsvvECLFi1IS0urck2r1aLTiZ/Henm1pWPHJcTGJtOq\n1Sv1IuIghVwikdQzX34Jy5dXHI8dC85o6/v1118TGBjIli1baNGifgTzclAUCxcu/ITFUlTjmk7n\nR0BATL0v4cilFYlEclUkJsLcuaqAA7z+utjxi4uLOX/+fI0m8NMF2z4LCg5hMCRgNM7HwyOUjh0X\n4+Nz8cY79YmckUskkiuipASWLq04joqCxx5zXjwrV65kxowZThs/K2sdiYmxJCUNBBSiozcQE7Nb\nmIiDnJFLJJLLoKxM7R6v1ar/3bAB7r5b3bT09wdRKdeZmZk0rrbIPnr0aDGDXwR390ZERr5HcPDt\naDSOrXJ4MS5rRp6UlMSEamXF1qxZ4/QbKJFIxDB0qNrjEkCng1mzxGWeKIrC3Llz6devH127dqW4\nuFjMwNViKCpKqfVaQEBPQkLudJqIw2XMyGfNmsWqVavw9fUtP3fo0CGWV97NkEgk1xSrVqmNGIYM\nUY9XrIBq3hlhaDQaDh06xHPPPcfQoUPxFJi7WFKSjtG4AINBDyjExu53qmBfjEvOyCMiIpg2bVr5\ncXZ2Nl988QWTJk1yaGASiUQcxcVw9GjFcWio+mNHlIifOHGCY/b+bJX46KOPGDFihDARP39+BUlJ\ng9i1qzOFhUeJivqK2NgDLinicBkz8oEDB5bnYFqtViZPnsxrr72Gh4dHjXKOEomkYbJnj5p58vXX\n6nHPns6JY8uWLXh5eREVFeWcAGwUFZ0kNHQinTuvwM3NR9i4pedLL/2gWtAol6HGaWlpvPTSS0ya\nNIk33niD4OBgSkpK+PPPPxk5ciSv15JvlJiYSPPmzesU1LWGyWTC39/f2WG4BPJeVODMe1FYqGHM\nmEYsXZpZpUiVKMrKyjh27BidOnUCnHcvFKUUjcYJN6AS1hIrBRsLyFuaR9EfRfj+4kuPHj2u6DUu\nO2tFURS6dOnCmjVrgApxr03E7bhqyUfRpKeny3thQ96LCkTfi8WLYeBAaNRIPZ4zByIiwhBZXruo\nqIjXX3+dRYsWERMTw9q1a9FoNELvhdmcx/nzyzAY9Hh4NKdTp8VCxq2MoiiYdpkwJBg4v+Q8Pp18\nCI8Pp8myJiQdS7ri17tsIXfFYuoSieTiWK3q2rePbWUgLU3tuGMX8s6dxcfk5eVFeHg4W7dupV07\ncXnWimIhO3sjBoOerKzvCQ7uT3j4CzRqNFRYDADFZ4sxzjdiTDBiLbUSOjGU7ju74x3pfVWve1lL\nK3UhMTHxir8eXKvIWWgF8l5U4Oh78fbbEBQEL7zgsCH+knXr1tG2bVtuuOGGSz7W0ffCai1h//6h\nNG58D02bjsHDQ1DBF8BSYCFzZSYGvQHTbhNN7mtCaHwoATcH1DpBrot2SkOQRHKNsH8/fP99hUX+\njTdwyvq3naysLJo0aeK8ACqh1Xpy000/CxtPsSrk/p6LQW8g87tMAnoH0PyR5nRe1Rk37/rPfJFC\nLpE0UMxm2LkTbr5ZPW7
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079d9160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x + 0, linestyle='solid')\n",
    "plt.plot(x, x + 1, linestyle='dashed')\n",
    "plt.plot(x, x + 2, linestyle='dashdot')\n",
    "plt.plot(x, x + 3, linestyle='dotted');\n",
    "\n",
    "# For short, you can use the following codes:\n",
    "plt.plot(x, x + 4, linestyle='-')  # solid\n",
    "plt.plot(x, x + 5, linestyle='--') # dashed\n",
    "plt.plot(x, x + 6, linestyle='-.') # dashdot\n",
    "plt.plot(x, x + 7, linestyle=':');  # dotted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD/CAYAAADsfV27AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtYVNXeB/DvcL8IyEVNxBTxgrc0UfNkppWWZr1pmpni\npezyaiaVmaamdgOtk5qEpZInwwxT6VUzq+PRFD0qSoHiBS9oKCAoyB1hLvv9Y4uDSgHjzFoz8P08\nT8+ZPYeZ9Xt28XW79v6tpVEURQEREdksO9kFEBHRnWGQExHZOAY5EZGNY5ATEdk4BjkRkY1jkBMR\n2bhaBXlycjLGjRt303tbt27F6NGjLVIUERHVnkNNPxAdHY3NmzfD3d39xnvHjx/Hpk2bLFoYERHV\nTo1X5K1atUJUVNSN46tXr2Lp0qWYM2eORQsjIqLaqTHIBw0aBHt7ewCAwWDA3LlzMWvWLLi6uoJN\noURE8tXpZuexY8eQnp6OBQsWYPr06Th79iwiIiIsVRsREdVCjXPklRRFQdeuXbF161YAQEZGBqZP\nn4533nmn2p9PTEw0T4VERA1MSEhInX6+1kGu0WgsXkx9lZmZCX9/f9llWAWeCyOeCyOeCyNTLoJr\nNbXSokULxMbG1vgeERGJx4YgIiIbxyAnIrJxDHIiIhvHICcisnEMciIiG8cgJyKycQxyIiIbxyAn\nIrJxDHIiIhvHICcisnEMciIiG8cgJyKycQxyIiJrUFFh8kcZ5EREsm3eDLzyiskfZ5ATEYmmKMDB\ng8bjwYOBL74w+esY5EREoul0wIIFQEGBeuzsDLi4mPx1DHIiIhG2bAH271dfOzoC27cDXl5m+WoG\nORGRCM7OgEOtd9esEwY5EZElnDoFPP208fixx4BevSwyFIOciMhcrlwBDAb1dVAQ8MEHQoZlkBMR\nmcuzzwIpKepre3ugc2chw1pmwoaIqCFISwNycoA+fdTjX39VA1wwXpETEZkqLc14BQ5ICXGAQU5E\nVHslJcBLL6nPgQPAwIHAiy/KrQm1DPLk5GSMGzcOAHDixAmMHTsW48ePx4svvoi8vDyLFkhEJJWi\nAHq9+trNTX36RFHk1nSLGoM8Ojoac+fOhVarBQCEh4dj3rx5+OabbzBo0CCsXLnS4kUSEUkTFgZ8\n/736WqMBRo5UG3qsSI1B3qpVK0RFRd04XrJkCTp06AAA0Ol0cHZ2tlx1RESiabXAiRPG4/nz1adR\nrFiNQT5o0CDYV5nA9/PzAwD8/vvvWLduHSZOnGix4oiIhEtJAcLDjce+voCddd9ONOnxw59++gkr\nVqzAypUr4e3t/Zc/l5mZaXJh9UlRURHPxXU8F0Y8F0ZSz4WiwH3VKpSGhkJxcwOaNQMWLQJs6N9N\nnYN88+bN+P777xETEwNPT8+//Vl/f3+TC6tPMjMzeS6u47kw4rkwkn4uvL3h5eGhhrhAiqIgLS0N\nQUFBN97Lysqq8/fU6e8LBoMB4eHhKC0txauvvorx48fj888/r/OgRERSrVsHLF5sPA4LExriBoMB\nGzZsQI8ePTBp0iQod/gUTK2uyFu0aIHY2FgAwMGqi6ETEdmKggLjsrH9+kmd9zYYDNi4cSPef/99\nDB06FBqN5o6+jy36RFT/5eYCDzyg3si0twdatpRajoODA9avX2+277PuW7FERKbav19dBwVQnzxJ\nTpbSQr9jxw6EV30KxgJ4RU5E9dN//qM28DRtqh47OUkpo3Pnzha/kcsrciKqH5KTgaVLjcdz5xpX\nJRTkwoUL0Fe281/XvHlzdOrUyaLjMsiJyHZVfdqjWTOgXTspZZw8eRLPP/88unfvjhNVu0IFYZAT\nkW0yGICQECA7Wz2+6y5g6FAppWzZsgWBgYE4ffo0unTpInx8zpETke0oLATKytSrbzs7YOtW4U08\n1Xn77beljs8rciKyHcuXA1u2GI9btBA6fElJCZ599tkbq8FaC16RE5H1ystTg7tycb6ZM9UnUSRx\nd3fHpEmTYGdli2hZVzVERFU5OgJnzhhvagoM8YqKCuRUPodexaOPPnrTirDWgEFORNYlLAxISlJf\ne3gAH34oNMBLS0uxbNkytG3bFl9++aWwce8Ep1aISDpNWZnxYNw4oMpqgKKlpaXht99+w6ZNm9Cr\nVy9pddQFg5yI5IqLg+eGDcB336nHPXtKLadLly6Ii4uTWkNdcWqFiMQyGIBdu4zHTz6JgkWLpJTy\n8ccfIz4+XsrY5sQgJyKxDAYgKkp9JhxQb2g6yJkc6N+/P9pJ6AYtNxgQnZmJgUlJ0BoMd/x9nFoh\nIsv7/nugeXN1HXAHB2DjRuElXLp0CXfddddN7913331CayjR67EqMxOfXryIzm5umNe6NRzMcCOX\nV+REZBlV10Fp0sS4qYNg8fHxGDJkCPr373/bglYirbl0CW0OHMC+wkL8X5cu+LlbNzzYuPEdbyoB\nMMiJyBJOngQef9x4/NBDwD33CC9DURQsWbIETz/9NI4cOSL1+e9Obm7Y3b07NnTujBAPD7N+N6dW\niMg8MjLU6RM7O6B9e7WdXjKNRmM1T6D0qmGz+jvBK3IiMo8XXgAql3C1swMCA4UOn5iYiI8++kjo\nmLc6UVKCV1JTUajTCR2XQU5Epjl5Etizx3j8889A587SymnVqhX69u0rZezDhYUYkZKCAUlJuNvF\nRXiwcmqFiEyTnQ2kpxuPBbbR5+fnw83NDU5Vtm/z8/PDgAEDhNUAAH8UFWFWWhqOl5ZiRsuW+KZj\nR7hLmIfnFTkR1U5xMTB2LFA5bdC/v9pOL1BOTg5mz56NoKAg7N+/X+jY1dEqCp5t2hRn77sP0wIC\npIQ4wCtyIvo7BgOg16tNO40aAaGhUpeRXbduHfLz83H48GEECp6Dr05vT0/0tuBNzNqq1RV5cnIy\nxl3/kzc9PR1jxoxBaGgo3nvvPYsWR0SSvfoqUPWpjyFDAImP8L3++utYvny50BCv7MLMKC8XNmZd\n1Rjk0dHRmDt37o0dMSIiIvDmm29i7dq1MBgM2LFjh8WLJCJBysqAP/4wHn/8MTBqlPAydDodXnzx\nRZSWlgofu1KJXo+lFy4g6MABbLpyBSUSm4lqUmOQt2rVClFRUTeOjx07hp7XVyd78MEHrWKeiojM\n5NQp4IsvjMceHlKmUhwcHDB06FCzdD3WVb5Wiw/Pn0fggQPYW1CALV27Yvs996C9m5vwWmqrxiAf\nNGjQTd1QSpW2W3d3dxQVFVmmMiKyPEVRN26o/D3u1g1YuVJwCQry8/Nve3/48OFwdXUVWgsAZGu1\nOFNWht3du2Njly7oYeYuzL9jUExbQKvONzur7lVXUlICz7+Z6M/MzDSpqPqmqKiI5+I6ngsjazkX\nbs7OuHb+PAy+vkLH1ev1+OmnnxAZGYnOnTtjyZIlQsf/Kx4Awr28gIICZBYUCBlTZ9Bha9pWfJ70\nOdb2XVvnz9c5yDt16oRDhw6hV69e2LNnD/r06fOXP+vv71/nguqjzMxMnovreC6MpJ2LNWuAS5fU\njYwBYMYM8TUAOHfuHGJiYhAREYF7771X+Lk4UVICZzs7tJFw1V+pXFeONclrsGjfIgR4BmDJ40sA\nE/7sqHOQz5w5E++++y60Wi2CgoIwePDguo9KRGLl5ABNm6qvH30UqNJII0tgYCD27t0LQOzf3g8X\nFiIiPR17CwqwskMHKUFeXFGMlYkr8en+T9H9ru5YM2wNHrj7AQDqUgN1Vasgb9GiBWJjYwEArVu3\nRkxMTJ0HIiJJrlwBBg5Un0axt1cXthJszZo18Pf3x6BBg4SPDajz8Lvz8xGeno4TpaV4S1IXZl5Z\nHiIPRiLqUBQeCnwI28ZsQ/e7ut/x97IhiKg+2rED6NIFuOsuwM/PGOKSdOjQAT4+PtLGz9PpMO3M\nGYQFBGBcs2ZwshPb1J5VlIXF+xdjddJqDOswDHtf2Iv2vu3N9v0McqL6KCkJ8PZWgxwQGuIFBQXw\numUTib+7lyaCr6Mjknv
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077be9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x + 0, '-g')  # solid green\n",
    "plt.plot(x, x + 1, '--c') # dashed cyan\n",
    "plt.plot(x, x + 2, '-.k') # dashdot black\n",
    "plt.plot(x, x + 3, ':r');  # dotted red"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\n",
    "\n",
    "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting the Plot: Axes Limits\n",
    "\n",
    "Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\n",
    "The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD/CAYAAAAOoUbCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvBw5OTk4ePAgUlNTsWD
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ab9f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "plt.xlim(-1, 11)\n",
    "plt.ylim(-1.5, 1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD6CAYAAABamQdMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5op7jRpAaKgsEEXmVtK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079fa160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "plt.xlim(10, 0)\n",
    "plt.ylim(1.2, -1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\n",
    "The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD/CAYAAAAOoUbCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvBw5OTk4ePAgUlNTsWD
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107508b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis([-1, 11, -1.5, 1.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD/CAYAAAD/qh1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN3w/JSUFkZGRSEpKQlJ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d1c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD6CAYAAAC8sMwIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsWNlA3udbzqoEBMjb9p
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1076ded30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeling Plots\n",
    "\n",
    "As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n",
    "\n",
    "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEVCAYAAAD6u3K7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6NpVd5KCtWoFjRvDggW
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d57438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.title(\"A Sine Curve\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"sin(x)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The position, size, and style of these labels can be adjusted using optional arguments to the function.\n",
    "For more information, see the Matplotlib documentation and the docstrings of each of these functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\n",
    "Again, Matplotlib has a built-in way of quickly creating such a legend.\n",
    "It is done via the (you guessed it) ``plt.legend()`` method.\n",
    "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD6CAYAAAC8sMwIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4cpUd65OUdeiFLIvhE6
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d7ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x), '-g', label='sin(x)')\n",
    "plt.plot(x, np.cos(x), ':b', label='cos(x)')\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label.\n",
    "More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aside: Matplotlib Gotchas\n",
    "\n",
    "While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands.\n",
    "In particular, functions to set limits, labels, and titles are slightly modified.\n",
    "For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n",
    "\n",
    "- ``plt.xlabel()``  → ``ax.set_xlabel()``\n",
    "- ``plt.ylabel()`` → ``ax.set_ylabel()``\n",
    "- ``plt.xlim()``  → ``ax.set_xlim()``\n",
    "- ``plt.ylim()`` → ``ax.set_ylim()``\n",
    "- ``plt.title()`` → ``ax.set_title()``\n",
    "\n",
    "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f67cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "ax.plot(x, np.sin(x))\n",
    "ax.set(xlim=(0, 10), ylim=(-2, 2),\n",
    "       xlabel='x', ylabel='sin(x)',\n",
    "       title='A Simple Plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}