{
 "metadata": {
  "name": "",
  "signature": "sha256:d9373713680ce89a073c585443d70acf0ee22ce6ba5745c12cdc0239eeb1028c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Matplotlib, A Simple Example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Tell iPython to load matplotlib and display all visuals created inline (that is, on this page)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Import matplotlib's pyplot module"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as pyplot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create a simple plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.plot([1.6, 2.7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10870d2d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGOFJREFUeJzt3X9sVfX9x/HXddSE0ckEDOXXVAqTC9fb24GphIm3LmyF\n8WOBJqLDsFq6ihAsaHCgCZWYyvCPpsUfWYyCiibblB+SaU2GXAxFKF8p1FVHEOlaKJB1lsAdSb2F\nz/ePOwul5fa2Pffec899PpKbtJzDPR8/wTdv3ueeV13GGCMAgGPclOgFAACsRWEHAIehsAOAw1DY\nAcBhKOwA4DAUdgBwmIiFvampSbm5uZo0aZI8Ho8qKyu7nNPS0qK8vDz5fD55PB5t2bIlVmsFAETB\nFelz7GfPntXZs2fl8/kUDAY1efJk7dixQ263u+Oc0tJStbW16YUXXlBLS4vuuusunTt3TgMGDIjL\nfwAAoLOIHXtGRoZ8Pp8kKT09XW63W83NzZ3OGTFihC5cuCBJunDhgoYOHUpRB4AEiroCNzQ0qLa2\nVjk5OZ1+vaioSA888IBGjhypixcv6i9/+YvliwQARC+qm6fBYFD5+fmqqKhQenp6p2NlZWXy+Xxq\nbm7WkSNHtGzZMl28eDEmiwUARMH04LvvvjO//OUvTXl5ebfHZ86cafbt29fx/QMPPGAOHTrU5bzM\nzEwjiRcvXrx49eKVmZnZU5nuImLHboxRYWGhJk6cqJKSkm7PmTBhgv7+979Lks6dO6djx45p7Nix\nXc47ceKEjDG8jNG6desSvga7vNgL9oK9iPw6ceJEpDLdrYgz9urqam3dulVer1fZ2dmSwqOXxsZG\nSVJxcbHWrl2rgoICZWVl6cqVK9q4caOGDBnS64UAAKwRsbD//Oc/15UrVyK+wbBhw7Rr1y5LFwUA\n6DuePE0Av9+f6CXYBntxFXtxFXvRPxEfULL0Qi6X4nQpAHCMvtROOnYAcBgKOwA4DIUdAByGwg4A\nDkNhBwCHobADgMNQ2AHAYSjsAOAwFHYAcBgKOwA4DIUdAByGH04KADZkjLR7d99+L4UdAGzmm2+k\nFSukr7/u2+9nFAMANtHWJj3/vHTPPdK0aVJdXd/eh44dAGxg927p8celn/5U+r//k+68s+/vRWEH\ngAQ6c0Z68kmpulqqrJTmzev/ezKKAYAEaG+XNm2SvF7p9tulL7+0pqhLdOwAEHc1NdJjj0m33CLt\n3StNnGjt+9OxA0CctLaGC/q8edLKldKePdYXdYnCDgAxZ4z05puS2y394Afhscsjj0guV2yuxygG\nAGKovl5aulS6dEnatSv8UcZYo2MHgBgIBqXVqyW/X3rwQengwfgUdYnCDgCWMkbavj08Oz9zRvri\nC2nZsvAIJl4YxQCARb6PAjhxIjxTz81NzDro2AGgn66PAjh6NHFFXaJjB4B+sTIKwCoRO/ampibl\n5uZq0qRJ8ng8qqys7Pa8QCCg7OxseTwe+f3+WKwTAGzlzBnp4YelRx+VNm4Mf+LFDkVdklzGGHOj\ng2fPntXZs2fl8/kUDAY1efJk7dixQ263u+Oc8+fPa9q0afr44481evRotbS0aNiwYV0v5HIpwqUA\nICm0t0uvviqtXy8tWSI9+6w0aFDsrteX2hlxFJORkaGMjAxJUnp6utxut5qbmzsV9nfffVcLFizQ\n6NGjJanbog4AThDrKACrRH3ztKGhQbW1tcrJyen068ePH9e3336r3NxcTZkyRW+//bbliwSARIpX\nFIBVorp5GgwGlZ+fr4qKCqWnp3c6FgqFdPjwYe3evVuXLl3S1KlTde+992r8+PExWTAAxIsx0ltv\nSU8/LS1YEI4CuPXWRK+qZz0W9lAopAULFmjRokX6zW9+0+X4mDFjNGzYMA0cOFADBw7U9OnTdfTo\n0W4Le2lpacfXfr+fG60AbCsRUQBS+MMogUCgX+8R8eapMUaLFy/W0KFDVV5e3u05//znP7V8+XJ9\n/PHHamtrU05Ojv785z9r4nX/TuHmKYBkEAyGb4xu3iyVloZHMPF8avR6lt88ra6u1tatW+X1epWd\nnS1JKisrU2NjoySpuLhYEyZMUF5enrxer2666SYVFRV1KeoAYHfGSDt2SE88Id1/fzgK4H+fHUk6\nETt2Sy9Exw7Apq6NAnjllcQ+NXq9vtROIgUApCy7RQFYhUgBACnJjlEAVqGwA0gpZ85ITz4pVVdL\nlZXW/QBpO2EUAyAltLdLmzZJXq90++3hz6Q7sahLdOwAUkCyRAFYhY4dgGMlWxSAVSjsABzHmPBP\nMHK7ww8Xffml9MgjksuV6JXFB6MYAI6SqCgAO6FjB+AIwaC0erXk90sPPigdPJiaRV2isANIcsZI\n27eHZ+dnzoSjAJYtS2y+S6IxigGQtK6NAnjzTWc8NWoFOnYAScepUQBWoWMHkFScHAVgFQo7gKSQ\nClEAVmEUA8DWUikKwCp07ABsK9WiAKxCxw7AdlI1CsAqFHYAtpHqUQBWYRQDwBaIArAOHTuAhCIK\nwHoUdgAJQRRA7DCKARB3RAHEFh07gLghCiA+6NgBxAVRAPFDYQcQU0QBxB+jGAAxQRRA4tCxA7Ac\nUQCJRccOwDJEAdgDhR1AvxEFYC8RC3tTU5Nyc3M1adIkeTweVVZW3vDcQ4cOacCAAdq2bZvliwRg\nX/X10v33h+fpu3ZJL78s3XproleV2iIW9rS0NJWXl6u+vl4HDhzQyy+/rK+++qrLeZcvX9bTTz+t\nvLw8GWNitlgA9kEUgH1FLOwZGRny+XySpPT0dLndbjU3N3c5b9OmTcrPz9dtt90Wm1UCsA2iAOwv\n6k/FNDQ0qLa2Vjk5OZ1+/fTp09q5c6c++eQTHTp0SC6GaoBjEQWQHKIq7MFgUPn5+aqoqFB6enqn\nYyUlJdqwYYNcLpeMMRFHMaWlpR1f+/1++f3+Pi0aQHy1tUkvviiVl0tPPSVt2ybdfHOiV+VMgUBA\ngUCgX+/hMj0MxUOhkGbPnq2ZM2eqpKSky/GxY8d2FPOWlhb98Ic/1Guvvaa5c+d2vtD/Cj+A5HJt\nFEBlJVEA8daX2hmxsBtjtHjxYg0dOlTl5eU9vllBQYHmzJmj+fPnW7I4AIlDFIA99KV2RhzFVFdX\na+vWrfJ6vcrOzpYklZWVqbGxUZJUXFzcx6UCsKv2dunVV6X166UlS6TXXpMGDUr0qtAbPY5iLLsQ\nHTtge9dGAbzyCk+N2kFfaidPngIgCsBhKOxACiMKwJlIdwRSVH29tHSpdOlSOAqAp0adg44dSDFE\nATgfhR1IEUQBpA5GMUAKIAogtdCxAw7W1iY9/3x41DJtmnT0KEU9FdCxAw71fRTAXXdJn38u3XFH\noleEeKGwAw5DFAAYxQAO0d4e/ilGXq90++3hz6RT1FMTHTvgANdGAezdy1OjqY6OHUhiRAGgOxR2\nIAkRBYBIGMUASYYoAPSEjh1IEkQBIFoUdsDmiAJAbzGKAWyMKAD0BR07YENEAaA/6NgBmyEKAP1F\nYQdsgigAWIVRDJBgRAHAanTsQAIRBYBYoGMHEoAoAMQShR2II6IAEA+MYoA4IQoA8ULHDsQYUQCI\nNwo7ECNEASBRGMUAMUAUABKpx469qalJubm5mjRpkjwejyorK7uc88477ygrK0ter1fTpk1TXV1d\nTBYL2B1RALCDHjv2tLQ0lZeXy+fzKRgMavLkyZoxY4bcbnfHOWPHjtWnn36qwYMHq6qqSr///e91\n4MCBmC4csBuiAGAXPRb2jIwMZWRkSJLS09PldrvV3NzcqbBPnTq14+ucnBydOnUqBksF7On7KID9\n+8NRAHPnJnpFSHW9unna0NCg2tpa5eTk3PCc119/XbNmzer3wgC7uz4KoL6eog57iPrmaTAYVH5+\nvioqKpSent7tOXv27NEbb7yh6urqbo+XlpZ2fO33++X3+3u1WMAuiAJArAQCAQUCgX69h8sYY3o6\nKRQKafbs2Zo5c6ZKSkq6Paeurk7z589XVVWVxo0b1/VCLpeiuBRga62t0po10s6d0saN0qJFPDWK\n2OpL7exxFGOMUWFhoSZOnHjDot7Y2Kj58+dr69at3RZ1INkRBYBk0mPHvm/fPk2fPl1er1eu//0p\nLisrU2NjoySpuLhYS5Ys0fbt2/WTn/xEUviTNDU1NZ0vRMeOJHVtFMCrr/LUKOKrL7UzqlGMFSjs\nSDbBoLR+vbR5s1RaGp6p89Qo4i0moxgg1RAFgGRHpABwDaIA4AR07ICIAoCz0LEj5REFAKehsCNl\nEQUAp2IUg5Rz+TJRAHA2OnakFKIAkAro2JESWlvDBX3ePGnlSmnPHoo6nIvCDkcjCgCpiFEMHOva\nKIBdu4gCQOqgY4fjBIPS6tWS3y89+KB08CBFHamFwg7HIAoACGMUA0cgCgC4io4dSY0oAKArOnYk\nLaIAgO5R2JF0iAIAImMUg6RBFAAQHTp2JAWiAIDo0bHD1ogCAHqPwg5bIgoA6DtGMbAdogCA/qFj\nh20QBQBYg8KOhCMKALAWoxgkFFEAgPXo2JEQRAEAsUPHjrgjCgCILQo74oYoACA+GMUg5ogCAOIr\nYmFvampSbm6uJk2aJI/Ho8rKym7PW7FihcaPH6+srCzV1tbGZKFITjU14Tn6+++HowBeeEEaNCjR\nqwKcLeIoJi0tTeXl5fL5fAoGg5o8ebJmzJght9vdcc6HH36or7/+WsePH9fBgwe1dOlSHThwIOYL\nh721tkpr1kg7d0obN0qLFvHUKBAvETv2jIwM+Xw+SVJ6errcbream5s7nfPBBx9o8eLFkqScnByd\nP39e586di9FyYXdEAQCJF/XN04aGBtXW1ionJ6fTr58+fVpjxozp+H706NE6deqUhg8fbt0qkRSI\nAgDsIarCHgwGlZ+fr4qKCqWnp3c5bozp9L3rBu1ZaWlpx9d+v19+vz/6lcK2gkFp/Xpp82aptDSc\nxshTo0DfBAIBBQKBfr2Hy1xfla8TCoU0e/ZszZw5UyUlJV2OP/bYY/L7/Vq4cKEkacKECdq7d2+X\njt3lcnX5CwDJzRhpxw7piSek+++XXnxRyshI9KoAZ+lL7Yw4YzfGqLCwUBMnTuy2qEvS3Llz9dZb\nb0mSDhw4oB//+MeMYVLAN99Ic+ZIa9eGZ+pvv01RB+wiYse+b98+TZ8+XV6vt2O8UlZWpsbGRklS\ncXGxJGn58uWqqqrSoEGDtHnzZv3sZz/reiE6dkdoawt35uXl0lNPhR84uvnmRK8KcK6+1M4eRzFW\nobAnv2ujACoriQIA4qEvtZNIAfSIKAAguRApgBsiCgBITnTs6FZNTfhji7fcEo4C4AdIA8mDjh2d\ntLaGC/q8edLKldKePRR1INlQ2CGJKADASRjFgCgAwGHo2FNYMCitXi35/dKDD0oHD1LUASegsKcg\nY6Tt28Oz8zNnpC++kJYtI98FcApGMSnmm2+kFSukEyfCM3V+gDTgPHTsKaKtTXr++fCoZdo06ehR\nijrgVHTsKeDaKIDPPycKAHA6CruDEQUApCZGMQ5EFACQ2ujYHYYoAAB07A5BFACA71HYkxxRAACu\nxygmiREFAKA7dOxJiCgAAJFQ2JMIUQAAosEoJkkQBQAgWnTsNkcUAIDeomO3MaIAAPQFhd2GiAIA\n0B+MYmyEKAAAVqBjtwmiAABYhY49wYgCAGA1CnuCEAUAIFYYxSQAUQAAYqnHjv3RRx/V8OHDdffd\nd3d7vKWlRXl5efL5fPJ4PNqyZYvVa3QMogAAxEOPhb2goEBVVVU3PP7SSy8pOztbR44cUSAQ0JNP\nPqn29nZLF5nsiAIAEE89jmLuu+8+NTQ03PD4iBEjVFdXJ0m6cOGChg4dqgEDmPB8jygAAPHW75un\nRUVFqq+v18iRI5WVlaWKigor1pX0iAIAkCj9LuxlZWXy+Xxqbm7WkSNHtGzZMl28eNGKtSWt3bvD\nDxnV1ISjANaskW6+OdGrApAq+j0z2b9/v5555hlJUmZmpu68804dO3ZMU6ZM6XJuaWlpx9d+v19+\nv7+/l7cVogAA9FcgEFAgEOjXe7iMMaankxoaGjRnzhx98cUXXY6tWrVKgwcP1rp163Tu3DlNnjxZ\ndXV1GjJkSOcLuVyK4lJJ6fJl6ZVXpPXrpSVLpGeflQYNSvSqADhBX2pnj4X9oYce0t69e9XS0qLh\nw4frueeeUygUkiQVFxerpaVFBQUFamxs1JUrV7RmzRo9/PDDliwuGVwbBfDKKzw1CsBaMSnsVnFa\nYW9tDc/Od+6UNm6UFi3iqVEA1utL7SRSoJeIAgBgd3zgvBeIAgCQDOjYo0AUAIBkQmGPgCgAAMmI\nUcwNEAUAIFnRsV+HKAAAyY6O/Rq7d0uPPy7ddVc4CuCOOxK9IgDoPQq7iAIA4CwpPYq5fFnatCkc\n2HX77eGPM1LUASS7lO3Yv48CGDxY2ruXKAAAzpFyHXtra7igz5snrVolffIJRR2As6RMYe8uCoB8\nFwBOlBKjGKIAAKQSR3fsRAEASEWOLOxEAQBIZY4bxRAFACDVOaZjJwoAAMIc0bETBQAAVyV1YScK\nAAC6SspRDFEAAHBjSdexEwUAAJElTcdOFAAARMf2hZ0oAADoHVuPYogCAIDes2XHThQAAPSdrQo7\nUQAA0H+2GcUQBQAA1kh4x04UAABYq8fC/uijj2r48OG6++67b3hOIBBQdna2PB6P/H5/1BffvTv8\nkFFNTTgKYM0a6eabo/7tAIBu9FjYCwoKVFVVdcPj58+f17Jly7Rr1y794x//0HvvvdfjRc+ckR5+\nWCoslF58Ufrgg9TKdwkEAolegm2wF1exF1exF/3TY2G/7777dOutt97w+LvvvqsFCxZo9OjRkqRh\nw4bd8FyiAML4Q3sVe3EVe3EVe9E//b55evz4cYVCIeXm5urixYt64okn9Mgjj3R77j33EAUAALHW\n78IeCoV0+PBh7d69W5cuXdLUqVN17733avz48V3OXbVK+u1veWoUAGLKROHkyZPG4/F0e2zDhg1m\n3bp1Hd8XFhaav/71r13Oy8zMNJJ48eLFi1cvXpmZmdGU6U763bHPmzdPy5cv1+XLl9XW1qaDBw9q\n1apVXc77+uuv+3spAEAUeizsDz30kPbu3auWlhaNGTNGzz33nEKhkCSpuLhYEyZMUF5enrxer266\n6SYVFRVpIgN0AEgYlzHGJHoRAADrWP7kaVVVlSZMmKDx48frj3/8Y7fnrFixQuPHj1dWVpZqa2ut\nXoJt9LQX77zzjrKysuT1ejVt2jTV1dUlYJWxF82fCUk6dOiQBgwYoG3btsVxdfEVzV709YG/ZNPT\nXrS0tCgvL08+n08ej0dbtmyJ/yLjJJoHQXtVN3s9lY+gvb3dZGZmmpMnT5rvvvvOZGVlmS+//LLT\nOX/729/MzJkzjTHGHDhwwOTk5Fi5BNuIZi/2799vzp8/b4wx5qOPPnLkXkSzD9+fl5uba37961+b\n9957LwErjb1o9qK1tdVMnDjRNDU1GWOM+fe//52IpcZcNHuxbt0684c//MEYE96HIUOGmFAolIjl\nxtynn35qDh8+fMMPqfS2blrasdfU1GjcuHG64447lJaWpoULF2rnzp2dzvnggw+0ePFiSVJOTo7O\nnz+vc+fOWbkMW4hmL6ZOnarBgwdLCu/FqVOnErHUmIpmHyRp06ZNys/P12233ZaAVcZHNHvRmwf+\nklk0ezFixAhduHBBknThwgUNHTpUAwbYJrfQUj09CNrbumlpYT99+rTGjBnT8f3o0aN1+vTpHs9x\nYkGLZi+u9frrr2vWrFnxWFpcRftnYufOnVq6dKkkyeXQBx2i2Yvjx4/r22+/VW5urqZMmaK33347\n3suMi2j2oqioSPX19Ro5cqSysrJUUVER72XaRm/rpqV//UX7P6S57n6tE/9H7s1/0549e/TGG2+o\nuro6hitKjGj2oaSkRBs2bJDL5ZIxpsufD6eIZi9688BfMotmL8rKyuTz+RQIBHTixAnNmDFDR48e\n1Y9+9KM4rNB+elM3LS3so0aNUlNTU8f3TU1NHf+kvNE5p06d0qhRo6xchi1EsxeSVFdXp6KiIlVV\nVUX8p1iyimYfPv/8cy1cuFBS+IbZRx99pLS0NM11WJBQNHsxZswYDRs2TAMHDtTAgQM1ffp0HT16\n1HGFPZq92L9/v5555hlJUmZmpu68804dO3ZMU6ZMieta7aDXddPKGwChUMiMHTvWnDx50rS1tfV4\n8/Szzz5z5A1DY6Lbi3/9618mMzPTfPbZZwlaZexFsw/X+t3vfmfef//9OK4wfqLZi6+++sr84he/\nMO3t7ea///2v8Xg8pr6+PkErjp1o9mLlypWmtLTUGGPM2bNnzahRo8x//vOfRCw3LiI94d/bumlp\nxz5gwAC99NJL+tWvfqXLly+rsLBQbrdbf/rTnySFH2iaNWuWPvzwQ40bN06DBg3S5s2brVyCbUSz\nF+vXr1dra2vHbDktLU01NTWJXLblotmHVBHNXqTKA3/R7MXatWtVUFCgrKwsXblyRRs3btSQIUMS\nvPLY6OlB0N7WTR5QAgCHSfiPxgMAWIvCDgAOQ2EHAIehsAOAw1DYAcBhKOwA4DAUdgBwGAo7ADjM\n/wPt7LgFtrZFVgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10656df10>"
       ]
      }
     ],
     "prompt_number": 27
    }
   ],
   "metadata": {}
  }
 ]
}