ethanwhite/python_ggplot_exploration.ipynb

## python_ggplot_exploration.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from ggplot import *\n",
      "from pandas import DataFrame\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Grab some data using the EcoData Retriever (http://ecodataretriever.org)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!retriever -q install csv MammalLH"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lhdata = pd.read_csv(\"MammalLH_species.csv\")\n",
      "lhdata.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>record_id</th>\n",
        "      <th>sporder</th>\n",
        "      <th>family</th>\n",
        "      <th>genus</th>\n",
        "      <th>species</th>\n",
        "      <th>mass_g</th>\n",
        "      <th>gestation_mo</th>\n",
        "      <th>newborn_g</th>\n",
        "      <th>weaning_mo</th>\n",
        "      <th>wean_mass_g</th>\n",
        "      <th>afr_mo</th>\n",
        "      <th>max_life_mo</th>\n",
        "      <th>litter_size</th>\n",
        "      <th>litters_year</th>\n",
        "      <th>refs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> Artiodactyla</td>\n",
        "      <td> Antilocapridae</td>\n",
        "      <td> Antilocapra</td>\n",
        "      <td>     americana</td>\n",
        "      <td>  45375</td>\n",
        "      <td> 8.13</td>\n",
        "      <td>  3246.36</td>\n",
        "      <td> 3.00</td>\n",
        "      <td>  8900</td>\n",
        "      <td> 13.53</td>\n",
        "      <td> 142</td>\n",
        "      <td> 1.85</td>\n",
        "      <td> 1.00</td>\n",
        "      <td> 1,2,6,9,23,26,27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> Artiodactyla</td>\n",
        "      <td>        Bovidae</td>\n",
        "      <td>       Addax</td>\n",
        "      <td> nasomaculatus</td>\n",
        "      <td> 182375</td>\n",
        "      <td> 9.39</td>\n",
        "      <td>  5480.00</td>\n",
        "      <td> 6.50</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> 27.27</td>\n",
        "      <td> 308</td>\n",
        "      <td> 1.00</td>\n",
        "      <td> 0.99</td>\n",
        "      <td>     1,2,17,23,26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> Artiodactyla</td>\n",
        "      <td>        Bovidae</td>\n",
        "      <td>   Aepyceros</td>\n",
        "      <td>      melampus</td>\n",
        "      <td>  41480</td>\n",
        "      <td> 6.35</td>\n",
        "      <td>  5093.00</td>\n",
        "      <td> 5.63</td>\n",
        "      <td> 15900</td>\n",
        "      <td> 16.66</td>\n",
        "      <td> 213</td>\n",
        "      <td> 1.00</td>\n",
        "      <td> 0.95</td>\n",
        "      <td>    1,2,8,9,23,29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> Artiodactyla</td>\n",
        "      <td>        Bovidae</td>\n",
        "      <td>  Alcelaphus</td>\n",
        "      <td>    buselaphus</td>\n",
        "      <td> 150000</td>\n",
        "      <td> 7.90</td>\n",
        "      <td> 10166.67</td>\n",
        "      <td> 6.50</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> 23.02</td>\n",
        "      <td> 240</td>\n",
        "      <td> 1.00</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>        1,2,17,23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td> Artiodactyla</td>\n",
        "      <td>        Bovidae</td>\n",
        "      <td>  Ammodorcas</td>\n",
        "      <td>       clarkei</td>\n",
        "      <td>  28500</td>\n",
        "      <td> 6.80</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1.00</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>              1,2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "   record_id       sporder          family        genus        species  \\\n",
        "0          1  Artiodactyla  Antilocapridae  Antilocapra      americana   \n",
        "1          2  Artiodactyla         Bovidae        Addax  nasomaculatus   \n",
        "2          3  Artiodactyla         Bovidae    Aepyceros       melampus   \n",
        "3          4  Artiodactyla         Bovidae   Alcelaphus     buselaphus   \n",
        "4          5  Artiodactyla         Bovidae   Ammodorcas        clarkei   \n",
        "\n",
        "   mass_g  gestation_mo  newborn_g  weaning_mo  wean_mass_g  afr_mo  \\\n",
        "0   45375          8.13    3246.36        3.00         8900   13.53   \n",
        "1  182375          9.39    5480.00        6.50          NaN   27.27   \n",
        "2   41480          6.35    5093.00        5.63        15900   16.66   \n",
        "3  150000          7.90   10166.67        6.50          NaN   23.02   \n",
        "4   28500          6.80        NaN         NaN          NaN     NaN   \n",
        "\n",
        "   max_life_mo  litter_size  litters_year              refs  \n",
        "0          142         1.85          1.00  1,2,6,9,23,26,27  \n",
        "1          308         1.00          0.99      1,2,17,23,26  \n",
        "2          213         1.00          0.95     1,2,8,9,23,29  \n",
        "3          240         1.00           NaN         1,2,17,23  \n",
        "4          NaN         1.00           NaN               1,2  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ggplot(aes(x='weaning_mo', y='gestation_mo', color='sporder'), data=lhdata) + \\\n",
      "    geom_point()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<ggplot: (5576561)>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kpITExMRGz6ntSK1+4GbbHq35ln1CrwS9ktS1Gxs8fuPGjaw+kEv0pLuwhvmW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5517a10>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = ggplot(aes(x='weaning_mo'), data=lhdata)\n",
      "p + geom_hist() + ggtitle(\"Histogram of Weaning Times\") + labs(\"Weaning Time\", \"Freq\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<ggplot: (5579953)>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2uA0//eAkqhuafVl+m16YEINpI/r4Zd9ERFoUFBQgMTHRq3V9euRw9uxZFBcX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\nYk3BRW0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5523c10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p2 = ggplot(aes(x='gestation_mo', color='sporder', fill=True, alpha=0.3),\n",
      "            data=lhdata)\n",
      "p2 + geom_density() + labs(\"Gestation Time\", \"Probability Density\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<ggplot: (5361853)>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZAKoFKRRhwoPhlTaugFyokTEX5bsM7Wqj5ozl82u/xWKZcwo6xPvuu28u7ZhV\nRKqP5BtPoaNxVMsqkA5kM8QPvIRIHUL97ybEmHihEAKJJqs00eoYufYBsp0DRBur58zmsTsFTMJE\nXSZqAnN1lcLCDgZOgsgOkXz9SVRtI2rRkrwzBIjG0JHTkLIbt/ON0RRgo9ehyQU5hBC49QlSezrQ\nqjKSx1oslrmh6LKbXC7Ho48+yosvvgjAWWedxeWXXz46hK404vueQVUvQicm9u5EIo2KNyPTbxBV\nr5JNXAhaEw92sU49j+5QEIvhJC8i0IvJHO0j3jI3axNN/WU2UZeJmsBcXaVQtId4zz33kE6n+dSn\nPsUnP/lJ+vv7+eY3vzkHps2MsHEpurqAE5MahEDVNFPVsw2Z7qLG/zl1/s/pEysYdNeDuwT6foDn\nvkjmzW7bS7RYTiKKOsT9+/fzR3/0R7S0tLB06VKuuuoq9u/fPwemzRBZPEGDlh6qqo6Gtm9R5e8i\n5axFiggZBTgJiK9HZl9EZHeS6xosv82YuwbMRF0magJzdZVCUYeotSYIjsXbfN8vW+KFuUIpiVMl\nSepXyWarQEgcNNlweFuicCC2Dodfkt67l2nubrRYLAucojHEX/u1X+Ozn/0sF198MUopfvrTny74\nWIMOBVXuGwxGVxLtfJ1U1VIiEjJj51lkBBlfQtj7JMHAOryaeFltWujfaSFM1GWiJjBXVykUdYiX\nXXYZK1euHE0K+8EPfnBhLcyeBBn24TpZQrcFhyO4A0eIRFaSDo/rCXpNSHcnmX0v4G36tfkx1mKx\nzBnTWnazceNGrrjiCq644ooF7wzRmmh4iNDNlx4NqxqIdL9GRKrxPcRhZHIFYdvPUEF5d6+YGr8x\nUZeJmsBcXaVQ1CG2t7fPhR1zhqN7EYQIZ7hz7EYQWhFLHSU7SY5Y4SVB5Mi++eLcGmqxWOacog7x\n9ttvnws75oy4OkBANUIeGx6HiXqSvW+Q1ZPnenSSy/DffKqskyumxm9M1GWiJjBXVykUdYgjFfNM\nQKohpMigZRX5PLd5B6cjcVw1hEj1TH5dPInKpgl69sydsRaLZc4p6hDf/va3c++99zI4ODjuz3R5\n+eWXufHGG7nxxht55ZVXpjz361//Orfccgs33XQTR48enfYzpktEHUaJquGWQIxVH6smNrCfQp1A\nEWsit/epWbdpBFPjNybqMlETmKurFIrOMn/3u98F4Jlnnhk9JoSYVpLYUgvV/8mf/AkAr7zyCt/7\n3vdG27PJMnLaAAAgAElEQVSCDonoLgJn8cgBhNDo4azZxKqJ9PaQy6aJxiYusZFVdYTdu1C5PmSk\ndvbsslgsFUNRh3jnnXfO+OYzLVQfi8Vw3enUR5k+nt8J0uNYt1Dk44gjEylCEDgJcl0HiC47fcL1\n0hP4qha/9RdEV79nVm0Dc+M3JuoyUROYq6sUZtfrHMfYQvXAaKH6Yg7xySef5D3vmdrpjE1V9Myz\nz9IvDtPQcgoAHR35+i9NTY2j7WZeI0g0AJBKpdBeADK/9Ka/rx8AP7GUoPsAB4MYCMGK4YqDBw8c\nAGBJXQvB4Zd47mAVCGdcYW/Atm3btiuwXQpTVt3L5XJ0dnayePFiHKf0Iu4zKVT//PPPc/ToUX77\nt3+74DlTVd2Ldr+BE6THnS/CDLXZXxGIZsRwsoZIMsdAWwI/dWzSaHuqjrPZS+Mp/xtZ3TzhuTqE\noGsf1edfjlPzlul+DdPC1Fx0JuoyUROYq2tWqu69+OKL3HXXXTQ0NJBKpbj22mvH1WieDqUWqt+7\ndy87d+7kiiuuKOk5xfDCdpQThTFbsLUG6Yz/LXBRZKINhO1vTOoQhQNaNhAc3jrrDtFiscw/BR3i\nQw89xOc+9zmam5tpbW3lW9/6Fv/v//2/km5eaqH6f/zHf6ShoYFbbrmFlStXcuWVV5aqZ1JiQRuh\nV8vY+vRaCaQ7PkmFKxQ5rxo1dATtZxBebKKmaBK/czeeP4D0Zi+jtom/zGCmLhM1gbm6SqGgQ3Rd\nl+bmfC9p6dKlpFKpGT1g06ZNbNq0acLx888/f8KxcpQ3lWEKSY5ARhCM2X4XCqR3nENEk0MiozHC\nnkO4zadNuJ9TJQm6qlHdLyAXXzjr9loslvmjoEPs6enh8ccfH92d0dXVNdoWQnDZZZfNmZEnQiQ4\nSugkJhxXSuAc30OUKp8CLFGP6tyDbjp1whIhIfPD5rD9eZzm3yi4hKhUtm7dyv8579fYN6A5lIZ0\nkF8kmvDglCSsqJI4cnaeNZeYGJcyUROYq6sUCjrEiy66iHT62OTEhRdeOK69UIiE7YRe/YTjWgmc\nyHiHGBGKnBZIN0JAgB7qQSQXTbhWxjz8vixe5ggivvSEbdRa00GCH7ZqYo6mPiJoiub30WRDzWt9\nsLNXcXajoDluXBkci6ViKOgQK77M6DSQ4RBSBARyYv0XrQXCOT6GqBlU+V6YiCUJu/YhJ3GITlwQ\ndNUTdj6DXPF7J2RjqDXbOhXeyo0sTYArjzk8AcRdQdyFTKB4ukOztkbzllo5az3TcmNij8NETWCu\nrlIwursRCdoI5cThMgBaIKVmZD8zgCc0/nDWbBmvRQ22oUN/wqVCArKWsOsNtMrN2D6lNU+3K3qy\nmhVVAneKIXHMlaysEuzp17zcrWwWb4ulDJR1Yfa8ojXRsB0/0jjFSfn9zHq4o+hJRdbP/0YIIRHS\nQfW24TSsmHhlTOAPSiKDuxE160s2T2nN852KIV+zJCHZsWMH69dPfR8pBMur4FBK4wjFWxfl14Zm\nMhn6+/vp6uqiu7ubvr4+hoaGyGazhGGI53nEYjEaGhpoampi2bJl1NTUzEkv08S4lImawFxdpWCs\nQ5ThEEJoEFNIFBohNXp4mOwKhT+m4yWq6gm79kzqEJ24wO9pyA+bZ+AQd/YqOjOaZYnSnJIUgmbX\n55k3jvLMkddJHXyNVCqFlBLP84hGo8RiMSKRCNFoFMjvKR8aGqK9vZ1t27aRy+WIxWJs2LCBc845\nh7q6uSm1arFUOsY6xGjYRiiL1EHRIKRiJHLgovG1ROerlSIjccKBTlR6ABkfv+ZQSBCyirBvH67f\nj/Bqpm1b61DI3n7NiqQY7aUV6x0CdBxtY/sLz3Ok9TBerAq3ZS2nr1pHfWR6w+empqbRv2ezWfbs\n2cNzzz3HaaedxqWXXkpNzfQ1TBcTexwmagJzdZWCmTFErYmEHYROsshp49ciCgESTcCxXpuIxgl7\nD056vYiG+Kk4Yd/Uac3GkgoU27qgJZHv7U2Hrs52vv/ow/z4iccJtGDdxrM59S3rWVoboz25kmAG\nrzEajbJq1So2bdpEX18f//Iv/8LTTz+94CsqWiwngpEOUYaDUGy4DKAEjju+boBLiK/GfC2JOnTP\nQbSaWF/AiUuCdBOq63m0Lu5ItNb8qlNT42kizvivfseOHRPOD3yf//npf/PEY98lVlXDuo1n0bC4\nBTk8E+0R4uqAQ+5iZjrFIqVk+fLlbNiwga1bt/LAAw/MeBH+ZJiYY89ETWCurlIw0iFGwyOFZ5fH\noMKJaxEdNMGYUgJSuiA1eqBzwvVCkJ94SWXRmbaiz9s/qOjLaeqixb/27q5OHvvOA7S3d3D6mWdT\n19g06XkJnSUjI3TLExvuep7HW9/6VlKpFF//+tfp7u4+oftZLAsR8xyiVnhhN6rIcBlABQInMr7n\n54gQ/7jaKiJWQ9i9f9J7iGhILlWH6n5+ymdlAsX2HmgpMIkyNoZ4YP8+fvDYIzQsXsqKU9ci5dSZ\nhqpVina3gSwT11uWysqVK2loaODuu+8el5hjppgYlzJRE5irqxTMc4hC4ntN+dQ0RdBK4kzYz6wm\nOEQZS6LT3ejcxJ06TlwSputR/a+jw2zBZ23r0iRdjVdk+92uV7fz1I+3sOYtb6VmUUNRDZCPe8Z0\nhkNe84yHzmNpbGxk1apV3HPPPbPiFC2WhYJ5DhHymbGnwbEUYMfciCsUWT3J1+K5qL6JzkEIEE6I\nyjjoob2TPqd1KKQjo2mIFnaGO3bsYOf2l3j+mV9y+hlnEo0XH/KPJaZ9Ahw6ndlZQlNbW8vq1au5\n99576ejomPF9TIxLmagJzNVVCmY6xGkjGC0lMIwrNLlwouMSiUWEXXvRk8zCOlFFdqgR1fX0hM+C\nUPFCV36oPNVC6AN7d/Or557htA0bcb2ZVTpM6hSdsp6MOPGhM0BdXR0rVqzgnnvuoa+vb1buabFU\nMie5QwTQyDF7mp3hBA/HIx0PdICepFSpiElUtgqV6kDlxjuO7b2aiKOJOYWd4eGDb9J+5DBrT8AZ\nQv5lxnWaw24zitnZhbJo0SKam5u57777yGYLhwQKYWJcykRNYK6uUjjpHaJWAjlmptlDkVGTOxMR\nSxB27Z94XICQPmGuGtX7wujx3pziwKCmKVbYOfX19vDfW37AmtPXn5AzHCFKMDx0nr3KgM3NzXie\nx0MPPUQYTlx+ZLGYwknvEJUSuGNmmiNSkw0n/1pErBY1eBTtT+wpOXFNbmgRuvcFtA7ze5U7NE2x\nwguws5kMT/znd1m68hQ6uyf2PGdKtU7R5cze0Blg1apVdHd388QTT5R0nYlxKRM1gbm6SqHsDrGU\nQvU7d+7kuuuu47777iu3WaMoX+LFj2XS9oTOJ4mdBCEEwnVRfRPXHIqIIEw7KD9ED+xmT78ipzVJ\nb/KvWGvNj37wPapqaqlrnFi/5UQQQFylOeQunrWhM8C6det49dVXefbZZ2ftnhZLJVFWhzhSqP76\n66/n+uuv5+GHH54ybZXv+/ze751YfsFSUaHEiR7rIbpSkZti7YqoWkTYtXuCDiFAeiG+v5R0+//w\nWh8smWKo/MufPUk6naFlZb506ooSC3gVI0qAQtAxS7POkN/VsmHDBn784x+zb9++aV1jYlzKRE1g\nrq5SKKtDHFuoPhKJjBaqL8TGjRtJJosvqJ5VtMgvvRF5B+cJRa5ADBFAuhF0mEGneid85iQ0QU+M\n/b0p6vXRgvkNd+18lT2732DV2reUNQVXUqfpcWpJieis3dN1XU4//XT+4z/+w+5msRhHWR3i2EL1\n99xzz2ih+tlgbLzjmWef5eCBYwkYevv66evvH2339Y9vp1Kpcft1s5ksqUzewbloegfTdHYeW3vX\n2dkxrt2X9ml/7dgSm4MHDnDwwAGkJ+jxXfZ3eoRvfHf08x07dozuVe5oP8rjjz5CNFmL47qj1//q\n+ecn3O9E2/mhc4ZtXYpXd742qT0zab/55psMDAzw0EMPkU6n2bp167j3MbY98vdCny/E9l133VVR\n9sxW+/h3Nt/2zFa7FKYsVH+izKRQ/Y4dO/jVr341ZW3mYoXqk9ldCCbOhspcMFqofixeIke6J0qm\nL58u7OmhxVzW3IcnJv9qtFaEXYeIrHsHInIsxZivJS/31NMsBknWvUbPiv8P7VSNfp5Op3j0wftp\nWXkKNfXjSxMcPHBg1ofNIwwSJ6lTtIRds3rfgwcPUlNTw/vf/34cZ/KdQSYmHTVRE5irq5RC9WXt\nIZZaqB6Yl9T4oe8QqTo2seIQjkvwcDxCSEQ0TtC5Z9zxfX4NsWiaSMonpIZY37FeZBiG/PgHm6lv\nbJ7gDGH2Y4hjqSJNr1NNnyiSH7JEVqxYQVtbGz/96U8LnmPi/8FM1ATm6iqFsiaILbVQ/WOPPcaL\nL75Ib28v6XSaq6++upzmjaICSSSZI7+FT+CJkKxyiMvCKb1k1SLCnjfRi09HOB49Kkpv4LBKDoEH\nQbaF2MA2MrW/hnbiPPPzp8jlfJaesnZONI1FAEmVotVbTDx3iMjY+tQnyNq1a/nVr35FU1MTZ5xx\nxqzd12KZD8qeMbuUQvWXX345l19+eblNmogWCMCJhIQ5d8rF2SMI6SBdj7DrTWhay+5sDYvpRgA6\nAaJXoVuqiPU9y4tHFrF/317WnnFmwfuVc8gM+aQVcZXhgLeENf5h5Kykgcj/6J1++uk88cQTo1v9\nxmLiMMxETWCurlI46RdmjxAGkmhNvoJeVISkCqxFHItINhJ2vM6b2QQxnSIu8j0v4YAQAb6/jOyh\nn7Dtl0+y5n+dUTSNV7mJEqA1HHJmJyvOCJFIhFNOOYVHHnmEnp7ZW2Buscw11iEOE2RcYjVZEJqI\nUKTD4s5LOC6Dbg0DHYdoEoPjPtNxReZwhmdfPMC5a3J4kam35ZWzdziWJBnSMkqbMzGOeSJUV1fT\n2NjIgw8+SDp9LE2aiT0OEzWBubpKwTrEEXQ+802kKkdMhgwV2L43lhwu+5MbWNL9LPK4+s0BIa/s\n3EVt7VtYljiEp2ZnudFsUK1T9Dk1tM/iom3IF7FyXZfvfOc7+P7EetYWS6VjHeIY/JRDsjlN3AlJ\nFZl3UAj20EKt7ifiSmTX7mOfKcWzL7yKTASsjDSS0fW0hBNTg41l7DrCciOAGjVIt1PLUad+VofP\nK1asYGhoaNQpFlsPprUmF2oyoSYd5P/rK42ah9UG08XUPb+m6ioFY8uQzgStHLRWNLT0s/vNqXtP\nh0UTKgxYpHsh2YDTswdVtxoicV7euQffz7Hm1OWIdIjKNlMdfZ3q8CADzsQaz/NB3ikO0ePUEODQ\nEnbN2kTLmjVr2LNnDw899BDLli0bPR5qzUBO05nVdGRg0Ifs6HLRsc/Ox2+jDtR4sDgGjXFBlTt1\nTkmL5USxDvE4/CGXZDJLbeMgIRpnkuQIXaKWLhVnVTjcq5MOOlaNPPoyr6Sb6ejs4n+tzTu+XCRL\nVSpBf2QFy8Ot7JLvQ4mJ8cS5iiGOZcQpDoo4b3otLPOPEplkQftMOPXUU9m3bx+v732TpRsG6NAJ\nerMAmqgLVQ4sigpcwbCTG/89a60JNGRCzev98EqvxpWCpQlYmRTUevPnHE2NtZmqqxTskHkCgtxg\nhGjUR7f0IeJZxvZeBkScN1UDy/zDSMasU0zUceTAHjrefJ23nLocZ7hUKI5GOz6xVDUBMZYHW/O1\nCyoEAVSTBjR7IivoltUnlCFHA1lcOp0awnW/weHatdz92I/p7+lmeRWsTEoWxyRJT+LJwk5NCIEn\nBdWeZElCsjIpaY5BV0aztU3xo1bF3v4Qf5KdRxbLTLEOcVIEftqlbzCG1zRAZFk3TnWKtOOyW7fQ\nErQSEWMnDTT7Ozo5HIa8bVkvEW98ADKI5IhlNTl/GUnVyqLwNY5nLmOIIwgd4ql+qsJWFge7WOVv\nIxQddDo+WQYRahChiy/iVghSIj9z/UZkBXu95fTIWiLax+0/yvL6BE89/gi7d756QjuRXCloiOWd\nY52neb1fs+WQ4sXOkEG/eF3s2cLUWJupukrBDpkLkHB8ujNRFg8GIEOCpKKrxmFd+DJeLoRQgKNw\nIyGtXX2E1UP8n7fUEcmmqXNfYiCyip72BsLQBQG5WJbqgTi9tatZyrNkRB0pp2VONQkdEted1Ib7\nqNateAyCFoTCAe2ghYAQAu2SceKEwiGm07gaQqrJiBbSoolAxAmES1pEScsY/nCFQ0/7VKnMhFhk\nTX0D8apqtj3/HPv27Ob8Cy+mpvbEZrhjrmSpm49L9mQ1Tx6BhmjI6bWC+qgomJTXYpmKsiZ3KBez\nndxhMvpyEfZn6ri4uYeUjrFbraBJH6FW9iPdEIRGBbBjTw8dHSnOWLsIz5WgNbK7HbVkKc6iJJ1H\nmsik83uInZwLOspAdR818hB73PeQkdMrNTodpNJEciGRnCISKNxQo4RmsL6fRvUqSd2K1pKcSJIT\ndYRE8okcC6ABnwg+Epc0EfrxVJacSNAnT2FALkOIBLKEQXbX0SN0HDnMqetO54xNZ1FVXT0r2rXW\nDPiaPh/ijmBdrWBJXBRMwWY5eSgluYPtIRag2ssxNCRpC+vY7beg0l0sq0qBECjfJeeHPP9KG45Q\nbDq9AWfk/3hCoOoakEdayerTaFraTldbA6mhJGEkwM1IqgZqGKxp4dTgB+xzLyElF8/YTicISaZC\nqtIBbqjRQhA6Ai0HqXb3UC32kPE1KbmIXnEqyOlHSQQQIUdktFVLIMFTKVaEL+AFv8AXCXrkafTJ\n1eRE7ZQOFqBhcQt1jc20Hz7Afz78bZYsXcpbNpzB4pZlo+nQZoIQgpqIoCYC6UDxao/mlR7BmmpY\nUSWJu9YxWopjHWIBhICqeJwfdC+mPjjM8kg/znA6sKPdKV7ZdZQlDTFWtkxSzMlxUTV1RNr2kpKn\n07CkC45qUoPVBLEcbgaq+hcxkJSs0f/FYec8Xm6NsWLlqmnZJkNNIu1TMxTghRrfkeQiDlkZUMVB\nGnmViOjDJ8kgLQy4pdV4LkYoEwySv6ejs9SrPTSHrxAKj36xkj65mrRsRAlv0j3ajuPQsvIUFi9f\nRW9XO1t/9hRBLktDYxMrVp1CY1Mz1TU1xBJVM5pJjruSuAuBUhwYhDf6FQ1ROK0mP5x2TnA4beqe\nX1N1lYJ1iECoISA/RNQIsjpGm1hGLBJwrnyBeMJHa+j3Ydf+brp7hzhtdR3Jqig9Y0bgGsFo2Xsv\ngaxSuK276QpPpbqpm4zWDA4mIZoh6oe4vbUcrvJIRJ5hdUzSH0TJiuMdbP4BUmkSOU1tKiDqa0IJ\nRz3QUU2MXurEbqo4jMalgwYC3ZhPNIFmSE2ylGYSp1DITUztPjx6RAsIkDogrtppDPciRUhO1JD1\nPESoyYpaAqIgxvyTExBrbKClsQGlQlIDA2zfvZPsKy+gAh+0IhKJEUvEScSriCcSxKsSxOMJvGiU\neCxONB4n6kVxI9445ymGra7yIKGhL6f5aZtAIGiMaZYmBLURQcwZ+Tqm5yS11qRUwICfPS5SWril\ntR6dTFLk38kIIxEcTf4cPfb64Wv0mGv0uKtH7l3gucedNwEx/q9HgjT7h3qQwx+MxGElgvz/xLhj\ncnjJ1MjxWi828RkLDOsQgS4l2B5oNC4Z0URW1JFQR4npPgQQ+JqOrhR9vSnqa6MsXreEwwJQ+Wl6\nIUYSh+UR6Pzfo9VElUP0yF4OhatY1NBNHwHdA1Xg+jgyTW0qxkDqdMJIOw3p79MtF3FIrmGAelzl\nUuVDXRbioSAtNe2uIoxlqBJ9LBKHqaUDJeCwrmGQpejhf86afGVALaBP52fEx9pYKHI8cXn0ZBy3\nZnDcJ0kQSdCaqMpQVTeIzD1JhJAomhwx0qKKtEiQpoocMTLECUQUP+HhJ5IghuOKWhGGASoICXL9\nBAOdqO6QMAhQYYhWIeHwf1HguA5OxMPzPCLROInqJPFkklgigRuNIoVAawi1S6hdhHYRQuPJAM/x\n8WSIIwOECDnewY3T3eTw4u5nx7smISZxOsP/Dsg7DY0e/zs09oUc952OtrQGIYbf6cijxraOXSEY\n/17F8R5v+D95Gya+ad2S4ODBV0cP6TFn5e99zEEz3M7/2xc0ROJcteYcFjrWIQL1UnCqW8OAaCKh\n+6nXO3GdgMEhn9b2Qdo7hzilLsKpq5NEIwLITP/mVfk0Yas7d5FSK1jVojgCDAwk8940lkEoQTSo\nQ+YWsYpBzhOvIUSOgAg5ESXrSnQkwBUZPDIIoVDaJUcVGVagtUMVMFkkUgNdJcQNTxitcUIfGQY4\nyscJwNEeTpif5Xb0IA5duCLEEeFwdS5ACrTjEEoXX8TJyTgZkWTIqWXQXcRQvJasaCKYoj5MGPio\nICD0A/xshkzbIIOZDnpzOUQYEksmqWtooLahkeq6OqKJCE4kiq8kgZKEKu8wtcr3fhyhcKUiIhUR\nJyTiKDyp8KTGEbpYuPSkosqdvZK388l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       "text": [
        "<matplotlib.figure.Figure at 0x51bba10>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}