{
 "metadata": {
  "name": "",
  "signature": "sha256:573cd9578967856f7dcd1df55351af8d3c475a4e3794ba81be885388f876b1a7"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "\n",
      "with open('creative_commons.txt', 'r') as f:\n",
      "    html = f.read()\n",
      "    \n",
      "name = '2014-06-16-seaborn'\n",
      "\n",
      "html = '''\n",
      "<small>\n",
      "<p> This post was written as an IPython notebook.\n",
      " It is available for <a href='https://ocefpaf.github.com/python4oceanographers/downloads/notebooks/%s.ipynb'>download</a>\n",
      " or as a static <a href='https://nbviewer.ipython.org/url/ocefpaf.github.com/python4oceanographers/downloads/notebooks/%s.ipynb'>html</a>.</p>\n",
      "<p></p>\n",
      "%s''' % (name, name, html)\n",
      "\n",
      "%matplotlib inline\n",
      "from matplotlib import style\n",
      "style.use('ggplot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This week I was helping a friend to explore her data-set with some simple\n",
      "statistics and plots.  So I decided to try\n",
      "[seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/index.html) out.\n",
      "\n",
      "It is a really nice library that, together with pandas, becomes a powerful tool\n",
      "to take the first steps while exploring your data.\n",
      "\n",
      "Here is a simple example of what we did."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import seaborn\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from io import BytesIO\n",
      "from pandas import read_csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kw = dict(na_values='NaN', sep=',', encoding='utf-8',\n",
      "          skipinitialspace=True, index_col=False)\n",
      "\n",
      "df = read_csv(\"./data/fish.csv\", **kw)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>Days</th>\n",
        "      <th>ID</th>\n",
        "      <th>Recovery</th>\n",
        "      <th>Extract weight</th>\n",
        "      <th>Lipid %</th>\n",
        "      <th>Weight (g)</th>\n",
        "      <th>Size (cm)</th>\n",
        "      <th>Liver weight (g)</th>\n",
        "      <th>LSI</th>\n",
        "      <th>CF</th>\n",
        "      <th>BDE 47 (ng/g)</th>\n",
        "      <th>BDE 99 (ng/g)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> A</td>\n",
        "      <td> 73.21</td>\n",
        "      <td> 0.10</td>\n",
        "      <td> 3.600000</td>\n",
        "      <td> 20.09</td>\n",
        "      <td> 12.8</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 0.696864</td>\n",
        "      <td> 0.957966</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0</td>\n",
        "      <td> B</td>\n",
        "      <td> 98.24</td>\n",
        "      <td> 0.22</td>\n",
        "      <td> 2.272727</td>\n",
        "      <td> 36.52</td>\n",
        "      <td> 15.5</td>\n",
        "      <td> 0.33</td>\n",
        "      <td> 0.903614</td>\n",
        "      <td> 0.980699</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "      <td> 89.71</td>\n",
        "      <td> 0.18</td>\n",
        "      <td> 3.500000</td>\n",
        "      <td> 28.74</td>\n",
        "      <td> 14.7</td>\n",
        "      <td> 0.25</td>\n",
        "      <td> 0.869868</td>\n",
        "      <td> 0.904763</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> A</td>\n",
        "      <td> 78.40</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 1.330769</td>\n",
        "      <td> 23.70</td>\n",
        "      <td> 14.0</td>\n",
        "      <td> 0.15</td>\n",
        "      <td> 0.632911</td>\n",
        "      <td> 0.863703</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1</td>\n",
        "      <td> B</td>\n",
        "      <td> 66.24</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 2.838462</td>\n",
        "      <td> 32.80</td>\n",
        "      <td> 15.0</td>\n",
        "      <td> 0.20</td>\n",
        "      <td> 0.609756</td>\n",
        "      <td> 0.971852</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "   Days ID  Recovery  Extract weight   Lipid %  Weight (g)  Size (cm)  \\\n",
        "0     0  A     73.21            0.10  3.600000       20.09       12.8   \n",
        "1     0  B     98.24            0.22  2.272727       36.52       15.5   \n",
        "2     0  C     89.71            0.18  3.500000       28.74       14.7   \n",
        "3     1  A     78.40            0.13  1.330769       23.70       14.0   \n",
        "4     1  B     66.24            0.13  2.838462       32.80       15.0   \n",
        "\n",
        "   Liver weight (g)       LSI        CF  BDE 47 (ng/g)  BDE 99 (ng/g)  \n",
        "0              0.14  0.696864  0.957966              0              0  \n",
        "1              0.33  0.903614  0.980699              0              0  \n",
        "2              0.25  0.869868  0.904763              0              0  \n",
        "3              0.15  0.632911  0.863703              0              0  \n",
        "4              0.20  0.609756  0.971852              0              0  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`Seaborn` makes it easy to control the figure aesthetics with `set_style` and\n",
      "`get_style`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kw = {'axes.edgecolor': '0', 'text.color': '0', 'ytick.color': '0', 'xtick.color': '0',\n",
      "      'ytick.major.size': 5, 'xtick.major.size': 5, 'axes.labelcolor': '0'}\n",
      "\n",
      "seaborn.set_style(\"whitegrid\", kw)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first plot will be a simple and naive correlation matrix.  It it just one\n",
      "line with `seaborn`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = seaborn.corrplot(df, annot=False, diag_names=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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BHrR9bc31z/TU1fbpwOm9VOFcYH/g1xSDjna0/Zykn1DMcAHFqK5Teqt/REQ0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\n+3flsR2BC4A3lcdOtH16XwV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       "text": [
        "<matplotlib.figure.Figure at 0x7f39c1a2c950>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Easy conclusion, the bigger the fish, the heavier it is ;).  But seriously now,\n",
      "`BDE 47` is positively correlated with `Days` and `BDE 99`, that is worth\n",
      "exploring.  `BDE 99` was part of the experiment.  However, `BDE 47` was not in\n",
      "the fish at the begging, it is a by-product of the `BDE 99` that appear as the\n",
      "fish metabolized it.\n",
      "\n",
      "We can explore this a little further.  Note that we used pandas `groupby` to \n",
      "aggregate the the data around the variables \"Days\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = df.groupby('Days')\n",
      "mean_df = g.mean()\n",
      "g.describe().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></th>\n",
        "      <th>BDE 47 (ng/g)</th>\n",
        "      <th>BDE 99 (ng/g)</th>\n",
        "      <th>CF</th>\n",
        "      <th>Extract weight</th>\n",
        "      <th>LSI</th>\n",
        "      <th>Lipid %</th>\n",
        "      <th>Liver weight (g)</th>\n",
        "      <th>Recovery</th>\n",
        "      <th>Size (cm)</th>\n",
        "      <th>Weight (g)</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Days</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
        "      <th>count</th>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "      <td> 3.000000</td>\n",
        "      <td>  3.000000</td>\n",
        "      <td>  3.000000</td>\n",
        "      <td>  3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.947809</td>\n",
        "      <td> 0.166667</td>\n",
        "      <td> 0.823449</td>\n",
        "      <td> 3.124242</td>\n",
        "      <td> 0.240000</td>\n",
        "      <td> 87.053333</td>\n",
        "      <td> 14.333333</td>\n",
        "      <td> 28.450000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.038974</td>\n",
        "      <td> 0.061101</td>\n",
        "      <td> 0.110916</td>\n",
        "      <td> 0.739127</td>\n",
        "      <td> 0.095394</td>\n",
        "      <td> 12.724725</td>\n",
        "      <td>  1.386843</td>\n",
        "      <td>  8.218838</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.904763</td>\n",
        "      <td> 0.100000</td>\n",
        "      <td> 0.696864</td>\n",
        "      <td> 2.272727</td>\n",
        "      <td> 0.140000</td>\n",
        "      <td> 73.210000</td>\n",
        "      <td> 12.800000</td>\n",
        "      <td> 20.090000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.931364</td>\n",
        "      <td> 0.140000</td>\n",
        "      <td> 0.783366</td>\n",
        "      <td> 2.886364</td>\n",
        "      <td> 0.195000</td>\n",
        "      <td> 81.460000</td>\n",
        "      <td> 13.750000</td>\n",
        "      <td> 24.415000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "            BDE 47 (ng/g)  BDE 99 (ng/g)        CF  Extract weight       LSI  \\\n",
        "Days                                                                           \n",
        "0    count              3              3  3.000000        3.000000  3.000000   \n",
        "     mean               0              0  0.947809        0.166667  0.823449   \n",
        "     std                0              0  0.038974        0.061101  0.110916   \n",
        "     min                0              0  0.904763        0.100000  0.696864   \n",
        "     25%                0              0  0.931364        0.140000  0.783366   \n",
        "\n",
        "             Lipid %  Liver weight (g)   Recovery  Size (cm)  Weight (g)  \n",
        "Days                                                                      \n",
        "0    count  3.000000          3.000000   3.000000   3.000000    3.000000  \n",
        "     mean   3.124242          0.240000  87.053333  14.333333   28.450000  \n",
        "     std    0.739127          0.095394  12.724725   1.386843    8.218838  \n",
        "     min    2.272727          0.140000  73.210000  12.800000   20.090000  \n",
        "     25%    2.886364          0.195000  81.460000  13.750000   24.415000  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = seaborn.jointplot(\"Days\", \"BDE 99 (ng/g)\", df, kind=\"reg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P8BaZmTWZWWM+ixERkfGXiMeorU5MzBaZmS0yszvMbC+wHvizme0ys/9lZqcV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\nM8Om5TiOgcdhTET8t8B2AZB0JrCatPS8StKdDX/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       "text": [
        "<matplotlib.figure.Figure at 0x7f39c195e4d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = seaborn.jointplot(\"Days\", \"BDE 47 (ng/g)\", df, kind=\"reg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FURYlSW2CpOUmhBBJKhCM0NMXwjBlXG2yZEKJEEIkmahh4u7yE45GsSgZV5ss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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f39c179b610>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The increase in `BDE 47` is clear.  `BDE 99` does not show a decrease in the\n",
      "same rate as `BDE 47` because it was part of the fish diet.\n",
      "\n",
      "The inspection of the residues is also a one-liner."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = seaborn.residplot(\"Days\", \"BDE 99 (ng/g)\", df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RCfQ/q2632+txLLB/RFwfETdFxELarweovPncExFfB74JfIP2/Z0iIhYAv5eZ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f39c18c42d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = seaborn.residplot(\"Days\", \"BDE 47 (ng/g)\", df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f39c175d0d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hopefully that is useful for others.  Do not forget to check\n",
      "seaborn [docs](http://web.stanford.edu/~mwaskom/software/seaborn/index.html)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML(html)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<small>\n",
        "<p> This post was written as an IPython notebook.\n",
        " It is available for <a href='https://ocefpaf.github.com/python4oceanographers/downloads/notebooks/2014-06-16-seaborn.ipynb'>download</a>\n",
        " or as a static <a href='https://nbviewer.ipython.org/url/ocefpaf.github.com/python4oceanographers/downloads/notebooks/2014-06-16-seaborn.ipynb'>html</a>.</p>\n",
        "<p></p>\n",
        "<a rel=\"license\" href=\"https://creativecommons.org/licenses/by-sa/4.0/\"><img\n",
        "alt=\"Creative Commons License\" style=\"border-width:0\"\n",
        "src=\"https://i.creativecommons.org/l/by-sa/4.0/88x31.png\" /></a><br /><span\n",
        "xmlns:dct=\"https://purl.org/dc/terms/\"\n",
        "property=\"dct:title\">python4oceanographers</span> by <a\n",
        "xmlns:cc=\"https://creativecommons.org/ns#\" href=\"https://ocefpaf.github.io/\"\n",
        "property=\"cc:attributionName\" rel=\"cc:attributionURL\">Filipe Fernandes</a> is\n",
        "licensed under a <a rel=\"license\"\n",
        "href=\"https://creativecommons.org/licenses/by-sa/4.0/\">Creative Commons\n",
        "Attribution-ShareAlike 4.0 International License</a>.<br />Based on a work at <a\n",
        "xmlns:dct=\"https://purl.org/dc/terms/\" href=\"https://ocefpaf.github.io/\"\n",
        "rel=\"dct:source\">https://ocefpaf.github.io/</a>.\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.HTML at 0x7feb341f5ad0>"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}