diff --git a/borja/Iteration1/.ipynb_checkpoints/reshape-checkpoint.ipynb b/borja/Iteration1/.ipynb_checkpoints/reshape-checkpoint.ipynb
new file mode 100644
index 0000000..122022c
--- /dev/null
+++ b/borja/Iteration1/.ipynb_checkpoints/reshape-checkpoint.ipynb
@@ -0,0 +1,1562 @@
+{
+ "metadata": {
+  "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": "3.5.0+"
+  },
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "http://seananderson.ca/2013/10/19/reshape.html"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Tabular Data Gym 2. Reshaping."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "import matplotlib.style\n",
+      "matplotlib.style.use('ggplot')\n",
+      "\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext rpy2.ipython"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "library(magrittr)\n",
+      "library(ggplot2)\n",
+      "library(dplyr)\n",
+      "library(reshape2)"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: \n",
+        "Attaching package: \u2018dplyr\u2019\n",
+        "\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n",
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from \u2018package:stats\u2019:\n",
+        "\n",
+        "    filter, lag\n",
+        "\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n",
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from \u2018package:base\u2019:\n",
+        "\n",
+        "    intersect, setdiff, setequal, union\n",
+        "\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "names(airquality) <- tolower(names(airquality))\n",
+      "head(airquality)"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  ozone solar.r wind temp month day\n",
+        "1    41     190  7.4   67     5   1\n",
+        "2    36     118  8.0   72     5   2\n",
+        "3    12     149 12.6   74     5   3\n",
+        "4    18     313 11.5   62     5   4\n",
+        "5    NA      NA 14.3   56     5   5\n",
+        "6    28      NA 14.9   66     5   6\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aq_pd = %R airquality\n",
+      "aq_pd.head()"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>ozone</th>\n",
+        "      <th>solar.r</th>\n",
+        "      <th>wind</th>\n",
+        "      <th>temp</th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>41</td>\n",
+        "      <td>190</td>\n",
+        "      <td>7.4</td>\n",
+        "      <td>67</td>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>36</td>\n",
+        "      <td>118</td>\n",
+        "      <td>8.0</td>\n",
+        "      <td>72</td>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>12</td>\n",
+        "      <td>149</td>\n",
+        "      <td>12.6</td>\n",
+        "      <td>74</td>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>18</td>\n",
+        "      <td>313</td>\n",
+        "      <td>11.5</td>\n",
+        "      <td>62</td>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>-2147483648</td>\n",
+        "      <td>-2147483648</td>\n",
+        "      <td>14.3</td>\n",
+        "      <td>56</td>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "        ozone     solar.r  wind  temp  month  day\n",
+        "1          41         190   7.4    67      5    1\n",
+        "2          36         118   8.0    72      5    2\n",
+        "3          12         149  12.6    74      5    3\n",
+        "4          18         313  11.5    62      5    4\n",
+        "5 -2147483648 -2147483648  14.3    56      5    5"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "Since `rpy2` does not treat NA well, we need to change it manually to `np.nan`."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def fix_nans_in_df(colname, df):\n",
+      "    df.ix[df[colname] < 0, colname] = np.NaN    \n",
+      "\n",
+      "for colname in aq_pd.columns.values:\n",
+      "    fix_nans_in_df(colname, aq_pd)\n",
+      "\n",
+      "aq_pd.head()"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>ozone</th>\n",
+        "      <th>solar.r</th>\n",
+        "      <th>wind</th>\n",
+        "      <th>temp</th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>41</td>\n",
+        "      <td>190</td>\n",
+        "      <td>7.4</td>\n",
+        "      <td>67</td>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>36</td>\n",
+        "      <td>118</td>\n",
+        "      <td>8.0</td>\n",
+        "      <td>72</td>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>12</td>\n",
+        "      <td>149</td>\n",
+        "      <td>12.6</td>\n",
+        "      <td>74</td>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>18</td>\n",
+        "      <td>313</td>\n",
+        "      <td>11.5</td>\n",
+        "      <td>62</td>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>14.3</td>\n",
+        "      <td>56</td>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "   ozone  solar.r  wind  temp  month  day\n",
+        "1     41      190   7.4    67      5    1\n",
+        "2     36      118   8.0    72      5    2\n",
+        "3     12      149  12.6    74      5    3\n",
+        "4     18      313  11.5    62      5    4\n",
+        "5    NaN      NaN  14.3    56      5    5"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "Melt/stack: changing wide format into long"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "R function `melt` from `reshape2` package change wide fromat into long. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "\n",
+      "aql <- melt(airquality)\n",
+      "aql %>% head"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: No id variables; using all as measure variables\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  variable value\n",
+        "1    ozone    41\n",
+        "2    ozone    36\n",
+        "3    ozone    12\n",
+        "4    ozone    18\n",
+        "5    ozone    NA\n",
+        "6    ozone    28\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "### Question 1\n",
+      "\n",
+      "One can get similar result using pandas' method `melt`. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pd.melt(aq_pd)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>variable</th>\n",
+        "      <th>value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>36</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>28</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>19</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>8</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>7</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>11</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>16</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>12</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>13</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>15</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>16</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>34</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>17</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>6</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>18</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>30</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>19</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>20</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>21</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>22</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>23</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>32</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>24</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>25</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>26</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>27</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>28</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>45</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>29</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>115</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>...</th>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>888</th>\n",
+        "      <td>day</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>889</th>\n",
+        "      <td>day</td>\n",
+        "      <td>2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>890</th>\n",
+        "      <td>day</td>\n",
+        "      <td>3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>891</th>\n",
+        "      <td>day</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>892</th>\n",
+        "      <td>day</td>\n",
+        "      <td>5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>893</th>\n",
+        "      <td>day</td>\n",
+        "      <td>6</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>894</th>\n",
+        "      <td>day</td>\n",
+        "      <td>7</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>895</th>\n",
+        "      <td>day</td>\n",
+        "      <td>8</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>896</th>\n",
+        "      <td>day</td>\n",
+        "      <td>9</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>897</th>\n",
+        "      <td>day</td>\n",
+        "      <td>10</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>898</th>\n",
+        "      <td>day</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>899</th>\n",
+        "      <td>day</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>900</th>\n",
+        "      <td>day</td>\n",
+        "      <td>13</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>901</th>\n",
+        "      <td>day</td>\n",
+        "      <td>14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>902</th>\n",
+        "      <td>day</td>\n",
+        "      <td>15</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>903</th>\n",
+        "      <td>day</td>\n",
+        "      <td>16</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>904</th>\n",
+        "      <td>day</td>\n",
+        "      <td>17</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>905</th>\n",
+        "      <td>day</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>906</th>\n",
+        "      <td>day</td>\n",
+        "      <td>19</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>907</th>\n",
+        "      <td>day</td>\n",
+        "      <td>20</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>908</th>\n",
+        "      <td>day</td>\n",
+        "      <td>21</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>909</th>\n",
+        "      <td>day</td>\n",
+        "      <td>22</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>910</th>\n",
+        "      <td>day</td>\n",
+        "      <td>23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>911</th>\n",
+        "      <td>day</td>\n",
+        "      <td>24</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>912</th>\n",
+        "      <td>day</td>\n",
+        "      <td>25</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>913</th>\n",
+        "      <td>day</td>\n",
+        "      <td>26</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>914</th>\n",
+        "      <td>day</td>\n",
+        "      <td>27</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>915</th>\n",
+        "      <td>day</td>\n",
+        "      <td>28</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>916</th>\n",
+        "      <td>day</td>\n",
+        "      <td>29</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>917</th>\n",
+        "      <td>day</td>\n",
+        "      <td>30</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>918 rows \u00d7 2 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "    variable  value\n",
+        "0      ozone     41\n",
+        "1      ozone     36\n",
+        "2      ozone     12\n",
+        "3      ozone     18\n",
+        "4      ozone    NaN\n",
+        "5      ozone     28\n",
+        "6      ozone     23\n",
+        "7      ozone     19\n",
+        "8      ozone      8\n",
+        "9      ozone    NaN\n",
+        "10     ozone      7\n",
+        "11     ozone     16\n",
+        "12     ozone     11\n",
+        "13     ozone     14\n",
+        "14     ozone     18\n",
+        "15     ozone     14\n",
+        "16     ozone     34\n",
+        "17     ozone      6\n",
+        "18     ozone     30\n",
+        "19     ozone     11\n",
+        "20     ozone      1\n",
+        "21     ozone     11\n",
+        "22     ozone      4\n",
+        "23     ozone     32\n",
+        "24     ozone    NaN\n",
+        "25     ozone    NaN\n",
+        "26     ozone    NaN\n",
+        "27     ozone     23\n",
+        "28     ozone     45\n",
+        "29     ozone    115\n",
+        "..       ...    ...\n",
+        "888      day      1\n",
+        "889      day      2\n",
+        "890      day      3\n",
+        "891      day      4\n",
+        "892      day      5\n",
+        "893      day      6\n",
+        "894      day      7\n",
+        "895      day      8\n",
+        "896      day      9\n",
+        "897      day     10\n",
+        "898      day     11\n",
+        "899      day     12\n",
+        "900      day     13\n",
+        "901      day     14\n",
+        "902      day     15\n",
+        "903      day     16\n",
+        "904      day     17\n",
+        "905      day     18\n",
+        "906      day     19\n",
+        "907      day     20\n",
+        "908      day     21\n",
+        "909      day     22\n",
+        "910      day     23\n",
+        "911      day     24\n",
+        "912      day     25\n",
+        "913      day     26\n",
+        "914      day     27\n",
+        "915      day     28\n",
+        "916      day     29\n",
+        "917      day     30\n",
+        "\n",
+        "[918 rows x 2 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Question 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "airquality %>%\n",
+      "    melt(id.vars = c(\"month\", \"day\")) %>%\n",
+      "    head()"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day variable value\n",
+        "1     5   1    ozone    41\n",
+        "2     5   2    ozone    36\n",
+        "3     5   3    ozone    12\n",
+        "4     5   4    ozone    18\n",
+        "5     5   5    ozone    NA\n",
+        "6     5   6    ozone    28\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now let's try to get it with pandas."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aql_pd = pd.melt(aq_pd, id_vars=['month','day'])\n",
+      "aql_pd.head(6)"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "      <th>variable</th>\n",
+        "      <th>value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>36</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>5</td>\n",
+        "      <td>6</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>28</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": [
+        "   month  day variable  value\n",
+        "0      5    1    ozone     41\n",
+        "1      5    2    ozone     36\n",
+        "2      5    3    ozone     12\n",
+        "3      5    4    ozone     18\n",
+        "4      5    5    ozone    NaN\n",
+        "5      5    6    ozone     28"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Question 3 Putting names to columns"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "\n",
+      "aql <-\n",
+      "  melt(airquality, id.vars = c(\"month\", \"day\"),\n",
+      "  variable.name = \"climate_variable\", \n",
+      "  value.name = \"climate_value\") %>%\n",
+      "  mutate(date=as.Date(paste0(\"2015-\",month,'-',day)))\n",
+      "head(aql)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day climate_variable climate_value       date\n",
+        "1     5   1            ozone            41 2015-05-01\n",
+        "2     5   2            ozone            36 2015-05-02\n",
+        "3     5   3            ozone            12 2015-05-03\n",
+        "4     5   4            ozone            18 2015-05-04\n",
+        "5     5   5            ozone            NA 2015-05-05\n",
+        "6     5   6            ozone            28 2015-05-06\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Your turn with pandas."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pd.melt(aq_pd,id_vars=['month','day'], var_name='climate_variable', value_name='climate_value').head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "      <th>climate_variable</th>\n",
+        "      <th>climate_value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>36</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": [
+        "   month  day climate_variable  climate_value\n",
+        "0      5    1            ozone             41\n",
+        "1      5    2            ozone             36\n",
+        "2      5    3            ozone             12\n",
+        "3      5    4            ozone             18\n",
+        "4      5    5            ozone            NaN"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Comments about ploting\n",
+      "\n",
+      "When one uses ggplot it is natural to use long format when ploting."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "aql %>%\n",
+      "  ggplot() +\n",
+      "  geom_line(aes(date, climate_value, colour=climate_variable))"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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LbB47DU0gF1Jx7Iy+swzX09lQJF8Z/IP+t1TRq8N/POEFOJwkVi9A11WrZrCH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7/8wAc+sDLLsoKxooLO5nxNwrqHhn9/dufK9LfTDe4c4kBWy4MONeO4dqM5v2LUF+rXWz+znYt3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\nO5NFnoE5e1WIxMw7RzHeufYvzi79LAAlClQcXKkWmOoFAYC5KQ63SCca2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DhEEZuYQQJQ\nCoTAEXjQVhh64LR+a2YpxwOlTOMBkpdvmUREcbQ9TeLNwoE0Ag0xlUgSWN+IeAwIwUG/lfQDAKTr\nlOORKKJEAgjJOM0OMKaIerzAC0iLJz1ohFE0Qp0jlHuwOWomlECL4sBSaG7358LG+t63MIgbepIQ\nqQoglGyKTehgmkhVGCwihHUz0qMaCA0i0JQe8xi0EqVWiGP8CzQhqbq3tGBmsCoOay7dYftNkCCy\nJuYYEREClB6ZB22UTTLzCvocNtG9KABjyrBs44GeeX59q9xGA+L1oWC6EEdMBdkB1h6ymMru3yu9\n9DwC1NOlwgvWNQfStYxJQoQxAeL2Ep5HmsYzLgQIAcbRiFXQYjMemEgCDWKatAxxurjtW/vs/kHs\nMJLRqkJTJf2We64qCJAVhu4JcQBmdJppux2lBMRj2qUCkTBxOqlzREcajRSU8uvfRd3z/KxlrIOV\n2VmtK5mXxsIQPGgqCAxBDBasLMURJAkBIHHiHGPK1L43GT2W8xzTuN/ML0w+oySPugft8eJg2hCH\nQmUHQDJRyTQdRGoUIybZpaVrwPOAEOV50DSR9QlsmpF4CDAiQDweygugawhhjnF0e9C2QI8XJpJA\nk3QetFlYDAjj5qbULSzmhyHQ0Sjx+qwkIbYK+1UFAFhG1s1oL4Ee1INGYyDQ1OmiI5EkHHFQPC5s\nXk9Tb4GRoAwzWGEyHrQ7f2geNAgia4U4TNNyBIdveBpQQk9FzynH41CQFBYl/ypKQwm4HQ3Ulz4G\nDYpCJBmSiUqVaTqINA0IQakQh3W6BBHreplv4fm1/5fu8IgxgXp8IAipZkIEGEWjxBboccNEEmia\nzoMGhBJTqrk9O3tugOHFoIk3q7cHbc1B5rDDMNWUW44Qw+kk437oYx6DxtEIcXSHOIa2a+aYgZWI\nlcKy/ppmy19fEGO5qJlbvYfgQSNBYBLQe9/ViIB0vcd4XqA8T3zJ1kQqSkgb5SoOlxsZRr9ACjIM\nZCSSs21FCSlR3NoCDIPiGoO6qzg4HgAIL0BmCzEhiAB1e6xJJgAgMG5EAFGSdM9txgETS6D7tU4l\nMaqnsbt2pHSKwdzQBRqpCvH6kj1X8TggZG174xkHBZo6ztem/tGdcJEMfhwFCvwx9KAJASVKnS4q\nO4DScVcUFY306XDRdeAGiwgjdrAyO4Styc6Hq+IQBNZADOIRMWHAuI8jJ5Ho+W3gOVJQlArFUCtJ\neKS7zYYExsTt6V9ppyqU462kKxEldv8+6nIRpwtrOk4Ng+Y5AEi5xgCAYirT6yoTALCWwIilDEMF\nwXJQeNaFTZNK8khdf9gcPRPpnaCilHZekllShkyTaWux/ooRN4wQh6IQb1ayICkeoy43KFEAYBkZ\nAFKdhJOzz8OakT7EQSlQ2jOtf/RBigKYoaIICFFJ7r/ddaxB0SjtJdCDF0EDALKmUGXqJMRDq4MW\nxOJYSVXOYjANGDkPGhIaSoU4WN4sKOoxTBBTA+1GD+L1WWFoFA5Zt3C7dqTCLCBJTGszKSkDhzOH\nFEzLXwoASNesEEeq0g4A2F07hH++0fvISNOSJ4oXrFEHHONCCZOMUHeSzYgwkQQ6bZIQAAAho7qW\n7d78jYdTxUGVKM3KQqaJTBPFYyQrx5puwTEO6BsYRVo8U4jDZBFkqsAbBbASoU6npWgj0qsysmAl\n2qdOy0gMvgucstaUnwxvGcbJfNfgiGK+ml2bf/kRF0GnJTmHDwAAzKJiY9LkXvchEIY0wOtooN4s\nJhhg9+52/ukPzIEGFIvxm9fH559p3UsEESg1ikuJ7HDGpZOK/xOs+ahW96Mgpjxo7O9iujpQr01s\nOBbHOPkw0K0QhwsbxM4QjismkkATIU2S0CIxcxb32XZkJGBYIQ5CkBYnLjcwDGhxpMXNrCyk68gw\neOwA6OvWaVp6D9p6zBEV3h4hkTB0uznUOe4q7ZASJY6eLzlKGIPU2AEAHjQGDQxDWe6wO6WsigUA\nAMM4kjbCDJgVk2lpcgKfNv9Ms2xS73uPRTOh14cCXcK6tYma6dLKl4TVbxilFSS1nFOSAMAsLqOy\njNTkxwAnuuPmgpAaX4MDXWCaqK01dWQUUxErgNXF3j2OAxumLdDjiokk0CBJaWPQAGDm5tHsXHbn\nDhhOmR2KqYAx5QXrM4piMep0UZ4HJcqxlgfdowtY1zImCRE9plNmWI5UVDbqiU9i8RGerD8SICVK\nHT1ZJjRoGyEAIHawNZIU4aFs5kaChK2NJyPqQSdqptPi0ox3SxIewozyo4F4fdyuHRSh+NeX6CfW\ncXt26gvO6rlXkqkkk6xsIjuSQwt6VXGkXGMAwH4/9Xh7L43DSrdAC0JqHAdKmHaXyrhiIgk0FSWU\nOeSnzZzFbdsKAPVo3ueGdygHxDEVHA5AKDn6Kx6jokRlB46pyRBHb9WIxzMmCSk5lts2zUmTzdMX\nPdQVuK/TTxyO8eZBYyVKe3nQVNcHj0EfpsyOYYa0+FXsjreaJj1WOS7T5Ubh4Kg+BfH4wDS10xcB\nQvopC5QrryG+rJ57C4vVpd8FhEB24O7Bs1TTaGrAk+VBmyYOBxM1x7HdeRoAYNQY4pICnZw4yrpx\nwshUY4e7OkfrRdpkZkIJ9KCt3mbNNBz0M20tb9NTthq+IR2xu+DfqjTCWhxECRxOpCrJEEcvDxpl\nCnFYjSqjsFNjcNZGovs0nfiycWfHMX7qwUHRKHH2SxIOXsUx2I4FklegnX5W2rt6QwUB6TpQikwT\njZwHfZgn9WalbSQZQUh2Tuz8JaYV+0aIZOX0uRshayMBlR2gJAU62aiSdI3jAIBDASoIRkUlbk0K\nNIpGkEkRywOA1eoNANW5S2YGZ6YNceDmQ/Kzf8bpJjfZjCoTSqCl9FUcyXsZ1qiqad3f0Aa+LlSU\n6WEWKBYDSnEshqx6A0G8P6a1mSYRBCLJSFWSVRy9BTpziIMCHY2lR4PwRVxrNoz9esIsKGLamsdV\nKTRSIuDsHYM+TB00DF4HLQhGVc3hn1QQgRBkGCNbBz04xOtL30gygmBsTDv+8JbIjtTqBpxIpEIc\nlvJifxfJyiH5Bdjfac0gxZ0dVHZY0f9UiMMnTSnp8lBn/yoOpETlfyzXFp7d23m3OTZMJIEGUUKm\niYwEv3k903Rg4P0kK2edpjswbiaDVtpTKv/1T9xnn6Q86Kgk3WXAKtEJokRlB1IUDltlc72ThPG0\nY+MpJTDIrJ/RYXU4erbTSShtdnuBENxdgzXmICOBdL13pwMyEv0X/fWDGfYSnHQHYSjDgq4hMpIx\n6MEhXl/acaBjgMOBY90hDr07xOF04WAQAHDAT3xZVBCJ14fbWwCA8XdS2Zn8+nfrOBCCFGVgiEN4\n55/G5KmJ4088di/HppuJJNDUSujHYvy2DylO8yUkWdnrEHuRx9Wgp9l7koJt+AJHI9zHW5CaHDrz\nvsunA3rf4aaSTGQZqYoVgwZAL4fCOqVgmsgwrFq6FwIhvZfHSmEMBPqfofAil2OSwDckEiSvoHfy\nZ2xB0SgVpT4xjYQxuAeNGeveo74EEQSkaXREqzgGh3h9OBgcD5cvySSh9UE1TSvEYRaXIlXBXZ3Y\n32lFQsyCIqa1BQBwVydyuKzOQ8oL1rKYPeHwB95s2nc3EIrF2C92avNGfm25zVCYSAINACBJ7KFG\n0HVSUDjwTuLLfld2LvV5FEL8Rs9of2HT+t4Sxm37UD/ldBwKcPu/sAR6ncM90zTe92RRQQCH05o4\nCgAKxdc1tW5UVKTrgDHluB1x7fpDrd872Jzo/lomPehBz2Sjnrh8f9MgDxgWGqXro9EznPIknm/Q\nE2ZBER4/At2vCBoAEvrgLq11XXL0v3BWsgsd0TqVIyMkOwkhozfTLni49RSEQsAwwerhIgRpcdA1\na/AeAADDmJOruN2f44Cf+rIBgBQUMYcaAQB3tlOHy2oXSJVLP9rpv3fK9H4VjdwnH5qTJo/Ubh2b\n4TLBBJoIIrvzM6N8ktWNmqD0nxHlQLe/vEtyxDBzEpASjt3Xaw8s+6+Pue0fWn9G4RBzcL8286TE\njBNx8yErWrpOEH8SaD8kSq0MS2UHiikcIwPANo01Kd2sxpAWt9znl0LhS73uMDGvbWxJKjQCAKCA\n3whn7Oh7J6qsjSpNg/r1Q2dDVJ0kCMUcN4nn9ukJUlB0NB70FjXWbhiHf9zQwNFIm9v3t0DoaX/w\nuUCoSU8gI3GYfecOa5fH0XrQlBdA05j2NpKTe5SHGiIX7m/8t3lndIxOlKNJT8zYudefQaN1Qv+3\nvXPWnn1n7d0PAIAxFSWkqtYw6JTIJqprmV07sL/L8qATNdOZA/uYznbc1QEOl9UcRHkeCEGJRL2W\n+LxfAJpSbvtH+sxZo/ECbYbCBBNokCRm3xdmRSUA/C0QmrFz7zVNzQ93JRM19WpsfiTIBfyVPJ+K\ncqCEjkNBds9Oa2wCt/1Dc2otSHJi5ixACGQ5bJJ/MdyijuaTIqHNsTiRZVCiHHYAwEcazmfZTWoM\naXHC84TCy8HId3ze58pL1qvqrrgG3R70ipByxcHmTF+n9YqKADarPRnOloRxZ1v/uqV45otlQiEV\nV/lLl3+x1wMAlTy3T9OSHvQRXWjHKb3yYPOzgfAR/Nu0XKWRadUzXw5H3ouqr4Ujp3zRcFFWkTZ4\nmR2k38k0XAgv6pqGmw6aRZkrl0eOBj1xMJGYQswFQWWrOvLV0NviWpzSv4fS/+q/FY2+GorcV5Tf\nYpiWo00dTqQqPYM4AABArajEoQBSFeLNsh6jz5onrnoN6TqVnQgYgGT4/qCqtlC6T5C07g+SSekj\ne/aszM4zyytH/NXZDJEJJtBUlJBpmOWVrQnjltb2x0oLny4rXhdNpkfeVdQzEhoT8E/iuYZuD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+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "autoscroll": "json-false",
+      "collapsed": false,
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "But with pandas' mathplotlib interface it is easier to use wild format.\n",
+      "\n",
+      "First let's create dates."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aq_pd2= aq_pd.copy()\n",
+      "\n",
+      "aq_pd2['date'] = \\\n",
+      "    [pd.Timestamp(s) \n",
+      "     for s in ('2015-'+aq_pd['month'].astype(int).astype(str)+'-'\n",
+      "               +aq_pd['day'].astype(int).astype(str))]"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Then selecting columns and adding aproprite index one get: "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aq_pd2_indexed = aq_pd2[['date', 'ozone', 'solar.r', 'wind', 'temp']]\n",
+      "aq_pd2_indexed.set_index('date')\n",
+      "aq_pd2_indexed.plot()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x7f51cbee6668>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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UDL/YV8GxE5lEGfxC08RkTur8nZWjm81SpBg2RjvA211tNg80007+fPN1GuBPXJsLKGks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\nhwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f51cbefd400>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Question 4. Time for dcast"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "aqw <- dcast(aql, month + day ~ variable)\n",
+      "head(aqw)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day ozone solar.r wind temp\n",
+        "1     5   1    41     190  7.4   67\n",
+        "2     5   2    36     118  8.0   72\n",
+        "3     5   3    12     149 12.6   74\n",
+        "4     5   4    18     313 11.5   62\n",
+        "5     5   5    NA      NA 14.3   56\n",
+        "6     5   6    28      NA 14.9   66\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The approprite pandas method seems to be `pivot_table`. Can you try?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Question 5. Melting with stack\n",
+      "\n",
+      "There is another pandas' method for maelting. It is called stack. Can you try it with `aq_pd`? What is the type of the result? Can you make it to be DataFrame with comlumns `varaiable` and `value`?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div>\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>variable</th>\n",
+        "      <th>value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>solar.r</td>\n",
+        "      <td>190.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>wind</td>\n",
+        "      <td>7.4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>temp</td>\n",
+        "      <td>67.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>month</td>\n",
+        "      <td>5.0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 208,
+       "text": [
+        "  variable  value\n",
+        "1    ozone   41.0\n",
+        "1  solar.r  190.0\n",
+        "1     wind    7.4\n",
+        "1     temp   67.0\n",
+        "1    month    5.0"
+       ]
+      }
+     ],
+     "prompt_number": 208
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Question 6. Melt, filter, group, agregate."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "aql %>% head"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day variable value\n",
+        "1     5   1    ozone    41\n",
+        "2     5   2    ozone    36\n",
+        "3     5   3    ozone    12\n",
+        "4     5   4    ozone    18\n",
+        "5     5   5    ozone    NA\n",
+        "6     5   6    ozone    28\n"
+       ]
+      }
+     ],
+     "prompt_number": 209
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "airquality %>%\n",
+      "    melt(id.vars = c(\"month\", \"day\")) %>%\n",
+      "    filter(!is.na(value)) %>%\n",
+      "    group_by(variable) %>%\n",
+      "    summarize(total= sum(value))  "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "# A tibble: 4 \u00d7 2\n",
+        "  variable   total\n",
+        "    <fctr>   <dbl>\n",
+        "1    ozone  4887.0\n",
+        "2  solar.r 27146.0\n",
+        "3     wind  1523.5\n",
+        "4     temp 11916.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 214
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "library(data.table)\n",
+      "data.table(airquality) %>%\n",
+      "    melt(id.vars = c(\"month\", \"day\")) %>%\n",
+      "    .[!is.na(value), .(total=sum(value)), by=variable]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "   variable   total\n",
+        "1:    ozone  4887.0\n",
+        "2:  solar.r 27146.0\n",
+        "3:     wind  1523.5\n",
+        "4:     temp 11916.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 215
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Time for doing this with pands:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": true,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/borja/Iteration1/reshape.ipynb b/borja/Iteration1/reshape.ipynb
new file mode 100644
index 0000000..122022c
--- /dev/null
+++ b/borja/Iteration1/reshape.ipynb
@@ -0,0 +1,1562 @@
+{
+ "metadata": {
+  "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": "3.5.0+"
+  },
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "http://seananderson.ca/2013/10/19/reshape.html"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Tabular Data Gym 2. Reshaping."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "import matplotlib.style\n",
+      "matplotlib.style.use('ggplot')\n",
+      "\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext rpy2.ipython"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "library(magrittr)\n",
+      "library(ggplot2)\n",
+      "library(dplyr)\n",
+      "library(reshape2)"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: \n",
+        "Attaching package: \u2018dplyr\u2019\n",
+        "\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n",
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from \u2018package:stats\u2019:\n",
+        "\n",
+        "    filter, lag\n",
+        "\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n",
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from \u2018package:base\u2019:\n",
+        "\n",
+        "    intersect, setdiff, setequal, union\n",
+        "\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "names(airquality) <- tolower(names(airquality))\n",
+      "head(airquality)"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  ozone solar.r wind temp month day\n",
+        "1    41     190  7.4   67     5   1\n",
+        "2    36     118  8.0   72     5   2\n",
+        "3    12     149 12.6   74     5   3\n",
+        "4    18     313 11.5   62     5   4\n",
+        "5    NA      NA 14.3   56     5   5\n",
+        "6    28      NA 14.9   66     5   6\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aq_pd = %R airquality\n",
+      "aq_pd.head()"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>ozone</th>\n",
+        "      <th>solar.r</th>\n",
+        "      <th>wind</th>\n",
+        "      <th>temp</th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>41</td>\n",
+        "      <td>190</td>\n",
+        "      <td>7.4</td>\n",
+        "      <td>67</td>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>36</td>\n",
+        "      <td>118</td>\n",
+        "      <td>8.0</td>\n",
+        "      <td>72</td>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>12</td>\n",
+        "      <td>149</td>\n",
+        "      <td>12.6</td>\n",
+        "      <td>74</td>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>18</td>\n",
+        "      <td>313</td>\n",
+        "      <td>11.5</td>\n",
+        "      <td>62</td>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>-2147483648</td>\n",
+        "      <td>-2147483648</td>\n",
+        "      <td>14.3</td>\n",
+        "      <td>56</td>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "        ozone     solar.r  wind  temp  month  day\n",
+        "1          41         190   7.4    67      5    1\n",
+        "2          36         118   8.0    72      5    2\n",
+        "3          12         149  12.6    74      5    3\n",
+        "4          18         313  11.5    62      5    4\n",
+        "5 -2147483648 -2147483648  14.3    56      5    5"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "Since `rpy2` does not treat NA well, we need to change it manually to `np.nan`."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def fix_nans_in_df(colname, df):\n",
+      "    df.ix[df[colname] < 0, colname] = np.NaN    \n",
+      "\n",
+      "for colname in aq_pd.columns.values:\n",
+      "    fix_nans_in_df(colname, aq_pd)\n",
+      "\n",
+      "aq_pd.head()"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>ozone</th>\n",
+        "      <th>solar.r</th>\n",
+        "      <th>wind</th>\n",
+        "      <th>temp</th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>41</td>\n",
+        "      <td>190</td>\n",
+        "      <td>7.4</td>\n",
+        "      <td>67</td>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>36</td>\n",
+        "      <td>118</td>\n",
+        "      <td>8.0</td>\n",
+        "      <td>72</td>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>12</td>\n",
+        "      <td>149</td>\n",
+        "      <td>12.6</td>\n",
+        "      <td>74</td>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>18</td>\n",
+        "      <td>313</td>\n",
+        "      <td>11.5</td>\n",
+        "      <td>62</td>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>14.3</td>\n",
+        "      <td>56</td>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "   ozone  solar.r  wind  temp  month  day\n",
+        "1     41      190   7.4    67      5    1\n",
+        "2     36      118   8.0    72      5    2\n",
+        "3     12      149  12.6    74      5    3\n",
+        "4     18      313  11.5    62      5    4\n",
+        "5    NaN      NaN  14.3    56      5    5"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "Melt/stack: changing wide format into long"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "R function `melt` from `reshape2` package change wide fromat into long. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "\n",
+      "aql <- melt(airquality)\n",
+      "aql %>% head"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/lib/python3/dist-packages/rpy2/robjects/functions.py:106: UserWarning: No id variables; using all as measure variables\n",
+        "\n",
+        "  res = super(Function, self).__call__(*new_args, **new_kwargs)\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  variable value\n",
+        "1    ozone    41\n",
+        "2    ozone    36\n",
+        "3    ozone    12\n",
+        "4    ozone    18\n",
+        "5    ozone    NA\n",
+        "6    ozone    28\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "### Question 1\n",
+      "\n",
+      "One can get similar result using pandas' method `melt`. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pd.melt(aq_pd)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>variable</th>\n",
+        "      <th>value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>36</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>28</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>19</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>8</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>7</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>11</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>16</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>12</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>13</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>15</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>16</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>34</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>17</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>6</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>18</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>30</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>19</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>20</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>21</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>22</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>23</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>32</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>24</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>25</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>26</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>27</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>28</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>45</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>29</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>115</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>...</th>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>888</th>\n",
+        "      <td>day</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>889</th>\n",
+        "      <td>day</td>\n",
+        "      <td>2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>890</th>\n",
+        "      <td>day</td>\n",
+        "      <td>3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>891</th>\n",
+        "      <td>day</td>\n",
+        "      <td>4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>892</th>\n",
+        "      <td>day</td>\n",
+        "      <td>5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>893</th>\n",
+        "      <td>day</td>\n",
+        "      <td>6</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>894</th>\n",
+        "      <td>day</td>\n",
+        "      <td>7</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>895</th>\n",
+        "      <td>day</td>\n",
+        "      <td>8</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>896</th>\n",
+        "      <td>day</td>\n",
+        "      <td>9</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>897</th>\n",
+        "      <td>day</td>\n",
+        "      <td>10</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>898</th>\n",
+        "      <td>day</td>\n",
+        "      <td>11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>899</th>\n",
+        "      <td>day</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>900</th>\n",
+        "      <td>day</td>\n",
+        "      <td>13</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>901</th>\n",
+        "      <td>day</td>\n",
+        "      <td>14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>902</th>\n",
+        "      <td>day</td>\n",
+        "      <td>15</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>903</th>\n",
+        "      <td>day</td>\n",
+        "      <td>16</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>904</th>\n",
+        "      <td>day</td>\n",
+        "      <td>17</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>905</th>\n",
+        "      <td>day</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>906</th>\n",
+        "      <td>day</td>\n",
+        "      <td>19</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>907</th>\n",
+        "      <td>day</td>\n",
+        "      <td>20</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>908</th>\n",
+        "      <td>day</td>\n",
+        "      <td>21</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>909</th>\n",
+        "      <td>day</td>\n",
+        "      <td>22</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>910</th>\n",
+        "      <td>day</td>\n",
+        "      <td>23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>911</th>\n",
+        "      <td>day</td>\n",
+        "      <td>24</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>912</th>\n",
+        "      <td>day</td>\n",
+        "      <td>25</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>913</th>\n",
+        "      <td>day</td>\n",
+        "      <td>26</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>914</th>\n",
+        "      <td>day</td>\n",
+        "      <td>27</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>915</th>\n",
+        "      <td>day</td>\n",
+        "      <td>28</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>916</th>\n",
+        "      <td>day</td>\n",
+        "      <td>29</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>917</th>\n",
+        "      <td>day</td>\n",
+        "      <td>30</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>918 rows \u00d7 2 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "    variable  value\n",
+        "0      ozone     41\n",
+        "1      ozone     36\n",
+        "2      ozone     12\n",
+        "3      ozone     18\n",
+        "4      ozone    NaN\n",
+        "5      ozone     28\n",
+        "6      ozone     23\n",
+        "7      ozone     19\n",
+        "8      ozone      8\n",
+        "9      ozone    NaN\n",
+        "10     ozone      7\n",
+        "11     ozone     16\n",
+        "12     ozone     11\n",
+        "13     ozone     14\n",
+        "14     ozone     18\n",
+        "15     ozone     14\n",
+        "16     ozone     34\n",
+        "17     ozone      6\n",
+        "18     ozone     30\n",
+        "19     ozone     11\n",
+        "20     ozone      1\n",
+        "21     ozone     11\n",
+        "22     ozone      4\n",
+        "23     ozone     32\n",
+        "24     ozone    NaN\n",
+        "25     ozone    NaN\n",
+        "26     ozone    NaN\n",
+        "27     ozone     23\n",
+        "28     ozone     45\n",
+        "29     ozone    115\n",
+        "..       ...    ...\n",
+        "888      day      1\n",
+        "889      day      2\n",
+        "890      day      3\n",
+        "891      day      4\n",
+        "892      day      5\n",
+        "893      day      6\n",
+        "894      day      7\n",
+        "895      day      8\n",
+        "896      day      9\n",
+        "897      day     10\n",
+        "898      day     11\n",
+        "899      day     12\n",
+        "900      day     13\n",
+        "901      day     14\n",
+        "902      day     15\n",
+        "903      day     16\n",
+        "904      day     17\n",
+        "905      day     18\n",
+        "906      day     19\n",
+        "907      day     20\n",
+        "908      day     21\n",
+        "909      day     22\n",
+        "910      day     23\n",
+        "911      day     24\n",
+        "912      day     25\n",
+        "913      day     26\n",
+        "914      day     27\n",
+        "915      day     28\n",
+        "916      day     29\n",
+        "917      day     30\n",
+        "\n",
+        "[918 rows x 2 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Question 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "airquality %>%\n",
+      "    melt(id.vars = c(\"month\", \"day\")) %>%\n",
+      "    head()"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day variable value\n",
+        "1     5   1    ozone    41\n",
+        "2     5   2    ozone    36\n",
+        "3     5   3    ozone    12\n",
+        "4     5   4    ozone    18\n",
+        "5     5   5    ozone    NA\n",
+        "6     5   6    ozone    28\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now let's try to get it with pandas."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aql_pd = pd.melt(aq_pd, id_vars=['month','day'])\n",
+      "aql_pd.head(6)"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "      <th>variable</th>\n",
+        "      <th>value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>36</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>5</td>\n",
+        "      <td>6</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>28</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": [
+        "   month  day variable  value\n",
+        "0      5    1    ozone     41\n",
+        "1      5    2    ozone     36\n",
+        "2      5    3    ozone     12\n",
+        "3      5    4    ozone     18\n",
+        "4      5    5    ozone    NaN\n",
+        "5      5    6    ozone     28"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Question 3 Putting names to columns"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "\n",
+      "aql <-\n",
+      "  melt(airquality, id.vars = c(\"month\", \"day\"),\n",
+      "  variable.name = \"climate_variable\", \n",
+      "  value.name = \"climate_value\") %>%\n",
+      "  mutate(date=as.Date(paste0(\"2015-\",month,'-',day)))\n",
+      "head(aql)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day climate_variable climate_value       date\n",
+        "1     5   1            ozone            41 2015-05-01\n",
+        "2     5   2            ozone            36 2015-05-02\n",
+        "3     5   3            ozone            12 2015-05-03\n",
+        "4     5   4            ozone            18 2015-05-04\n",
+        "5     5   5            ozone            NA 2015-05-05\n",
+        "6     5   6            ozone            28 2015-05-06\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Your turn with pandas."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pd.melt(aq_pd,id_vars=['month','day'], var_name='climate_variable', value_name='climate_value').head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>month</th>\n",
+        "      <th>day</th>\n",
+        "      <th>climate_variable</th>\n",
+        "      <th>climate_value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>5</td>\n",
+        "      <td>1</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>36</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>5</td>\n",
+        "      <td>3</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>5</td>\n",
+        "      <td>5</td>\n",
+        "      <td>ozone</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": [
+        "   month  day climate_variable  climate_value\n",
+        "0      5    1            ozone             41\n",
+        "1      5    2            ozone             36\n",
+        "2      5    3            ozone             12\n",
+        "3      5    4            ozone             18\n",
+        "4      5    5            ozone            NaN"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Comments about ploting\n",
+      "\n",
+      "When one uses ggplot it is natural to use long format when ploting."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "aql %>%\n",
+      "  ggplot() +\n",
+      "  geom_line(aes(date, climate_value, colour=climate_variable))"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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LbB47DU0gF1Jx7Iy+swzX09lQJF8Z/IP+t1TRq8N/POEFOJwkVi9A11WrZrCH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7/8wAc+sDLLsoKxooLO5nxNwrqHhn9/dufK9LfTDe4c4kBWy4MONeO4dqM5v2LUF+rXWz+znYt3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\nO5NFnoE5e1WIxMw7RzHeufYvzi79LAAlClQcXKkWmOoFAYC5KQ63SCca2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DhEEZuYQQJQ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+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "autoscroll": "json-false",
+      "collapsed": false,
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "source": [
+      "But with pandas' mathplotlib interface it is easier to use wild format.\n",
+      "\n",
+      "First let's create dates."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aq_pd2= aq_pd.copy()\n",
+      "\n",
+      "aq_pd2['date'] = \\\n",
+      "    [pd.Timestamp(s) \n",
+      "     for s in ('2015-'+aq_pd['month'].astype(int).astype(str)+'-'\n",
+      "               +aq_pd['day'].astype(int).astype(str))]"
+     ],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Then selecting columns and adding aproprite index one get: "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "aq_pd2_indexed = aq_pd2[['date', 'ozone', 'solar.r', 'wind', 'temp']]\n",
+      "aq_pd2_indexed.set_index('date')\n",
+      "aq_pd2_indexed.plot()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x7f51cbee6668>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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UDL/YV8GxE5lEGfxC08RkTur8nZWjm81SpBg2RjvA211tNg80007+fPN1GuBPXJsLKGks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\nhwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f51cbefd400>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Question 4. Time for dcast"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "aqw <- dcast(aql, month + day ~ variable)\n",
+      "head(aqw)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day ozone solar.r wind temp\n",
+        "1     5   1    41     190  7.4   67\n",
+        "2     5   2    36     118  8.0   72\n",
+        "3     5   3    12     149 12.6   74\n",
+        "4     5   4    18     313 11.5   62\n",
+        "5     5   5    NA      NA 14.3   56\n",
+        "6     5   6    28      NA 14.9   66\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The approprite pandas method seems to be `pivot_table`. Can you try?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Question 5. Melting with stack\n",
+      "\n",
+      "There is another pandas' method for maelting. It is called stack. Can you try it with `aq_pd`? What is the type of the result? Can you make it to be DataFrame with comlumns `varaiable` and `value`?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {
+      "autoscroll": "json-false",
+      "ein.tags": [
+       "worksheet-0"
+      ],
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "html": [
+        "<div>\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>variable</th>\n",
+        "      <th>value</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>ozone</td>\n",
+        "      <td>41.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>solar.r</td>\n",
+        "      <td>190.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>wind</td>\n",
+        "      <td>7.4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>temp</td>\n",
+        "      <td>67.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>month</td>\n",
+        "      <td>5.0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 208,
+       "text": [
+        "  variable  value\n",
+        "1    ozone   41.0\n",
+        "1  solar.r  190.0\n",
+        "1     wind    7.4\n",
+        "1     temp   67.0\n",
+        "1    month    5.0"
+       ]
+      }
+     ],
+     "prompt_number": 208
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Question 6. Melt, filter, group, agregate."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "aql %>% head"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "  month day variable value\n",
+        "1     5   1    ozone    41\n",
+        "2     5   2    ozone    36\n",
+        "3     5   3    ozone    12\n",
+        "4     5   4    ozone    18\n",
+        "5     5   5    ozone    NA\n",
+        "6     5   6    ozone    28\n"
+       ]
+      }
+     ],
+     "prompt_number": 209
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "airquality %>%\n",
+      "    melt(id.vars = c(\"month\", \"day\")) %>%\n",
+      "    filter(!is.na(value)) %>%\n",
+      "    group_by(variable) %>%\n",
+      "    summarize(total= sum(value))  "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "# A tibble: 4 \u00d7 2\n",
+        "  variable   total\n",
+        "    <fctr>   <dbl>\n",
+        "1    ozone  4887.0\n",
+        "2  solar.r 27146.0\n",
+        "3     wind  1523.5\n",
+        "4     temp 11916.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 214
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%R\n",
+      "library(data.table)\n",
+      "data.table(airquality) %>%\n",
+      "    melt(id.vars = c(\"month\", \"day\")) %>%\n",
+      "    .[!is.na(value), .(total=sum(value)), by=variable]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "   variable   total\n",
+        "1:    ozone  4887.0\n",
+        "2:  solar.r 27146.0\n",
+        "3:     wind  1523.5\n",
+        "4:     temp 11916.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 215
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Time for doing this with pands:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": true,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file