pandas_introduction.ipynb

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   "source": [
    "<CENTER>\n",
    "<img src=\"img/PyDataLogoBig-Paris2015.png\" width=\"50%\">\n",
    "\n",
    "  <header>\n",
    "    <h1>Introduction to Pandas</h1>\n",
    "    <h3>April 3rd, 2015</h3>\n",
    "    <h2>Joris Van den Bossche</h2>\n",
    "    <p></p>\n",
    "Source: <a href=\"https://github.com/jorisvandenbossche/2015-PyDataParis\">https://github.com/jorisvandenbossche/2015-PyDataParis</a>\n",
    "  </header>\n",
    "</CENTER>"
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   "source": [
    "# About me: Joris Van den Bossche\n",
    "\n",
    "- PhD student at Ghent University and VITO, Belgium\n",
    "- bio-science engineer, air quality research\n",
    "- pandas core dev\n",
    "\n",
    "->\n",
    "\n",
    "- https://github.com/jorisvandenbossche\n",
    "- [@jorisvdbossche](https://twitter.com/jorisvdbossche)\n",
    "\n",
    "\n",
    "Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)"
   ]
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   "source": [
    "\n",
    "# Content of this talk\n",
    "\n",
    "- Why do you need pandas?\n",
    "- Basic introduction to the data structures\n",
    "- Guided tour through some of the pandas features with a **case study about air quality**\n",
    "\n",
    "If you want to follow along, this is a notebook that you can view or run yourself:\n",
    "\n",
    "- All materials (notebook, data, link to nbviewer): https://github.com/jorisvandenbossche/2015-PyDataParis\n",
    "- You need `pandas` > 0.15 (easy solution is using Anaconda)"
   ]
  },
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     "slide_type": "slide"
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   "source": [
    "Some imports:"
   ]
  },
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   "execution_count": 1,
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    "slideshow": {
     "slide_type": "-"
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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "pd.options.display.max_rows = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "# Let's start with a showcase"
   ]
  },
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   "cell_type": "markdown",
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     "slide_type": "subslide"
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   "source": [
    "## Case study: air quality in Europe\n",
    "\n",
    "AirBase (The European Air quality dataBase): hourly measurements of all air quality monitoring stations from Europe\n",
    "\n",
    "Starting from these hourly data for different stations:"
   ]
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   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
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   "outputs": [],
   "source": [
    "import airbase\n",
    "data = airbase.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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       "<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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>14.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>16.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198895 rows × 4 columns</p>\n",
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       "                     BETR801  BETN029  FR04037  FR04012\n",
       "1990-01-01 00:00:00      NaN     16.0      NaN      NaN\n",
       "1990-01-01 01:00:00      NaN     18.0      NaN      NaN\n",
       "1990-01-01 02:00:00      NaN     21.0      NaN      NaN\n",
       "1990-01-01 03:00:00      NaN     26.0      NaN      NaN\n",
       "...                      ...      ...      ...      ...\n",
       "2012-12-31 20:00:00     16.5      2.0       16       47\n",
       "2012-12-31 21:00:00     14.5      2.5       13       43\n",
       "2012-12-31 22:00:00     16.5      3.5       14       42\n",
       "2012-12-31 23:00:00     15.0      3.0       13       49\n",
       "\n",
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   "source": [
    "to answering questions about this data in a few lines of code:\n",
    "\n",
    "**Does the air pollution show a decreasing trend over the years?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab4c292c>"
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     "execution_count": 4,
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       "/wcPQG2cwFg6/wAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab4ad70c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['1999':].resample('A').plot(ylim=[0,100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**How many exceedances of the limit values?**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0xab02004c>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaaca5a8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exceedances = data > 200\n",
    "exceedances = exceedances.groupby(exceedances.index.year).sum()\n",
    "ax = exceedances.loc[2005:].plot(kind='bar')\n",
    "ax.axhline(18, color='k', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**What is the difference in diurnal profile between weekdays and weekend?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab3cb5cc>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "/ZSwMke76/1wG4wF5iiltiqlXlNKhehXmlf4B/BM9/dGoBN5H7g8iMOAxgtuW7sPVwvXMQN/0jRt\n",
       "JvA48K5sA9fQNO1jwHLBXTJHu4tdYhvsBH6nadpkoBj4d10K8xKappk1TWtWSoXSFcr/xsU55JXv\n",
       "A1f/AW6kay7q8/1rmmZzcQ3e7hjwLoCmaYVALRCva0Xe68Lf/auZo1043ypN0/Z3f78aGK1nMd5A\n",
       "KZUEfAm8rWna+8j7wOVBvB2YDaCUygUOubh/0bVox7MASqkEuo5SVF72FaKv7Jc52nW3QSl1Xff3\n",
       "04A9ehbT3yml4oCNwFOapr3ZfbfXvw9cfbHWKmCGUmp79+3FLu5fwOvAG0qpc7/si+WohMudROiQ\n",
       "KQAAAGtJREFUuyJU5mjXz7lt8DjwklKqk64PpI/pV5JXeJquQ8/PKKXOnSv+FfAXb34fyFzTQggh\n",
       "hI7kIh0hhBBCRxLEQgghhI4kiIUQQggdSRALIYQQOpIgFkIIIXQkQSyEEELoSIJYCCGE0NH/B3AI\n",
       "SxP7Ps+PAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab482fac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['weekday'] = data.index.weekday\n",
    "data['weekend'] = data['weekday'].isin([5, 6])\n",
    "data_weekend = data.groupby(['weekend', data.index.hour])['FR04012'].mean().unstack(level=0)\n",
    "data_weekend.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will come back to these example, and build them up step by step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Why do you need pandas?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Why do you need pandas?\n",
    "\n",
    "When working with *tabular or structured data* (like R dataframe, SQL table, Excel spreadsheet, ...):\n",
    "\n",
    "- Import data\n",
    "- Clean up messy data\n",
    "- Explore data, gain insight into data\n",
    "- Process and prepare your data for analysis\n",
    "- Analyse your data (together with scikit-learn, statsmodels, ...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Pandas: data analysis in python\n",
    "\n",
    "For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.\n",
    "\n",
    "What is ``pandas``?\n",
    "\n",
    "* Pandas can be thought of as NumPy arrays with labels for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.\n",
    "* Pandas can also be thought of as `R`'s `data.frame` in Python.\n",
    "* Powerful for working with missing data, working with time series data, for reading and writing your data, for reshaping, grouping, merging your data, ...\n",
    "\n",
    "It's documentation: http://pandas.pydata.org/pandas-docs/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Key features\n",
    "\n",
    "* Fast, easy and flexible input/output for a lot of different data formats\n",
    "* Working with missing data (`.dropna()`, `pd.isnull()`)\n",
    "* Merging and joining (`concat`, `join`)\n",
    "* Grouping: `groupby` functionality\n",
    "* Reshaping (`stack`, `pivot`)\n",
    "* Powerful time series manipulation (resampling, timezones, ..)\n",
    "* Easy plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Basic data structures\n",
    "\n",
    "Pandas does this through two fundamental object types, both built upon NumPy arrays: the ``Series`` object, and the ``DataFrame`` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Series\n",
    "\n",
    "A Series is a basic holder for **one-dimensional labeled data**. It can be created much as a NumPy array is created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.1\n",
       "1    0.2\n",
       "2    0.3\n",
       "3    0.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([0.1, 0.2, 0.3, 0.4])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Attributes of a Series: `index` and `values`\n",
    "\n",
    "The series has a built-in concept of an **index**, which by default is the numbers *0* through *N - 1*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the underlying numpy array representation with the `.values` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1,  0.2,  0.3,  0.4])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can access series values via the index, just like for NumPy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10000000000000001"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Unlike the NumPy array, though, this index can be something other than integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series(np.arange(4), index=['a', 'b', 'c', 'd'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2['c']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In this way, a ``Series`` object can be thought of as similar to an ordered dictionary mapping one typed value to another typed value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium           11.3\n",
       "France            64.3\n",
       "Germany           81.3\n",
       "Netherlands       16.9\n",
       "United Kingdom    64.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = pd.Series({'Germany': 81.3, 'Belgium': 11.3, 'France': 64.3, 'United Kingdom': 64.9, 'Netherlands': 16.9})\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.299999999999997"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['France']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "but with the power of numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium           11300\n",
       "France            64300\n",
       "Germany           81300\n",
       "Netherlands       16900\n",
       "United Kingdom    64900\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can index or slice the populations as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.300000000000001"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['Belgium']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium    11.3\n",
       "France     64.3\n",
       "Germany    81.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['Belgium':'Germany']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Many things you can do with numpy arrays, can also be applied on objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fancy indexing, like indexing with a list or boolean indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France         64.3\n",
       "Netherlands    16.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population[['France', 'Netherlands']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France            64.3\n",
       "Germany           81.3\n",
       "United Kingdom    64.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population[population > 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Element-wise operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium           0.113\n",
       "France            0.643\n",
       "Germany           0.813\n",
       "Netherlands       0.169\n",
       "United Kingdom    0.649\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A range of methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.739999999999995"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Alignment!\n",
    "\n",
    "Only, pay attention to **alignment**: operations between series will align on the index:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s1 = population[['Belgium', 'France']]\n",
    "s2 = population[['France', 'Germany']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium    11.3\n",
       "France     64.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "France     64.3\n",
       "Germany    81.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium      NaN\n",
       "France     128.6\n",
       "Germany      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 + s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## DataFrames: Multi-dimensional Data\n",
    "\n",
    "A DataFrame is a **tablular data structure** (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can think of it as multiple Series object which share the same index.\n",
    "\n",
    "\n",
    "<img src=\"img/dataframe.png\" width=110%>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "One of the most common ways of creating a dataframe is from a dictionary of arrays or lists.\n",
    "\n",
    "Note that in the IPython notebook, the dataframe will display in a rich HTML view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area    capital         country  population\n",
       "0   30510   Brussels         Belgium        11.3\n",
       "1  671308      Paris          France        64.3\n",
       "2  357050     Berlin         Germany        81.3\n",
       "3   41526  Amsterdam     Netherlands        16.9\n",
       "4  244820     London  United Kingdom        64.9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
    "        'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
    "        'area': [30510, 671308, 357050, 41526, 244820],\n",
    "        'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n",
    "countries = pd.DataFrame(data)\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Attributes of the DataFrame\n",
    "\n",
    "A DataFrame has besides a `index` attribute, also a `columns` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4], dtype='int64')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'area', u'capital', u'country', u'population'], dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To check the data types of the different columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area            int64\n",
       "capital        object\n",
       "country        object\n",
       "population    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "An overview of that information can be given with the `info()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5 entries, 0 to 4\n",
      "Data columns (total 4 columns):\n",
      "area          5 non-null int64\n",
      "capital       5 non-null object\n",
      "country       5 non-null object\n",
      "population    5 non-null float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 160.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "countries.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Also a DataFrame has a `values` attribute, but attention: when you have heterogeneous data, all values will be upcasted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30510L, 'Brussels', 'Belgium', 11.3],\n",
       "       [671308L, 'Paris', 'France', 64.3],\n",
       "       [357050L, 'Berlin', 'Germany', 81.3],\n",
       "       [41526L, 'Amsterdam', 'Netherlands', 16.9],\n",
       "       [244820L, 'London', 'United Kingdom', 64.9]], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If we don't like what the index looks like, we can reset it and set one of our columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population\n",
       "country                                      \n",
       "Belgium          30510   Brussels        11.3\n",
       "France          671308      Paris        64.3\n",
       "Germany         357050     Berlin        81.3\n",
       "Netherlands      41526  Amsterdam        16.9\n",
       "United Kingdom  244820     London        64.9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = countries.set_index('country')\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To access a Series representing a column in the data, use typical indexing syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium            30510\n",
       "France            671308\n",
       "Germany           357050\n",
       "Netherlands        41526\n",
       "United Kingdom    244820\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "As you play around with DataFrames, you'll notice that many operations which work on NumPy arrays will also work on dataframes.\n",
    "\n",
    "Let's compute density of each country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium           370.370370\n",
       "France             95.783158\n",
       "Germany           227.699202\n",
       "Netherlands       406.973944\n",
       "United Kingdom    265.092721\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['population']*1000000 / countries['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Adding a new column to the dataframe is very simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density\n",
       "country                                                  \n",
       "Belgium          30510   Brussels        11.3  370.370370\n",
       "France          671308      Paris        64.3   95.783158\n",
       "Germany         357050     Berlin        81.3  227.699202\n",
       "Netherlands      41526  Amsterdam        16.9  406.973944\n",
       "United Kingdom  244820     London        64.9  265.092721"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['density'] = countries['population']*1000000 / countries['area']\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can use masking to select certain data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area    capital  population     density\n",
       "country                                              \n",
       "Belgium      30510   Brussels        11.3  370.370370\n",
       "Netherlands  41526  Amsterdam        16.9  406.973944"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['density'] > 300]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "And we can do things like sorting the items in the array, and indexing to take the first two rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30510</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "      <td>370.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>London</td>\n",
       "      <td>64.9</td>\n",
       "      <td>265.092721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area    capital  population     density\n",
       "country                                                  \n",
       "Netherlands      41526  Amsterdam        16.9  406.973944\n",
       "Belgium          30510   Brussels        11.3  370.370370\n",
       "United Kingdom  244820     London        64.9  265.092721\n",
       "Germany         357050     Berlin        81.3  227.699202\n",
       "France          671308      Paris        64.3   95.783158"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.sort_index(by='density', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "One useful method to use is the ``describe`` method, which computes summary statistics for each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>269042.800000</td>\n",
       "      <td>47.740000</td>\n",
       "      <td>273.183879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>264012.827994</td>\n",
       "      <td>31.519645</td>\n",
       "      <td>123.440607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30510.000000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41526.000000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>244820.000000</td>\n",
       "      <td>64.300000</td>\n",
       "      <td>265.092721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>357050.000000</td>\n",
       "      <td>64.900000</td>\n",
       "      <td>370.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>671308.000000</td>\n",
       "      <td>81.300000</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                area  population     density\n",
       "count       5.000000    5.000000    5.000000\n",
       "mean   269042.800000   47.740000  273.183879\n",
       "std    264012.827994   31.519645  123.440607\n",
       "min     30510.000000   11.300000   95.783158\n",
       "25%     41526.000000   16.900000  227.699202\n",
       "50%    244820.000000   64.300000  265.092721\n",
       "75%    357050.000000   64.900000  370.370370\n",
       "max    671308.000000   81.300000  406.973944"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The `plot` method can be used to quickly visualize the data in different ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab20740c>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "MXFnAYYxxhhj4s4CDGOMMcbE3f8DrlS9r+DDbd8AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab206a6c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, for this dataset, it does not say that much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab258d8c>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "kqSCLF5JkgqyeCVJKsjilSSpoP8PdSLVvuvAQy8AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab1222ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries['population'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab12eaec>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaae3e58c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries.plot(kind='scatter', x='population', y='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The available plotting types: ‘line’ (default), ‘bar’, ‘barh’, ‘hist’, ‘box’ , ‘kde’, ‘area’, ‘pie’, ‘scatter’, ‘hexbin’.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "countries = countries.drop(['density'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Some notes on selecting data\n",
    "\n",
    "One of pandas' basic features is the labeling of rows and columns, but this makes indexing also a bit more complex compared to numpy. We now have to distuinguish between:\n",
    "\n",
    "- selection by label\n",
    "- selection by position."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For a DataFrame, basic indexing selects the columns.\n",
    "\n",
    "Selecting a single column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium            30510\n",
       "France            671308\n",
       "Germany           357050\n",
       "Netherlands        41526\n",
       "United Kingdom    244820\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "or multiple columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30510</td>\n",
       "      <td>370.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>244820</td>\n",
       "      <td>265.092721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area     density\n",
       "country                           \n",
       "Belgium          30510  370.370370\n",
       "France          671308   95.783158\n",
       "Germany         357050  227.699202\n",
       "Netherlands      41526  406.973944\n",
       "United Kingdom  244820  265.092721"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[['area', 'density']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "But, slicing accesses the rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>41526</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "      <td>406.973944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               area    capital  population     density\n",
       "country                                               \n",
       "France       671308      Paris        64.3   95.783158\n",
       "Germany      357050     Berlin        81.3  227.699202\n",
       "Netherlands   41526  Amsterdam        16.9  406.973944"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries['France':'Netherlands']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For more advanced indexing, you have some extra attributes:\n",
    "    \n",
    "* `loc`: selection by label\n",
    "* `iloc`: selection by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "357050"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['Germany', 'area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>area</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>671308</td>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "      <td>95.783158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>357050</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>81.3</td>\n",
       "      <td>227.699202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           area capital  population     density\n",
       "country                                        \n",
       "France   671308   Paris        64.3   95.783158\n",
       "Germany  357050  Berlin        81.3  227.699202"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc['France':'Germany', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>capital</th>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               capital  population\n",
       "country                           \n",
       "Belgium       Brussels        11.3\n",
       "Netherlands  Amsterdam        16.9"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.loc[countries['density']>300, ['capital', 'population']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Selecting by position with `iloc` works similar as indexing numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>capital</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>Brussels</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Paris</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          capital  population\n",
       "country                      \n",
       "Belgium  Brussels        11.3\n",
       "France      Paris        64.3"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.iloc[0:2,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The different indexing methods can also be used to assign data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries.loc['Belgium':'Germany', 'population'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many, many more interesting operations that can be done on Series and DataFrame objects, but rather than continue using this toy data, we'll instead move to a real-world example, and illustrate some of the advanced concepts along the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Case study: air quality data of European monitoring stations (AirBase)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## AirBase (The European Air quality dataBase)\n",
    "\n",
    "AirBase: hourly measurements of all air quality monitoring stations from Europe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8#tab-data-by-country width=700 height=350></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<iframe src=http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8#tab-data-by-country width=700 height=350></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Importing and cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Importing and exporting data with pandas\n",
    "\n",
    "A wide range of input/output formats are natively supported by pandas:\n",
    "\n",
    "* CSV, text\n",
    "* SQL database\n",
    "* Excel\n",
    "* HDF5\n",
    "* json\n",
    "* html\n",
    "* pickle\n",
    "* ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries.to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Now for our case study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "I downloaded some of the raw data files of AirBase and included it in the repo:\n",
    "\n",
    "> station code: BETR801, pollutant code: 8 (nitrogen dioxide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-01-01\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -1 ./data/BETR8010000800100hour.1-1-1990.31-12-2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Just reading the tab-delimited data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>1990-01-01</th>\n",
       "      <th>-999.000</th>\n",
       "      <th>0</th>\n",
       "      <th>-999.000.1</th>\n",
       "      <th>0.1</th>\n",
       "      <th>-999.000.2</th>\n",
       "      <th>0.2</th>\n",
       "      <th>-999.000.3</th>\n",
       "      <th>0.3</th>\n",
       "      <th>-999.000.4</th>\n",
       "      <th>...</th>\n",
       "      <th>-999.000.19</th>\n",
       "      <th>0.19</th>\n",
       "      <th>-999.000.20</th>\n",
       "      <th>0.20</th>\n",
       "      <th>-999.000.21</th>\n",
       "      <th>0.21</th>\n",
       "      <th>-999.000.22</th>\n",
       "      <th>0.22</th>\n",
       "      <th>-999.000.23</th>\n",
       "      <th>0.23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-01-03</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-01-04</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-01-05</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-06</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>...</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1990-01-01  -999.000  0  -999.000.1  0.1  -999.000.2  0.2  -999.000.3  0.3  \\\n",
       "0  1990-01-02      -999  0        -999    0        -999    0        -999    0   \n",
       "1  1990-01-03        51  1          50    1          47    1          48    1   \n",
       "2  1990-01-04      -999  0        -999    0        -999    0        -999    0   \n",
       "3  1990-01-05        51  1          51    1          48    1          50    1   \n",
       "4  1990-01-06      -999  0        -999    0        -999    0        -999    0   \n",
       "\n",
       "   -999.000.4  ...   -999.000.19  0.19  -999.000.20  0.20  -999.000.21  0.21  \\\n",
       "0        -999  ...            57     1           58     1           54     1   \n",
       "1          51  ...            84     1           75     1         -999     0   \n",
       "2        -999  ...            69     1           65     1           64     1   \n",
       "3          51  ...          -999     0         -999     0         -999     0   \n",
       "4        -999  ...          -999     0         -999     0         -999     0   \n",
       "\n",
       "   -999.000.22  0.22  -999.000.23  0.23  \n",
       "0           49     1           48     1  \n",
       "1         -999     0         -999     0  \n",
       "2           60     1           59     1  \n",
       "3         -999     0         -999     0  \n",
       "4         -999     0         -999     0  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not really what we want."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "With using some more options of `read_csv`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colnames = ['date'] + [item for pair in zip([\"{:02d}\".format(i) for i in range(24)], ['flag']*24) for item in pair]\n",
    "\n",
    "data = pd.read_csv(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\",\n",
    "                   sep='\\t', header=None, na_values=[-999, -9999], names=colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/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>date</th>\n",
       "      <th>00</th>\n",
       "      <th>flag</th>\n",
       "      <th>01</th>\n",
       "      <th>flag</th>\n",
       "      <th>02</th>\n",
       "      <th>flag</th>\n",
       "      <th>03</th>\n",
       "      <th>flag</th>\n",
       "      <th>04</th>\n",
       "      <th>...</th>\n",
       "      <th>19</th>\n",
       "      <th>flag</th>\n",
       "      <th>20</th>\n",
       "      <th>flag</th>\n",
       "      <th>21</th>\n",
       "      <th>flag</th>\n",
       "      <th>22</th>\n",
       "      <th>flag</th>\n",
       "      <th>23</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-01-03</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-05</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  00  flag  01  flag  02  flag  03  flag  04  ...   19  flag  20  \\\n",
       "0  1990-01-01 NaN     0 NaN     0 NaN     0 NaN     0 NaN  ...  NaN     0 NaN   \n",
       "1  1990-01-02 NaN     1 NaN     1 NaN     1 NaN     1 NaN  ...   57     1  58   \n",
       "2  1990-01-03  51     0  50     0  47     0  48     0  51  ...   84     0  75   \n",
       "3  1990-01-04 NaN     1 NaN     1 NaN     1 NaN     1 NaN  ...   69     1  65   \n",
       "4  1990-01-05  51     0  51     0  48     0  50     0  51  ...  NaN     0 NaN   \n",
       "\n",
       "   flag  21  flag  22  flag  23  flag  \n",
       "0     0 NaN     0 NaN     0 NaN     0  \n",
       "1     1  54     1  49     1  48     1  \n",
       "2     0 NaN     0 NaN     0 NaN     0  \n",
       "3     1  64     1  60     1  59     1  \n",
       "4     0 NaN     0 NaN     0 NaN     0  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what did we do:\n",
    "\n",
    "- specify that the values of -999 and -9999 should be regarded as NaN\n",
    "- specified are own column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For now, we disregard the 'flag' columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>date</th>\n",
       "      <th>00</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>03</th>\n",
       "      <th>04</th>\n",
       "      <th>05</th>\n",
       "      <th>06</th>\n",
       "      <th>07</th>\n",
       "      <th>08</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>58</td>\n",
       "      <td>59.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-01-03</td>\n",
       "      <td>51.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>74</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8388</th>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>26.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>35.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>50.5</td>\n",
       "      <td>62.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>55</td>\n",
       "      <td>53.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>38.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8389</th>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>21.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>36</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8390</th>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>11.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
       "      <td>25.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8391</th>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>27.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8392 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    00    01    02    03    04    05    06    07    08  ...   \\\n",
       "0     1990-01-01   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "1     1990-01-02   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "2     1990-01-03  51.0  50.0  47.0  48.0  51.0  52.0  58.0  57.0   NaN  ...    \n",
       "3     1990-01-04   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "...          ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n",
       "8388  2012-12-28  26.5  28.5  35.5  32.0  35.5  50.5  62.5  74.5  76.0  ...    \n",
       "8389  2012-12-29  21.5  16.5  13.0  13.0  16.0  23.5  23.5  27.5  46.0  ...    \n",
       "8390  2012-12-30  11.5   9.5   7.5   7.5  10.0  11.0  13.5  13.5  17.5  ...    \n",
       "8391  2012-12-31   9.5   8.5   8.5   8.5  10.5  15.5  18.0  23.0  25.0  ...    \n",
       "\n",
       "        14    15  16    17     18    19    20    21    22    23  \n",
       "0      NaN   NaN NaN   NaN    NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1     55.0  59.0  58  59.0   58.0  57.0  58.0  54.0  49.0  48.0  \n",
       "2     69.0  74.0 NaN   NaN  103.0  84.0  75.0   NaN   NaN   NaN  \n",
       "3      NaN  71.0  74  70.0   70.0  69.0  65.0  64.0  60.0  59.0  \n",
       "...    ...   ...  ..   ...    ...   ...   ...   ...   ...   ...  \n",
       "8388  56.5  52.0  55  53.5   49.0  46.5  42.5  38.5  30.5  26.5  \n",
       "8389  48.0  41.5  36  33.0   25.5  21.0  22.0  20.5  20.0  15.0  \n",
       "8390   NaN  25.0  25  25.5   24.5  25.0  18.5  17.0  15.5  12.5  \n",
       "8391   NaN   NaN  28  27.5   26.0  21.0  16.5  14.5  16.5  15.0  \n",
       "\n",
       "[8392 rows x 25 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop('flag', axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we want to reshape it: our goal is to have the different hours as row indices, merged with the date into a datetime-index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Intermezzo: reshaping your data with `stack`, `unstack` and `pivot`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The docs say:\n",
    "\n",
    "> Pivot a level of the (possibly hierarchical) column labels, returning a\n",
    "DataFrame (or Series in the case of an object with a single level of\n",
    "column labels) having a hierarchical index with a new inner-most level\n",
    "of row labels.\n",
    "\n",
    "<img src=\"img/stack.png\" width=70%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  B  C\n",
       "0  one  a  0\n",
       "1  one  b  1\n",
       "2  two  a  2\n",
       "3  two  b  3"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':['one', 'one', 'two', 'two'], 'B':['a', 'b', 'a', 'b'], 'C':range(4)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use `stack`/`unstack`, we need the values we want to shift from rows to columns or the other way around as the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>a</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       C\n",
       "A   B   \n",
       "one a  0\n",
       "    b  1\n",
       "two a  2\n",
       "    b  3"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(['A', 'B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>B</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "B    a  b\n",
       "A        \n",
       "one  0  1\n",
       "two  2  3"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df['C'].unstack()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  B  C\n",
       "0  one  a  0\n",
       "1  one  b  1\n",
       "2  two  a  2\n",
       "3  two  b  3"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = result.stack().reset_index(name='C')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`pivot` is similar to `unstack`, but let you specify column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>B</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "B    a  b\n",
       "A        \n",
       "one  0  1\n",
       "two  2  3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='A', columns='B', values='C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`pivot_table` is similar as `pivot`, but can work with duplicate indices and let you specify an aggregation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  B  C\n",
       "0  one  a  0\n",
       "1  one  b  1\n",
       "2  two  a  2\n",
       "3  two  b  3\n",
       "4  one  a  4\n",
       "5  two  b  5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A':['one', 'one', 'two', 'two', 'one', 'two'], 'B':['a', 'b', 'a', 'b', 'a', 'b'], 'C':range(6)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>B</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "B    a  b\n",
       "A        \n",
       "one  2  1\n",
       "two  1  2"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='A', columns='B', values='C', aggfunc='count') #'mean'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Back to our case study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use `stack` to create a timeseries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_stacked = data.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          \n",
       "1990-01-02  09    48.0\n",
       "            12    48.0\n",
       "            13    50.0\n",
       "            14    55.0\n",
       "                  ... \n",
       "2012-12-31  20    16.5\n",
       "            21    14.5\n",
       "            22    16.5\n",
       "            23    15.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now, lets combine the two levels of the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_stacked = data_stacked.reset_index(name='BETR801')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_stacked.index = pd.to_datetime(data_stacked['date'] + data_stacked['level_1'], format=\"%Y-%m-%d%H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_stacked = data_stacked.drop(['date', 'level_1'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02 09:00:00</th>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02 12:00:00</th>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02 13:00:00</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02 14:00:00</th>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170794 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BETR801\n",
       "1990-01-02 09:00:00     48.0\n",
       "1990-01-02 12:00:00     48.0\n",
       "1990-01-02 13:00:00     50.0\n",
       "1990-01-02 14:00:00     55.0\n",
       "...                      ...\n",
       "2012-12-31 20:00:00     16.5\n",
       "2012-12-31 21:00:00     14.5\n",
       "2012-12-31 22:00:00     16.5\n",
       "2012-12-31 23:00:00     15.0\n",
       "\n",
       "[170794 rows x 1 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For this talk, I put the above code in a separate function, and repeated this for some different monitoring stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import airbase\n",
    "no2 = airbase.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- FR04037 (PARIS 13eme): urban background site at Square de Choisy\n",
    "- FR04012 (Paris, Place Victor Basch): urban traffic site at Rue d'Alesia\n",
    "- BETR802: urban traffic site in Antwerp, Belgium\n",
    "- BETN029: rural background site in Houtem, Belgium\n",
    "\n",
    "See http://www.eea.europa.eu/themes/air/interactive/no2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Exploring the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Some useful methods:\n",
    "\n",
    "`head` and `tail`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BETR801  BETN029  FR04037  FR04012\n",
       "1990-01-01 00:00:00      NaN       16      NaN      NaN\n",
       "1990-01-01 01:00:00      NaN       18      NaN      NaN\n",
       "1990-01-01 02:00:00      NaN       21      NaN      NaN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>14.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>16.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BETR801  BETN029  FR04037  FR04012\n",
       "2012-12-31 19:00:00     21.0      2.5       28       67\n",
       "2012-12-31 20:00:00     16.5      2.0       16       47\n",
       "2012-12-31 21:00:00     14.5      2.5       13       43\n",
       "2012-12-31 22:00:00     16.5      3.5       14       42\n",
       "2012-12-31 23:00:00     15.0      3.0       13       49"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 198895 entries, 1990-01-01 00:00:00 to 2012-12-31 23:00:00\n",
      "Data columns (total 4 columns):\n",
      "BETR801    170794 non-null float64\n",
      "BETN029    174807 non-null float64\n",
      "FR04037    120384 non-null float64\n",
      "FR04012    119448 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 7.6 MB\n"
     ]
    }
   ],
   "source": [
    "no2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Getting some basic summary statistics about the data with `describe`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>170794.000000</td>\n",
       "      <td>174807.000000</td>\n",
       "      <td>120384.000000</td>\n",
       "      <td>119448.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.914561</td>\n",
       "      <td>16.687756</td>\n",
       "      <td>40.040005</td>\n",
       "      <td>87.993261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.230921</td>\n",
       "      <td>13.106549</td>\n",
       "      <td>23.024347</td>\n",
       "      <td>41.317684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>339.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>358.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BETR801        BETN029        FR04037        FR04012\n",
       "count  170794.000000  174807.000000  120384.000000  119448.000000\n",
       "mean       47.914561      16.687756      40.040005      87.993261\n",
       "std        22.230921      13.106549      23.024347      41.317684\n",
       "min         0.000000       0.000000       0.000000       0.000000\n",
       "25%        32.000000       7.000000      23.000000      61.000000\n",
       "50%        46.000000      12.000000      37.000000      88.000000\n",
       "75%        61.000000      23.000000      54.000000     115.000000\n",
       "max       339.000000     115.000000     256.000000     358.000000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Quickly visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaa1d544c>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "5sc7fzoLz7WpJUkqzGFqSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqbD/D6Ecvg/j\n",
       "52/aAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa1d588c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " no2.plot(kind='box', ylim=[0,250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaabe97ec>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "a//LqO0KvTEibixjVwMrO+B3oF7v1wB/1CHb/1Hg/oj4MTCX2nb/v3TO3/96/W9khn//ffaBJEkC\n",
       "2vvwgSRJ+hgMBZIkCTAUSJKkwlAgSZIAQ4EkSSoMBZIkCTAUSJKk4v8D1jWZAhmsUMYAAAAASUVO\n",
       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaabe92ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2['BETR801'].plot(kind='hist', bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa7b3e74c>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "BzCYa0SHi8ApAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa3f42ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does not say too much .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can select part of the data (eg the latest 500 data points):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa7c325ec>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "snPI/6KpRTzsTZnoUhTlyzM7ZYFAIBAIBALBbEJ0lhMIBAKBQCAQnJGclqkRAoFAIBAIBALBVBGO\n",
       "sEAgEAgEAoHgjEQ4wgKBQCAQCASCMxLhCAsEAoFAIBAIzkiEIywQCAQCgUAgOCMRjrBAIBAIBAKB\n",
       "4IxEOMICgUAgEAgEgjMS4QgLBAKBQCAQCM5I/j/8ZLY3hmCSewAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa3e604c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2[-500:].plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can use some more advanced time series features -> next section!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Working with time series data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "When we ensure the DataFrame has a `DatetimeIndex`, time-series related functionality becomes available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.tseries.index.DatetimeIndex'>\n",
       "[1990-01-01 00:00:00, ..., 2012-12-31 23:00:00]\n",
       "Length: 198895, Freq: None, Timezone: None"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Indexing a time series works with strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BETR801</th>\n",
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       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 09:00:00</th>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 10:00:00</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 11:00:00</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 12:00:00</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>57</td>\n",
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      "text/plain": [
       "                     BETR801  BETN029  FR04037  FR04012\n",
       "2010-01-01 09:00:00       17        7       19       41\n",
       "2010-01-01 10:00:00       18        5       21       48\n",
       "2010-01-01 11:00:00       17        4       23       63\n",
       "2010-01-01 12:00:00       18        4       22       57"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2[\"2010-01-01 09:00\": \"2010-01-01 12:00\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A nice feature is \"partial string\" indexing, where we can do implicit slicing by providing a partial datetime string.\n",
    "\n",
    "E.g. all data of 2012:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>2012-01-01 00:00:00</th>\n",
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       "      <th>2012-01-01 03:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>16.5</td>\n",
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       "      <td>47</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>14.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>16.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8784 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BETR801  BETN029  FR04037  FR04012\n",
       "2012-01-01 00:00:00     21.0      1.0       17       56\n",
       "2012-01-01 01:00:00     18.0      1.0       16       50\n",
       "2012-01-01 02:00:00     20.0      1.0       14       46\n",
       "2012-01-01 03:00:00     16.0      1.0       17       47\n",
       "...                      ...      ...      ...      ...\n",
       "2012-12-31 20:00:00     16.5      2.0       16       47\n",
       "2012-12-31 21:00:00     14.5      2.5       13       43\n",
       "2012-12-31 22:00:00     16.5      3.5       14       42\n",
       "2012-12-31 23:00:00     15.0      3.0       13       49\n",
       "\n",
       "[8784 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2['2012']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Or all data of January up to March 2012:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>2012-01-02</th>\n",
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       "      <td>40.0</td>\n",
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       "      <th>2012-01-04</th>\n",
       "      <td>16</td>\n",
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       "      <td>41.0</td>\n",
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       "      <td>39.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16</td>\n",
       "      <td>13.0</td>\n",
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       "      <th>2012-02-26</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>54.5</td>\n",
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       "      <th>2012-02-27</th>\n",
       "      <td>59</td>\n",
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       "      <td>59</td>\n",
       "      <td>65.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>42</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-28</th>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27</td>\n",
       "      <td>43.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>49.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59</td>\n",
       "      <td>61.0</td>\n",
       "      <td>53.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-29</th>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>35.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>34</td>\n",
       "      <td>47.0</td>\n",
       "      <td>51.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>67</td>\n",
       "      <td>73.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>70</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            00  01    02    03  04    05    06    07    08    09 ...    14  \\\n",
       "date                                                             ...         \n",
       "2012-01-01  21  18  20.0  16.0  13  17.0  15.0  13.0  15.0  15.0 ...  31.5   \n",
       "2012-01-02 NaN NaN  10.5  12.0  12  39.0  49.5  52.5  45.0  48.0 ...  32.0   \n",
       "2012-01-03  18 NaN   NaN  14.0  23  31.5  36.0  40.0  32.5  26.0 ...  24.0   \n",
       "2012-01-04  16  16   NaN  13.0  14  17.0  26.0  33.0  36.0  36.0 ...  41.0   \n",
       "...         ..  ..   ...   ...  ..   ...   ...   ...   ...   ... ...   ...   \n",
       "2012-02-26  56  53  56.0  53.0  53   NaN   NaN  48.0  50.0  52.5 ...  25.0   \n",
       "2012-02-27  59  47  50.0  51.5  59  65.0   NaN   NaN  70.0  62.0 ...  56.0   \n",
       "2012-02-28  24  23  20.0  21.0  27  43.0  55.0   NaN   NaN  47.0 ...  49.0   \n",
       "2012-02-29  45  39  35.0  32.5  34  47.0  51.5  52.5   NaN   NaN ...  50.0   \n",
       "\n",
       "              15    16  17    18    19    20  21    22  23  \n",
       "date                                                        \n",
       "2012-01-01  33.5  32.5  30  25.0  20.0  14.0  13  15.0  14  \n",
       "2012-01-02  38.0  43.0  61  56.0  46.0  39.0  33  24.0  20  \n",
       "2012-01-03  28.0  25.0  28  25.0  26.0  22.0  21  20.0  19  \n",
       "2012-01-04  42.0  52.5  48  39.0  32.5  23.0  16  13.0  12  \n",
       "...          ...   ...  ..   ...   ...   ...  ..   ...  ..  \n",
       "2012-02-26  32.0  37.5  50  50.0  44.0  54.5  54  54.5  67  \n",
       "2012-02-27  61.0  70.0  68  60.0  56.0  54.0  42  36.0  28  \n",
       "2012-02-28  55.0  61.0  59  61.0  53.5  52.0  52  50.0  48  \n",
       "2012-02-29  56.0  61.0  67  73.5  73.0  72.5  70  69.0  62  \n",
       "\n",
       "[60 rows x 24 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2012-01':'2012-03']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Time and date components can be accessed from the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2, ..., 21, 22, 23])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1990, 1990, 1990, ..., 2012, 2012, 2012])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.index.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The power of pandas: `resample`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very powerfull method is **`resample`: converting the frequency of the time series** (e.g. from hourly to daily data).\n",
    "\n",
    "The time series has a frequency of 1 hour. I want to change this to daily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>53.923077</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>29.136364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>65.250000</td>\n",
       "      <td>42.681818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>63.846154</td>\n",
       "      <td>40.136364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              BETR801    BETN029  FR04037  FR04012\n",
       "1990-01-01        NaN  21.500000      NaN      NaN\n",
       "1990-01-02  53.923077  35.000000      NaN      NaN\n",
       "1990-01-03  63.000000  29.136364      NaN      NaN\n",
       "1990-01-04  65.250000  42.681818      NaN      NaN\n",
       "1990-01-05  63.846154  40.136364      NaN      NaN"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.resample('D').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "By default, `resample` takes the mean as aggregation function, but other methods can also be specified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>103</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>74</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>84</td>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BETR801  BETN029  FR04037  FR04012\n",
       "1990-01-01      NaN       41      NaN      NaN\n",
       "1990-01-02       59       59      NaN      NaN\n",
       "1990-01-03      103       47      NaN      NaN\n",
       "1990-01-04       74       58      NaN      NaN\n",
       "1990-01-05       84       67      NaN      NaN"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.resample('D', how='max').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The string to specify the new time frequency: http://pandas.pydata.org/pandas-docs/dev/timeseries.html#offset-aliases  \n",
    "These strings can also be combined with numbers, eg `'10D'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Further exploring the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa96348ac>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4f1d72c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.resample('M').plot() # 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# no2['2012'].resample('D').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "clear_cell": true,
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa7bdc64c>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa53a32ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.loc['2009':, 'FR04037'].resample('M', how=['mean', 'median']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Question: The evolution of the yearly averages with, and the overall mean of all stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa93b380c>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "B8VTnO/xUy5bAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa93b96ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2_1999 = no2['1999':]\n",
    "no2_1999.resample('A').plot()\n",
    "no2_1999.mean(axis=1).resample('A').plot(color='k', linestyle='--', linewidth=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Analysing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Intermezzo - the groupby operation (split-apply-combine)\n",
    "\n",
    "By \"group by\" we are referring to a process involving one or more of the following steps\n",
    "\n",
    "* **Splitting** the data into groups based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure\n",
    "\n",
    "<img src=\"img/splitApplyCombine.png\">\n",
    "\n",
    "Similar to SQL `GROUP BY`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The example of the image in pandas syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>data</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    data key\n",
       "0      0   A\n",
       "1      5   B\n",
       "2     10   C\n",
       "3      5   A\n",
       "..   ...  ..\n",
       "5     15   C\n",
       "6     10   A\n",
       "7     15   B\n",
       "8     20   C\n",
       "\n",
       "[9 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
    "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate('sum')  # np.sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Back to the air quality data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: how does the *typical monthly profile* look like for the different stations?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we add a column to the dataframe that indicates the month (integer value of 1 to 12):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "no2['month'] = no2.index.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now, we can calculate the mean of each month over the different years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.927088</td>\n",
       "      <td>20.304075</td>\n",
       "      <td>47.634409</td>\n",
       "      <td>82.472813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.168021</td>\n",
       "      <td>19.938929</td>\n",
       "      <td>50.564499</td>\n",
       "      <td>83.973207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.598322</td>\n",
       "      <td>19.424205</td>\n",
       "      <td>47.862715</td>\n",
       "      <td>96.272138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.491741</td>\n",
       "      <td>18.183433</td>\n",
       "      <td>40.943117</td>\n",
       "      <td>95.962862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49.220250</td>\n",
       "      <td>14.605979</td>\n",
       "      <td>39.706019</td>\n",
       "      <td>93.000316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50.894911</td>\n",
       "      <td>17.660149</td>\n",
       "      <td>44.010934</td>\n",
       "      <td>86.297836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.254468</td>\n",
       "      <td>19.372193</td>\n",
       "      <td>45.564683</td>\n",
       "      <td>87.173878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>48.644117</td>\n",
       "      <td>21.007089</td>\n",
       "      <td>45.262243</td>\n",
       "      <td>81.817977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         BETR801    BETN029    FR04037    FR04012\n",
       "month                                            \n",
       "1      50.927088  20.304075  47.634409  82.472813\n",
       "2      54.168021  19.938929  50.564499  83.973207\n",
       "3      54.598322  19.424205  47.862715  96.272138\n",
       "4      51.491741  18.183433  40.943117  95.962862\n",
       "...          ...        ...        ...        ...\n",
       "9      49.220250  14.605979  39.706019  93.000316\n",
       "10     50.894911  17.660149  44.010934  86.297836\n",
       "11     50.254468  19.372193  45.564683  87.173878\n",
       "12     48.644117  21.007089  45.262243  81.817977\n",
       "\n",
       "[12 rows x 4 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.groupby('month').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "clear_cell": true,
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa93495cc>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9340e4c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.groupby('month').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Question: The typical diurnal profile for the different stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa4a46acc>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa95f68ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.groupby(no2.index.hour).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Question: What is the difference in the typical diurnal profile between week and weekend days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2.index.weekday?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2['weekday'] = no2.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Add a column indicating week/weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no2['weekend'] = no2['weekday'].isin([5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekend</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\">False</th>\n",
       "      <th>0</th>\n",
       "      <td>40.008066</td>\n",
       "      <td>17.487512</td>\n",
       "      <td>34.439398</td>\n",
       "      <td>52.094663</td>\n",
       "      <td>6.520355</td>\n",
       "      <td>1.998157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.281875</td>\n",
       "      <td>17.162671</td>\n",
       "      <td>31.585121</td>\n",
       "      <td>44.721629</td>\n",
       "      <td>6.518121</td>\n",
       "      <td>1.997315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.601189</td>\n",
       "      <td>16.800076</td>\n",
       "      <td>30.865143</td>\n",
       "      <td>43.518539</td>\n",
       "      <td>6.520511</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.633946</td>\n",
       "      <td>16.591031</td>\n",
       "      <td>32.963500</td>\n",
       "      <td>51.942135</td>\n",
       "      <td>6.518038</td>\n",
       "      <td>2.002360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.853566</td>\n",
       "      <td>16.791971</td>\n",
       "      <td>40.780162</td>\n",
       "      <td>72.547472</td>\n",
       "      <td>6.514098</td>\n",
       "      <td>2.003883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BETR801    BETN029    FR04037    FR04012     month   weekday\n",
       "weekend                                                                  \n",
       "False   0  40.008066  17.487512  34.439398  52.094663  6.520355  1.998157\n",
       "        1  38.281875  17.162671  31.585121  44.721629  6.518121  1.997315\n",
       "        2  38.601189  16.800076  30.865143  43.518539  6.520511  2.000000\n",
       "        3  42.633946  16.591031  32.963500  51.942135  6.518038  2.002360\n",
       "        4  49.853566  16.791971  40.780162  72.547472  6.514098  2.003883"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_weekend = no2.groupby(['weekend', no2.index.hour]).mean()\n",
    "data_weekend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>69.817219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.721629</td>\n",
       "      <td>60.697248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>54.407904</td>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend      False      True \n",
       "0        52.094663  69.817219\n",
       "1        44.721629  60.697248\n",
       "2        43.518539  54.407904\n",
       "3        51.942135  53.534933\n",
       "4        72.547472  57.472830"
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     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_weekend_FR04012 = data_weekend['FR04012'].unstack(level=0)\n",
    "data_weekend_FR04012.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa95081cc>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "SxP7Ps+PAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa951094c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_weekend_FR04012.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Question: What are the number of exceedances of hourly values above the European limit 200 µg/m3 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exceedances = no2 > 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# group by year and count exceedances (sum of boolean)\n",
    "exceedances = exceedances.groupby(exceedances.index.year).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0xa94c7b0c>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa954bc0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = exceedances.loc[2005:].plot(kind='bar')\n",
    "ax.axhline(18, color='k', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Question:  Visualize the typical week profile for the different stations as boxplots.\n",
    "\n",
    "Tip: the boxplot method of a DataFrame expects the data for the different boxes in different columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "clear_cell": true,
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BETR801  BETN029  FR04037  FR04012  month  weekday  \\\n",
       "1990-01-01 00:00:00      NaN       16      NaN      NaN      1        0   \n",
       "1990-01-01 01:00:00      NaN       18      NaN      NaN      1        0   \n",
       "1990-01-01 02:00:00      NaN       21      NaN      NaN      1        0   \n",
       "1990-01-01 03:00:00      NaN       26      NaN      NaN      1        0   \n",
       "1990-01-01 04:00:00      NaN       21      NaN      NaN      1        0   \n",
       "\n",
       "                    weekend  week  \n",
       "1990-01-01 00:00:00   False     1  \n",
       "1990-01-01 01:00:00   False     1  \n",
       "1990-01-01 02:00:00   False     1  \n",
       "1990-01-01 03:00:00   False     1  \n",
       "1990-01-01 04:00:00   False     1  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a weekday and week column\n",
    "no2['weekday'] = no2.index.weekday\n",
    "no2['week'] = no2.index.week\n",
    "no2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "clear_cell": true,
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/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>weekday</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week</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>1</th>\n",
       "      <td>24.625000</td>\n",
       "      <td>23.875000</td>\n",
       "      <td>26.208333</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>40.208333</td>\n",
       "      <td>24.625000</td>\n",
       "      <td>22.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.125000</td>\n",
       "      <td>44.125000</td>\n",
       "      <td>57.583333</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>40.791667</td>\n",
       "      <td>34.750000</td>\n",
       "      <td>32.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.208333</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>51.958333</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>28.291667</td>\n",
       "      <td>18.416667</td>\n",
       "      <td>18.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.333333</td>\n",
       "      <td>49.500000</td>\n",
       "      <td>49.375000</td>\n",
       "      <td>48.916667</td>\n",
       "      <td>63.458333</td>\n",
       "      <td>34.250000</td>\n",
       "      <td>25.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47.791667</td>\n",
       "      <td>38.791667</td>\n",
       "      <td>54.333333</td>\n",
       "      <td>50.041667</td>\n",
       "      <td>51.458333</td>\n",
       "      <td>46.541667</td>\n",
       "      <td>35.458333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekday          0          1          2          3          4          5  \\\n",
       "week                                                                        \n",
       "1        24.625000  23.875000  26.208333  17.500000  40.208333  24.625000   \n",
       "2        39.125000  44.125000  57.583333  50.750000  40.791667  34.750000   \n",
       "3        45.208333  66.333333  51.958333  28.250000  28.291667  18.416667   \n",
       "4        35.333333  49.500000  49.375000  48.916667  63.458333  34.250000   \n",
       "5        47.791667  38.791667  54.333333  50.041667  51.458333  46.541667   \n",
       "\n",
       "weekday          6  \n",
       "week                \n",
       "1        22.375000  \n",
       "2        32.250000  \n",
       "3        18.333333  \n",
       "4        25.250000  \n",
       "5        35.458333  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table so that the weekdays are the different columns\n",
    "data_pivoted = no2['2012'].pivot_table(columns='weekday', index='week', values='FR04037')\n",
    "data_pivoted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joris/miniconda/lib/python2.7/site-packages/pandas/tools/plotting.py:2633: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9290aac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = data_pivoted.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Exercise**: Calculate the correlation between the different stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "clear_cell": true,
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BETR801</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464085</td>\n",
       "      <td>0.561676</td>\n",
       "      <td>0.394446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETN029</th>\n",
       "      <td>0.464085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401864</td>\n",
       "      <td>0.186997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04037</th>\n",
       "      <td>0.561676</td>\n",
       "      <td>0.401864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.433466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04012</th>\n",
       "      <td>0.394446</td>\n",
       "      <td>0.186997</td>\n",
       "      <td>0.433466</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BETR801   BETN029   FR04037   FR04012\n",
       "BETR801  1.000000  0.464085  0.561676  0.394446\n",
       "BETN029  0.464085  1.000000  0.401864  0.186997\n",
       "FR04037  0.561676  0.401864  1.000000  0.433466\n",
       "FR04012  0.394446  0.186997  0.433466  1.000000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2[['BETR801', 'BETN029', 'FR04037', 'FR04012']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "clear_cell": true,
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/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>BETR801</th>\n",
       "      <th>BETN029</th>\n",
       "      <th>FR04037</th>\n",
       "      <th>FR04012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BETR801</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.581701</td>\n",
       "      <td>0.663855</td>\n",
       "      <td>0.459885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETN029</th>\n",
       "      <td>0.581701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.527390</td>\n",
       "      <td>0.312484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04037</th>\n",
       "      <td>0.663855</td>\n",
       "      <td>0.527390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04012</th>\n",
       "      <td>0.459885</td>\n",
       "      <td>0.312484</td>\n",
       "      <td>0.453584</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BETR801   BETN029   FR04037   FR04012\n",
       "BETR801  1.000000  0.581701  0.663855  0.459885\n",
       "BETN029  0.581701  1.000000  0.527390  0.312484\n",
       "FR04037  0.663855  0.527390  1.000000  0.453584\n",
       "FR04012  0.459885  0.312484  0.453584  1.000000"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2[['BETR801', 'BETN029', 'FR04037', 'FR04012']].resample('D').corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "no2 = no2[['BETR801', 'BETN029', 'FR04037', 'FR04012']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Further reading\n",
    "\n",
    "- the documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
    "- Wes McKinney's book \"Python for Data Analysis\"\n",
    "- lots of tutorials on the internet, eg http://github.com/jvns/pandas-cookbook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What's new in pandas\n",
    "\n",
    "Some recent enhancements of the last year (versions 0.14 to 0.16):\n",
    "\n",
    "- Better integration for categorical data (`Categorical` and `CategoricalIndex`)\n",
    "- The same for `Timedelta` and `TimedeltaIndex`\n",
    "- More flexible SQL interface based on `sqlalchemy`\n",
    "- MultiIndexing using slicers\n",
    "- `.dt` accessor for accesing datetime-properties from columns\n",
    "- Groupby enhancements\n",
    "- And a lot of enhancements and bug fixes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How can you help?\n",
    "\n",
    "**We need you!**\n",
    "\n",
    "Contributions are very welcome and can be in different domains:\n",
    "\n",
    "- reporting issues\n",
    "- improving the documentation\n",
    "- testing release candidates and provide feedback\n",
    "- triaging and fixing bugs\n",
    "- implementing new features\n",
    "- spreading the word\n",
    "\n",
    "-> https://github.com/pydata/pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Thanks for listening! Questions?\n",
    "\n",
    "\n",
    "- https://github.com/jorisvandenbossche\n",
    "- <mailto:jorisvandenbossche@gmail.com>\n",
    "- [@jorisvdbossche](https://twitter.com/jorisvdbossche)\n",
    "\n",
    "\n",
    "Slides and data: Source: https://github.com/jorisvandenbossche/2015-PyDataParis\n",
    "\n",
    "\n",
    "Slides presented with 'live reveal' https://github.com/damianavila/RISE\n"
   ]
  }
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