solved - 07 - Case study - air quality data.ipynb

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   "source": [
    "<p><font size=\"6\"><b> Case study: air quality data of European monitoring stations (AirBase)</b></font></p><br>\n",
    "**AirBase (The European Air quality dataBase): hourly measurements of all air quality monitoring stations from Europe. **\n",
    "\n",
    "> *© 2016, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AirBase is the European air quality database maintained by the European Environment Agency (EEA). It contains air quality monitoring data and information submitted by participating countries throughout Europe. The air quality database consists of a multi-annual time series of air quality measurement data and statistics for a number of air pollutants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "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=900 height=350></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "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=900 height=350></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the data files that are available from AirBase were included in the data folder: the hourly **concentrations of nitrogen dioxide (NO2)** for 4 different measurement stations:\n",
    "\n",
    "- 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": "code",
   "execution_count": 2,
   "metadata": {
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     "slide_type": "-"
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_rows = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Processing a single file\n",
    "\n",
    "We will start with processing one of the downloaded files (`BETR8010000800100hour.1-1-1990.31-12-2012`). Looking at the data, you will see it does not look like a nice csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
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   "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\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\") as f:\n",
    "    print(f.readline())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we will need to do some manual processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Just reading the tab-delimited data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\", sep='\\t')#, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "    <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",
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       "      <th>-999.000.23</th>\n",
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       "      <th>0</th>\n",
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       "      <td>54.0</td>\n",
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       "      <td>49.0</td>\n",
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       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>1990-01-03</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1990-01-04</td>\n",
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       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
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       "      <td>69.0</td>\n",
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       "      <td>65.0</td>\n",
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       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-01-05</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
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       "      <td>-999.0</td>\n",
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       "      <td>-999.0</td>\n",
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       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-06</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 49 columns</p>\n",
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       "   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  0      -999.0    0      -999.0    0      -999.0    0   \n",
       "1  1990-01-03      51.0  1        50.0    1        47.0    1        48.0    1   \n",
       "2  1990-01-04    -999.0  0      -999.0    0      -999.0    0      -999.0    0   \n",
       "3  1990-01-05      51.0  1        51.0    1        48.0    1        50.0    1   \n",
       "4  1990-01-06    -999.0  0      -999.0    0      -999.0    0      -999.0    0   \n",
       "\n",
       "   -999.000.4  ...   -999.000.19  0.19  -999.000.20  0.20  -999.000.21  0.21  \\\n",
       "0      -999.0  ...          57.0     1         58.0     1         54.0     1   \n",
       "1        51.0  ...          84.0     1         75.0     1       -999.0     0   \n",
       "2      -999.0  ...          69.0     1         65.0     1         64.0     1   \n",
       "3        51.0  ...        -999.0     0       -999.0     0       -999.0     0   \n",
       "4      -999.0  ...        -999.0     0       -999.0     0       -999.0     0   \n",
       "\n",
       "   -999.000.22  0.22  -999.000.23  0.23  \n",
       "0         49.0     1         48.0     1  \n",
       "1       -999.0     0       -999.0     0  \n",
       "2         60.0     1         59.0     1  \n",
       "3       -999.0     0       -999.0     0  \n",
       "4       -999.0     0       -999.0     0  \n",
       "\n",
       "[5 rows x 49 columns]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above data is clearly not ready to be used! Each row contains the 24 measurements for each hour of the day, and also contains a flag (0/1) indicating the quality of the data. Furthermore, there is no header row with column names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>: <br><br> Clean up this dataframe by using more options of `read_csv` (see its [docstring](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html))\n",
    "\n",
    " <ul>\n",
    "  <li>specify the correct delimiter</li>\n",
    "  <li>specify that the values of -999 and -9999 should be regarded as NaN</li>\n",
    "  <li>specify are own column names (for how the column names are made up, see See http://stackoverflow.com/questions/6356041/python-intertwining-two-lists)\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Column names: list consisting of 'date' and then intertwined the hour of the day and 'flag'\n",
    "hours = [\"{:02d}\".format(i) for i in range(24)]\n",
    "column_names = ['date'] + [item for pair in zip(hours, ['flag']*24) for item in pair]"
   ]
  },
  {
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   "execution_count": 7,
   "metadata": {
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    "collapsed": true,
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   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\",\n",
    "                   sep='\\t', header=None, names=column_names, na_values=[-999, -9999])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    "scrolled": true
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       "    <tr style=\"text-align: right;\">\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1990-01-04</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>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-05</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
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       "      <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",
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       "<p>5 rows × 49 columns</p>\n",
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       "         date    00  flag    01  flag.1    02  flag.2    03  flag.3    04  \\\n",
       "0  1990-01-01   NaN     0   NaN       0   NaN       0   NaN       0   NaN   \n",
       "1  1990-01-02   NaN     0   NaN       0   NaN       0   NaN       0   NaN   \n",
       "2  1990-01-03  51.0     1  50.0       1  47.0       1  48.0       1  51.0   \n",
       "3  1990-01-04   NaN     0   NaN       0   NaN       0   NaN       0   NaN   \n",
       "4  1990-01-05  51.0     1  51.0       1  48.0       1  50.0       1  51.0   \n",
       "\n",
       "    ...       19  flag.19    20  flag.20    21  flag.21    22  flag.22    23  \\\n",
       "0   ...      NaN        0   NaN        0   NaN        0   NaN        0   NaN   \n",
       "1   ...     57.0        1  58.0        1  54.0        1  49.0        1  48.0   \n",
       "2   ...     84.0        1  75.0        1   NaN        0   NaN        0   NaN   \n",
       "3   ...     69.0        1  65.0        1  64.0        1  60.0        1  59.0   \n",
       "4   ...      NaN        0   NaN        0   NaN        0   NaN        0   NaN   \n",
       "\n",
       "   flag.23  \n",
       "0        0  \n",
       "1        1  \n",
       "2        0  \n",
       "3        1  \n",
       "4        0  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For the sake of this tutorial, we will disregard the 'flag' columns (indicating the quality of the data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "<br><br>\n",
    "Drop all 'flag' columns ('flag1', 'flag2', ...) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_columns = [col for col in data.columns if 'flag' in col]\n",
    "# we can now use this list to drop these columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "clear_cell": true,
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = data.drop(flag_columns, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>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>58.0</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>74.0</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>4</th>\n",
       "      <td>1990-01-05</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "4  1990-01-05  51.0  51.0  48.0  50.0  51.0  58.0  65.0  66.0  69.0  ...    \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  58.0  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  74.0  70.0   70.0  69.0  65.0  64.0  60.0  59.0  \n",
       "4   NaN   NaN   NaN   NaN    NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "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. Here we have a wide and long dataframe, and want to make this a long, narrow timeseries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "<b>REMEMBER</b>: \n",
    "\n",
    " <ul>\n",
    "  <li>Recap: reshaping your data with [`stack` and `unstack`](./pandas_07_reshaping_data.ipynb)</li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "<img src=\"img/schema-stack.svg\" width=70%>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    "<br><br>\n",
    "\n",
    "Reshape the dataframe to a timeseries. \n",
    "The end result should look like:<br><br>\n",
    "\n",
    "\n",
    "<div class='center'>\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 style=\"text-align:center\">170794 rows × 1 columns</p>\n",
    "</div>\n",
    "\n",
    " <ul>\n",
    "  <li>Reshape the dataframe so that each row consists of one observation for one date + hour combination</li>\n",
    "  <li>When you have the date and hour values as two columns, combine these columns into a datetime (tip: string columns can be summed to concatenate the strings) and remove the original columns</li>\n",
    "  <li>Set the new datetime values as the index, and remove the original columns with date and hour values</li>\n",
    "\n",
    "</ul>\n",
    "\n",
    "\n",
    "**NOTE**: This is an advanced exercise. Do not spend too much time on it and don't hesitate to look at the solutions. \n",
    "\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": 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",
       "            15    59.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use stack to reshape the data to move the hours (the column labels) into a column.\n",
    "# But we don't want to move the 'date' column label, therefore we first set this as the index.\n",
    "# You can check the difference with \"data.stack()\"\n",
    "data2 = data.set_index('date')\n",
    "data_stacked = data2.stack()\n",
    "data_stacked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>level_1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>09</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>12</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>13</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>14</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>15</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date level_1     0\n",
       "0  1990-01-02      09  48.0\n",
       "1  1990-01-02      12  48.0\n",
       "2  1990-01-02      13  50.0\n",
       "3  1990-01-02      14  55.0\n",
       "4  1990-01-02      15  59.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We reset the index to have the date and hours available as columns\n",
    "data_stacked = data_stacked.reset_index()\n",
    "data_stacked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "clear_cell": true,
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Now we combine the dates and the hours into a datetime, and set this as the index\n",
    "data_stacked.index = pd.to_datetime(data_stacked['date'] + data_stacked['level_1'], format=\"%Y-%m-%d%H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</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>1990-01-02 15:00:00</th>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        0\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",
       "1990-01-02 15:00:00  59.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the origal date and hour columns\n",
    "data_stacked = data_stacked.drop(['date', 'level_1'], axis=1)\n",
    "data_stacked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rename the remaining column to the name of the measurement station\n",
    "data_stacked = data_stacked.rename(columns={0: 'BETR801'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1990-01-02 15:00:00</th>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "1990-01-02 15:00:00     59.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_stacked.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final data is now a time series. In pandas, this means that the index is a `DatetimeIndex`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1990-01-02 09:00:00', '1990-01-02 12:00:00',\n",
       "               '1990-01-02 13:00:00', '1990-01-02 14:00:00',\n",
       "               '1990-01-02 15:00:00', '1990-01-02 16:00:00',\n",
       "               '1990-01-02 17:00:00', '1990-01-02 18:00:00',\n",
       "               '1990-01-02 19:00:00', '1990-01-02 20:00:00',\n",
       "               ...\n",
       "               '2012-12-31 12:00:00', '2012-12-31 13:00:00',\n",
       "               '2012-12-31 16:00:00', '2012-12-31 17:00:00',\n",
       "               '2012-12-31 18:00:00', '2012-12-31 19:00:00',\n",
       "               '2012-12-31 20:00:00', '2012-12-31 21:00:00',\n",
       "               '2012-12-31 22:00:00', '2012-12-31 23:00:00'],\n",
       "              dtype='datetime64[ns]', length=170794, freq=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_stacked.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13451c8cc0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56i3RdVqYtHwLv/cznainobmm341eQPbQcewq3h+wQBVutUys3+eUCfTJLj0k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\nnAV6Y4zJcP8PB/eYT6yvfSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13451517f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_stacked.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing a collection of files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have seen the code steps to process one of the files. We have however multiple files for the different stations with the same structure. Therefore, to not have to repeat the actual code, let's make a function from the steps we have seen above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Write a function `read_airbase_file(filename, station)`, using the above steps the read in and process the data, and that returns a processed timeseries.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def read_airbase_file(filename, station):\n",
    "    \"\"\"\n",
    "    Read hourly AirBase data files.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    filename : string\n",
    "        Path to the data file.\n",
    "    station : string\n",
    "        Name of the station.\n",
    "       \n",
    "    Returns\n",
    "    -------\n",
    "    DataFrame\n",
    "        Processed dataframe.\n",
    "    \"\"\"\n",
    "    \n",
    "    ...\n",
    "    \n",
    "    return ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_airbase_file(filename, station):\n",
    "    \"\"\"\n",
    "    Read hourly AirBase data files.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    filename : string\n",
    "        Path to the data file.\n",
    "    station : string\n",
    "        Name of the station.\n",
    "       \n",
    "    Returns\n",
    "    -------\n",
    "    DataFrame\n",
    "        Processed dataframe.\n",
    "    \"\"\"\n",
    "    \n",
    "    # construct the column names    \n",
    "    hours = [\"{:02d}\".format(i) for i in range(24)]\n",
    "    colnames = ['date'] + [item for pair in zip(hours, ['flag']*24) for item in pair]\n",
    "    \n",
    "    # read the actual data\n",
    "    data = pd.read_csv(filename, sep='\\t', header=None, na_values=[-999, -9999], names=colnames)\n",
    "    \n",
    "    # drop the 'flag' columns\n",
    "    data = data.drop([col for col in data.columns if 'flag' in col], axis=1)\n",
    "\n",
    "    # reshape\n",
    "    data = data.set_index('date')\n",
    "    data_stacked = data.stack()\n",
    "    data_stacked = data_stacked.reset_index()\n",
    "    \n",
    "    # parse to datetime and remove redundant columns \n",
    "    data_stacked.index = pd.to_datetime(data_stacked['date'] + data_stacked['level_1'], format=\"%Y-%m-%d%H\")\n",
    "    data_stacked = data_stacked.drop(['date', 'level_1'], axis=1)\n",
    "    data_stacked = data_stacked.rename(columns={0: station})\n",
    "    \n",
    "    return data_stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the function on the data file from above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "filename = \"data/BETR8010000800100hour.1-1-1990.31-12-2012\"\n",
    "station = filename.split(\"/\")[-1][:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BETR801'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "clear_cell": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1990-01-02 15:00:00</th>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "1990-01-02 15:00:00     59.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = read_airbase_file(filename, station)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to use this function to read in all the different data files from AirBase, and combine them into one Dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Use the `glob.glob` function to list all 4 AirBase data files that are included in the 'data' directory, and call the result `data_files`.</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "clear_cell": false,
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "clear_cell": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/FR040370000800100hour.1-1-1999.31-12-2012',\n",
       " 'data/BETR8010000800100hour.1-1-1990.31-12-2012',\n",
       " 'data/FR040120000800100hour.1-1-1999.31-12-2012',\n",
       " 'data/BETN0290000800100hour.1-1-1990.31-12-2012']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_files = glob.glob(\"data/*0008001*\")\n",
    "data_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "<b>EXERCISE</b>:\n",
    "\n",
    " <ul>\n",
    "  <li>Loop over the data files, read and process the file using our defined function, and append the dataframe to a list.</li>\n",
    "  <li>Combine the the different DataFrames in the list into a single DataFrame where the different columns are the different stations. Call the result `combined_data`.</li>\n",
    "\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "clear_cell": true,
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "dfs = []\n",
    "\n",
    "for filename in data_files:\n",
    "    station = filename.split(\"/\")[-1][:7]\n",
    "    df = read_airbase_file(filename, station)\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_data = pd.concat(dfs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FR04037</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04012</th>\n",
       "      <th>BETN029</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 01:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     FR04037  BETR801  FR04012  BETN029\n",
       "1990-01-01 00:00:00      NaN      NaN      NaN     16.0\n",
       "1990-01-01 01:00:00      NaN      NaN      NaN     18.0\n",
       "1990-01-01 02:00:00      NaN      NaN      NaN     21.0\n",
       "1990-01-01 03:00:00      NaN      NaN      NaN     26.0\n",
       "1990-01-01 04:00:00      NaN      NaN      NaN     21.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we don't want to have to repeat this each time we use the data. Therefore, let's save the processed data to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "combined_data.to_csv(\"airbase_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with time series data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We processed the individual data files above, and saved it to a csv file `airbase_data.csv`. Let's import the file here (if you didn't finish the above exercises, a version of the dataset is also available in `data/airbase_data.csv`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "alldata = pd.read_csv('airbase_data.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only use the data from 1999 onwards:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = alldata['1999':].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Som first exploration with the *typical* functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1999-01-01 00:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 01:00:00</th>\n",
       "      <td>33.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 02:00:00</th>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 03:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BETR801  BETN029  FR04037  FR04012\n",
       "1999-01-01 00:00:00     27.0     13.0     68.0    105.0\n",
       "1999-01-01 01:00:00     33.0     15.0     70.0    116.0\n",
       "1999-01-01 02:00:00     24.0     16.0     72.0    114.0\n",
       "1999-01-01 03:00:00     26.0     18.0     72.0     91.0\n",
       "1999-01-01 04:00:00      NaN     19.0     74.0     81.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() # tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 122736 entries, 1999-01-01 00:00:00 to 2012-12-31 23:00:00\n",
      "Data columns (total 4 columns):\n",
      "BETR801    106628 non-null float64\n",
      "BETN029    111989 non-null float64\n",
      "FR04037    120384 non-null float64\n",
      "FR04012    119448 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>106628.000000</td>\n",
       "      <td>111989.00000</td>\n",
       "      <td>120384.000000</td>\n",
       "      <td>119448.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.728969</td>\n",
       "      <td>16.42093</td>\n",
       "      <td>40.040005</td>\n",
       "      <td>87.993261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.708333</td>\n",
       "      <td>12.31968</td>\n",
       "      <td>23.024347</td>\n",
       "      <td>41.317684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>34.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>339.000000</td>\n",
       "      <td>105.00000</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  106628.000000  111989.00000  120384.000000  119448.000000\n",
       "mean       46.728969      16.42093      40.040005      87.993261\n",
       "std        21.708333      12.31968      23.024347      41.317684\n",
       "min         1.000000       1.00000       0.000000       0.000000\n",
       "10%        21.000000       4.00000      14.000000      37.000000\n",
       "50%        45.000000      12.00000      37.000000      88.000000\n",
       "90%        75.000000      34.00000      70.000000     139.000000\n",
       "max       339.000000     105.00000     256.000000     358.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(percentiles=[0.1, 0.5, 0.9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Quickly visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba14531978>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AWIBABgDAAgSyg7iOBQDoKALZQVzHAgB0FIEMIOxQAwChiEAGEHaoAYBQRCADAGABAhkA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\n+dMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4868e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='box', ylim=[0,250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba153ede80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FX//61yorK1Nubq6ioqL09NNPa+rUqYqJibnlDZaUlGjIkCHavHmz/vznP2v58uWm5d0d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba259871d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['BETR801'].plot(kind='hist', bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13445fdbe0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lmDFjRnCZadOmAQCmTZuGYcOGAQDy8/Ph9XoBAIWFhcjJyQk2HASAL774wtBSZIAlyURE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\n0RbStFJIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1344583438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fba1590af60>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AMZxh0X44itaHkUnLSKck2+PW7/24OMMWMZwxRjGzmAQN3SAIgpjskBiGOXTD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9jVM1c2oOkKJqaLWFlqkKYFIWa4Q1OK6vg4UC7DhQQrhhludZzY7nNhsBReMjNMZBLT4wWeIt4f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Izu6tYc++Mfzg4QswdO3yrjufQDdUgX+2U7mcBtfxE8ehqDKmp7f/updb2wLD3/72t7GwsIBv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rBwjGPGObLYexzBSeWGdZx8bhHFb/zPrcYBLYLhWO92MlzBYEJTMdXIjjHMOjVJhVvRREX3\nyNj51vYGgM2G+WguiVFGwi2O349fYiLq8HCspaUEWh3H09UESctguKpNMFzroUzCpeRgPnKY2h82\nAa0n17l8Y/+gZMK0Ie5dpJERQ09JndJGc7ibKNRq4h94iMTHn+j1QNiFazDJkLqj7HVOemtJ3bHj\nWFGQNKx3SoeiQnQYdWoO5TsnczpvvuwmE03+jutwTypJdJW+EWkJTqcXh250F51ai0JSODSE9ZGU\nmcrdwxw6w5UZVuj0nY7d9AUGrdono6BbIkkSmshI1OHhHh2gK3Ob5AzKWm4BFtQ7gogYQ+fT4349\n8Bc8Mu4et0N3BcO5HoanuCTbhjtLYMrMFfgpNYRrz96Ny5kS6GfkbxMe4IZB13b4vNlJM7gm9XIC\nNUZGRZ7e1NZdDBoDFpsFi+30MbTt4bq57HhMfMtxzF1fU6AmoMtlEinO344rM2y2NvK/fUvZWbrX\na7vhQVqSMxzTLPP2OzJwCoVERX0Ty7/JweCvYljuD9R8/y2NOUfQxMZ1Wb6pM4KmTAO7ndL33wW6\n1xVf2VjFumNfs+rIJ7y69+1u11HXhjnHfRceY3D9MZBld7BidA4sqdu6BWtdLeWrV2HKPkDtjz9Q\n8vabyLKMLMs0ncxHHR7htT/VpqSCQsFgqQpboB8afxVTJ0ahMdd4nV3uCq4SFPORw0SF6EjTOALY\nQkUgmohIFE1mIpu6VybRYDWhlBzXyOo20zZdZRJtg2EA/bAR2M1mKj9fg6TRYBh5qhkspyYPrUpL\ntL71bkGMIYpr005pXLfKEp8BCrW6x8Zfe4N/Sirq8HDSGk6w92AhjZZTDfCV1SaU5SVU+AUxbEDv\n7JZIksSAuCCq6pqoqGl0y6sBVBocPi+iFzLD3tJ3Ii1BK/pazXBbonQR2GQb5ebT7zDbQxXsyAyr\nz2K98LlAtTsYdpRRtMx6FNQXIiERo+88GIbW9aUJxjjUCpXHzHCFuZJAjZFYw6ma3ymxF6DpyyPL\nvUCpUHaaeVFICmYkTOHpSY92e8x7exicTXTe1g17UybR8lC6EwwH+Bkd3fbWjrfg4VSZRHJgIgAl\nJkdd64pDH7G7bB+rDn/SqoynM+ZcNgqbpMBUWMTGPYXIssxnPxyj2WrnpmF6bKWnehB6Q+UhYNJk\ndOlDQJbRxMZ1a/St66YRYH9FtlvLuquog0OoUhkIrixgRH0uKJXuLKVfTAx+iUk07N9H1fp1YLcT\ndu08tAPSqN/1E7U//oCtphp7fX2XgliFvz9+CYkYq4t5+NbR3PrnSYTjzNb2RjDsnIBmytqHLMvE\n2muxSCo+OVDrLk+JbyxF0mhQdPHGx9RsIkBjRK/WnZYZdu2mtZQhdBF21dUO27KMYcRI941EdVMN\n5eYKUgKT2p2EGRNwajfuRL3nKWZ9GUmSMI6/ALXdSmrVUXYdPjWV74OPtqGSbRj6Jfaq5KmrVOJQ\nfnWrYTfr9UPQ+6sI8/FU2pb0zUhL0OdqhtviumB7Wyqh0OoImDjZY3mAwIHLkbsyw66shyzLFNQX\nE64L7VaAqlKoSApIoLC+uFXzCYDNbqOqqZpQbSgROsd2pVqhYkbC5DM5lPMeo3NwR72XpRLFplIM\nar174ma7SB7/4RWubJk3smyVjVWoFSr6B6Vg1Bj4oXAb7x1cTWbJLlSSklpLHVuKdnhtW6/ToAwN\nJ8Ray1vrDvLCqj1syy4hLtxAfLFDZir06mvwT0klYMKkTt6t60gKBVG3/BZt2kBCL7+yW9vTLn/n\nKh/qbumIUafmpDYSf7uFkMYqjGPHoTSeCt6CZswEu52qDetAkggYP4GoW28DhYLqb792l0hovGie\na4nOWbrQeNRRo+wutYjv+WBYqdejG5xO49GjVH/9FZSX0BAQxsH8WraXniox0Q1O7/J30dBsQqfW\nEuQXSFVjVasdylOZ4dMVdxT+WqJ/fzv+/ZIJvuhiNp78ked++g8HnMo9nnoLWmoLH6rx3Ijc1wmc\nOAnUGmaU/8SuHY5jrmmwUJ3jSJIkDu3dhlJXMLzvaAX64SPxT0nFevPdlDVKjBkY0aOyal2lb0Za\nghaZ4bOr2eoJdzDsZROdJElE/eZWh5MXeKSmqQaDWk+YszzBlfVwCcC3zNx2ldSgZGRkd3Oci6qm\nGuyynVD/EPRqHdPiJjI39Yp2txkF3mN0Z4Y7D4YtNgsV5spOmxBbBg3dyQy7ZOMOOEdTe6KqsZqT\n9UUkGOPwV/lx46B52GQbPxRuI8Q/mD+P/ANqhYrvnYNdvEUfG42/zUJGtB/7j1Yiy3D5+Djqtm9D\nYTAQMvsSEh58BG1y50OYuoMqMIj4+xZgHDO2W68vcfq7QSGOzHV3g+Hx6VHkax3fdZMugIjrrm/1\neMAFE9E79Vx16UNQBQahDg1Fnz6EpmN51GzaCHhuxPVEy9IFAEsbebGeJvKmW1Do9ZQtfxdsNmIG\np2LUqfmpvMVzbrjJ4+uXZq/kXzsXtypHsdltNNqa0Kv1pAYlY7E3s6Nkt/vx2qY6NEqNx5Hw/olJ\nJDz8GP5JSWwu3MbRmuOszXOMavYUDLdMSh2syW31UJmpgkd+eJqlB1Z2W3GlqzQ7NcCbu6D1Dw6p\nschfXY+/3ULajjVU1ZrZf7TCXc/t343dkq4QH2EgLlzP9uxS9tQoCbrrfjZVOpI7E4Z4t+PZW/z8\np1ecq/TxzHCkrmuZYYF31DTVEqoNOW1og7t5Tt9585wn4p2SYUUNJQwJG+T+u6vUJcypW3utUy1E\ncGYYNM5g2Isx5keqjyIju8tjPHGGiWFGRgzlgyOfsr1kFzMSpnh83o6S3cjI7tHag0PTmDfgKsrM\nFVza7yK0Kn/mDbj6tIEHneHaHv/9xEi21PhTVm1mkLWMorpaAqdf2ONNXD1NsakUhaQgLTiVHwq3\ndTsYHp4aRvBtV5H/di3Jc69wT2xzIUkSUTfdQplWS+C0Ge6/G8dfQMO+vdT/tANlYKB7wpi3tCsv\nZjCiDOw5JYmWqENCiLnjLio/XwvIhF14IXcog9n4UyCqmHqiZkz3KOlmtjayvXineyhOcmAScGrg\nhl6lZWbCFDYVbGHD8W8ZEzUChaSgxlLrlkfriIZmE4XOPozqphrUCrXbR7alZWa4pLGc47X57mbb\nDce/oaqpmq3FOygylXDfqDt7tSnOYrPwt63POoYqaQz8bcIC/LqwWxgweQp5m7cTfzSLw++tYm/U\nSBKcSh9d3WnoKgqFxG1XpvPE2ztY/MmpOQVhgf6kxvbOb9Bb+rbnOY+R3EM3fNvk5S2h/sEoJAWl\npvLOn3ye0GxrxmQ1n6Zv6S2N1kYabU0EagLQq3SoFWp3sHFq8lz3755dJRBtv7MKp6ZwV4c4CDrG\nXSbhRWY4y5mpHdJZzXLLzHA3omGjxsDgkDT2V2Szq3QfgX5GAjQBhGlDMDWbkZHRq3VkluxCKSkd\nnfROpsS1ngQ3IWZMl+27xio3FxZw4RRHyVTR/xYDp0ZWny1kWaayscrjeSDLMsUNpYS3KCfq6s1A\nSxKTIkn8630eH1caDETd8rtWfzMMH4Hk54/c1EjULb9DqevaKHmlwYAmJhZz7hEasvbTXFaKduCg\nXg3edAPS0LXQcx6Aa7t8qMfXAByqPOJW7NhWvNMdDLvKvPRqHSH+wYyNGsnWoh0cqswhLSSVeksD\nEYGdD/E4WnMMmVMZ536BiW4Zwba07N2xS/DWgfd5YPTdNDSb2Fb8E5G6CEL8g8iuPEx+fUGPDO7x\nxN7yA1Q31eCv9KfOUs/hqhwyWkzW7AxJkuh322858sgjBO74lvoEJXGWcpSBge5hKb1JXLiBO+dm\nsHlvETKOe/qJGR1r5/sCEQz3VaS+XSahVCgJ04ZQ1keC4cL6YsK0oWg6kNDqaczWRuotDYTrHCUN\nH+asYWvRDh4bfx/BXurztsSlJBHkF4AkSQT7Bbprhgu7IKvmiTD/ECQkysytv7Pyxkr344Kew+jO\nDHccDMuyTFb5QfyV/u4LvifONDMMMDZqBPsrslmyf6nzbSSuH/gL1uZ9iclqYlDIAArqixgWlo5e\n7V13d3FDKQEaIzp1x8oGukGOi3bdT5kETpmKvdFM/a6dqCMi8e/XtdKIRmsT1U01PaZv/XX+Rj7K\nWcs9o/7Y6nsoNZVjUOuxylZMVjP9g5LPuGa4uyj8/Ij+7W3YLU3o04d06z2CZ82m5K03KHjhnwAE\nTb+wJ5fYY2RVOPSmVZKSnSV7uLb/lagUKuqdwbDO+dscHJLG1qIdFJtKiTFEISMT0E7zXFtczcTD\nwtLZU57luUQC3OpOAJPjJ/J1yRb+tWsxNrsNu2xnduJ0/FX+ZFceJrN4V68Gw5nFjsExc/tfxnsH\nV7O/4mCXgmGAwLBgskdexpjtq5hz4ksAgqZf2uNr9URGcigZyX1Lqahv7sEL+pzOcHtEaMNosJpa\naT6eDY5U5fLU9ufZcPwbn9pdcegjnt7+PKZmR53YvvJsLPZmMkt2dev9TnVBO+7Og/yDqGt21IVV\nNVWjV+nOSPdXrVQT5BfoMTMcJjLDPYpLTaK+EzWJUlMZ5Y2VDAzpj7KTm9/WahLdi4aHh2dwdepl\nzEqczkUJ01AplCw7uIqqpmqa7VZ2l+0nWh/JVamXefV+VruVp7Y/z9+3PesOYDyhiYzCPzkZ04Es\nrNXV1O/ciWyxEDB+glfHI8sy+8oPsKlgK09ue44ntz1Hft2Zd/c32Sx8efw7gFY19ZWNVTy1/Xn+\ntnURXxz7FnBI3+lUWvyUmm4PIjkTDCNGEjCu67JwLgImTsYwyjGhMWDyFIyjzv4I9bbIskxWxUEM\naj2TYydgspo5UuVoXGuZGQbciYfqpppT0+e8KJPIqc5DISm4YdC1/HLAVcyI99y0KbVo7Loi9WLG\nR48mv66AwoZiJsWOZ0zUCNJD09CrdOwo2e215GhXqbPUc6DyMAnGWMZHjUan0pJVfrBbEn+zr51G\n+VDHMauTUwm59PKeXu45hcgM91WUfU9nuC3hujCogDJzOXp17xbeu2hoNpFXc5z00IHui+e6Y18D\nkNemMaw3kWWZA5WHsNibKTOXY7Aa3Fum24t3clHCNGotdZysLyI91LsOXZesWqBzelyw8781TTVc\n238OVtl2xltJEbowDlXl0GSzoFao2FuWRX59ASpJ2e3yDkH7uDPDnZRJHHAOOPHqd9KqTKJ7KBVK\nZiacUnUJ8Q9ixeGP6R+UzPxB88ipPsrIiKEdDippiUqhYm7q5Xycs5bX9r3N0xMf6TCjHDD+AhqP\nHqVu+1Ya9u8DTmnrdsaBysMs3vtWq79tK95BvPFKr17viR8Kt1HvrO0uadEUvKN4N1a7lXq7lW9P\nbgYgzhDt2LnxDz4rwfCZIkkSUbf8joYx49AP61xXu8RURoW5ksFe+rEzxdRs4q0Dy6mx1DIuahSp\nwcl8e3IzJ+sLGRQ6wJ180aucwbDfqf4K1+5ay+Zfq93KnrL9DAsf4i6DaLJZOFF3kgRjHDq1jqlx\nnfz+WtykqtR+zB/0S8ZEjkApKegf7NDPVkgKRkUOY2PBFjYVbGVa/MSe+UBasO7Y19hlO2OiRqJU\nKBkUMoCfSvdQ1FDilf58SyKCdYTfcTP1u4ehGzioT8cavuD8Pvo+zLmSGQY4WVfIxpM/YumC5mh3\nsNqt/Gf36/x375t85+xiz6s5waGqHMDRZNZdEfyuUmwqdTvlcnOle5iFSlJS1FDCyfoiVh35lFf2\nvO4edew6hq1FO9oNkFqWSQBE6sLdr0kIiHPrvZ4J4c5axzJTOT8UbuN/+5dSairHT+XXrr6moPv4\nKTVoFOpOyyRc27UDglM7fc8z1Rluj8mxE/jziD/wh6G/IVQbzLjoUV4Hwi6mx0/i8uTZWO3WTodx\nGMeMQ1KpqFz3OabsA/inpKKJ8K7UYV+5YzrdpUkzeWD0XejVpzJx1U01/FiY6XEyXH1zA1s8PL6l\nMBO1QoVCUribgmVZZlvJTlQKFfeP/hPXD/wFt6Rfz/DwDMBxE2G2mtlSmOlucD1XUPj5YRw9BoW6\n48ar707+wNPbX+A/e17v0pCVM2Ft3pdkVRxkYHB/rkq91N007PqMXTtoBmdNfoDGiIREVWP1KVm1\nFjf2ByuP8EbWe2wq2Or+W17NceyyvePSiBa0zAy7TryBIf3dgbCL2UkzMKj1fJS7tsd/E/vKD/D9\nyR+I0kcyKcahSe3SRd9TltXRSz0iKZUYR40+rYHzfERc/foqfXzoBpwKrD7O/ZwVhz/mi14uU1hz\n9AuO1zkCy49z1nK4Koflhz4EINgviPrmhm6L4Ldlc8HWVkFsW1oOr6gwV7r/7cq4bSnKJNu5Zbzd\nWeMF8EnuOpZmr+TVvW9hs9uw2q2sP/YNZaYKjlQ7tgFDnbW70+Mn88Dou9zKHT2B6wam1FzOvvJs\n99/7B6V4eongDDBoDB2WSciyTE71UYL8AgntaglMD0XDkiTRPzjZoxSVt4yOHI6E1Or33h5Ko5HQ\nOVdjq6ttNXmtM2RZ5kDFQbQqfy5OupCEgDhGRQyjzlLPwaocthfv5N2Dq1rJbLXkq+Pfs+zgKrKd\nmXgXTTYLRQ0lJBjjCdOGUNxQiizL5NcXUNxQQkboIBID4rkgZiyjIoe5S1lcJUvLDq5y+yFvKTWV\n8W3+Zq+Gn5wtdpftZ9XhT9Aq/dEo1Lx3cHWriZi9gc1uY0fJbgxqPX8cdgsBGiOh2mD8lBp3cHmi\nrgCAOKfMpFLh2NXyVCYRZ3Q8b38Lf5fr9NdeB8OuzLCy4yE+QX6B3DDoWqx2K29kvddjCaKaplqW\nZa9CpVBxS/qv3VrzQ8MHo1aoyCzZ5bNE0M+VvhtpneecS5lhs7URgO9O/uj+/67Q0Gzio5y1HKs9\n4fE5ByuP8NWJ7wnXhnLrkBuwyXZe3PUaJ+sLuSB6LOOjHXVvBT0wHaiqsZr3D33IxpNbqLPUs/rI\nZ6dlcnOqTwmvlzdWkFOdh59Sw+ykGehUWjYVbKHR5rjQuTJXWRWH+CZ/ExISebUn+DzvSzYXbuOz\no+t5cderZFUcJP76+04AACAASURBVCWwn3u7S6NUkxAQ16Ndtq4u+ML6Ig5X5RCtj+TvEx5kficj\niwXdI9Q/2Ll92/4I5FJTGfXNDaQG9fPqez5TneHeJNg/iAHBKRytOUa+M2Dx+NzZl6BLH4JCq8U4\n2jvN3xJTKRWNVQwKGeAOSCfEjHHIaTXVMCpiGApJwYbj37ab/c2rdZRRHatp7Wfy6wqQkUkMiCNK\nF4nJaqbGUssHhz8FcPuWthhbDEc5Vpvv9dhtgM+ObuCDI5+ycPsL7sl+fQFZltlRvIu3st7n3exV\nqBUq7h75e+b2vxyz1cw3+ZvafV25uZLVRz4742PJrjxMfXMDoyKHu79jhaQgRh9NsamUZruV47X5\nBGiMBPmdkuIK8gukuqnWXWrWskwiyC+QOEMMOdVH3TcfOdV5SEikdNKw6saZGfbmHM0IG8zUuAso\nbihh+aEPz6h+2HUtenn3EuqbG7g65bJWTdRalZaMsMGUmEo7PecEHdN3I63znXMgMxzsH4TKOR/e\nX+mH2WrmxV2v8knuOo9blW2RZZml2Sv46sT3PPfTK62c7dcnNvKf3a/zn92v80bWu0iSxG/Sf83I\niKHcOfy3BPsFEWuI5hcDriTO0Hor7Uw46Qyow3WhnKwv5Jv8Taw5uqHVmnOrj6FVOTrnj9XmU2Iq\nJTkwCY1Sw8iIoe7jj9JHUt/cwHcnf2DpgRWoJCV3j7iNUP9gNhz/ls/zHJ28rnrji5Nm0JuEO29g\nthTtoNluJT10IKHaYPxVZ28M5s+Z4REZyMhsK9rBysOfsKUws1UGJ6erGao+FgC3ZVLseACe++kV\n9pTt9/g8SaEg9k9/pt/CZ1tNXuuI/U75uZYjsxOMcTw98REmRI8hVBvCmMgRFDeUsLuNbbtsd2cU\nj9edbPXYCecOUKIxzq1MsfTASnJrjjEiYqjHEd0DglNRSArCtaEOLdwazzfzbSmoL0JCoryxkvXO\nngdfUmGuYmn2ylY3+bIs8072Ct488D6ZJbtotDXxywFXE62PZHz0GHQqLT+V7MYu22loNrE0eyUl\npjKyKg7x9Pbn+SZ/E6/tfZstRTtYcejjbgWBW4t/AmCcU9/aRawhCrts50hVLtVNNSQYWycJgv0C\nsck2dzKkbf9DeuhArLKNw1U5WO1W8mqPE2OIcitSdEbLzLA3XOUMWrcV/8Q/f/pPt3YAbHYbS/Yv\nY3PBVgobihkRMbTd2uaxzs/qrQPLWX3kM6xdHMTRk1Q31bD80Eccqsw5a2voLqKBro9yLmSGFZKC\nMG0oxaZSrh90LR8eWUN+XQH5dQVoFBou6de5ZM/Ggi3sK88mKSCBqsZqPjyyhlh9NGkhqRyoOMTB\nqiMAKCUl1/S/wi10PjCkP3+/YAGyLKNUKIlxB8PFZ3xcrveINUSTFpxKuDaUrUU70Kl11FrqGB4+\nhKqmakZHDudQVY47AHcFNGOjRrG5cBtqhZr5g67lhZ/+y4c5awC4JvVy+gen8Jv0X/P8zv/S0Gxi\nUux4CuqK8FNq3JOteotwbShh/iFuOTVPF3pBzzAqYhirj3zGp0fXuzVND1Yd4TfpvwYgx1lrnhLo\nXTDcqoGuD0bGIyOGIg2Zz5tZ77E270uGhXuW/5JUKpQGg1fva5ftbC7YilJSnvabdTUqgqNmc0fJ\nblY6GwJrLXV8nvcV/YOT3Znb47X5yLLs/vxcwXFCQDw2503swaojBPsF8eu0azx+zgOCU3h+6pNk\nVxzi1X1vk1OTR1pI53XfFpuFUlM5yYFJVDfVsLtsP9fZLF0anHCmfHniO7YW7SBaH+ku7SpsKGZ7\n8U7iDDHcOHgewX6B7mBRrVAxMnIYmwu2cqgqh6L6YrYW7aCmqZZSUzk2u41BIQPIrjzMsuyV6FU6\n5qRc3Kk6Skv2l2ezq3QvsYbo06TJXNnQLUWZACQGtH7cNaToWG0+WpX/aQ2c6aED2XD8G1Yc/hi9\nWkez3er9OQenMsNeXo81SjV/GXk7q498xr7yA5itZrYW7aCisZK5qZd7de6uzfuSY7UnGB05nLmp\nlztqo9t53aCQAUTqwikxlVLiHApztZdKMG05UXeSz3I30GA1MSR0IJf2u6jd58myTGbJLjYXbEVG\n5qKEaejVehbvfROT1UxWxUEeH39/l77/M2FvWRbbin/isn6z2m0kzK48zKaTW7ik30zCw9u/5olg\nuK9yDmSGAUZGDiOn6ijDwtIZHj6EOksDi3b8m8+PfUlaSCrJgYnk1xWy6vAnTIgZw/ioUewp289X\nJ75ncGgaG45/i16t43cZ86luquG5n17h7QPLeeKCB7lz+G/d4yYVknSaILpCUrhb6sO0IWgUanaX\n7WXRjjL+MPRmr8cJN9utvLr3LcrNFcQZY91d+jH6aBSSglmJ03n34Ad8cdwhrfSTsyZxZsJUKsxV\n7uxKapBDJzU5MJHBIWlE6SNICkjg3tF/YsWhDwnVhjDNKd/TLzCRuamXs7HgRy5JupBATYBPghul\nQsm9o+9k5eGPqbPUe79NKOgWjiEXA9hfcZAoXQRqpZodJbuZlTidCF04+8uzCdQYvdbK7Qmd4d5m\nREQGmwuSOVh1hOqmmlbb2d1lZ8keyswVTIwZ1yr4bUukLpwrUy7mo5y1PLntOczWRmyyzZ2lVkgK\n6psbqGysJlTrqPk9XpuPVqUlXBvqHqcrIXFz+q861U1WK1SkOG+CW/YRdERRQwkyMnHGaAYEJ7Pu\n2NfsLctiTNQIr15/pljtVnaW7AEca3YFwwecPQ4XJkxpV898bORINhdsZVvRTuqctbmu+uspsROY\nm3o5/937JgpJwXVpc73ebdpfns0nuesoN1egkpTcOGjeab7QlexwNYq5kiIugv0c8mp22c7A4P6n\nNQMnBcSTGBBPYX0RDc0N6FRaRkV2rqThoquZYQCtyp8bBl3rvvE6VptPZslOQvyDme5Bxu1E7UlW\n53zGgKAUvjj+LWH+IVyXNhdtB5+lSqHikXH3YLKa+eeOl/nqxPdkhA32erfJxc7SvbyZ9R522Y6E\nxMm6QqbHT0ar8scu2/k2fzM/Fm7HJtuwyXYqG6vcg3/ezHoPrcqfRlsTyYGJHK05zvaSXUzwUGLU\nk5SYynjzwPtYbBb2l2cT7B9EUkAiv+h/BQaNnqrGat7Y/y4mq5n9FQd5P/nldt9HBMN9FPfJ18eD\n4cv6XQQtzrlAPyM3D/4VL+56lbey3uMvI2/n9f1LKTNXkFuTx+ojn7rrivOcNcK/HXIDQX6BBPkF\n8ssBV7GjZBd22Y5SofR6iIZCUjAxdhzbi3dyvDafrYU7mJU03avXZpVnk115GIWkoMxcgVqhRqvy\nJ8SpXzk2aiR7yrII9Asgp/ooJaYy0kMHEm+MJUwbQl7tcVSSkkRnNkOSJO4Yfqv7/eONMdw7+s7T\n7E6Pn+TRKfYmRo2BW4fc4HO75yszE6ZSY6lj/qBfUmYq53/7l7K9eCfJgYmYrGYujJ/itZJHKzWJ\nvhoNA0PCBnGw6ggHKg5xQYx3NcGeaLZbWXf8G+eN6bROnz8jfjKlpnL2l2cTqQtHr9a5m1OHhqWz\nu2wfr+x53X0T22A1MTC4P5IkEWOIZkBQCsPCh3gdTOjVOmL0UW6Fgtf2vU2sIYYrkme3+/yWo9VT\nncHwlye+IzWoX7eG9bRHTnUeKw59xI2D57GlKJOTdUXMS7uKWEM0WRWH3CONj1Yfwy7bUUgKsioO\nIiF53J1KDkwkUhfOzlJHIO2v9KfR1ohCUjAzYRpqpZq7RtzWpXWWmyt5M+t9mu3NBPkFcFm/We6G\nt5YkGOOI0kW4lT4Sja2D4ZY3XO3tdikVSu4f/acura0VZ5CccgX2V6deRnblIT7OWUtqUDLx7Rzn\n6pzPyKnOc2sg35z+6w4DYffyJAUGtZ4bBv2SF3b+l40nf+xSMGyz2/goZy1KScEfh91CbnUe6459\nzaGqHOINsSzNXsGR6qNoFGp3eeCgkAFcl3Y1J+oKeH3/MiyWZq5IvphxUSP565ZnWHv0CxKMse4b\nq+zKw6w+8hm/y7jRrZR0phQ3lPK//Uux2CzMiJ/MgcrD1FnqyCzZycGqw8xJuZTvT/6AyWpmatzE\nDuVXezwYXrhwIXv27EGSJB566CEyMjJ62sT5gfvk65sT6Dqif3AyFyfNYN2xr3nkx6cBuCB6LA1W\nE2WmcvoFBnJh/BR+LNxOrCG61fScybHjmeysO+wqv+h/JZcmzeTBzU+wrWQnMxMdGY/Kxipe2r2E\ncnMFKYH9uGP4rfgpNciyjF22s905JGNOyiV8lLOWZntzq5o0lULF7cN+AzguZB8c/pQ5KZcAp0YY\nJwYkdFmOSnB+0D84hQVj7gYcTadalZbM4l3uZqMxbeojO6bvNtC1JD00jQ+OOMZMn2kw/Fnueoob\nSpgUM44wbedTqxSSgl8PvMb97zJTBX/bugiFpOCCmDHsLttHsamUMP8QNEoNQf6Bbp/jahjrKjMT\npnKg8hAKSUFlYzVZFYcI04byUc4aGppNjI8azYSYMbxzYIX7Bj/GEE2kLpyxUSPZXryTR358GrVC\nxbUD5jAxZhw2u63VNnNRQwmv7n2LqXETmR4/iRJTGf/ZvYSKxipi9FHcMOhad8ZUISkobCjm37te\nw+TMdj+9/YVWa44zxHCyvpDihlKC/QPJrTlGYkC8x8y7JElclDidZdkrHcccN5WG5gZCtSHuLHtb\n7LKdRmsjaoUatVLt7qVQSApsdhtvZb1Ho62RGwfNY1z0KI+fr0ap5r7Rd7I270skJLesmotg/1PB\ncG/oIUuSBAoFUhcyw20J9DMyf9Av+e/eN3kz6z0WjLnLrQoBuIPg5MBE9Go9GWGD6BfYNf3+lMAk\nInXh7C3Pwmw1o1VpabJZeGnX/0gMiOPaAXPafV1myS4qG6uYGncBg0IG4K/0Y92xr/k+/wfy6wsw\nWxsZFpbOrwZec9rvI0wbSlnyxVQ11TArcRoKScHFSTNYm/clizL/zeXJsxkWPoS3st6n0dqIsp0b\nf5vdxpcnvmPjyR+ZlzaXjLBBfHXie9Yf+5omLxpTZ8RP5pr+V3ANjt/cN/mb+Cx3vfu3ekH0WK7t\nf2WHu689GgxnZmZy/Phxli9fTm5uLg8//DDLly/vSRPnDW5dQ0UfvuJ1wCVJMzFbGzlZX0iMPopr\n+l9xWpnDwJD+PW5Xp9YxJGwwu8v28fiWZ7DLMjq1lnJzBRG6MHJr8hwlG9FjWHH4I+ot9TQ0m4g1\nRDMjfjJfn9hIraXO49jjWEN0q4ula4Rx/y5uSQnOT9RKNSMjMvihcDv7yg8Qo49yN396Qytf3odd\nQ4QunHBtKFkVh/ipZDcJbTJ53iAjs714J1/nbyRCF8bc/ld0ay3hulCu6X8FVruVtOBUpsROIN4Y\ny4ToMT1WmjQuepQ7mJuZMJW3DyxnWfZKFJKCUP9gthbvYGfpHix2h9SWhES0PhKAGwfNIzWoHzuK\nd5NfX8jKQx+zq3QfudV5XJFyMRmhg7Fj582s9ygzV/BhzhrCtCGszfuSisYqEo3xHK/L558//YdL\nki5kduIMkgMTuTB+Cl/nb0Sr8ufq1MvYVbqPZqf9OEMMMYYo3ju4mj1lWVQ1VWGX7Z0OfhkbOYLP\n876ksrGKIWEDOx0f/p/dr3Ow6ggahZoZ8ZPZUboHBRLz0q4mu/Iwec6a2LFe3BD6q/y5xsNvwFUm\nEW+M7bXhQZJCAWeoxT4kbBDT4ybx7cnN/HfvW1yTejl+Sj/qmut4/+BqwNF8lxKU1L01ShJjIkey\nJm8DPxZmMjQsnfXHviav9jgDnJrI9ZaGVqpPpeZyPs1d58zwOxJIiQHx6NU6DlfnAjBvwNVMjh3v\n8XyZ3abx+9J+F5FgjGPZwVV8nPs5H+d+DjgSVmHOhtPKxipkGUxWE6sOf+LeKV6avZJofSRHa45h\nUOuJN8Z6PF6VpGJy7HiGR5xKurqOY1DIANYd+5phYekO2cdOzvUeDYa3bNnCzJkzAUhJSaG2tpaG\nhgb0QtC560jnRs2wJ5QKpce70N5mbNRIdpfto6KxCoWkoKqpmjGRI7h+0LU8u+MlthRluhsxFJLC\nMdEncgQKScHoyOF8k7+JWC+n+QyPyOBEXQGT47o/HlVwfnFRwnQarU1YZVuHF5h2OUfKJACuTLmE\ndw4s542s987ofYL9gvjtkPln1GDWshxpXtrVZ7SezhgVMYy1R7+gvLGSK5JnMy5qFE9vf4H65gZ3\nk1mYNsSt6yxJEhNjxjExZhxZFQd5Zc8b7rKt1Uc+Y/WRz9zvPTC4P4eqctxT+MZHj2b+oF9ysPII\nS7NXsjbvS1SSillJ07ky5WJUChWDnMMhJjoHNbhw7UysyXMo5UTpIk57TluUCqXbXlJA51nL4RFD\n8FNqyK05xvrj37h/sy/t/h/g0FS/Lu3qM74pCfIL5Irk2T0ymMgjSmXr4RvdZE7qpZQ3VrCvPJuF\nmf9q9dj0uEndDoRdjIkawZq8DXyYs8bduB1viOGSfjMpNZXxxLbnTlN7UkgKrk651K2drZAUDA5J\nI7NkF5NixzOlG9e3IWGDeGTsPXx+7CuqG6tJCIhjWpxjKt+qw5+wsWBLq+ePihhGQkAcH+Ws5WjN\nMYaHZ/CrtLmn7QJ4S6whmt92oRywR4Ph8vJyhgw51T0cHBxMeXm5CIa7wbmgJtFXyQgbxFUpl5IY\nEIef0o995dlcmDAFtULF7zNu5uv8jTTbmhkekUGQXwC7y/YzOdZxss9OnIFGoWZ0pHfNLFqVP/PS\nrurNwxH8zAjXhXLLkOu79dq+rDPclpERQ4nRR/L9yS3ujGRXCdQYmZk41V2neC6gVCi5Kf06cqoc\nzWkKScEdw27lcHUuM+Ins6UwkyAPtcHpoQO5cdA8rLKVIaGD+Tr/e0zNjjKHIL9AZiVO40DlYUfj\npV8AFyVMAxy7bA+P/Qvf5m8mPcxRM6tSqLgy5WKP64zQhnFVyqWUmMoI04YwI36KVz0aA4JT3FnG\nzpgcO4HJsROoaarju5ObyQgbhF12ZPwlSWJ63KQe+W4lSeLipM7Vi87IhkLRaixzd3Fdh7YX73TX\nsktIDI8Y0iPqPmHaEOYNuMotJahRapjpvP4F+QUxI36ye3oqgFJSMD56zGklGZf0m0m4NpSLvKjT\n94RBo+eX7STFRkUOx2q3IuO4vx8UmsbIiKHIsoxKUmHUGBzKND50cpLcg2NLHnvsMaZNm8aMGY6U\n+a9//WsWLlxIYqLnu7WysvbF6M936nf9ROF/XiLy5lsInDTlbC9HIBD0Ab767ABHshxNRFMvGcDg\nYac34QgEgp7n6H1/QWkMIPGxv53tpQjOgPDw9lWmejQzHBERQXl5ufvfpaWlhId33DXoaWHnO+Gz\nptFv1rSzvQyBQNCH+NUtHW9jCwSC3iH8rSVnewmCXqRH9+AnTpzIhg2O+qOsrCwiIyPR6byb8CIQ\nCAQCgUAgEPiaHs0MjxgxgvT0dK677jqUSiWPPfZYT769QCAQCAQCgUDQo/RozbBAIBAIBAKBQHAu\n8bOdQHf48GHuuOMObr75Zq6//nqKiop46KGHsFqtqNVqnn32WUJDOxdw7yn7u3bt4tlnn0WlUuHn\n58eiRYsIDm5fqLw37LvYtGkTv/vd7zh48GCv2fa0hgcffJD9+/e7j/vWW29l6tSpPrNvtVp54IEH\nOHHiBAaDgX//+98Yjb1Xs97W/t13301VVRWyLFNTU8Pw4cP5+9//7jP7mZmZvPDCC6hUKnQ6Hc8+\n+6xPj//o0aM89thjSJJEv379ePzxx1H0olrKokWL2LlzJzabjdtuu42MjAzuu+8+ZFkmPDycRYsW\noVb37qCUlmv4/e9/z8yZM3nnnXdYtGgRmZmZaLW9q5LQ3mfw4IMP+swPtrXv+tx95Qfb2r/ooosA\n3/rBtmv45ptvfOoH2/4Gp02b5lM/2Pb416xZ41M/2NZ+cHAwzz//vM/8YFv7KSkpPvODjY2NLFiw\ngIqKCiwWC7fffjsDBw70qR9su4Y//vGPTJ061ad+0CvknyEmk0meP3++/Oijj8rLli2TZVmWH3jg\nAXn9+vWyLMvysmXL5EWLFvnU/t133y2fPHlSlmVZfumll+RXX33Vp/ZlWZabmprkG264QZ48eXKv\n2e5oDQsWLJC/++67Xrftyf67774rP/XUU7Isy/LKlSvlb775xqf2W/Lggw/Ke/fu9an9uXPnyseO\nHZNlWZYXL14sv/baaz61f/vtt8ubNm2SZVmWX3nlFXnNmjW9Zn/r1q3ybbfdJsuyLFdVVcnTpk2T\nFyxY4PYBzz//vPz+++/3mn1Pa/j444/l559/Xp4+fbpsMpl8bn/BggXyunXrZFnufT/Ynn1f+sH2\n7Muyb/2gp+/AV36wPfu+9IOevgMXve0H27N/zTXX+MwPtmffl35w7dq18pIlS2RZluWCggJ51qxZ\nPveD7a3Bl37QW36WIrZ+fn4sWbKEiIgI998ef/xxZs2aBUBISAg1NTU+tf+vf/2L2NhYZFmmtLSU\nyMhIn9oHWLx4MTfccEOvZ8M6WoOvaM/+t99+yxVXOCYYXXvttUyfPt2n9l3k5eVRX1/fq6PK27Mf\nEhJCZWUlADU1Nb2akWvP/vHjx93HPHHiRDZv3txr9seOHcuLL74IQEBAACaTiczMTLfs4/Tp0/nx\nxx97zX57azCbzcycOZO//OUvvWq3I/uPP/44s2fPBnrfD7Zn35d+sD37siz71A+29zu02+3IPqpO\nbM/+d9995zM/6Ok7AN/4wfaOPzAw0Gd+sD37vvSDl156KbfeeisAhYWFREdH+9wPtreGiy66yGd+\n0Ft+lsGwQqFAo2k9rcjf3x9JkrDb7bz33ntcfvnlPrUPjq25iy++mIqKCubM6b3pbO3Zz8vL49Ch\nQ8yePdsnjtjTZ7Bs2TJuuukm7rnnHqqrq31qv6CggO+//5758+dzzz33UFtb61P7Lt555x1uuMH7\nyTg9ZX/BggXccccdXHLJJezcuZO5c+f61H5aWhrfffcdAJs3b6aioqLX7EuShL+/PwAffPAB06ZN\nw2w2uwOg0NBQysrKes1+2zWsWrWKqVOn+nQAUXv2/fz8fOYH27MPvvOD7dk/fvy4T/1g2zVMmzYN\nhULhMz/Y3nngSz/Y3nfgGqTgCz/Y3uf/0EMP+cwPtvf5p6Wl8e233wK97wddXHfdddx///08+OCD\nPveDbdfw0EMP9UmVsZ9lMOwJu93Offfdx/jx4xk/frzP7U+ePJkNGzbQr18/Xn31VZ/a/sc//sGC\nBQt8arMtc+bM4Z577uHtt98mLS2Nl156yaf2ZVkmJSWFpUuXkpqayuLFi31qH6C5uZmdO3cyduxY\nn9t+8skneeWVV1i3bh0jR47k3Xff9an9+++/n3Xr1nHzzTcjy7JPgpGvvvqK1atX8+ijj7ay56vM\nnGsNH374IY8++qjPbHZk39d+sK19X/vBlvYXLlx4VvxgyzXMmTOHe++916d+sOV5YLfbfe4H2/4G\nfO0HW9o/G36w5ed///33s379ep/6weXLl/PKK69w7733njU/2HINfZHzKhh+8MEH6devH3fccYfP\nbX/11Vfu/581axY7d+70me2SkhLy8vK47777mDdvHmVlZcyfP99n9l2MHz+egQMd4yYvvPBCDh8+\n7FP7YWFhjBkzBoBJkyaRm5vrU/sAmZmZDB061Od2AQ4dOsTw4cMBuOCCC8jKyvKp/aioKBYvXsxb\nb73FsGHDiI2N7VV7mzZt4rXXXmPJkiUYDAb0ej0WiwVwnBO+KOFpuwYXvhoz2p59X/rBtvZ97Qdb\n2m9oaDgrfrDtZ+BrP9jWvq/9YHu/QV/6wbb2fe0H29r3pR/MysqiuLgYgIEDB2K3233uB9uuwWaz\nuctUfDluuTPOm2D4008/RaPRcOedd54V+y+99JK7c3nv3r3069fPZ7YjIyP54osvWL58OStWrCA8\nPJylS5f6zL6Lu+66i/z8fAC2bdvGgAEDfGp/ypQpbNy4EXCcoL78Dlzs27fPfSH0NeHh4e4L3759\n+zock94bvPTSS3z//fcAfPjhh71aq1hfX8+zzz7L4sWL3Z3iEyZMcA8F2rBhA5MnT+41+57W4MIX\nGZn27PvSD7Zn35d+sK39s+EH2/sMfOkH27PvSz/o6RzwlR9sz74v/aCnc8BXfjAzM5M33ngDgPLy\nckwmExMmTGD9+vWAb/xg2zWYzWZCQkIA32amO+NnqTOclZXFP/7xDwoLC1GpVERGRlJZWYlGo0Gv\n1yNJEqmpqb02FKQ9+/fddx9PPfUUarXaLSnk+kH4wv7LL79MQEAA4MhGfP31171iu6M1zJ8/n1df\nfRWtVoter+fpp5/26Wfw3HPP8eSTT1JWVoZer+eZZ57x+Xfw73//m1GjRnHJJZf0it2O7P/f//0f\nzzzzDGq1mqCgIJ5++ulW2cretn/vvffyxBNPADB69GgeeOCBXrENsHLlSl5++WWSkpKQZRlJknjm\nmWd4+OGHsVgsxMTEsHDhQpRKpU/XMG7cOLZu3crevXvJyMhg+PDhvbZt2NI+OC48xcXFGI1Gn/jB\n9o7/rrvu4p///KdP/GB79hctWkRUVBTgGz/Y3hrmzp3LsmXLfOIHPX0GCxcu9Ikf9GR/yZIlPvGD\nnn6Dzz33nE/8oCf7zzzzDND7frCpqYmHHnqI4uJimpqa+NOf/kR6ejr333+/z/xg2zXceeedHDx4\nkB9++MEnftBbfpbBsEAgEAgEAoFA4A3nTZmEQCAQCAQCgUDQFhEMCwQCgUAgEAjOW0QwLBAIBAKB\nQCA4b1Gd7QX0FAUFBVxxxRUMGTIEWZZRqVTcdtttTJgw4WwvTSAQCAQCgUDQR/nZBMMAycnJvPPO\nOwDk5+fzhz/8gRdeeMHnEl4CgUAgEAgEgnODn1Uw3JL4+Hhuv/12li1bRlpaGmvWrEGpVDJz5kxu\nvvlm6urqgdUYRAAABBJJREFUuPfee6mvr8doNPLCCy+g1WrP9rIFAoFAIBAIBD7kZ10znJ6ezsaN\nG/niiy94//33WbZsGevXr6e4uJjXX3+dyZMn8+677zJhwgR+/PHHs71cgUAgEAgEAoGP+dlmhgEa\nGhrQ6XQcP36cG2+8EVmWMZvNnDx5kgMHDvDnP/8ZgJtuuuksr1QgEAgEAoFAcDb4WQfD+/btw2Kx\nMHXqVP72t7+1emzJkiXY7faztDKBQCAQCAQCQV/gZ1Um0XKY3okTJ3j77bdZtmwZW7dupbGxEVmW\neeqpp7BYLGRkZLB161YAVqxYwccff3y2li0QCAQCgUAgOEv8bMYxFxQUcOWVV5Keno7FYsFut/OX\nv/yFCRMm8P777/PBBx+gUqm48MILue2226ivr+f++++nrq4Og8HAc889h06nO9uHIRAIBAKBQCDw\nIT+bYFggEAgEAoFAIOgqP6syCYFAIBAIBAKBoCuIYFggEAgEAoFAcN5yzqtJLFq0iJ07d2Kz2bjt\nttvIyMjgvvvuQ5ZlwsPDWbRoEWq1mtraWv7v//4PvV7Piy++CIDNZuPhhx/mxIkT2O127r//fkaO\nHHmWj0ggEAgEAoFA4CvO6czwtm3byM3NZfny5fzvf//j6aef5sUXX+SGG25g2bJlJCQksHr1agD+\n+te/Mnr06Fav/+STT9DpdLz33ns8+eSTLFy48GwchkAgEAgEAoHgLHFOB8Njx451Z3kDAgIwmUxk\nZmYyY8YMAKZPn+6eLPfUU0+dlvWdM2cOCxYsACAkJISamhofrl4gEAgEAoFAcLY5p4NhSZLw9/cH\n4IMPPmDatGmYzWbUajUAoaGhlJWVAbQrm6ZUKtFoNAC8/fbbXH755T5auUAgEAgEAoGgL3BOB8Mu\nvvrqK1avXs2jjz7aavCGt6px7777LgcOHOCOO+7orSUKBAKBQCAQCPog53wwvGnTJl577TWWLFmC\nwWBAr9djsVgAKCkpISIiosPXr1q1iu+++45XXnkFpVLpiyULBAKBQCAQCPoI53QwXF9fz7PPPsvi\nxYsxGo0ATJgwgQ0bNgCwYcMGJk+e7H6+LMutssX5+fmsWLGCl19+2V1aIRAIBAKBQCA4fzinJ9Ct\nXLmSl19+maSkJGRZRpIknnnmGR5++GEsFgsxMTEsXLgQSZK46aabqK+vp6SkhNTUVO644w5+/PFH\nPv/8c6Kjo92vf+ONN1CpznnFOYFAIBAIBAKBF5zTwbBAIBAIBAKBQHAmnNNlEgKBQCAQCAQCwZkg\ngmGBQCAQCAQCwXmLCIYFAoFAIBAIBOctIhgWCAQCgUAgEJy3iGBYIBAIBAKBQHDeIoJhgUAgEAgE\nAsF5iwiGBQKBQCAQCATnLSIYFggEAoFAIBCct/w/LoKQG6a+AngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4df99a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[-500:].plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "<b>REMINDER</b>: <br><br>\n",
    "\n",
    "Take a look at the [Timeseries notebook](05 - Time series data.ipynb) when you require more info about...\n",
    "\n",
    " <ul>\n",
    "  <li>`resample`</li>\n",
    "  <li>string indexing of DateTimeIndex</li>\n",
    "</ul><br><br>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: plot the monthly mean and median concentration of the 'FR04037' station for the years 2009-2012\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1339a7be80>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4F/DFuMPvBZ7Sfn4KeN/GLg2lRfctbdDNJkFVuTVn+5GcH/QQlXDAsZAUawp2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1339a0d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc['2009':, 'FR04037'].resample('M').mean().plot()\n",
    "data.loc['2009':, 'FR04037'].resample('M').median().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a654cc0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OHYviZpjopNpuxSSM4qLlSF+DXlijJEu+xbesZpPgv75jO51jHv79hCMFi1sZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13701ba5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc['2009':, 'FR04037'].resample('M').agg(['mean', 'median']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: plot the monthly mininum and maximum daily concentration of the 'BETR801' station\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "daily = data['FR04037'].resample('D').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a552438>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lvPw9UuxPfSn++4rXBUFzTjH7zjIq95uOoi+pTtO6SXJ+tOGSYn9xk4sbqX4x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\nA6tNx/ErGwTXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a560588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily.resample('M').agg(['min', 'max']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: make a bar plot of the mean of the stations in year of 2012\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a49aa58>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bgaeq6ut91zLpkpwHvAP4XuA+4I1V9dkkU8C1VbVY95YW0MpV47NFkg8Bb6+qj83b/ovA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\n6JLUCANdkhrx/wEaQwlA96QyEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a4f4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['2012'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: The evolution of the yearly averages with, and the overall mean of all stations (indicate the overall mean with a thicker black line)?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a4a67b8>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HxSJL1q2TGQ/Mlqlx/WRq71h5JDFR5r/5D1l98mRrZ6/dSE1Nbe0sSJPJ5J7euHGjHDNm\njJRSyuPHj8u4uLiz7vfOO+/IBx98sM6ybt26yW7dusmUlBQppZQff/yxvPOOO6Q1K0v+b+lSOX7k\nSFl1IkNu37pVDh06VEopZXZ2tty9e7eUUkqz2Sx79uzp3n/IkCFyy5YtUkoply1bJufPn99gXhr6\nHYFdsoni8GV7LS+EIPylFzk25Uayn55Ht5Xvq6YLwPLjj5x6YQHVBw9iGnEVnebPxxATA4DvhOuR\nDgeVe/dSvnkzZV9vdg2I9/JCDD174D02Ae+EBIwDB7Tpm5WNIe12KnbupHTtWsq+/AppsaALCyNw\n5p34TZ6MITb2sqn9dlRNMbTyE088wUsvvcTKlSsBEBoN+ogINvz4I3fcfjvOMjNXBAZSXFhIdlYW\nEZ07u0fZ9PHxoU+fPmRlZdG3b18OHz7MmDFjABg/fjzXX3+9exC3lnRZR0F9p06E/fEPZD/xJIX/\nWkbw7AdaO0utxl5QQN6fX6F09Wp04eF0XrIEn+uvqxfYhFaL16BBeA0aROgTT2DNyHAVAJu3UPjO\nOxS+/TbawEC8r74a74SxeI8cicZkaqWzujBSSqr27qV07TrMGzbgKCpC4+vrfrrb68ohaniPprJh\nHpza17RphvWHiYvOuUntMAxVVVVNMrTytGnTWLp0ab1x8LNzcoju3x9Dz57YT52ic3Awx3fupNP4\n8Wh8fRFCkJ6ezk8//cSwYa4b43FxcaxevZpf/epXfPzxx5w8efJCf4EmcVkHfQC/xETKv/qa/Ndf\nx3vMaDxbYayL1iTtdor/8x/y//4azupqgmbNInj2A42+CekRGUngzJkEzpyJw2ymfOtWyjdvoeyr\nryj99FOEXo/X8OF4J4zFJyEBfU0tpy2pPnaM0rVrMa9Lco1iaTDgnZCA3+RETGPGtOubckpdRqPR\nPXzyzp07ufPOO91vy7qYoZW1Wi2/+93vWLhwIRMnTqy3XuPhgUdkpOulLhoN1pMn0ZhMVPv4cPPN\nN7NkyRJ3u/2///1vHn74YRYsWMCUKVPwaKX/d5d90AcI++MfsOzaRfbTTxO1alWrjgTYkiw//MCp\nBS9SffgwppEj6TT/mUsaAkBbUyv2S0xE2mxYfvyJ8s2bKd+82TXi4wsLMMTG4nONqxnIMy6u1WrO\nttxc13DF69ZRlZpaM4rlMILnzDnn06hKEzlPjbwlXHXVVRQUFJCfn39J6dxxxx0sXLiQfv36uZd1\n7ty5Tk09KyeHqOHD0RsMVGZlcfOMGdw6eTI3TZni3iY2NpbPP/8cgMOHD5OUlHRJ+bpYHSLoa/39\nCX/pJU7efz/5S/5Gp6efau0sNStbXh55f34F89q16CLC6fz3v+EzfnyTtlELvR7TsKGYhg2l07yn\nqT52nPLNX1O2eTMF//gnBUvfRBsSjM/YsXgnXIPpquFojMZGpS0dDpyVlTgtFmRlpevVebXTlZU4\nLZU4qypd8xbXMln1y7TtVA6Vu390DW7Wrx+d/m8ePhMnom+DY5srzefgwYM4HA6CgoKwWCwXnY5e\nr+exxx5j0aJFXHPNNYBrCOXXX3+dW2+9le+++w4/Pz8iIiKQUvLgI4/Qp08f5k6dSnVaGrrQULSB\ngeTn5xMaGorT6eTFF19k9uzZTXWqF6RDBH0A79Gj8J9+K0Xvvot3wlhMQ4e2dpaanLTZKFq5koLX\nXkdarQTNfoDgBx5odLC9FIaYaAwx9xJ0773Yi4upSE6mbPMWzOs3UPLxKoTBgGn4cLT+fu7g7Kys\n/CWQ185bLHVfkt0YGg0aoxHhZUTjaUTr60vwb3+L7+TEZh3cTGl76g2tvHw52ppOB2e26d9zzz2N\nHlr53nvv5cUXX3TPT5o0ifXr19OjRw+8vLx45513ANi+fTsrVq6kf//+XHX77UibjefmzmXS+PGs\n/PRT3vzXvwDXi1fuvvvuJjnnC3XBT+ReipZ4IvdcnBYLx266CWx2otesvqwu8Su+/57cBQuoTjuC\nacxown7/ezyiolo7W0irFcuuXZRt3kLFtm1IqxWNlxFh9ELj6flLsK6d9zIijDXzXkaEp6d7WmM0\nIjyN7mmN0bWt8PBQPW3agMvlidymJKXEaTZjO3UKabOh9fVFFxZ2zvtIbeKJ3MuFxsuLiEWLOHH7\nDHIXLiTipZdaO0uXzJabR96f/oQ5KQl9RARd3ngd72uuaTNBUHh4YBoxAtOIEa2dFUVpcUIItH5+\naHx8sBcUYC8owJGWhi44GF1wcKt0fe5QQR/A64orCLr/fgr/+U98rrkGn3HjWjtLF0XabBS9t4KC\nN95A2u0E/3YOQfff3yJNOYqiXBih0aAPDUUbEID91Cns+fk4ikvQhXVq8aGcO1zQBwh58LeUJyeT\n84c/YoyPb9Rr6tqSim+/5dSCF7EePYr31VfT6Znf4xEZ2drZUhTlPDR6vevF7YGB2HNOYcvMxFFU\nhD48vMUqbB3ySRTh4UHE4kU4y8o49dxztOR9jUthO3WKrMcfJ+Ouu5HV1XRZupSu//yHCviK0s5o\nTSY8useg79wZabVSffQo1qwspN3e7MfukEEfwLNXL0IefZSyL76k9LM2MUDoWUmrlcJ//YujkxIp\n++prgufOJWbdWnyuSWjtrCmKcpGEEOgCAjD07IkuKBhHSQnVhw/jKC9HWq3NdtwOG/QBAu+aideQ\nIeS+9BK2rKzWzk6DKnbs4NivbiLvlb9gGjaMmHVrCZn7oHodpKJcJoRWiz48DEOPHggvL5xmM8du\n/BXlycnNcrwOHfSFVkv4ooXgdJL9+2eQTmdrZ8nNUVZG9tNPk3HPvUibjS7/eJOuby5VL39XlHPQ\narXEx8e7P+np6WzZsgU/Pz/i4+OJjY3lySefdG8vpeThhx+mR48eDBgwgB9//LFOeg6HgyuuuILJ\nkye7lxUVFTF+/Hh69uzJ+PHjKS4uBuD77793H3fgwIF8WjNkeVlZWZ08BQcH8+ijj9bLu8ZgwBAV\nhTYwEKTk5KwHyHjgAaqPH2/aH6mphutszKclh1a+EMWrVsnU3rGy8N13WzsrUkopK77/Xh5OSJCp\nfeNk7pIl0lFV1dpZUpTzamtDK9favHmzTExMlFJKabFYZO/eveW2bduklFImJSXJCRMmSKfTKXfu\n3OkeIrnWX/7yFzl9+nT3/lJK+bvf/U4uXLhQSinlwoUL5VNPPSWllLKiokLabDYppWuI5ZCQEPf8\n6QYNGiS/+eabs55DamqqdFZXy4Jl/5YHBw2Wqf36N+nQyh26pl/L79e/xjshgby//JXqM0bTa0lO\nq5W8V17hxJ0zETo9USvfJ/SRRzrMWEGK0tyMRiPx8fFk1TTnrl69mjvvvBMhBMOHD6ekpIScnBwA\nMjMzSUpK4r777quTxurVq5k5cyYAM2fO5LPPPgPAy8sLXc3w7VVVVQ12wzx8+DB5eXnnHd1TeHgQ\ndM/ddN+0Eb8pN1zaSZ+hsa9LfAy4D5DAPuBuwAv4EIgC0oFpUsriJs1dCxFCEL7gBY7dMIXsp54m\n6sMPEHp9i+ahOi2NrN89RfXBg/hPnUqneU+3myGLFeVMi79fzMGig02aZmxgLE8Pffqc25w+DEN0\ndLS7iaVWcXExaWlp7nHts7Ky6Hpak2mXLl3IysoiPDycRx99lD/96U+UlZXVSSM3N9c9Zn5YWBi5\nubnudd999x333HMPJ06cYMWKFe5CoNYHH3zAb37zm0b3y9cFB7seIn355UZt3xjnrekLIToDDwND\npJT9AC1wKzAP+EpK2RP4qma+3dIFBxP2wvNUpaZS8OabLXZc6XRStHw5x2++BXteHl2WvkH4ghdU\nwFeUi1A7tPKePXvqBPytW7cycOBAOnfuzPXXX09YWNg501m3bh2hoaEMHjz4nNsJIeoE8GHDhpGS\nksIPP/zAwoULqaqqqrP9Bx98wPRWfn1rYx/O0gFGIYQNVw0/G/g/YGzN+uXAFuDcxXAb5zt+POU3\n3kjBP9/C++qrMQ4c2KzHs+XmkvN//0fFjp14jx1L+IsL0AUHN+sxFaUlnK9G3tJGjx7NunXrOH78\nOMOHD2fatGnEx8fXGyI5MzOTzp0788knn7BmzRrWr19PVVUVZrOZGTNm8P7779OpUydycnIIDw8n\nJyeH0AZGb+3Tpw/e3t7s37+fIUNcQ+bs3bsXu91+3oKkuZ23pi+lzAJeATKAHKBUSvk50ElKmVOz\n2SmgU0P7CyFmCSF2CSF2Xeq41i2h0/xn0IWGkv30PJyVlc12HPOGDRybciOWn/YQ9vzzdHlzqQr4\nitLMoqOjmTdvHosXLwZcQyS/9957SCn59ttv8fPzIzw8nIULF5KZmUl6ejoffPAB11xzDe+//757\nn+XLlwOwfPlybrzxRgCOHz+OvebhqhMnTnDw4EGiThv08L///W+r1/Khcc07AcCNQDQQAZiEEDNO\n30ZKKXG199cjpXxLSjlESjkkJCSkCbLcvLQ+PkQsfBlrejp5r/ylydN3lJWR9dRTZD32OB5R3Yj5\n9H8E/GZamxkgTVEud7NnzyY5OZn09HQmTZpETEwMPXr04P7772fp0qXn3X/evHl88cUX9OzZky+/\n/JJ581wt29u2bWPgwIHEx8dz0003sXTpUoJPq8h99NFHbSLon3doZSHEVGCClPLemvk7geHAOGCs\nlDJHCBEObJFS9j5XWq09tPKFyF24kKLl79F12b/wHjmySdKs+P57sufNw56bR/CcOQTPfkC9rF25\nbKihlZtGcw+t3JgumxnAcCGEl3BVR8cBB4A1wMyabWYCbXssgwsU8thjeMTEkPP7Z3CUll5SWrVd\nMTNm3oXQ64n6z0pC5j6oAr6iKC2uMW363wGrgB9xddfUAG8Bi4DxQog04Nqa+cuGxtOTiMWLsRcW\ncurFix93vzotjfRpv6HwX8vwnzaNmE8/bfYbxIqiKGfTqKqmlPJZ4NkzFlfjqvVftoz9+xE8ZzYF\nr72Oz7hr8J0wodH7SqeT4hUryPvLX9H4+NBl6VI1QJqiKK1OPZF7HsGzZuHZvz+nnn0OW15eo/ax\nnTpFxr33krtwEaaRI4lZs1oFfEVR2gQV9M9D6PVELF6Ms6qKnPnzzzv2fm1XzMq9PxP2wvN0WfpG\nu3tJi6Ioly8V9BvBEBNN6JNPUpG8lZKPPm5wmzpdMaOjXF0xp6mumIqitC0q6DdSwO234XXVcHIX\nL8aakVFnXcX333PsxhsxJ60neO5colauxKNbt1bKqaJ0XK05tHJhYSEJCQl4e3szd+5c9/YWi4XE\nxERiY2OJi4tz9+tvLSroN5LQaIh4+WWEVkv20/OQDgdOq5XcP/+ZjJl3odF7qK6YitLKTh97Z8+e\nPe4nYkePHs2ePXv46aefWLduHdu3bwdgw4YNpKWlkZaWxltvvcWcOXPqpPe3v/2tXp/5RYsWMW7c\nONLS0hg3bhyLFrk6Lnp6erJgwQJeeeWVevl68sknOXjwID/99BPbt29nw4YNzXD2jaOC/gXQh4cT\n9of5VP70E7kvvUz6tN9QtOzf+E+bRvSn/1NdMRWljWvOoZVNJhOjRo3C84y32nl5eZGQ4OrI4eHh\nwaBBg8jMzGzW8zwXVSW9QL433EDZV19T/J//oA0KosubS/FJUD1zFOV0p15+meoDTTu0sqFPLGG/\n//05t2ntoZXPp6SkhLVr1/LII480ep+mpoL+BRJCEP7C8xj798PvpptUzxxFaUNqm3fOVDu0clpa\nGo8++ugFDa28ZcuWs2535tDK52K325k+fToPP/wwMTExjdqnOaigfxG0fn4EnXHJpyjKL85XI29p\nLT20ckNmzZpFz549G3w/bktSbfqKonQYzTm08rnMnz+f0tJSlixZ0nwn10iqpq8oSocye/ZsXnnl\nFffQyuvXr6dHjx54eXnxzjvvnHf/efPmMW3aNJYtW0a3bt346KOP3OuioqIwm81YrVY+++wzPv/8\nc3x9fXnppZeIjY1l0KBBAMydO7feDeKWct6hlZtSexpaWVGUC6OGVm4abWFoZUVRFOUyoYK+oihK\nB6KCvqIoTaYlm4svRy3x+zXmHbm9hRB7TvuYhRCPCiEChRBfCCHSar4Dmj23iqK0WZ6enhQWFqrA\nf5GklBQCNMC/AAAgAElEQVQWFtZ7orepnbf3jpTyEBAPIITQAlnAp8A84Csp5SIhxLya+aebMa+K\norRhXbp0ITMzk/z8/NbOSrvl6elJly5dmvUYF9plcxxwVEp5QghxIzC2ZvlyYAsq6CtKh6XX64mO\njm7tbCjncaFt+rcC/62Z7iSlzKmZPgV0amgHIcQsIcQuIcQuVQNQFEVpXY0O+kIID2AKUO8tItLV\niNdgQ56U8i0p5RAp5ZCQkJCLzqiiKIpy6S6kpj8R+FFKWTukXK4QIhyg5rtxL5BVFEVRWs2FBP3p\n/NK0A7AGmFkzPRNY3VSZUhRFUZpHo4K+EMIEjAf+d9riRcB4IUQacG3NvKIoitKGNar3jpSyAgg6\nY1khrt48iqIoSjuhnshVFEXpQFTQVxRF6UBU0FcURelAVNBXFEXpQFTQVxRF6UBU0FcURelAVNBX\nFEXpQFTQVxRF6UBU0FcURelAVNBXFEXpQFTQVxRF6UBU0FcURelAVNBXFEXpQFTQVxRF6UBU0FcU\nRelAGvsSFX8hxCohxEEhxAEhxFVCiEAhxBdCiLSa74DmzqyiKIpyaRpb0/8bsFFKGQsMBA4A84Cv\npJQ9ga9q5hVFUZQ27LxBXwjhB4wBlgFIKa1SyhLgRmB5zWbLgV81VyYVRVGUptGYmn40kA+8I4T4\nSQjxr5p35naSUubUbHMK6NRcmVQURVGaRmOCvg4YBLwppbwCqOCMphwppQRkQzsLIWYJIXYJIXbl\n5+dfan4VRVGUS9CYoJ8JZEopv6uZX4WrEMgVQoQD1HznNbSzlPItKeUQKeWQkJCQpsizoiiKcpHO\nG/SllKeAk0KI3jWLxgGpwBpgZs2ymcDqZsmhoiiK0mR0jdzuIWClEMIDOAbcjavA+EgIcS9wApjW\nPFlUFEVRmkqjgr6Ucg8wpIFV45o2O4qiKEpzUk/kKoqidCAq6CuKonQgKugriqJ0ICroK4qidCAt\nGvSzs7MZNWoUn3zyCXa7vSUPrSiKogDC9TBty9Dr9bI22Hfr1o2HHnqI++67Dz8/vxbLg6IoSnsj\nhNgtpWyoB+WFp9WSQV8IUe9g3t7e3H333Tz88MP06NGjxfKiKIrSXlxWQf+0ddxwww08+uijjB07\nFiFEi+VLURSlLWvKoN+ibfp9+/blvvvuw2Aw1FsnpWTNmjVcc801XHvttbRkYaQoitJRtGjQNxqN\nvP3225w8eZIFCxYQFhbW4HZDhw5VNX1FUZRm0CpdNkNCQpg/fz4nTpxgxYoVDBo0yL1Oq9Xy4IMP\n1ttn6dKl7N27tyWzqSiKctlp0Tb9IUOGyF27dtVbLqVk27ZtvPrqqxiNRlauXFln/YkTJ4iJicHp\ndJKQkMCjjz5KYmIiWq22pbLeYUgp1VWWorQx7bZN/2yEEIwePZr//e9/rFixot761157DafTCcDm\nzZu58cYb6d27N6+99hplZWUtnd12zel0smHDBu644w5sNlu99X/+85+58cYb+fzzz92/uaIolxEp\nZYt9Bg8eLC+U2WyWfn5+tW/mqvfx8/OTTzzxhDx+/PgFp92RlJSUyCVLlsiePXu6f7uPP/64zjZ2\nu11GRka61/fs2VMuWbJEFhcXt1KuFUWRUkpgl2yiONzmg77dbpefffaZHDt27FkDPyA1Go28+eab\n5a5duy74GJezlJQUOWfOHGkymer9ZmPGjKmz7Weffdbgb+vl5SUfeOAB+fPPP7fSWShKx9biQR9I\nB/YBe2oPDgQCXwBpNd8B50vnYoL+6X788Uc5c+ZM6eHhcdbg/+67717SMS4HtQXluHHjzllQAvLA\ngQPu/ebMmXPe7ceMGSM//PBDabVaW/EMFaVjaa2gH3zGsj8B82qm5wGLz5fOpQb9Wjk5OfKPf/yj\nDAkJqROQOnXqJKuqqupsa7PZ5I8//tgkx20PvvrqKxkVFXXOwK3X6+WMGTPkd999V2dfp9Mpt2/f\nLm+77Tap1+vPmUbv3r2lw+FopbNUlI6lKYP+pdzIvRFYXjO9HPjVJaR1QcLCwnj++efJyMhg2bJl\n9O/fH4Df/va39R78SkpKYtCgQVx55ZW8/fbbl/2N3y5dupCent7guoiICF544QVOnjzJotfeIlMX\nwbxPfuaav2xh4t+28qdNh9CFx7L8vRVkZGSwYMECOnfu3GBa48aNQ6NpE/0AlDZMSklaWhr//ve/\nefHFFxvcpqEOBUrzaVSXTSHEcaAUcAD/lFK+JYQokVL616wXQHHt/NkMiB8kf9y9C522aYOFlJLN\nmzfTv39/QkJC6qxLTExk/fr17nlvb29uv/12Zs2aVef5gPbGZrPx6aefMmbMmHoPuU2YMIFNmza5\n50eOHMmtd91PYNxodp008/3xIjKLKwHw9dRxZVQgFquDH9KLsDslvp46RvcKIaF3KCOi/dm5eROv\nv/46W7ZscaeZkpJC37596xz3+eefp3Pnztx22214eXk138krbZbdbmfv3r1s27aNrVu3sm3bNnJz\ncwHQ6/WUlpZiNBrr7BMbG0tlZSWxsbH06dOH2NhY93RoaKjqQkwrjL0jhOgspcwSQoTiar9/CFhz\nepAXQhRLKQMa2HcWMAvAI6z74Mh7/k5koBcxId7EhJjoHuxNdIiJmGATgSaPJv0HPnHiBNHR0Zzt\nHAcPHsysWbOYPn06Pj4+TXbc5pSXl8dbb73FP/7xD7Kysnjuued49tln62yzbt06bpk6leHjpxB+\n1a84LkM5Za4CINDkwdCoQIbFBDIsOojeYT5oNa7f3FxlY3taAZsP5bH5UD75ZdUADOzix9jeoXQV\nBXz1yQqys7NZvXp1vXx17doVq9WKv78/99xzD3PmzFGD6F3mbDYb27dvdwf4HTt2UF5eftbtN2/e\nzNixY93z1dXVmEwmHA5Hg9v7+/vXKQxmzJhBREREU59Gm9eqA64JIZ4DyoH7gbFSyhwhRDiwRUrZ\n+1z7mqK95diF09Fb+2AuiiEz34DV8UtfcD+jnpgQEzHB3jXfJmJCvOkW5IWn/sIfxEpJSeHJJ59k\n06ZNZw384Kr933bbbcybN4/o6OgLPk5L+OGHH3jttdf48MMPsVqt7uVhYWEcP57O8eJqvjtWyPfp\nRXx3tIC8gkK0Xn6E+hgYFhPE0OhAhkcH0iPUu1EFq9MpSc0xs6WmAPgpoxinhCCTB2N6BpPQpxNj\negbj7+UBwMsvv8wzzzxTJw0hBBMmTGDu3LlMmDBBNQc1ktPpxGw2U1RURHFxMTqdjrCwMEJCQtrc\nb1heXo6/v/9Zg/aZFixYwPz5893zqampxMXFNfp4+/bto1+/fu55p9PJc889R+/evYmNjaV37954\ne3s3/gTaiRYN+kIIE6CRUpbVTH8BvACMAwqllIuEEPOAQCnlU+dKKyI2QvZZ0Ic8Sx4APfx70D9w\nKBGGeER1dzIKqjmWX8Hxggp3zdSVB+gSYCQm2JvoYBPdQ0zuK4UwX8/zBrHjx4+zbNkyli1bxqlT\np866XWpqKn369DlnWi3JarXy8ccf89prr/Hdd9+ddbvIm/8P0WMkAJ39jQyLdtXkh0YHERXk1SRX\nT8UVVpLT8tl8MI9vDudTbLGhETC4WwBX9wph0V3XknUy46z7x8TEMGfOHCZNmkRMTAyenp6XnKe2\nTEqJ2WymuLjY/SkqKqKyspI77rijzra7du1i9uzZ7u1KSkoarKRotVrCwsJITk4mJibGvdxut7Nh\nwwbCw8MJDw+nU6dO6HS6JjmPjIwMtm7d6v68+uqrXHfddXW2GTp0KD/88MNZ0zCZTIwYMYJRo0Zx\nww03cMUVV7jXbdiwgcmTJzfqQUCNRkNFRUWd/zvp6en1Kmpdu3YlNjaWvn37EhcXR1xcHH379sXf\n/5ytz21aSwf9GODTmlkd8B8p5UtCiCDgIyASOAFMk1IWnSutIUOGyB9++IGjJUfZnr2dbVnb2J27\nG5vThlFn5MqwKxkRMYJRnUcR5BFBeqGFo/nl7oLgWIFr2mL9pVZh1GuJDja5rgxCvIkJNmEy6NBq\nXDVNrRBoNQIhQNrtbN/yOav/+x47k7+u84c1ZNgI/rtmo3sfjRAUF+VzKjuL+CsGoRECjQCtRqDR\n/JKuTlOb/sUF1tLSUoB6L5JJTEzkiy++OOdNLqE3EDb4Oibdei8TRw1mWEwgXQKavy3d4ZTszSxh\ny0HXVcC+rFLspXk4Uz+nZM8mKs3F59z/66+/JiEhoc6yKVOmYDAYCAgIcH8CAwMbnPb19W3yGq+U\nEpvNRkVFBRaLBYvFctbpu+++u87xU1NTeeyxx+oE+OLi4gYDmU6nw2q11vn/8t133zF8+PBG57Ww\nsJDAwED3fGZmJl27dnXPCyEIDQ11FwJnfiZNmtRgoet0Ojlw4ECdIH/y5Mk62zzzzDP1bsg+/vjj\nvPrqq+75kJAQRo8ezejRoxk1ahTx8fHnLISqqqpIS0vj4MGD7s+BAwc4dOgQFovFvV337t05cuRI\nnX03btzIxIkTz/OLuURERHDo0KF2eSXQlEH/vNUBKeUxYGADywtx1fYviBCCHgE96BHQg5lxM7HY\nLOzK3cX2rO1sz95OcmYyAF28uzCy80hGdR7FA3FD8dJ71R6XXHM1x/LLOVZQwbF8V2Hwc2Yp6/fl\n4Dxva1UQDH+MiD63Ub73c8r3fYGjvIjjIVdx7V+T62xZuvMjSpLfwyOsB94DJ2DqMwaNoeGgqtXU\nLQRc3xq0OLCb87EV52AtOUV10SmqinKoLMzGUpiNzVLGwJvn0m/CDHTaX/ZPyTafNeAHhHXlptvv\n4amHZtO7W8MjlTYnrUYwKDKAQZEBPH5db/LKqthyKJ8thwbwTept5O7dTPlPSVRnH25w/9MDFoDD\n4WDt2rWNPv6OHTu46qqr6iybMWMGPj4+BAYG4uvrS3V1tTtQn/m9YcMGPDw83PumpqYyYMCARjdR\nTJ8+vc6NapvNxueff96ofe12OxUVFXUCT0BAvVthZ1VbMJ4uJyenzryUktzcXHJzc9mzZ0+9NMxm\nc52gf/LkSX7zm99w6NAhiorOWW9j27Zt9ZZNmjSJoqIid6Dv2bPnBVWCPD096d+/v7sXXi2n00lm\nZiYHDhzg4MGDDRYcBw4caPRxHA5HvYC/Y8cOpk2bVu+qoL1fGZxL01wDXgIvvRdjuoxhTJcxAJw0\nn2R79na2Z21nzdE1fHjoQ3QaHYNCBzGy80hGRoykV0Avwvw8GdEjuE5a1XYHJ4sqqbI5cDglTun6\nOJy4pp0SpwSHezqR6mor337zBYNGJqDVG9z72OxO5q6YA4D11BGKTr2OJfkdho2fwugbfkOXnv1w\nSInd7qS62orQ6bE7JQ6nE7tT8sXyJezfuh5z/imkPPela3l+FpU2B/bqmv0dEod3aL3trhl/HU88\n+kibax8P9fFk2pCuTBvSFZvjCnafGMHmQ/ezelMy+7/6iIoDW8HxSwG24bCZXWXH0WtdBVyVufSC\njlds13MkrxydRqDTCmzV1fUG6TuXioqKOkHf09Oz0QEfwGKx1An6FxK0AYqLi88b9H18fNxXNlar\nlZycHEpKSggLC6sXUM8M+ufi7e1dr9NCRkYGO3fubNT+u3btwmazodfr3cuuvfZarr322kbnobE0\nGg2RkZFERkZy/fXXN7jN0KFDeeqpp9wFw9GjR8/aVNTQvYP9+/eTlZVFVlYWX3zxRZ11ERERdQqC\ngQMHMnTo0Es/sVbW6kH/TF19u3Kr763cGnsrVoeVPXl72Ja9je1Z23l196u8uvtVQowh7mag4eHD\n8fd0lcgGnZYeoRd+6TYp/p56yzZt2kR+dt1L26rKCr5Z81++WfNf+vfvjxCCY8eOceedd/LGG2/U\n2TZ/k4HtedmNOn4Po4VP5oyos2wJP/LYtv8RFhbGtGnTePDBB+nVq9cFnlnL02s1DI8JYnhMEP83\nsQ9ZJXey5tsD/HvZv0n59musZUUs3XEKjcHs3sdW3LjfqdYDHx1Ea8p1z9vLCi5o/398mUpUt674\nG/X4eemxl1/YwHKnNznA2YO+l5dXnWap2s+ZNdbAwEC+/fZbdzOWv79/g7XayspKiovrN535+Phw\n3XXXkZOTQ05ODgUFZ/89wsPD6y07V6FhMBgYOnQoo0aNYvTo0YwYMaJOwG9tI0eOZOTIke756upq\njhw5woEDB0hJSSElJYXU1FQOHTrUYNBPSUk5a9rZ2dlkZ2e7C4MhQ4bUu3dRUFBAcXExPXr0aDdd\nS9tc0D+dh9aDoeFDGRo+lMcHP06eJY8d2TvYnrWdzSc3s/roajRCQ7+gfq6rgM4j6RfUD63m0odc\n9vPzY+LEiWzcuLHBm2r79u1zTx87dqze+tNvtJ3L6TXO091zzz3cc889+Pr6NjLHbVNnfyNzJgxi\nzoRBOJ0Sq8N1JeRwSGxOJw6npNRcxnfjO1FUVExJcTFFxUWUlLimS0tKMJeWYC4ppsxcSllpCa/f\nPRo0OhxOid0hOZ52kHlLG5+n179IQR9Y4p53VtcEcaFB4+GJVu+JzuCJh6cRg6cXnkYjRi8vTCYT\n3iYvko+WEGP1ItjHQLcgL7y9vUlKSqoX3M/2b3smrVbLsGHDzrud0Wis18cdICEhoc49EqvVSm5u\nLtnZ2e6CoPYTFBRUb//Tg76fnx8jR450t8cPGTKkXd10NxgM7tr5Lbfc4l5us9nqFdZwYc1DDRUa\n//3vf3n44YcJCwtjzJgx7k9cXFybuho/XZsYT/9iOJwO9hfuZ0fWDrZlb2Nf/j4kEl8PX3oH9ibS\nJ5KuPl2J9I10T9feF7gQ6enp7p4/Z6sR9e7dm4MHD9ZZ9tlnn3HTTTcBrtpVTEwMMTExREdHu6dj\nYmIIDw9vs/852ouioiI2bNjg7iFTVlaGp6cnXjWB+vRvo9HIoCuHY9PoKbHYKLZYKamwUlhmocwK\npVU2SmuXW2yUVtoosdgoqbRSZat/RaAR0DXQi+4h3nQPMdEj1Ltm2psAU+OCfmvLzMwkJSWF8PBw\n4uLiOtR7KqxWK0eOHKlzVZCSksLhw4ex2+11tl28eDFPPVW3g+LUqVNZtWpVvXQDAgIYNWqUuxC4\n4oorLukKqd2+GL0pg/6ZSqpK+DbnW3bm7OR46XEyzBkUVhXW2SbYGOwuALr5dqOrb1cifVyFgrfH\nuZuFbDYbSUlJ/POf/6zX7z8wMJCCgoI6l3elpaVkZWURFRXVZp9OrbRXsil9E6uPrMagM3BHnzsY\nETGi3VymtrQqm8NdAJRYbOSaqziaX8HR/HKO5rk6FljtvxQMgSYPuoeY3IVA91ATPUJ86BxgdD8Q\np7RNpxcGtQXB3LlzGTVqNHll1aQXVnCioIIHJg6htCj/vOmZTCaef/55nnjiiYvKjwr6jVRhq+Bk\n2UlOmE9wsuwkGeYMMsoyOGk+SV5lXp1tAz0DXVcGPpHuwqCbbze6+nTFz1C3O+XJkyfZt28fgYGB\nxMTEEBIS0q4C5aGiQ6w6vIqkY0mU2cqI8o2iwlZBfmU+PQN6MrPvTCZFT0KvbTttt+2BwynJKq50\nFQK1nzxXoVBY8csDdR46DTHBJvfVQfeaq4OYEBNeHm26xbVDsDmcZBVXcqLIQkZhBemFFk4UWjhR\nWEFGkYXqmoLdWV1B/qcvU519EGmrPm+6Dz//KrfefgchPgZCfQ34GHS8//77hISEMGLEiHM25aqg\n3wQsNguZ5ZmcNJ8koyzjl4KhLINTFXUf4PIz+NVrLoryjaJPUB90mvbxR2qxWdiUvolVh1fxc8HP\neGg8GB81nlt63sLgToOxO+2sP76ed1Pe5UjJEUKNodzW5zam9p6Kr0f7vq/QFhRXWE8rDCo4muea\nziiy1OlmHOHn6S4EXN8mgkyuXmVSgsT1DdSZl9SMmFuznDrL665z73NGei1BCPDy0OHjqcPboMPb\nU4fJQ9fiVz6VVgcZRRbSCyvIKLRwoqiiJrBbyCqpxHHaP4qnXkO3QBPdgrzoFuRFZJCJqCAvugWa\n0OsE2UXl7PhuFzu3b+XnXd9ydN8uqirqD+wY8cC/0Pv/0sXaQwvHl0zHbilDCA2donsTGz+MQcOu\nYuSokfSM7EKor4Egkwd6nVYF/eZUZa8iqzzrlyuD064WcipycNZ0wfTx8GFkxEjGdBnDqM6jCPC8\nsK57LeFg0UF3rb7cVk6MXwxTe03lhu431LuCAVdw2JG9g3dS3uG7nO/w0nlxc6+bmdFnBhHeHW/M\nk+ZWbXdwotDiLgSO5JW7m4xOfwjxcufloXUXAj4GHSaDrs68t6drWe20t0GPt8FVeNRu6+Opw6DT\nuK+6SyxWVyAvsnCioKKm5u4K9HlldWvmfkY9UTUBvVugV02AdwX6UB/DBV3JO51O9u/fT3JyMsnJ\nyWzduhWNRsuWH1PJL7OSX15Nnrma/fv38/cHbzxrOvqgrhi6xuHZtR8Fa19RQb+1WB1WssqzOFx8\nmK2ZW9matZWiqiIEggEhA7i6y9WM6TKGXgG9Wq3Jx2KzsOH4BlYdXsX+wv0YtAauj7qeW3rdQnxI\nfKPzdaDwAMtTl7Px+EYArou6jrvi7qJvUN/z7KlcKiklp8xVHMkrp6zKjsBVSwbX0+WueeFe7lrm\nWlFv3Wn7IEDjXnd6Wi1zXk4JFdV2yqvtVFTbKatyTZfXflfXnS+rslNhdX07zv/kJTqNwNtTh9Mp\nMVfVvRHbyddAt0ATkUFe7gBfW2P382q+pkwpJfn5+YSG1n32ZunSpTz44IPn3V8IgZRSBf22wimd\npBam8k3mNyRnJpNamApAJ69O7ofOhoUPw6ir39WuqaUWprpr9Ra7hR7+Pbil1y1MjpncYK2+sU5V\nnOL91PdZlbaKClsFQ8OGMjNuJqM7j25X9zKU9ktKSbXd6S4k6hQY1baaQsLhnnZKiDytxh4Z6IXR\no231Stq2bRsrV64kOTmZ1NTUs243cOBA9u7dq4J+W5VvyWdb1ja+yfyGndk7sdgtGLQGrgy70l0I\ndPZu+MUkF6PCVsH64+tZdXgVqYWpeGo93bX6gSEDmzQol1nL+OTwJ6w4sII8Sx7d/bozM24miTGJ\neGjbR/dERWmL8vPz3e8gSE5O5qeffnI/WfzQQw/x2muvqaDfHlgdVnbn7iY5M5nkzGQyylyjUPbw\n78HoLqO5usvVDAwZeME3g6WUpBSmsOrwKtYfX0+lvZKeAT2Z2msqiTGJzX7j1eawsTF9I8tTlnOo\n+BDBxmBu73M7U3tNvaQrCkVRXMxmMzt27GDr1q2MHz+ehIQEFfTbo/TSdL7J/IatmVvZnbsbu7Tj\n6+HruhncdQyjIka5h5RoSJm1jPXH1rMqbRUHiw5i1BmZEDWBW3rdQv/g/i3e1CKl5Nucb1mespzt\n2dsx6oz8uuevmdFnBl18urRoXhTlcqa6bF4Gyqxl7MzeSXJmsvtmsEZoGBA8gKu7Xs3ozqPpFeAa\na2dfwT5WHV7FxvSNVNoriQ2M5ZaetzApZhI+Hm3jjV+Hig7xXup7rD++Hqd0Mr7beO6Ku4t+wf3O\nv7OiKOekgv5lximdpBSkkJyVXOdmcJgpDG+9N0dKjmDUGZkUPYmpvabSN6hvm72BmluRy8qDK/n4\n0MeU28oZ3Gkwd8XdxZguY9AINdyEolwMFfQvc/mWfLZmbSU5M5niqmImd5/MpOhJmPSm1s5ao5Vb\ny/lf2v94/8D75FTkEOUbxcy4mYzvNl61+yvKBWqVoC+E0AK7gCwp5WQhRCDwIRAFpON6c9Y5X5mk\ngn7HY3Pa+CL9C95NeZcDRa4RDaP9ookPiWdgyEDiQ+OJ9otWVwGKcg6tFfQfB4YAvjVB/09A0Wnv\nyA2QUj59rjRU0O+4pJTszd/Lrtxd7Mnbw978vZRUu4Y39vHwYUDIAHdBMCBkQLu6qlGU5tbiQV8I\n0QVYDrwEPF4T9A8BY6WUOUKIcGCLlLL3udJRQV+pJaXkhPkEe/JdBcCevD0cLTmKRKIRGnr493AV\nAqEDiQ+Jp6tP1zZ7H0NRmltrBP1VwELAB3iyJuiXSCn9a9YLoLh2/ox9ZwGzACIjIwefOHGiKfKt\nXIbKrGXsy9/nKgTy9/Bz/s+U28oB1yiop18NxAXHtchTzorSFrToi9GFEJOBPCnlbiHE2Ia2kVJK\nIUSDpYeU8i3gLXDV9C8hr8plzsfDhxGdRzCis+vVkQ6ng2Olx1xXA3l72Zu/ly0ntwCgEzp6B/Ym\nPjTeXRCEmeq/P1ZRlLrOW9MXQiwE7gDsgCfgC/wPuBLVvKO0sOKqYn7O/9ndLLS/YD+V9koAQr1C\nGRgy0H1foE9gHzx17edVf4pyNq3WZbOmpl/bvPNnoPC0G7mBUsqnzrW/CvpKU7M77RwuPuy+Obw3\nfy9Z5VkAaIWWXgG96Bfcj/7B/ekf3J9ov+gmeYeyorSkthL0g4CPgEjgBK4um0Xn2l8FfaUl5Fvy\n2V+wn30F+9hXsI+UghTKbK6XWnjpvIgLjnMXAv2C+9HJq5NqFmpmVoeVoqoiSqpLMOlM+Bp88dZ7\nqwK4kdTDWYpyAZzSyQnzCfYX7Ofn/J/ZX7Cfg8UHsTtd462HGEPoF9yPASED6Bfcj7iguDYzvEVb\nZnVYKawspLCq0P1dUFlQZ1lBZQGFVYWUWeu/SUog8NZ742vwxdfD1/3tZ/Bzzdcs8/Pw+2WbmvUm\nvalDPduhgr6iXCKrw8qhokP8XOAqBPYX7CfdnO5eH+0X7b4a6B/cn14BvTrEO4PPDOS1QbuhoN5Q\nIAfw0fsQZAwi0DOQIGMQQZ5BBBuDCTIG4Wfww2KzYLaaKa0uxWw1uz7V5nrLagvlhmiEBh8PH1ch\ncEah4GvwJcQYwsCQgfQK7IVe0/7/3VTQV5RmUFpdSkphCvvy97muCgp+pqjK1WLpofEgNijW3STU\nP7g/kT6R7aZZyCmdFFUVkWvJJa8iz/VtcX3XThdUFjQqkNcG8CDPIIKMNQG9ZjrIGIRBa7jk/Eop\nqQwcNq8AABDJSURBVLRXNqpwMFvNlFWXUWotda93SNerJj21nvQL7lenl9e5RrJtq1TQV5QWIKXk\nVMUp99XAvoJ9pBamunsLeWo98ff0x9/gj5+HH34G18ff4H/WaV8P3wt+f8L52Bw28irzyK2oG8hP\nn8+vzK9Xc9YKLSFeIYR6hdLJq5M7eJ8Z1JsqkLcUKSW5llx3V989eXs4WHQQu3Sdf+0wILUFQZRf\nVJtvKlJBX1Faid1p52jJUfYX7OdY6TH+v727jW3rvA44/j96o94tyZZpWbbsLHZd2EljJ1qXBPnQ\nLUuWDhgSbFi2fFkGdDM67EMzDBic7cuKdkD20mErMmwz0GH+0A1IkQ7JWnRDZqRIizhJ5VT1S2LH\nTWs71rttkZJli7LEsw/PQ/GSJiVauiRF8vwA4l7ee3n5HJE6597nvjCWiDGTmCGWiLlxvxWa2tLM\npaOxg85IJ10RVzBS4xlFoik9vphcZPzmuEvggUSeGqb2RoJaGlqItkaXE/rW1q1E29LPo61Repp7\nauZA6q3FW5y9epbhqWGGJ4cZnhomnogD0NnUuXwfqENbD3Fg8wFaG1vL3OJMlvSN2cCSmmTu9hyx\nRIx4Ik48EU8XhUCBiC/Eic/HiS+4+fm6VrJ1RbryJvLUtI7GjorpeioHVeXizMXlU32HJ4f5OP4x\n4PaA9vXsy9gb6GvvK2t7LekbU4UWk4vMLszeUSwa6hqWk3pva69dcFYk8UR8+cK/4clhTl89vdyV\nF22NLheAg1sPsq9nX0kPEFvSN8aYIgte+Jc6PjA6Nwq44zkHthxgT9ceupu76Yp00R3ppqu5i57m\nHve8uTu0YyGW9I0xpgwm5iaWbwg4PDnMldkrxBIxlNx5tKWhZbkIdDX7whDxhSHH801Nm3IeZynp\nDdeMMcY40bYoT7Y9yZO7n1yetpRcYmZhhunENLH5GNOJaabnp4klYsvD6/PXic3HuBi/SCwRY+72\nXM71C0JnpJPuSHd6D6K5O9QYLOkbY8w61NfV093skjQF/hJoYilBbN4d0E8ViWChSBWQkRsjnLl6\nJtT2WtI3xpgSi9RHiLZFibZFC1pefie8M7E29hUJxhhjQmVJ3xhjaoglfWOMqSGW9I0xpoasmvRF\npFlE3hORn4jIWRH5sp/eIyJviMgFPwz3vCJjjDGhK2RLPwH8iqo+ABwEnhKRh4EjwHFV3Qsc98+N\nMcZsYKsmfXVu+KeN/qHA08AxP/0Y8ExRWmiMMSY0BfXpi0i9iAwDk8AbqvouEFXVMb/IOJDzhFMR\nOSwiQyIyNDU1FUqjjTHGrE1BSV9Vl1T1ILAD+KyI3Jc1XyH3zSdU9aiqDqrqYG9v77obbIwxZu3u\n6uwdVY0BbwJPARMi0gfgh5PhN88YY0yYCjl7p1dEuvx4C/AEcA54HXjeL/Y88FqxGmmMMSYchdx7\npw84JiL1uCLxiqp+R0ROAK+IyBeAS8CzRWynMcaYEKya9FX1FHAox/RrwOPFaJQxxpjisCtyjTGm\nhljSN8aYGmJJ3xhjaoglfWOMqSGW9I0xpoZY0jfGmBpiSd8YY2qIJX1jjKkhlvSNMaaGWNI3xpga\nYknfGGNqiCV9Y4ypIZb0jTGmhljSN8aYGmJJ3xhjakghv5y1U0TeFJEPROSsiHzJT+8RkTdE5IIf\ndhe/ucYYY9ajkC39ReBPVXU/8DDwxyKyHzgCHFfVvcBx/9wYY8wGtmrSV9UxVX3fj88CHwL9wNPA\nMb/YMeCZYjXSGGNMOAr5jdxlIrIb99OJ7wJRVR3zs8aBaKgtM8W3cBPGhmHkfahvhIGHIXof1NWX\nu2XGmCIpOOmLSDvwKvCCqs6IyPI8VVUR0TyvOwwcBhgYGFhfa83aJZdg6hxcGYKRky7RT34AupS5\nXFMH7PwsDDziikD/Q9DUWp42G2NCV1DSF5FGXML/pqp+20+eEJE+VR0TkT5gMtdrVfUocBRgcHAw\nZ2EwIVOF+BWf3Idcgh8dhttzbn5zl0vm+z7vhv0PweI8XH4HLp9wwzf/ClCoa4C+g64ApApB25ay\nhmeMWTtRXTkPi9ukPwZcV9UXAtP/Frimqi+JyBGgR1X/bKV1DQ4O6tDQUAjNNhluxWD0fZfkr5x0\nwzlfg+ubYNtnXGLfMeiGPb8AgT213Ouchk9+lC4CIydhKeHmbflUZhHovmf19Rlj1kxETqrqYCjr\nKiDpPwb8ADgNJP3kP8f1678CDACXgGdV9fpK66ropH8rBp+8C5fedolw4iw0tUFLD7T2QEt3erg8\nLXvY7frO12MxAeNn/Fa8f1y7kJ6/eW86ufc/CNH7oaFpfe+Zet/RH6eLwOV3YD7m5rVHM4tA9H6o\nv6vDRcaYFZQ06YepopL+zBhcfhsunUgn+VR3x/ZDrstjcd5tEd+ahpvX4dZ1N0zezr/eSGegQKxQ\nHFLPlxZc98yI74sfP+2mAbRtDST4h1y7WrpK8uchmYSr59NF4NIJiF928xrbYOcvwsCjrgjsGHQF\n0hizJpb0w6YK136a3oq/fAKmL7p5wQS26xHoH1z5wKYqLNzILALBopBdIG5dh5vTkIiv3MbGNpfU\n+x9Md9V09m+sbpX4lfRewOV3YOIMoCD10PeAKwCb90DndujYBh3boa0X6uzCcGNWYkl/vZYWYeK0\n34p/2yWouSk3r3Wz66bY5bdStz1Qmq6KpUW/13A9syiIuGS/ZV/ldZnMxwPHBU64PZXF+cxl6hqg\nfRt09kGHf3T2uYIQHJZzT0EVbt+E+RkXU2LGjd+eg+Si2+vRJXeGlC75aUugycC01DCwbHIxMG+F\nZRtb3N+oI+qG7Vtd0WzbWnnfCbMmYSb92vjG3L7lEk4qyX/yI1iYdfO6BuDex91W/MCjsGVvebae\n6xugvdc9qkXzJtj7q+4BrrDNTbqus9lRmB2HmVGYHXPDqXPws++7pJot0pmjIGQVifatua8xWEwE\nEnb8zuSd8M8zxgPz5+N3ntq6buLaKvWBYV36eV1DelrihtsIyLWOti2ZhaA9mh62R9OFwk67NV51\nJv3sg66jP073g2/dD5951m/JPwKb+svb1lpS3+C6djq3Aw/lXy5xI10IZsf8uC8UM2Nw9S24Me62\nlIOk3iW6ts2u0KcSeeqso7zEFZXmTleoIp2ujZFPu+fNnX5+anyTG29qzUzOGQk8kMiXlwnOu8sN\ni8UFVzBnJ1zsNybS46nh1Dk3PfvvAq792YUgYxiFhmaQOt++usDDtzf1PO/8DdTVaPIqbffOzhYd\n+pN7Um/tB5L1nJXn53xN4Hly0ffHK9Q1uq6R1Fb8wC+5g6Sm8iWXYO5quhAEi8PNq9DYmpnEmzcF\nxjszx5s6que4QjLp9gpmx31xmPTjE344mS4Uqes2QiOFFY1SFIeGFujaCV27oHtX5rCjr+I+78rt\n09+zVYe+9tuw/JZ+ZLkNqz0vYBkR6P2024q3q0mNyS8x6/cS/GNpIX18Qf3xBE26/63lYw7Z85L+\n2EOy8HmlsDAHscswfcltDBDIIfVNsGnnncWgexd07XZnzm2wvZbK7dPvGoCn/6mkb2mMySPS4R5b\n9pS7JcW1mIDYJxC76IpA7FJ6ODp85/GSpnaXq3LtJXTvcn+zCladffrGGJPSEHGFLV9xS8zeWQxS\nw5+/dWc3WEtPugh07UyfWNDZ708u2Lb+izCLyJK+Maa2RTpg233ukU0Vbl7zReBiZlEYPwXnv5fj\nRAHxZ1P5QtDZ569NySoOkfZSRHcHS/rGGJOP+NNi27bAjhxnnKm662tmRtxJBDMj6TPPZkZh+udw\n6YfuTLJskU3pU4/zFYfWzaEfX7Ckb4wxayXiDvy29sC2+/MvtzDnr0vJUxxSp9tqMvN19U2uKITI\nkr4xxhRbUxtsvtc98lladIl/ZjR9KnKqOHA6tKZY0jfGmI2gvsFdLJrzgtFvhPY2lXWFgjHGmHWx\npG+MMTVk1aQvIv8mIpMiciYwrUdE3hCRC35o9zYwxpgKUMiW/r8DT2VNOwIcV9W9wHH/3BhjzAa3\natJX1beA7Pu6Po373Vz88JmQ22WMMaYI1tqnH1XVMT8+DkRDao8xxpgiWveBXHW36cx7q04ROSwi\nQyIyNDU1td63M8YYsw5rTfoTItIH4IeT+RZU1aOqOqiqg729VfSrUMYYU4HWenHW68DzwEt++Foh\nLzp58uSMiFxY43vejQHgcpHfYxOwyq+Zh6IUsUB1xVNNsUB1xVNNsUDp4jkQ2ppUdcUH8J/AGHAb\nuAJ8AdiMO2vnAvB/QM9q6/HrOlrIcut9AFMleI+qiaXa4qmmWKotnmqKpVLjWXVLX1WfyzPr8UIL\nS8B/r+E1axErwXtUUyxQXfFUUyxQXfFUUyxQgfGU9IpcVS3VH6jou1vVFAtUVzzVFAtUVzzVFAtU\nZjzVehuGo+VuQIiqKRaorniqKRaorniqKRYIMZ6S/jC6McaY8qrWLX1jjDE5VETSF5GdIvKmiHwg\nImdF5Et+et4bv4nIiyLyUxE5LyK/Fpj+kIic9vO+LhLyb5GVKBYRaRWR74rIOb+el0oZR9jxZK3z\n9eAN/kol5O9Zk4gcFZGP/Gf0WxUez3P+/+aUiPyPiGzZyLGIyGa//A0ReTlrXWXNAWHGs6Y8UIrT\njUI4XakPeNCPdwAfAfuBvwGO+OlHgL/24/uBnwAR4B7gY6Dez3sPeBgQ4HvA5ysxFqAV+GW/TBPw\ng1LHEvZn4+f/JvAfwJlKjgX4MvBVP14HbKnUeHDX80ymYvCv/8sNHksb8BjwReDlrHWVNQeEGc9a\n8kBJAw3xD/Ya8ARwHugL/BHP+/EXgRcDy/8v8Ihf5lxg+nPAv1ZiLDnW84/AH1bqZ+PH24Ef+i9/\nyZN+yLF8ArSVO4Yw4gEagSlgl0+U/wIc3sixBJb7/awkueFywHriybGeVfNARXTvBInIbuAQ8C75\nb/zWj/unS7nip/X78ezpZbHOWILr6QJ+A3fBXNmEEM9XgK8BN4vd1tWsJxb/eQB8RUTeF5FviUhZ\nb0q4nnhU9TbwR7gfah3FFeXwfr/vLhUYSz4bKgfAuuMJrqegPFBRSV9E2oFXgRdUdSY4T12Zq5hT\nkcKKRUQacFdNf11VfxZ6Qwu03nhE5CBwr6r+V/FaWZgQPpsGYAfwtqo+CJwA/q4YbS1ECJ9NIy7p\nHwK2A6dwewUlV005AMqTByom6fsv3qvAN1X1235yvhu/jQA7Ay/f4aeN+PHs6SUVUiwpR4ELqvoP\nxW11fiHF8wgwKCIXcV08nxKR7xe/9ZlCiuUabm8l9fpvAQ8Wuek5hRTPQQBV/dgnoleAR0vQ/Ax3\nGUs+GyIHQGjxpBScByoi6fuj698APlTVvw/MSt34DTJv/PY68LsiEhGRe4C9wHt+t2lGRB726/w9\nCrxZXFjCisWv66u4Gz69UIq25xLiZ/PPqrpdVXfjDlh9pKqfK0UMKSHGorjL8z/nl3sc+KDIzb9D\niN+1EWC/iKRuk/sE8GGx2x+0hlhy2gg5AMKLx6/r7vJAuQ9gFHiQ4zHcbs4pYNg/fp0VbvwG/AXu\n7IPzBI5mA4PAGT/vZfwFapUWC24LRXH/fKn1/EElfzaB+bspz9k7YX7PdgFv+XUdBwYqPJ4v+u/a\nKVxB21wBsVzE/erfDVzf/X4/vaw5IMx41pIH7IpcY4ypIRXRvWOMMSYclvSNMaaGWNI3xpgaYknf\nGGNqiCV9Y4ypIZb0jTGmhljSN8aYGmJJ3xhjasj/AxKBFFPxolsgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a4693c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.resample('A').mean().plot()\n",
    "data.mean(axis=1).resample('A').mean().plot(color='k', linestyle='--', linewidth=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Combination with groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`resample` can actually be seen as a specific kind of `groupby`. E.g. taking annual means with `data.resample('A', 'mean')` is equivalent to `data.groupby(data.index.year).mean()` (only the result of `resample` still has a DatetimeIndex).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a40c7b8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KRSnjdHQO8DnwGrAouM4ioHf3ZXCGUn74QyKGD6f4pz/DX1vbqX01N8UsWHQr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\nMNM3DMOoIMz0DcMwKoi/0s0SasbMk9AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a485860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(data.index.year).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "But, `groupby` is more flexible and can also do resamples that do not result in a new continuous time series, e.g. by grouping by the hour of the day to get the diurnal cycle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: how does the *typical monthly profile* look like for the different stations?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1\\. add a column to the dataframe that indicates the month (integer value of 1 to 12):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['month'] = data.index.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "2\\. Now, we can calculate the mean of each month over the different years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>51.955189</td>\n",
       "      <td>19.832429</td>\n",
       "      <td>47.634409</td>\n",
       "      <td>82.472813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.721187</td>\n",
       "      <td>20.238004</td>\n",
       "      <td>50.564499</td>\n",
       "      <td>83.973207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.737157</td>\n",
       "      <td>19.741470</td>\n",
       "      <td>47.862715</td>\n",
       "      <td>96.272138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.445248</td>\n",
       "      <td>17.865095</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>47.193898</td>\n",
       "      <td>14.292315</td>\n",
       "      <td>39.706019</td>\n",
       "      <td>93.000316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>48.649218</td>\n",
       "      <td>17.007418</td>\n",
       "      <td>44.010934</td>\n",
       "      <td>86.297836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.186465</td>\n",
       "      <td>17.900151</td>\n",
       "      <td>45.564683</td>\n",
       "      <td>87.173878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>50.194588</td>\n",
       "      <td>20.200031</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      51.955189  19.832429  47.634409  82.472813\n",
       "2      53.721187  20.238004  50.564499  83.973207\n",
       "3      53.737157  19.741470  47.862715  96.272138\n",
       "4      49.445248  17.865095  40.943117  95.962862\n",
       "...          ...        ...        ...        ...\n",
       "9      47.193898  14.292315  39.706019  93.000316\n",
       "10     48.649218  17.007418  44.010934  86.297836\n",
       "11     50.186465  17.900151  45.564683  87.173878\n",
       "12     50.194588  20.200031  45.262243  81.817977\n",
       "\n",
       "[12 rows x 4 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('month').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3\\. plot the typical monthly profile of the different stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a485f98>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LXn2V4jlzCf3FL0h4dK6ch+Jitqoqqt5/n4q3/kvz3r14hYURftllhF999TEvQ9prQl/b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\nABcZPXr3VAkZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a343f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('month').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('month', axis=1, errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: plot the weekly 95% percentiles of the concentration in 'BETR801' and 'BETN029' for 2011\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2011 = data['2011'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a244a90>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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57fnd2NPQa5gSy/GLlJ5beNiSkO18ScGBfij5AKad7zjbFkJl+WUfe+Ym2dmk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\nRzl6hUKhCHL+HxyjQMYYqtRLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a346cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2011 = data['2011'].dropna()\n",
    "df2011.groupby(df2011.index.week)[['BETN029', 'BETR801']].quantile(0.95).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a110908>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sVW4gFmYJEvOBIT2NjnRG9R66Gkgq8H7fSuSdnkbqSxKTBFz4Pcrijr7g/bEk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\nzDownR7PiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a35dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2011[['BETN029', 'BETR801']].resample('W').agg(lambda x: x.quantile(0.75)).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: The typical diurnal profile for the different stations?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f133a052ef0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SxsXFyePHj0sppczMzJQhISHF+4eGhsr9+/dLKaX87rvv5Ntvv33bWKrC66hU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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a05dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(data.index.hour).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "clear_cell": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: What are the number of exceedances of hourly values above the European limit 200 µg/m3 for each year/station?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exceedances = data > 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# group by year and count exceedances (sum of boolean)\n",
    "exceedances = exceedances.groupby(exceedances.index.year).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1999</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</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>2009</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BETR801  BETN029  FR04037  FR04012\n",
       "1999      0.0      0.0      3.0     39.0\n",
       "2000      0.0      0.0      0.0     17.0\n",
       "2001      0.0      0.0      0.0     88.0\n",
       "2002      0.0      0.0      0.0     12.0\n",
       "...       ...      ...      ...      ...\n",
       "2009      0.0      0.0      0.0     58.0\n",
       "2010      0.0      0.0      0.0    104.0\n",
       "2011      1.0      0.0      0.0     62.0\n",
       "2012      6.0      0.0      0.0     13.0\n",
       "\n",
       "[14 rows x 4 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exceedances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f133a017d68>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mNhXYAkxIRFARCUO6znCarGmKAfLy8tizZw+fffYZCxcuZPny5Rx66KHce++9DBs2rP7U\nzRtuuKHZm7yJoGmK05CmKZZ0pmmKU0PTFIuISKtU9CIigVPRi0jSpcMQcSaL9/lT0YtIUmVnZ7Nj\nxw6VfSe5Ozt27Gj2ydr9oS8HF5GkGjRoEJWVlegT8J2XnZ3NoEGDOn17Fb2IJFX37t0ZMmRIqmMc\n0DR0IyISOBW9iEjgVPQiIoFT0YuIBE5FLyISOBW9iEjgVPQiIoFT0YuIBE5FLyISOBW9iEjgVPQi\nIoFT0YuIBO6AmtQsU76iT0QkkXRELyISOBW9iEjgVPQiIoFT0YuIBE5FLyISOBW9iEjgVPQiIoFT\n0YuIBE5FLyISOBW9iEjgVPQiIoE7oOa6EZEDR0tzWx2o81rFdURvZoeb2VNmtsnMNprZaWbW18z+\nZGbl0e8+iQorIiL7L96hm58Dy9x9GHASsBGYDqxw96HAiui6iIikSKeL3swOA74BzAVw98/cfTcw\nHiiJNisBLo43pIiIdF48R/RDgO3Ao2a21sweNrNDgP7uXhVt8z7Qv6Ubm1mRmZWZWdn27dvjiCEi\nIm2Jp+izgFHAL939ZOAfNBmmcXcHvKUbu/scdy9w94KcnJw4YoiISFviKfpKoNLdV0XXnyJW/NVm\nlgsQ/d4WX0QREYlHp4ve3d8H3jOz46NF44A3gMVAYbSsEFgUV0IREYlLvOfR/zvwuJkdDPwvcA2x\nF49SM5sKbAEmxHkfIgcEnfctyRJX0bv7OqCghVXj4tmviIgkjqZAEBEJnIpeRCRwKnoRkcCp6EVE\nAqeiFxEJnIpeRCRwKnoRkcCp6EVEAqeiFxEJnIpeRCRwKnoRkcCp6EVEAqeiFxEJnIpeRCRwKnoR\nkcCp6EVEAqeiFxEJnIpeRCRwKnoRkcCp6EVEAqeiFxEJnIpeRCRwKnoRkcCp6EVEAqeiFxEJnIpe\nRCRwKnoRkcCp6EVEAqeiFxEJnIpeRCRwcRe9mXUzs7VmtiS63tfM/mRm5dHvPvHHFBGRzkrEEf1N\nwMYG16cDK9x9KLAiui4iIikSV9Gb2SDgW8DDDRaPB0qiyyXAxfHch4iIxCfeI/pZwH8AXzRY1t/d\nq6LL7wP947wPERGJQ6eL3swuBLa5+5rWtnF3B7yV2xeZWZmZlW3fvr2zMUREpB3xHNGfDlxkZhXA\nAuBsM3sMqDazXIDo97aWbuzuc9y9wN0LcnJy4oghIiJt6XTRu/sd7j7I3fOAK4Fn3X0ysBgojDYr\nBBbFnVJERDotGefRFwPnmFk58M3ouoiIpEhWInbi7s8Dz0eXdwDjErFfERGJnz4ZKyISOBW9iEjg\nVPQiIoFT0YuIBE5FLyISuIScdSOSzjYOy2+2LH/Txha2FAmTjuhFRAKnohcRCZyKXkQkcCp6EZHA\nqehFRAKnohcRCZyKXkQkcCp6EZHAqehFRAKnohcRCZyKXkQkcCp6EZHAqehFRAKnohcRCZyKXkQk\ncCp6EZHAqehFRAKnohcRCZyKXkQkcCp6EZHAqehFRAKnohcRCZyKXkQkcCp6EZHAqehFRALX6aI3\ns6PN7Dkze8PMXjezm6Llfc3sT2ZWHv3uk7i4IiKyv+I5oq8FbnP34cCpwL+Z2XBgOrDC3YcCK6Lr\nIiKSIp0uenevcvdXossfARuBgcB4oCTarAS4ON6QIiLSeQkZozezPOBkYBXQ392rolXvA/1buU2R\nmZWZWdn27dsTEUNERFoQd9GbWS/gt8DN7r6n4Tp3d8Bbup27z3H3AncvyMnJiTeGiIi0Iq6iN7Pu\nxEr+cXf/XbS42sxyo/W5wLb4IoqISDyyOntDMzNgLrDR3X/WYNVioBAojn4vam9fb775JmPHjm20\n7MILL+T2228HaLaus+v3vrulfv2ZvXpxbd9+Cd1/otbvfXdLo3yFUe6eDbZLZb669d+KLhc2eF7r\nMqZDvrr1DfPVmTBzZtrkC+HfZ88u+P93f9dnyr/PeNd3RKeLHjgduAp4zczWRcv+D7GCLzWzqcAW\nYEIc9yEiInGy2DB6ahUUFHhZWVnS72fjsPwWl+dv2pj0+94fmZwz3TKCciaacqYPM1vj7gXtbadP\nxoqIBE5FLyISOBW9iEjgVPQiIoFT0YuIBE5FLyISOBW9iEjgVPQiIoGL55OxIiISh676cKSO6EVE\nAqeiFxEJnIpeRCRwKnoRkcCp6EVEAqeiFxEJnIpeRCRwKnoRkcCp6EVEAqeiFxEJnIpeRCRwKnoR\nkcCp6EVEAqfZK6VDRpSMaLasNAU5RNJN3vSnmy2rKP5WCpK0Tkf0IiKB0xF9iulIWUSSTUf0IiKB\n0xG9pL2WxkAh/cZBRdKVjuhFRAKnohcRCZyKXkQkcCp6EZHAJa3ozex8M3vTzN4ys+nJuh8REWlb\nUs66MbNuwIPAOUAlsNrMFrv7G8m4v3SUCZ+WE0ln+n8ocZJ1RP814C13/193/wxYAIxP0n2JiEgb\nzN0Tv1Ozy4Hz3X1adP0q4BR3v6HBNkVAUXT1eODNBMc4AvggwftMBuVMLOVMrEzImQkZITk5B7t7\nTnsbpewDU+4+B5iTrP2bWZm7FyRr/4minImlnImVCTkzISOkNmeyhm62Akc3uD4oWiYiIl0sWUW/\nGhhqZkPM7GDgSmBxku5LRETakJShG3evNbMbgD8C3YBH3P31ZNxXG5I2LJRgyplYyplYmZAzEzJC\nCnMm5c1YERFJH/pkrIhI4FT0IiKBU9GLiARORS8iEjgVvYhI4IIoejM7osn1yWb2gJkVmZmlKldT\nZnaJmfWNLueY2Xwze83MfmNmg1Kdr46Z/czMTk91jvaYWV8z+28zm2Yx/2VmS8zsJ2bWJ9X56pjZ\nWWY228wWmdnvzKzYzI5Lda6mzOw8M/ulmS2Ofn5pZuenOldHmdl/pzpDQ9HzOdXM8posv7bLs4Rw\neqWZveLuo6LL3wfGAP8DXAhUuvstqcxXx8zecPfh0eXfAC8BTwLfBL7j7uekMl8dM9sObAFygN8A\nT7j72tSmas7MlgKvAYcC+dHlUmKzpp7k7imfSM/MZgADgBXAxcA7wGbgX4H73P3JFMarZ2azgK8C\n84nNOAuxT7RPAcrd/aZUZesoM3vX3Y9JdQ4AM7sPOAN4Bfg2MMvdfxGtq++rLssTSNGvdfeTo8uv\nAGPc/R9m1h14xd1HpDZhjJm96e7HR5fXuPvoBuvWufvI1KX7Ut3zaWZfBSYS+2RzN+AJYqW/OaUB\nI3XPWfRXW6W7D2y6LoXx6nK8Vvfvz8yygBfc/fToL44X3f3E1CaMMbPN7v7VFpYbsNndh6YgVjNm\ntqe1VUAPd0/Z/F0NmdlrwMnRh0cPJ3bg+aa739Kwr7pKEEM3QA8zO9nMRgPd3f0fAO7+ObAvtdEa\ned7MfmhmPaLLl0DsT3vgw9RGa8QB3H2zu9/t7icAE4BsYGlKkzV2UFSYRwO96v5ENrN+wMEpzNXQ\nF3XDdcBRxF4wcfddxMopXdSY2T+3sPyfgZquDtOG3cBQdz+0yU9voCrV4RrIcvdaAHffTeyo/lAz\ne5IU/NtMi1e/BKgCfhZd/sDMct29KvofvjaFuZq6AfgvvpyS+RYz+wfwB+CqlKVqrlkBufurwKvA\nHV0fp1UzgE3R5WuBh83MgeHA/01ZqsbuA9aa2WZi03FfD7H3aID1qQzWxNXAL82sN18O3RxN7ADk\n6hRlasl8YDBQ3cK6/+niLG1528zOdPcXANx9HzDVzO4BLuvqMEEM3bQm+qarf3L3vanO0pSZHUbs\nVX9HqrM0ZWa93P3jVOfoiOi/sUV/ImcBI4Gt7p42R3fREf1XiH0Zz+5U52mLmQ0A6obAtrr7+6nM\nk6miv9px909aWDfQ3bt0Nt9QjujrxhK/RoN/pMDL6VbyTXOaWV3OtHnFdfeP23g+0yZn5Avga2bW\nMGe6ldMuYkM2Z0cngaXrc0lU7I2ePzMb5u6bWrlJ2kinnC0VfAO9uyxIJIgjejM7F3gIKOfLee8H\nAccB/+ruy1OVrSHlTKxMyJkJGduTTmeztEU527jPQIp+I/Av7l7RZPkQYKm756ckWBPKmViZkDMT\nMgKY2QOtrQIK3f3QrszTGuXsnFCGbrL48g2khrYC3bs4S1uUM7EyIWcmZAS4BrgN+LSFdZO6OEtb\nlLMTQin6R4DVZrYAeC9adjSx87/npixVc8qZWJmQMxMyQuxb4Ta4+9+arjCzu7o+TquUsxOCGLoB\nMLPhwEU0fvNwsbu/kbpUzSlnYmVCzgzJ2BeoSbeTF5pSzs4JpuhFRKRlQXwy1swOiyaK2mRmO81s\nh5ltjJYdnup8dZQzsTIhZyZkBOVMtHTLGUTRE5vIahcw1t37uns/4KxoWWlKkzWmnImVCTkzISMo\nZ6KlVc4ghm6swWRh+7OuqylnYmVCzkzICMqZaOmWM5Qj+i1m9h9m1r9ugZn1N7P/5MszHdKBciZW\nJuTMhIygnImWVjlDKfqJQD/gBTPbZWY7geeBvsRmXUwXyplYmZAzEzKCciZaWuUMYugGYvNcEPto\n+UsNJ+Qys/PdfVnqkjWmnImVCTkzISMoZ6KlVU53z/gf4EZiU/8uBCqA8Q3WvZLqfMp54ObMhIzK\nGX7OlD8hCXpSXwN6RZfzgDLgpuj62lTnU84DN2cmZFTO8HOGMgXCQR79aeTuFWY2FnjKzAaTXt/i\no5yJlQk5MyEjKGeipVXOUN6MrTaz+u8HjZ7gC4EjgLT4vtiIciZWJuTMhIygnImWVjmDeDPWzAYB\ntd7Ct+GY2enuvjIFsZpRzsTKhJyZkBGUM9HSLWcQRS8iIq0LZehGRERaoaIXEQmcil5EJHAqehGR\nwP1/JS+JBWk5ebcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a06f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = exceedances.loc[2005:].plot(kind='bar')\n",
    "ax.axhline(18, color='k', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: And are there exceedances of the yearly limit value of 40 µg/m3 since 200 ?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yearly = data['2000':].resample('A').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BETR801    13\n",
       "BETN029     0\n",
       "FR04037     5\n",
       "FR04012    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(yearly > 40).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f1339f6bf60>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EjRnNoN/9DufYsZ1cWiGE6F49vhuG9ii7nYxHHwWLhdw77yLk97e5vtaasn/8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\nyCt0Uy6F/q6EhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1339e9b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly.plot()\n",
    "plt.axhline(40, linestyle='--', color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: What is the difference in the typical diurnal profile between week and weekend days? (and visualise it)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "clear_cell": false,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.index.weekday?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['weekday'] = data.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Add a column indicating week/weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['weekend'] = data['weekday'].isin([5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">False</th>\n",
       "      <th>0</th>\n",
       "      <td>38.693423</td>\n",
       "      <td>17.415094</td>\n",
       "      <td>34.439398</td>\n",
       "      <td>52.094663</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.128589</td>\n",
       "      <td>16.985991</td>\n",
       "      <td>31.585121</td>\n",
       "      <td>44.721629</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.837973</td>\n",
       "      <td>16.530217</td>\n",
       "      <td>30.865143</td>\n",
       "      <td>43.518539</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.446300</td>\n",
       "      <td>16.362120</td>\n",
       "      <td>32.963500</td>\n",
       "      <td>51.942135</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.478893</td>\n",
       "      <td>16.696870</td>\n",
       "      <td>40.780162</td>\n",
       "      <td>72.547472</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BETR801    BETN029    FR04037    FR04012  weekday\n",
       "weekend                                                       \n",
       "False   0  38.693423  17.415094  34.439398  52.094663      2.0\n",
       "        1  37.128589  16.985991  31.585121  44.721629      2.0\n",
       "        2  37.837973  16.530217  30.865143  43.518539      2.0\n",
       "        3  42.446300  16.362120  32.963500  51.942135      2.0\n",
       "        4  50.478893  16.696870  40.780162  72.547472      2.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_weekend = data.groupby(['weekend', data.index.hour]).mean()\n",
    "data_weekend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <td>38.693423</td>\n",
       "      <td>40.634184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.128589</td>\n",
       "      <td>37.774169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.837973</td>\n",
       "      <td>35.909899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.446300</td>\n",
       "      <td>35.236208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.478893</td>\n",
       "      <td>36.435609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend      False      True \n",
       "0        38.693423  40.634184\n",
       "1        37.128589  37.774169\n",
       "2        37.837973  35.909899\n",
       "3        42.446300  35.236208\n",
       "4        50.478893  36.435609"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_weekend_BETR801 = data_weekend['BETR801'].unstack(level=0)\n",
    "data_weekend_BETR801.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1339dc1c18>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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X93Ujd2eW3NeT8REBvL8pnjnfRFNYUrMy1rbEuka5/P4MXDhi2mM27Qyj3qjRrjExMXz5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\nn1rfFxkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133a336668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_weekend_BETR801.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "clear_cell": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.drop(['weekday', 'weekend'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: Visualize the typical week profile for the different stations as boxplots (where the values in one boxplot are the daily means for the different weeks for a certain weekday).\n",
    "</div>\n",
    "\n",
    "Tip: the boxplot method of a DataFrame expects the data for the different boxes in different columns). For this, you can either use `pivot_table` as a combination of `groupby` and `unstack`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>weekday</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-01 00:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 01:00:00</th>\n",
       "      <td>33.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 02:00:00</th>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 03:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BETR801  BETN029  FR04037  FR04012  weekday  week\n",
       "1999-01-01 00:00:00     27.0     13.0     68.0    105.0        4    53\n",
       "1999-01-01 01:00:00     33.0     15.0     70.0    116.0        4    53\n",
       "1999-01-01 02:00:00     24.0     16.0     72.0    114.0        4    53\n",
       "1999-01-01 03:00:00     26.0     18.0     72.0     91.0        4    53\n",
       "1999-01-01 04:00:00      NaN     19.0     74.0     81.0        4    53"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a weekday and week column\n",
    "data['weekday'] = data.index.weekday\n",
    "data['week'] = data.index.week\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "clear_cell": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>28.545455</td>\n",
       "      <td>25.727273</td>\n",
       "      <td>30.260870</td>\n",
       "      <td>22.704545</td>\n",
       "      <td>44.473684</td>\n",
       "      <td>27.113636</td>\n",
       "      <td>39.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.227273</td>\n",
       "      <td>60.113636</td>\n",
       "      <td>50.250000</td>\n",
       "      <td>42.550000</td>\n",
       "      <td>58.704545</td>\n",
       "      <td>68.181818</td>\n",
       "      <td>68.568182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.704545</td>\n",
       "      <td>113.863636</td>\n",
       "      <td>61.045455</td>\n",
       "      <td>43.636364</td>\n",
       "      <td>43.363636</td>\n",
       "      <td>28.826087</td>\n",
       "      <td>22.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.944444</td>\n",
       "      <td>71.659091</td>\n",
       "      <td>57.565217</td>\n",
       "      <td>59.833333</td>\n",
       "      <td>65.818182</td>\n",
       "      <td>50.181818</td>\n",
       "      <td>29.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47.775000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>41.318182</td>\n",
       "      <td>44.227273</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>76.409091</td>\n",
       "      <td>68.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekday          0           1          2          3          4          5  \\\n",
       "week                                                                         \n",
       "1        28.545455   25.727273  30.260870  22.704545  44.473684  27.113636   \n",
       "2        55.227273   60.113636  50.250000  42.550000  58.704545  68.181818   \n",
       "3        73.704545  113.863636  61.045455  43.636364  43.363636  28.826087   \n",
       "4        46.944444   71.659091  57.565217  59.833333  65.818182  50.181818   \n",
       "5        47.775000   53.750000  41.318182  44.227273  72.250000  76.409091   \n",
       "\n",
       "weekday          6  \n",
       "week                \n",
       "1        39.590909  \n",
       "2        68.568182  \n",
       "3        22.340909  \n",
       "4        29.500000  \n",
       "5        68.500000  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table so that the weekdays are the different columns\n",
    "data_pivoted = data['2012'].pivot_table(columns='weekday', index='week', values='BETR801')\n",
    "data_pivoted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wKWpEHxHn/Xrs0FVrPiaVIjPP+9VsNtd8TJtD10UfES+JiGZEPBIRD0fEe9rLL4mIByPi\na+3vL+pf3F/PX3RJerZeRvRngb/OzF3A64B3R8Qu4CCwmJlXAovt+9LQ8x2hStV10Wfmycz8Qvv2\nT4AWcBlwNXCkvdoR4JpeQ0obwXeEKlVfdsZGxE7g1cBDwEhmnmw/9AQwssZz9gP7AUZGRlhaWupH\nlPM6ffr0QLe/Ecxfnbr//tQ9f52zw5DkX2ukst4v4GLgGPBH7fs/+pXHf3ihbezevTsHqdlsDnT7\ng/bSGz9RdYSe1D1/3X9/6py/7r87g84PHM119HRPI/qIuAj4KPCRzPxYe/F3ImI0M09GxChwqpfX\n2Ew8PE7SIPRy1E0ADaCVmbeueuh+YF/79j7gvu7jbS5r/TV2jlhSL3oZ0b8BeDvwlYj4UnvZzcAt\nwD0RMQM8BlzbW0RJUi+6LvrMXAbWmmuY6na7kqT+KurMWEnSs1n0klQ4L2omqXJ1P+Js2PM7opdU\nubofcTbs+R3Rq2+GfVQjbVaO6NU3wz6qkTYri16SCmfRS1LhLHpJKpxFL0mFs+glqXAWvSQVzqKX\npMJZ9JJUuBiGk1Yi4rusXLt+UC4FvjfA7Q+a+atl/urUOTsMPv9LM/PFF1ppKIp+0CLiaGbuqTpH\nt8xfLfNXp87ZYXjyO3UjSYWz6CWpcJul6G+vOkCPzF8t81enztlhSPJvijl6SdrMNsuIXpI2reKL\nPiLeHBGPRsTXI+Jg1Xk6EREfjohTEXG86iydioiXREQzIh6JiIcj4j1VZ+pERGyPiM9FxJfb+d9f\ndaZuRMSWiPhiRHyi6iydiohvRsRXIuJLEXG06jydiogXRsS9EfHViGhFxOsry1Ly1E1EbAH+C/g9\n4ATweWA6Mx+pNNg6RcQbgdPAP2XmeNV5OhERo8BoZn4hIp4PHAOuqdHPPoAdmXk6Ii4CloH3ZOZ/\nVhytIxFxA7AH+M3MvKrqPJ2IiG8CezKzlsfRR8QR4D8y886IeA7wvMz8URVZSh/Rvxb4emZ+IzN/\nBtwNXF1xpnXLzM8CP6g6Rzcy82RmfqF9+ydAC7is2lTrlytOt+9e1P6q1agoIi4H3gLcWXWWzSYi\nXgC8EWgAZObPqip5KL/oLwO+ter+CWpUNqWIiJ3Aq4GHqk3Smfa0x5eAU8CDmVmr/MAHgPcC/1d1\nkC4l8O8RcSwi9lcdpkMvA74L/EN76uzOiNhRVZjSi14Vi4iLgY8Cf5mZ/1t1nk5k5rnM/G3gcuC1\nEVGb6bOIuAo4lZnHqs7Sg4n2z/8PgHe3pzLrYivwGuDvM/PVwJNAZfsISy/6bwMvWXX/8vYybYD2\n3PZHgY9k5seqztOt9lvuJvDmqrN04A3AW9vz3HcDvxMRd1UbqTOZ+e3291PAx1mZiq2LE8CJVe8C\n72Wl+CtRetF/HrgyIl7W3hlyHXB/xZk2hfbOzAbQysxbq87TqYh4cUS8sH37uazs0P9qtanWLzNv\nyszLM3MnK7/3n8nM6yuOtW4RsaO9E5/2lMebgNocfZaZTwDfioiXtxdNAZUdiLC1qhfeCJl5NiL+\nHPgUsAX4cGY+XHGsdYuIBWAvcGlEnADel5mNalOt2xuAtwNfac9zA9ycmf9WYaZOjAJH2kdu/QZw\nT2bW7hDFGhsBPr4yXmAr8M+Z+UC1kTo2C3ykPcj8BvCOqoIUfXilJKn8qRtJ2vQsekkqnEUvSYWz\n6CWpcBa9JBXOopekwln0klQ4i16SCvf/VAU4M7n8vWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1339dd4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = data_pivoted.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative method using `groupby` and `unstack`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "clear_cell": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1339cc15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['2012'].groupby(['weekday', 'week'])['BETR801'].mean().unstack(level=0).boxplot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: The maximum daily 8 hour mean should be below 100 µg/m³. What are the number of exceedances of this limit for each year/station?\n",
    "</div>\n",
    "\n",
    "Tip: have a look at the `rolling` method to perform moving window operations.\n",
    "\n",
    "Note: this is not an actual limit for NO2, but a nice exercise to introduce the `rolling` method. Other pollutans, such as 03 have actually such kind of limit values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1339bf3940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exceedances = data.rolling(8).mean().resample('D').max() > 100\n",
    "exceedances = exceedances.groupby(exceedances.index.year).sum()\n",
    "ax = exceedances.loc[2005:].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>QUESTION</b>: Calculate the correlation between the different stations\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "clear_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>0.472954</td>\n",
       "      <td>0.561676</td>\n",
       "      <td>0.394446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETN029</th>\n",
       "      <td>0.472954</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  0.472954  0.561676  0.394446\n",
       "BETN029  0.472954  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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['BETR801', 'BETN029', 'FR04037', 'FR04012']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "clear_cell": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>0.604825</td>\n",
       "      <td>0.663855</td>\n",
       "      <td>0.459885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETN029</th>\n",
       "      <td>0.604825</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  0.604825  0.663855  0.459885\n",
       "BETN029  0.604825  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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['BETR801', 'BETN029', 'FR04037', 'FR04012']].resample('D').mean().corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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