2.3_WaPOR_data_check.ipynb

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    "# WaPOR data check\n",
    "\n",
    "## 1. Does RS-derived Precipitation data agree with in-situ measurement?\n",
    "### Example: Extract point time series from raster maps\n",
    "Sometimes, we can obtain precipitation measurement from in-situ meteorological monitoring station or from global database (For example, NOAA Global Historical Climatology Network). This data can be used to validate remotely sensed precipitation data.\n",
    "First, we'll read data from a text file or spread sheet. Using python code, this can be done systematically. For example, below is a code to read data from the given csv file. "
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEER SHEVA\n",
      "[31.25, 34.8164]\n"
     ]
    },
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1921.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1921.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1921.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1921.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "     Year  Month    Data\n",
       "0  1921.0    9.0 -9999.0\n",
       "1  1921.0   10.0     1.1\n",
       "2  1921.0   11.0     7.4\n",
       "3  1921.0   12.0    55.7\n",
       "4  1922.0    1.0    51.7"
      ]
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     "execution_count": 1,
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   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "ts_fh=r\"E:\\Exercise\\2.3\\Jordan\\BEER SHEVA.csv\"\n",
    "\n",
    "# Read station name\n",
    "Station_name=os.path.split(ts_fh)[-1].split(\".\")[0]\n",
    "print(Station_name)\n",
    "\n",
    "# Read metadata (coordinates)\n",
    "f = open(ts_fh,'r') #open file to read\n",
    "first=f.readline().split(\";\")\n",
    "LatLon=[float(first[1]),float(first[3])]\n",
    "f.close()\n",
    "print(LatLon)\n",
    "\n",
    "# Read data\n",
    "obs_data=np.loadtxt(ts_fh,delimiter=';',skiprows=1,usecols=(1,2,4))\n",
    "obs_ts=pd.DataFrame(obs_data,columns=['Year','Month','Data'])\n",
    "obs_ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a selected month and year, the observed value can be obtained from the dataframe by *.loc* method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "        Year  Month  Data\n",
       "1048  2009.0    1.0   0.8"
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   "source": [
    "year=2009\n",
    "month=1\n",
    "obs_ts.loc[obs_ts['Year']==year].loc[obs_ts['Month']==month]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we know the coordinates of the station, we can extract the corresponding value in a raster map (RS data) for later comparison. Below is the example function *PixelCoordinates* to get corresponding row and column index of a point in raster mapset given the geographical coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "text": [
      "E:\\Exercise\\2.3\\Jordan\\P\\0_L1_monthly_PCP_200902.tif\n",
      "(2009, 2)\n",
      "(174, 1060)\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "from osgeo import gdal\n",
    "def PixelCoordinates(lon,lat,fh):\n",
    "    \"\"\"\n",
    "    Find the corresponding pixel to a latitude and longitude.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    lon : float or int\n",
    "        Longitude to find.\n",
    "    lat : float or int\n",
    "        Latitude to find.\n",
    "    fh : str\n",
    "        Filehandle pointing to the file to be searched.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    xpixel : int\n",
    "        The index of the longitude.\n",
    "    ypixel : int\n",
    "        The index of the latitude.\n",
    "    \"\"\"\n",
    "    SourceDS = gdal.Open(fh, gdal.GA_ReadOnly)\n",
    "    xsize = SourceDS.RasterXSize\n",
    "    ysize = SourceDS.RasterYSize\n",
    "    GeoT = SourceDS.GetGeoTransform()\n",
    "    assert (lon >= GeoT[0]) & (lon <= GeoT[0] + xsize * GeoT[1]), 'longitude is not on the map'\n",
    "    assert (lat <= GeoT[3]) & (lat >= GeoT[3] + ysize * GeoT[5]), 'latitude is not on the map'\n",
    "    location = GeoT[0]\n",
    "    xpixel = -1\n",
    "    while location <= lon:\n",
    "        location += GeoT[1]\n",
    "        xpixel += 1\n",
    "    location = GeoT[3]\n",
    "    ypixel = -1\n",
    "    while location >= lat:\n",
    "        location += GeoT[5]\n",
    "        ypixel += 1\n",
    "    return xpixel, ypixel   \n",
    "\n",
    "P_fhs=glob.glob(r'E:\\Exercise\\2.3\\Jordan\\P\\*.tif')\n",
    "fh=P_fhs[1]\n",
    "print(fh)\n",
    "year=int(os.path.split(fh)[1].split(\"_\")[-1][0:4])\n",
    "month=int(os.path.split(fh)[1].split(\"_\")[-1][4:6])\n",
    "print(year,month)\n",
    "xy=PixelCoordinates(LatLon[1],LatLon[0],fh)\n",
    "print(xy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once obtain the row and column index in raster array from the point coordinates, we can extract the value at the point from the array. For example, below is the code to get the remotely sensed value at Beer Sheva station from the Precipitation raster file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.0\n"
     ]
    }
   ],
   "source": [
    "def OpenRasterAsArray(in_fh,nan_value=True,bandnumber=1,dtype='float32'):  \n",
    "    SourceDataSet = gdal.Open(in_fh, gdal.GA_ReadOnly)    \n",
    "    Subdataset = SourceDataSet.GetRasterBand(bandnumber)\n",
    "    NDV = Subdataset.GetNoDataValue()\n",
    "    if NDV is None:\n",
    "        NDV=-9999\n",
    "    Array = Subdataset.ReadAsArray().astype(dtype)\n",
    "    if nan_value:\n",
    "        Array[Array == NDV] = np.nan\n",
    "    return Array\n",
    "\n",
    "map_array=OpenRasterAsArray(fh)\n",
    "px_value=map_array[xy[1]][xy[0]]\n",
    "print(px_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise (+)\n",
    "Extract a time series from the raster dataset at the location of the meteorological station. Store data in a dataframe.\n",
    "\n",
    "**Hint**:\n",
    "You can loop over the list of raster files and append a list of year, month, data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "'''Write your code here'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.200001</td>\n",
       "      <td>1</td>\n",
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       "      <td>57.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2009</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        Data  Month  Year\n",
       "0  23.200001      1  2009\n",
       "1  57.000000      2  2009\n",
       "2  29.400000      3  2009\n",
       "3   2.400000      4  2009\n",
       "4   1.500000      5  2009"
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   ],
   "source": [
    "years=[]\n",
    "months=[]\n",
    "values=[]\n",
    "for fh in P_fhs:\n",
    "    years.append(int(os.path.split(fh)[1].split(\"_\")[-1][0:4]))\n",
    "    months.append(int(os.path.split(fh)[1].split(\"_\")[-1][4:6]))\n",
    "    xy=PixelCoordinates(LatLon[1],LatLon[0],fh)\n",
    "    map_array=OpenRasterAsArray(fh)\n",
    "    px_value=map_array[xy[1]][xy[0]]\n",
    "    values.append(px_value)\n",
    "rs_ts=pd.DataFrame({'Year':years,'Month':months,'Data':values})\n",
    "rs_ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Compare RS-derived time series with observation\n",
    "Sometimes, observation and remote sensing data have different available period. Thus, it's necessary to match the 2 timeseries by time. For example, below is how we match observed data with remote sensing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Data</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>66.2</td>\n",
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       "      <th>2</th>\n",
       "      <td>29.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>16.1</td>\n",
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       "      <th>3</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>4</td>\n",
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       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.600000</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17.600000</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>2009</td>\n",
       "      <td>32.3</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65.599998</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>54.4</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49.200001</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>74.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.4</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.100000</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>49.400002</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>4</td>\n",
       "      <td>2011</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>16.600000</td>\n",
       "      <td>11</td>\n",
       "      <td>2016</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>51.299999</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>51.200001</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>55.900002</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>16.200001</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>6.700000</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>34.599998</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>80.300003</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>52.299999</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>36.400002</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>-9999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Data  Month  Year  Observed\n",
       "0    23.200001      1  2009       0.8\n",
       "1    57.000000      2  2009      66.2\n",
       "2    29.400000      3  2009      16.1\n",
       "3     2.400000      4  2009       0.9\n",
       "4     1.500000      5  2009       0.0\n",
       "5     0.000000      6  2009       0.0\n",
       "6     0.000000      7  2009       0.0\n",
       "7     0.000000      8  2009       0.0\n",
       "8     0.000000      9  2009       0.0\n",
       "9     6.600000     10  2009       3.2\n",
       "10   17.600000     11  2009      18.8\n",
       "11   21.900000     12  2009      32.3\n",
       "12   65.599998      1  2010      54.4\n",
       "13   49.200001      2  2010      74.9\n",
       "14   20.500000      3  2010      21.8\n",
       "15    0.000000      4  2010       3.0\n",
       "16    0.600000      5  2010       5.4\n",
       "17    0.000000      6  2010       0.0\n",
       "18    0.000000      7  2010       0.0\n",
       "19    0.000000      8  2010       0.0\n",
       "20    0.200000      9  2010       0.3\n",
       "21    0.000000     10  2010       1.8\n",
       "22    9.100000     11  2010       0.9\n",
       "23   18.900000     12  2010      13.7\n",
       "24   37.500000      1  2011      81.7\n",
       "25   49.400002      2  2011      29.7\n",
       "26   23.600000      3  2011       5.6\n",
       "27    5.100000      4  2011      14.3\n",
       "28    1.300000      5  2011       3.9\n",
       "29    0.000000      6  2011       0.0\n",
       "..         ...    ...   ...       ...\n",
       "90    0.000000      7  2016   -9999.0\n",
       "91    0.000000      8  2016   -9999.0\n",
       "92    0.000000      9  2016   -9999.0\n",
       "93    7.700000     10  2016   -9999.0\n",
       "94   16.600000     11  2016   -9999.0\n",
       "95   51.299999     12  2016   -9999.0\n",
       "96   51.200001      1  2017   -9999.0\n",
       "97   55.900002      2  2017   -9999.0\n",
       "98   16.200001      3  2017   -9999.0\n",
       "99    6.700000      4  2017   -9999.0\n",
       "100   1.300000      5  2017   -9999.0\n",
       "101   0.000000      6  2017   -9999.0\n",
       "102   0.000000      7  2017   -9999.0\n",
       "103   0.000000      8  2017   -9999.0\n",
       "104   0.000000      9  2017   -9999.0\n",
       "105   6.300000     10  2017   -9999.0\n",
       "106  19.000000     11  2017   -9999.0\n",
       "107  34.599998     12  2017   -9999.0\n",
       "108  80.300003      1  2018   -9999.0\n",
       "109  52.299999      2  2018   -9999.0\n",
       "110  15.900000      3  2018   -9999.0\n",
       "111   0.700000      4  2018   -9999.0\n",
       "112   2.100000      5  2018   -9999.0\n",
       "113   0.000000      6  2018   -9999.0\n",
       "114   0.000000      7  2018   -9999.0\n",
       "115   0.000000      8  2018   -9999.0\n",
       "116   0.800000      9  2018   -9999.0\n",
       "117   1.200000     10  2018   -9999.0\n",
       "118  28.000000     11  2018   -9999.0\n",
       "119  36.400002     12  2018   -9999.0\n",
       "\n",
       "[120 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_values=[]\n",
    "for i in range(len(rs_ts)):\n",
    "    month=rs_ts.iloc[i]['Month']\n",
    "    year=rs_ts.iloc[i]['Year']\n",
    "    obs_value=-9999\n",
    "    #check if the month and year has observed data\n",
    "    for j in range(len(obs_ts)):\n",
    "        jline=obs_ts.iloc[j]\n",
    "        if (year == jline['Year']) & (month == jline['Month']):        \n",
    "            obs_value=jline['Data']    \n",
    "        else:\n",
    "            continue \n",
    "    obs_values.append(obs_value)\n",
    "rs_ts['Observed']=obs_values\n",
    "rs_ts.head()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When there's remote sensing data but no observed data available (-9999 value), these values should be dropped out of analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>Data</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.200001</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2009</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.600000</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17.600000</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>2009</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65.599998</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>54.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49.200001</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>74.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.100000</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18.900000</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>49.400002</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>4</td>\n",
       "      <td>2011</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.300000</td>\n",
       "      <td>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.0</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>58</th>\n",
       "      <td>12.600000</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>63.799999</td>\n",
       "      <td>12</td>\n",
       "      <td>2013</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>28.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>78.900002</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>69.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4.200000</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>10.100000</td>\n",
       "      <td>10</td>\n",
       "      <td>2014</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>43.200001</td>\n",
       "      <td>11</td>\n",
       "      <td>2014</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>29.200001</td>\n",
       "      <td>12</td>\n",
       "      <td>2014</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>82.300003</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>86.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>81.300003</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>10.400000</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>16.200001</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>24.799999</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "      <td>27.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>38.299999</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>84.800003</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>77.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>45.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>32.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>12.700000</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Data  Month  Year  Observed\n",
       "0   23.200001      1  2009       0.8\n",
       "1   57.000000      2  2009      66.2\n",
       "2   29.400000      3  2009      16.1\n",
       "3    2.400000      4  2009       0.9\n",
       "4    1.500000      5  2009       0.0\n",
       "5    0.000000      6  2009       0.0\n",
       "6    0.000000      7  2009       0.0\n",
       "7    0.000000      8  2009       0.0\n",
       "8    0.000000      9  2009       0.0\n",
       "9    6.600000     10  2009       3.2\n",
       "10  17.600000     11  2009      18.8\n",
       "11  21.900000     12  2009      32.3\n",
       "12  65.599998      1  2010      54.4\n",
       "13  49.200001      2  2010      74.9\n",
       "14  20.500000      3  2010      21.8\n",
       "15   0.000000      4  2010       3.0\n",
       "16   0.600000      5  2010       5.4\n",
       "17   0.000000      6  2010       0.0\n",
       "18   0.000000      7  2010       0.0\n",
       "19   0.000000      8  2010       0.0\n",
       "20   0.200000      9  2010       0.3\n",
       "21   0.000000     10  2010       1.8\n",
       "22   9.100000     11  2010       0.9\n",
       "23  18.900000     12  2010      13.7\n",
       "24  37.500000      1  2011      81.7\n",
       "25  49.400002      2  2011      29.7\n",
       "26  23.600000      3  2011       5.6\n",
       "27   5.100000      4  2011      14.3\n",
       "28   1.300000      5  2011       3.9\n",
       "29   0.000000      6  2011       0.0\n",
       "..        ...    ...   ...       ...\n",
       "58  12.600000     11  2013       0.6\n",
       "59  63.799999     12  2013     146.0\n",
       "60  28.500000      1  2014       4.7\n",
       "61  26.000000      2  2014       3.2\n",
       "62  78.900002      3  2014      69.6\n",
       "63   4.200000      4  2014       0.0\n",
       "64   1.900000      5  2014      35.1\n",
       "65   0.000000      6  2014       0.0\n",
       "66   0.000000      7  2014       0.0\n",
       "67   0.000000      8  2014       0.0\n",
       "68   0.000000      9  2014       0.0\n",
       "69  10.100000     10  2014      11.6\n",
       "70  43.200001     11  2014      36.3\n",
       "71  29.200001     12  2014      12.9\n",
       "72  82.300003      1  2015      86.9\n",
       "73  81.300003      2  2015      85.5\n",
       "74  16.500000      3  2015       6.6\n",
       "75  10.400000      4  2015       9.1\n",
       "76   0.000000      5  2015       0.4\n",
       "77   0.000000      6  2015       0.0\n",
       "78   0.000000      7  2015       0.0\n",
       "79   0.000000      8  2015       0.0\n",
       "80   0.000000      9  2015       0.0\n",
       "81  16.200001     10  2015      22.8\n",
       "82  24.799999     11  2015      27.2\n",
       "83  38.299999     12  2015      43.7\n",
       "84  84.800003      1  2016      77.9\n",
       "85  50.500000      2  2016      45.4\n",
       "86  32.500000      3  2016       9.8\n",
       "87  12.700000      4  2016      28.0\n",
       "\n",
       "[88 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_ts.replace(-9999,np.nan)\n",
    "rs_ts.drop(rs_ts.loc[rs_ts['Observed']==-9999].index,inplace=True)\n",
    "rs_ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One common method to validate remotely sensed data with observed data is using scatter plot and regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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RKEYVimdEAwAz6wp0iLyc5+6fxnuvSO2LsoqHlVtYzN03ApdFHiIiUtdt3Ag3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\nDA3DMAzDMFKGGRqGYRiGYaSM/wc9FfXbjTeaQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124aae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "#plot scatter points\n",
    "plt.scatter(rs_ts['Data'].values,rs_ts['Observed'].values)\n",
    "plt.xlabel(\"WaPOR Precipitation [mm/month]\")\n",
    "plt.ylabel(\"Observed Precipitation [mm/month]\")\n",
    "plt.title(Station_name)\n",
    "\n",
    "# regression line\n",
    "#fitting RS and Observed data into a 1st order polynomial function (linear). z is the set of parameters of the fitted function\n",
    "z = np.polyfit(rs_ts['Data'].values, rs_ts['Observed'].values, 1) \n",
    "#define the fitted linear function using parameter as p(x) where x is observed value\n",
    "p = np.poly1d(z)\n",
    "function = \"%.2f * x+ (%.2f)\"%(z[0],z[1]) \n",
    "#plot x=[0, max_x_value] and p(x)\n",
    "plt.plot([0,max(rs_ts['Observed'])],p([0,max(rs_ts['Observed'])]),\"r\",label=function)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "#adjust plot\n",
    "plt.ylim(0,max(rs_ts['Observed']))\n",
    "plt.xlim(0,max(rs_ts['Observed']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise (optional ++++)\n",
    "- Write a program to validate and bias-correct Remotely sensed precipitation data using observed data\n",
    "- Collect in-situ Precipitation data in your study area to validate a remote sensing product (Ex. WaPOR, TRMM)\n",
    "- Calculate Bias coefficient of remotely sensed data compare to observed data. Correct remotely sensed data if necessary (High bias value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Can RS-derived data close the water balance?\n",
    "A csv of yearly P, ET, and dS derived from GRACE of Litani basin, Lebanon was prepared. Annual P, ET can be computed using the exampled code from previous exercises. The dS (Total storage change) is calculated from GRACE GFSC Mascon solution. For more details, you can have a look at the given scripts in *GRACE_for_basin* folder.\n",
    "First, load the csv data as pandas Dataframe. The unit of the data values is MCM/year or Mm3/yr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>year</th>\n",
       "      <th>P</th>\n",
       "      <th>ET</th>\n",
       "      <th>dS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1387.340</td>\n",
       "      <td>1049.236000</td>\n",
       "      <td>-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>1376.750</td>\n",
       "      <td>927.632158</td>\n",
       "      <td>-77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>1775.850</td>\n",
       "      <td>932.551200</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>1612.000</td>\n",
       "      <td>1039.102000</td>\n",
       "      <td>-83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>992.138</td>\n",
       "      <td>713.588700</td>\n",
       "      <td>-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>1153.029</td>\n",
       "      <td>829.601700</td>\n",
       "      <td>-106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>1292.685</td>\n",
       "      <td>785.900000</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year         P           ET   dS\n",
       "0  2010  1387.340  1049.236000  -70\n",
       "1  2011  1376.750   927.632158  -77\n",
       "2  2012  1775.850   932.551200   38\n",
       "3  2013  1612.000  1039.102000  -83\n",
       "4  2014   992.138   713.588700  -28\n",
       "5  2015  1153.029   829.601700 -106\n",
       "6  2016  1292.685   785.900000  -41"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "Litani_csv=r\"E:\\Exercise\\2.3\\Litani\\P_ET_dS_stats.csv\"\n",
    "df=pd.read_csv(Litani_csv,sep=';',index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate a new column as function of existing columns using the dataframe method *apply*\n",
    "\n",
    "*df.apply(**lamda** row: function(row['column1'],row['column2'],...), axis=1)*\n",
    "\n",
    "For example, below is how to calculate new column P-ET-dS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": 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>year</th>\n",
       "      <th>P</th>\n",
       "      <th>ET</th>\n",
       "      <th>dS</th>\n",
       "      <th>P-ET-dS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1387.340</td>\n",
       "      <td>1049.236000</td>\n",
       "      <td>-70</td>\n",
       "      <td>408.104000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>1376.750</td>\n",
       "      <td>927.632158</td>\n",
       "      <td>-77</td>\n",
       "      <td>526.117842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>1775.850</td>\n",
       "      <td>932.551200</td>\n",
       "      <td>38</td>\n",
       "      <td>805.298800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>1612.000</td>\n",
       "      <td>1039.102000</td>\n",
       "      <td>-83</td>\n",
       "      <td>655.898000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>992.138</td>\n",
       "      <td>713.588700</td>\n",
       "      <td>-28</td>\n",
       "      <td>306.549300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>1153.029</td>\n",
       "      <td>829.601700</td>\n",
       "      <td>-106</td>\n",
       "      <td>429.427300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>1292.685</td>\n",
       "      <td>785.900000</td>\n",
       "      <td>-41</td>\n",
       "      <td>547.785000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year         P           ET   dS     P-ET-dS\n",
       "0  2010  1387.340  1049.236000  -70  408.104000\n",
       "1  2011  1376.750   927.632158  -77  526.117842\n",
       "2  2012  1775.850   932.551200   38  805.298800\n",
       "3  2013  1612.000  1039.102000  -83  655.898000\n",
       "4  2014   992.138   713.588700  -28  306.549300\n",
       "5  2015  1153.029   829.601700 -106  429.427300\n",
       "6  2016  1292.685   785.900000  -41  547.785000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['P-ET-dS']=df.apply(lambda row: row['P'] - row['ET'] - row['dS'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discharges were measured at the outlet of Litani basin and stored in a csv file. To compare the yearly discharge at outlet, we need to read the data from csv file and convert into yearly data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>m3/s</th>\n",
       "      <th>km3/month</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>01/01/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.859613</td>\n",
       "      <td>0.045157</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15.560821</td>\n",
       "      <td>0.037645</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.270065</td>\n",
       "      <td>0.056970</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9.422667</td>\n",
       "      <td>0.024424</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.005290</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.171233</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111000</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.049742</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1.275767</td>\n",
       "      <td>0.003307</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.295968</td>\n",
       "      <td>0.003471</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.321500</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.275323</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.322226</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.010679</td>\n",
       "      <td>0.038733</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25.507548</td>\n",
       "      <td>0.068319</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11.943733</td>\n",
       "      <td>0.030958</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.294903</td>\n",
       "      <td>0.011503</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.241033</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.201226</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.223645</td>\n",
       "      <td>0.003277</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216467</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.231226</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3.917333</td>\n",
       "      <td>0.010154</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>7.865871</td>\n",
       "      <td>0.021068</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.810548</td>\n",
       "      <td>0.050382</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.174448</td>\n",
       "      <td>0.065583</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43.235290</td>\n",
       "      <td>0.115801</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>23.709667</td>\n",
       "      <td>0.061455</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.681677</td>\n",
       "      <td>0.020575</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4.936933</td>\n",
       "      <td>0.012797</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.968000</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.667000</td>\n",
       "      <td>0.004321</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.323000</td>\n",
       "      <td>0.014257</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.677000</td>\n",
       "      <td>0.039311</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.071000</td>\n",
       "      <td>0.038879</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.040176</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.028512</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.903000</td>\n",
       "      <td>0.007775</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161000</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161000</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.104000</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2.401000</td>\n",
       "      <td>0.006431</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.117000</td>\n",
       "      <td>0.021741</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.745000</td>\n",
       "      <td>0.021156</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.323000</td>\n",
       "      <td>0.016936</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5.067000</td>\n",
       "      <td>0.013134</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.458000</td>\n",
       "      <td>0.014619</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year  month  day       m3/s  km3/month  days\n",
       "datetime                                                      \n",
       "01/01/2010 00:00  2010      1    1  16.859613   0.045157    31\n",
       "01/02/2010 00:00  2010      2    1  15.560821   0.037645    28\n",
       "01/03/2010 00:00  2010      3    1  21.270065   0.056970    31\n",
       "01/04/2010 00:00  2010      4    1   9.422667   0.024424    30\n",
       "01/05/2010 00:00  2010      5    1   2.005290   0.005371    31\n",
       "01/06/2010 00:00  2010      6    1   0.171233   0.000444    30\n",
       "01/07/2010 00:00  2010      7    1   0.111000   0.000297    31\n",
       "01/08/2010 00:00  2010      8    1   0.049742   0.000133    31\n",
       "01/09/2010 00:00  2010      9    1   1.275767   0.003307    30\n",
       "01/10/2010 00:00  2010     10    1   1.295968   0.003471    31\n",
       "01/11/2010 00:00  2010     11    1   1.321500   0.003425    30\n",
       "01/12/2010 00:00  2010     12    1   1.275323   0.003416    31\n",
       "01/01/2011 00:00  2011      1    1   1.322226   0.003541    31\n",
       "01/02/2011 00:00  2011      2    1  16.010679   0.038733    28\n",
       "01/03/2011 00:00  2011      3    1  25.507548   0.068319    31\n",
       "01/04/2011 00:00  2011      4    1  11.943733   0.030958    30\n",
       "01/05/2011 00:00  2011      5    1   4.294903   0.011503    31\n",
       "01/06/2011 00:00  2011      6    1   1.241033   0.003217    30\n",
       "01/07/2011 00:00  2011      7    1   1.201226   0.003217    31\n",
       "01/08/2011 00:00  2011      8    1   1.223645   0.003277    31\n",
       "01/09/2011 00:00  2011      9    1   0.216467   0.000561    30\n",
       "01/10/2011 00:00  2011     10    1   0.231226   0.000619    31\n",
       "01/11/2011 00:00  2011     11    1   3.917333   0.010154    30\n",
       "01/12/2011 00:00  2011     12    1   7.865871   0.021068    31\n",
       "01/01/2012 00:00  2012      1    1  18.810548   0.050382    31\n",
       "01/02/2012 00:00  2012      2    1  26.174448   0.065583    29\n",
       "01/03/2012 00:00  2012      3    1  43.235290   0.115801    31\n",
       "01/04/2012 00:00  2012      4    1  23.709667   0.061455    30\n",
       "01/05/2012 00:00  2012      5    1   7.681677   0.020575    31\n",
       "01/06/2012 00:00  2012      6    1   4.936933   0.012797    30\n",
       "...                ...    ...  ...        ...        ...   ...\n",
       "01/07/2014 00:00  2014      7    1   0.000000   0.000000    31\n",
       "01/08/2014 00:00  2014      8    1   0.000000   0.000000    31\n",
       "01/09/2014 00:00  2014      9    1   0.000000   0.000000    30\n",
       "01/10/2014 00:00  2014     10    1   0.968000   0.002593    31\n",
       "01/11/2014 00:00  2014     11    1   1.667000   0.004321    30\n",
       "01/12/2014 00:00  2014     12    1   5.323000   0.014257    31\n",
       "01/01/2015 00:00  2015      1    1  14.677000   0.039311    31\n",
       "01/02/2015 00:00  2015      2    1  16.071000   0.038879    28\n",
       "01/03/2015 00:00  2015      3    1  15.000000   0.040176    31\n",
       "01/04/2015 00:00  2015      4    1  11.000000   0.028512    30\n",
       "01/05/2015 00:00  2015      5    1   2.903000   0.007775    31\n",
       "01/06/2015 00:00  2015      6    1   0.167000   0.000433    30\n",
       "01/07/2015 00:00  2015      7    1   0.161000   0.000431    31\n",
       "01/08/2015 00:00  2015      8    1   0.161000   0.000431    31\n",
       "01/09/2015 00:00  2015      9    1   0.014000   0.000036    30\n",
       "01/10/2015 00:00  2015     10    1   0.104000   0.000279    31\n",
       "01/11/2015 00:00  2015     11    1   2.120000   0.005495    30\n",
       "01/12/2015 00:00  2015     12    1   2.401000   0.006431    31\n",
       "01/01/2016 00:00  2016      1    1   8.117000   0.021741    31\n",
       "01/02/2016 00:00  2016      2    1   8.745000   0.021156    28\n",
       "01/03/2016 00:00  2016      3    1   6.323000   0.016936    31\n",
       "01/04/2016 00:00  2016      4    1   5.067000   0.013134    30\n",
       "01/05/2016 00:00  2016      5    1   0.672000   0.001800    31\n",
       "01/06/2016 00:00  2016      6    1   0.094000   0.000244    30\n",
       "01/07/2016 00:00  2016      7    1   0.130000   0.000348    31\n",
       "01/08/2016 00:00  2016      8    1   0.000000   0.000000    31\n",
       "01/09/2016 00:00  2016      9    1   0.140000   0.000363    30\n",
       "01/10/2016 00:00  2016     10    1   0.235000   0.000629    31\n",
       "01/11/2016 00:00  2016     11    1   0.220000   0.000570    30\n",
       "01/12/2016 00:00  2016     12    1   5.458000   0.014619    31\n",
       "\n",
       "[84 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qoutlet_csv=r\"E:\\Exercise\\2.3\\Litani\\Qoutlet_litani.csv\"\n",
    "Qoutlet=pd.read_csv(Qoutlet_csv,sep=';',index_col=0,skiprows=1)\n",
    "Qoutlet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Always check for nodata values and exclude them from calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": 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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>m3/s</th>\n",
       "      <th>km3/month</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>01/01/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.859613</td>\n",
       "      <td>0.045157</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15.560821</td>\n",
       "      <td>0.037645</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.270065</td>\n",
       "      <td>0.056970</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9.422667</td>\n",
       "      <td>0.024424</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.005290</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.171233</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111000</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.049742</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1.275767</td>\n",
       "      <td>0.003307</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.295968</td>\n",
       "      <td>0.003471</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.321500</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.275323</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.322226</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.010679</td>\n",
       "      <td>0.038733</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25.507548</td>\n",
       "      <td>0.068319</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11.943733</td>\n",
       "      <td>0.030958</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.294903</td>\n",
       "      <td>0.011503</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.241033</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.201226</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.223645</td>\n",
       "      <td>0.003277</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216467</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.231226</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3.917333</td>\n",
       "      <td>0.010154</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>7.865871</td>\n",
       "      <td>0.021068</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.810548</td>\n",
       "      <td>0.050382</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.174448</td>\n",
       "      <td>0.065583</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43.235290</td>\n",
       "      <td>0.115801</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>23.709667</td>\n",
       "      <td>0.061455</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.681677</td>\n",
       "      <td>0.020575</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4.936933</td>\n",
       "      <td>0.012797</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.968000</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.667000</td>\n",
       "      <td>0.004321</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.323000</td>\n",
       "      <td>0.014257</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.677000</td>\n",
       "      <td>0.039311</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.071000</td>\n",
       "      <td>0.038879</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.040176</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.028512</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.903000</td>\n",
       "      <td>0.007775</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161000</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161000</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.104000</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2.401000</td>\n",
       "      <td>0.006431</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.117000</td>\n",
       "      <td>0.021741</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.745000</td>\n",
       "      <td>0.021156</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.323000</td>\n",
       "      <td>0.016936</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5.067000</td>\n",
       "      <td>0.013134</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.458000</td>\n",
       "      <td>0.014619</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year  month  day       m3/s  km3/month  days\n",
       "datetime                                                      \n",
       "01/01/2010 00:00  2010      1    1  16.859613   0.045157    31\n",
       "01/02/2010 00:00  2010      2    1  15.560821   0.037645    28\n",
       "01/03/2010 00:00  2010      3    1  21.270065   0.056970    31\n",
       "01/04/2010 00:00  2010      4    1   9.422667   0.024424    30\n",
       "01/05/2010 00:00  2010      5    1   2.005290   0.005371    31\n",
       "01/06/2010 00:00  2010      6    1   0.171233   0.000444    30\n",
       "01/07/2010 00:00  2010      7    1   0.111000   0.000297    31\n",
       "01/08/2010 00:00  2010      8    1   0.049742   0.000133    31\n",
       "01/09/2010 00:00  2010      9    1   1.275767   0.003307    30\n",
       "01/10/2010 00:00  2010     10    1   1.295968   0.003471    31\n",
       "01/11/2010 00:00  2010     11    1   1.321500   0.003425    30\n",
       "01/12/2010 00:00  2010     12    1   1.275323   0.003416    31\n",
       "01/01/2011 00:00  2011      1    1   1.322226   0.003541    31\n",
       "01/02/2011 00:00  2011      2    1  16.010679   0.038733    28\n",
       "01/03/2011 00:00  2011      3    1  25.507548   0.068319    31\n",
       "01/04/2011 00:00  2011      4    1  11.943733   0.030958    30\n",
       "01/05/2011 00:00  2011      5    1   4.294903   0.011503    31\n",
       "01/06/2011 00:00  2011      6    1   1.241033   0.003217    30\n",
       "01/07/2011 00:00  2011      7    1   1.201226   0.003217    31\n",
       "01/08/2011 00:00  2011      8    1   1.223645   0.003277    31\n",
       "01/09/2011 00:00  2011      9    1   0.216467   0.000561    30\n",
       "01/10/2011 00:00  2011     10    1   0.231226   0.000619    31\n",
       "01/11/2011 00:00  2011     11    1   3.917333   0.010154    30\n",
       "01/12/2011 00:00  2011     12    1   7.865871   0.021068    31\n",
       "01/01/2012 00:00  2012      1    1  18.810548   0.050382    31\n",
       "01/02/2012 00:00  2012      2    1  26.174448   0.065583    29\n",
       "01/03/2012 00:00  2012      3    1  43.235290   0.115801    31\n",
       "01/04/2012 00:00  2012      4    1  23.709667   0.061455    30\n",
       "01/05/2012 00:00  2012      5    1   7.681677   0.020575    31\n",
       "01/06/2012 00:00  2012      6    1   4.936933   0.012797    30\n",
       "...                ...    ...  ...        ...        ...   ...\n",
       "01/07/2014 00:00  2014      7    1   0.000000   0.000000    31\n",
       "01/08/2014 00:00  2014      8    1   0.000000   0.000000    31\n",
       "01/09/2014 00:00  2014      9    1   0.000000   0.000000    30\n",
       "01/10/2014 00:00  2014     10    1   0.968000   0.002593    31\n",
       "01/11/2014 00:00  2014     11    1   1.667000   0.004321    30\n",
       "01/12/2014 00:00  2014     12    1   5.323000   0.014257    31\n",
       "01/01/2015 00:00  2015      1    1  14.677000   0.039311    31\n",
       "01/02/2015 00:00  2015      2    1  16.071000   0.038879    28\n",
       "01/03/2015 00:00  2015      3    1  15.000000   0.040176    31\n",
       "01/04/2015 00:00  2015      4    1  11.000000   0.028512    30\n",
       "01/05/2015 00:00  2015      5    1   2.903000   0.007775    31\n",
       "01/06/2015 00:00  2015      6    1   0.167000   0.000433    30\n",
       "01/07/2015 00:00  2015      7    1   0.161000   0.000431    31\n",
       "01/08/2015 00:00  2015      8    1   0.161000   0.000431    31\n",
       "01/09/2015 00:00  2015      9    1   0.014000   0.000036    30\n",
       "01/10/2015 00:00  2015     10    1   0.104000   0.000279    31\n",
       "01/11/2015 00:00  2015     11    1   2.120000   0.005495    30\n",
       "01/12/2015 00:00  2015     12    1   2.401000   0.006431    31\n",
       "01/01/2016 00:00  2016      1    1   8.117000   0.021741    31\n",
       "01/02/2016 00:00  2016      2    1   8.745000   0.021156    28\n",
       "01/03/2016 00:00  2016      3    1   6.323000   0.016936    31\n",
       "01/04/2016 00:00  2016      4    1   5.067000   0.013134    30\n",
       "01/05/2016 00:00  2016      5    1   0.672000   0.001800    31\n",
       "01/06/2016 00:00  2016      6    1   0.094000   0.000244    30\n",
       "01/07/2016 00:00  2016      7    1   0.130000   0.000348    31\n",
       "01/08/2016 00:00  2016      8    1   0.000000   0.000000    31\n",
       "01/09/2016 00:00  2016      9    1   0.140000   0.000363    30\n",
       "01/10/2016 00:00  2016     10    1   0.235000   0.000629    31\n",
       "01/11/2016 00:00  2016     11    1   0.220000   0.000570    30\n",
       "01/12/2016 00:00  2016     12    1   5.458000   0.014619    31\n",
       "\n",
       "[84 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qoutlet.drop(Qoutlet.loc[Qoutlet['m3/s']==-9999].index,inplace=True)\n",
    "Qoutlet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the *apply* method, convert discharge in m3/s to Mm3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": 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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>m3/s</th>\n",
       "      <th>km3/month</th>\n",
       "      <th>days</th>\n",
       "      <th>Mm3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>01/01/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.859613</td>\n",
       "      <td>0.045157</td>\n",
       "      <td>31</td>\n",
       "      <td>45.156787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15.560821</td>\n",
       "      <td>0.037645</td>\n",
       "      <td>28</td>\n",
       "      <td>37.644739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.270065</td>\n",
       "      <td>0.056970</td>\n",
       "      <td>31</td>\n",
       "      <td>56.969741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9.422667</td>\n",
       "      <td>0.024424</td>\n",
       "      <td>30</td>\n",
       "      <td>24.423552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.005290</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>31</td>\n",
       "      <td>5.370970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.171233</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>30</td>\n",
       "      <td>0.443837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111000</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>31</td>\n",
       "      <td>0.297302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.049742</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>31</td>\n",
       "      <td>0.133229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1.275767</td>\n",
       "      <td>0.003307</td>\n",
       "      <td>30</td>\n",
       "      <td>3.306787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.295968</td>\n",
       "      <td>0.003471</td>\n",
       "      <td>31</td>\n",
       "      <td>3.471120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.321500</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>30</td>\n",
       "      <td>3.425328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2010 00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.275323</td>\n",
       "      <td>0.003416</td>\n",
       "      <td>31</td>\n",
       "      <td>3.415824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.322226</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>31</td>\n",
       "      <td>3.541450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.010679</td>\n",
       "      <td>0.038733</td>\n",
       "      <td>28</td>\n",
       "      <td>38.733034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25.507548</td>\n",
       "      <td>0.068319</td>\n",
       "      <td>31</td>\n",
       "      <td>68.319418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11.943733</td>\n",
       "      <td>0.030958</td>\n",
       "      <td>30</td>\n",
       "      <td>30.958157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.294903</td>\n",
       "      <td>0.011503</td>\n",
       "      <td>31</td>\n",
       "      <td>11.503469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.241033</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>30</td>\n",
       "      <td>3.216758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.201226</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>31</td>\n",
       "      <td>3.217363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.223645</td>\n",
       "      <td>0.003277</td>\n",
       "      <td>31</td>\n",
       "      <td>3.277411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216467</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>30</td>\n",
       "      <td>0.561082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.231226</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>31</td>\n",
       "      <td>0.619315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3.917333</td>\n",
       "      <td>0.010154</td>\n",
       "      <td>30</td>\n",
       "      <td>10.153728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2011 00:00</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>7.865871</td>\n",
       "      <td>0.021068</td>\n",
       "      <td>31</td>\n",
       "      <td>21.067949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.810548</td>\n",
       "      <td>0.050382</td>\n",
       "      <td>31</td>\n",
       "      <td>50.382173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.174448</td>\n",
       "      <td>0.065583</td>\n",
       "      <td>29</td>\n",
       "      <td>65.582698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43.235290</td>\n",
       "      <td>0.115801</td>\n",
       "      <td>31</td>\n",
       "      <td>115.801402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>23.709667</td>\n",
       "      <td>0.061455</td>\n",
       "      <td>30</td>\n",
       "      <td>61.455456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.681677</td>\n",
       "      <td>0.020575</td>\n",
       "      <td>31</td>\n",
       "      <td>20.574605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2012 00:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4.936933</td>\n",
       "      <td>0.012797</td>\n",
       "      <td>30</td>\n",
       "      <td>12.796531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.968000</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>31</td>\n",
       "      <td>2.592691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.667000</td>\n",
       "      <td>0.004321</td>\n",
       "      <td>30</td>\n",
       "      <td>4.320864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2014 00:00</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.323000</td>\n",
       "      <td>0.014257</td>\n",
       "      <td>31</td>\n",
       "      <td>14.257123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.677000</td>\n",
       "      <td>0.039311</td>\n",
       "      <td>31</td>\n",
       "      <td>39.310877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.071000</td>\n",
       "      <td>0.038879</td>\n",
       "      <td>28</td>\n",
       "      <td>38.878963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.040176</td>\n",
       "      <td>31</td>\n",
       "      <td>40.176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.028512</td>\n",
       "      <td>30</td>\n",
       "      <td>28.512000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.903000</td>\n",
       "      <td>0.007775</td>\n",
       "      <td>31</td>\n",
       "      <td>7.775395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>30</td>\n",
       "      <td>0.432864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161000</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>31</td>\n",
       "      <td>0.431222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161000</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>31</td>\n",
       "      <td>0.431222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>30</td>\n",
       "      <td>0.036288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.104000</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>31</td>\n",
       "      <td>0.278554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>30</td>\n",
       "      <td>5.495040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2015 00:00</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2.401000</td>\n",
       "      <td>0.006431</td>\n",
       "      <td>31</td>\n",
       "      <td>6.430838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/01/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.117000</td>\n",
       "      <td>0.021741</td>\n",
       "      <td>31</td>\n",
       "      <td>21.740573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/02/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.745000</td>\n",
       "      <td>0.021156</td>\n",
       "      <td>28</td>\n",
       "      <td>21.155904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.323000</td>\n",
       "      <td>0.016936</td>\n",
       "      <td>31</td>\n",
       "      <td>16.935523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5.067000</td>\n",
       "      <td>0.013134</td>\n",
       "      <td>30</td>\n",
       "      <td>13.133664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>31</td>\n",
       "      <td>1.799885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/06/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>30</td>\n",
       "      <td>0.243648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/07/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>31</td>\n",
       "      <td>0.348192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/09/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>30</td>\n",
       "      <td>0.362880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/10/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>31</td>\n",
       "      <td>0.629424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/11/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>30</td>\n",
       "      <td>0.570240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2016 00:00</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5.458000</td>\n",
       "      <td>0.014619</td>\n",
       "      <td>31</td>\n",
       "      <td>14.618707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year  month  day       m3/s  km3/month  days         Mm3\n",
       "datetime                                                                  \n",
       "01/01/2010 00:00  2010      1    1  16.859613   0.045157    31   45.156787\n",
       "01/02/2010 00:00  2010      2    1  15.560821   0.037645    28   37.644739\n",
       "01/03/2010 00:00  2010      3    1  21.270065   0.056970    31   56.969741\n",
       "01/04/2010 00:00  2010      4    1   9.422667   0.024424    30   24.423552\n",
       "01/05/2010 00:00  2010      5    1   2.005290   0.005371    31    5.370970\n",
       "01/06/2010 00:00  2010      6    1   0.171233   0.000444    30    0.443837\n",
       "01/07/2010 00:00  2010      7    1   0.111000   0.000297    31    0.297302\n",
       "01/08/2010 00:00  2010      8    1   0.049742   0.000133    31    0.133229\n",
       "01/09/2010 00:00  2010      9    1   1.275767   0.003307    30    3.306787\n",
       "01/10/2010 00:00  2010     10    1   1.295968   0.003471    31    3.471120\n",
       "01/11/2010 00:00  2010     11    1   1.321500   0.003425    30    3.425328\n",
       "01/12/2010 00:00  2010     12    1   1.275323   0.003416    31    3.415824\n",
       "01/01/2011 00:00  2011      1    1   1.322226   0.003541    31    3.541450\n",
       "01/02/2011 00:00  2011      2    1  16.010679   0.038733    28   38.733034\n",
       "01/03/2011 00:00  2011      3    1  25.507548   0.068319    31   68.319418\n",
       "01/04/2011 00:00  2011      4    1  11.943733   0.030958    30   30.958157\n",
       "01/05/2011 00:00  2011      5    1   4.294903   0.011503    31   11.503469\n",
       "01/06/2011 00:00  2011      6    1   1.241033   0.003217    30    3.216758\n",
       "01/07/2011 00:00  2011      7    1   1.201226   0.003217    31    3.217363\n",
       "01/08/2011 00:00  2011      8    1   1.223645   0.003277    31    3.277411\n",
       "01/09/2011 00:00  2011      9    1   0.216467   0.000561    30    0.561082\n",
       "01/10/2011 00:00  2011     10    1   0.231226   0.000619    31    0.619315\n",
       "01/11/2011 00:00  2011     11    1   3.917333   0.010154    30   10.153728\n",
       "01/12/2011 00:00  2011     12    1   7.865871   0.021068    31   21.067949\n",
       "01/01/2012 00:00  2012      1    1  18.810548   0.050382    31   50.382173\n",
       "01/02/2012 00:00  2012      2    1  26.174448   0.065583    29   65.582698\n",
       "01/03/2012 00:00  2012      3    1  43.235290   0.115801    31  115.801402\n",
       "01/04/2012 00:00  2012      4    1  23.709667   0.061455    30   61.455456\n",
       "01/05/2012 00:00  2012      5    1   7.681677   0.020575    31   20.574605\n",
       "01/06/2012 00:00  2012      6    1   4.936933   0.012797    30   12.796531\n",
       "...                ...    ...  ...        ...        ...   ...         ...\n",
       "01/07/2014 00:00  2014      7    1   0.000000   0.000000    31    0.000000\n",
       "01/08/2014 00:00  2014      8    1   0.000000   0.000000    31    0.000000\n",
       "01/09/2014 00:00  2014      9    1   0.000000   0.000000    30    0.000000\n",
       "01/10/2014 00:00  2014     10    1   0.968000   0.002593    31    2.592691\n",
       "01/11/2014 00:00  2014     11    1   1.667000   0.004321    30    4.320864\n",
       "01/12/2014 00:00  2014     12    1   5.323000   0.014257    31   14.257123\n",
       "01/01/2015 00:00  2015      1    1  14.677000   0.039311    31   39.310877\n",
       "01/02/2015 00:00  2015      2    1  16.071000   0.038879    28   38.878963\n",
       "01/03/2015 00:00  2015      3    1  15.000000   0.040176    31   40.176000\n",
       "01/04/2015 00:00  2015      4    1  11.000000   0.028512    30   28.512000\n",
       "01/05/2015 00:00  2015      5    1   2.903000   0.007775    31    7.775395\n",
       "01/06/2015 00:00  2015      6    1   0.167000   0.000433    30    0.432864\n",
       "01/07/2015 00:00  2015      7    1   0.161000   0.000431    31    0.431222\n",
       "01/08/2015 00:00  2015      8    1   0.161000   0.000431    31    0.431222\n",
       "01/09/2015 00:00  2015      9    1   0.014000   0.000036    30    0.036288\n",
       "01/10/2015 00:00  2015     10    1   0.104000   0.000279    31    0.278554\n",
       "01/11/2015 00:00  2015     11    1   2.120000   0.005495    30    5.495040\n",
       "01/12/2015 00:00  2015     12    1   2.401000   0.006431    31    6.430838\n",
       "01/01/2016 00:00  2016      1    1   8.117000   0.021741    31   21.740573\n",
       "01/02/2016 00:00  2016      2    1   8.745000   0.021156    28   21.155904\n",
       "01/03/2016 00:00  2016      3    1   6.323000   0.016936    31   16.935523\n",
       "01/04/2016 00:00  2016      4    1   5.067000   0.013134    30   13.133664\n",
       "01/05/2016 00:00  2016      5    1   0.672000   0.001800    31    1.799885\n",
       "01/06/2016 00:00  2016      6    1   0.094000   0.000244    30    0.243648\n",
       "01/07/2016 00:00  2016      7    1   0.130000   0.000348    31    0.348192\n",
       "01/08/2016 00:00  2016      8    1   0.000000   0.000000    31    0.000000\n",
       "01/09/2016 00:00  2016      9    1   0.140000   0.000363    30    0.362880\n",
       "01/10/2016 00:00  2016     10    1   0.235000   0.000629    31    0.629424\n",
       "01/11/2016 00:00  2016     11    1   0.220000   0.000570    30    0.570240\n",
       "01/12/2016 00:00  2016     12    1   5.458000   0.014619    31   14.618707\n",
       "\n",
       "[84 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qoutlet['Mm3']=Qoutlet.apply(lambda row: (row['m3/s']*86400*row['days'])/(1000000), axis=1)\n",
    "Qoutlet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember how to sum data by year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>month</th>\n",
       "      <th>day</th>\n",
       "      <th>m3/s</th>\n",
       "      <th>km3/month</th>\n",
       "      <th>days</th>\n",
       "      <th>Mm3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>2010</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>70.618988</td>\n",
       "      <td>0.184059</td>\n",
       "      <td>365</td>\n",
       "      <td>184.059216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>74.975890</td>\n",
       "      <td>0.195169</td>\n",
       "      <td>365</td>\n",
       "      <td>195.169133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>137.816463</td>\n",
       "      <td>0.362003</td>\n",
       "      <td>366</td>\n",
       "      <td>362.003126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>75.646636</td>\n",
       "      <td>0.197106</td>\n",
       "      <td>365</td>\n",
       "      <td>197.105962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>24.170000</td>\n",
       "      <td>0.063410</td>\n",
       "      <td>365</td>\n",
       "      <td>63.409997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>64.779000</td>\n",
       "      <td>0.168189</td>\n",
       "      <td>365</td>\n",
       "      <td>168.189264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>35.201000</td>\n",
       "      <td>0.091539</td>\n",
       "      <td>365</td>\n",
       "      <td>91.538640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month  day        m3/s  km3/month  days         Mm3\n",
       "year                                                     \n",
       "2010     78   12   70.618988   0.184059   365  184.059216\n",
       "2011     78   12   74.975890   0.195169   365  195.169133\n",
       "2012     78   12  137.816463   0.362003   366  362.003126\n",
       "2013     78   12   75.646636   0.197106   365  197.105962\n",
       "2014     78   12   24.170000   0.063410   365   63.409997\n",
       "2015     78   12   64.779000   0.168189   365  168.189264\n",
       "2016     78   12   35.201000   0.091539   365   91.538640"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qoutlet_y=Qoutlet.groupby(['year']).sum()\n",
    "Qoutlet_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare Qoutlet with P-ET-dS of Litani basin. What does it mean when P-ET-dS is not close to Qoutlet?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5kzGjqxLJkMHriExKa9cOateGDh3g33+9jsYYk9IsWTFBTdX1UvnxR9dKP29e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\ntvb/ABaKCqatqiS3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101ef400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Qoutlet']=Qoutlet_y['Mm3'].values\n",
    "from matplotlib import pyplot as plt\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(df['P-ET-dS'].values,label='P-ET-dS')\n",
    "ax.plot(df['Qoutlet'].values,label='Qoutlet')\n",
    "ax.legend()\n",
    "ax.set_xticklabels(df['year'].values)\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Flows [MCM/year]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise (++):\n",
    "One of the reasons for the gap between remotely sensed water balance and in-situ measured discharge at outlet is unaccounted flows such as inter-basin transfer.\n",
    "For Litani basin, this is the case. A great amount of water is transfered out of the basin from a reservoir before the outlet. Given the recorded monthly inter-basin transfer from 2010 to 2016 (**Qibt_litani.csv**), use this data to check the Water Balance of Litani. \n",
    "\n",
    "**Hint**:\n",
    "Make use of the example codes to calculate yearly inter-basin transfer. For the years with no data available, use average value of all years for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''Write your code here'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": 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>month</th>\n",
       "      <th>day</th>\n",
       "      <th>m3/s</th>\n",
       "      <th>km3/month</th>\n",
       "      <th>days</th>\n",
       "      <th>Mm3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>2012</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>153.625170</td>\n",
       "      <td>0.405575</td>\n",
       "      <td>366</td>\n",
       "      <td>405.574560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>145.317777</td>\n",
       "      <td>0.381064</td>\n",
       "      <td>365</td>\n",
       "      <td>381.064489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>16.756049</td>\n",
       "      <td>0.043818</td>\n",
       "      <td>365</td>\n",
       "      <td>43.817969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>44.244843</td>\n",
       "      <td>0.116738</td>\n",
       "      <td>365</td>\n",
       "      <td>116.738317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>31.708597</td>\n",
       "      <td>0.083957</td>\n",
       "      <td>365</td>\n",
       "      <td>83.957234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month  day        m3/s  km3/month  days         Mm3\n",
       "year                                                     \n",
       "2012     78   12  153.625170   0.405575   366  405.574560\n",
       "2013     78   12  145.317777   0.381064   365  381.064489\n",
       "2014     78   12   16.756049   0.043818   365   43.817969\n",
       "2015     78   12   44.244843   0.116738   365  116.738317\n",
       "2016     78   12   31.708597   0.083957   365   83.957234"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qibt_csv=r\"E:\\Exercise\\2.3\\Litani\\Qibt_litani.csv\"\n",
    "Qibt=pd.read_csv(Qibt_csv,sep=';',index_col=0,skiprows=1)\n",
    "Qibt.drop(Qibt.loc[Qibt['m3/s']==-9999].index,inplace=True)\n",
    "Qibt['Mm3']=Qibt.apply(lambda row: (row['m3/s']*86400*row['days'])/(1000000), axis=1)\n",
    "Qibt_y=Qibt.groupby(['year']).sum()\n",
    "Qibt_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[206.2305136514304,\n",
       " 206.2305136514304,\n",
       " 405.5745600039744,\n",
       " 381.06448882338242,\n",
       " 43.817968550419195,\n",
       " 116.7383172252672,\n",
       " 83.957233654108791]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qibt_avg=Qibt_y['Mm3'].mean()\n",
    "Qibt_values=[Qibt_avg,Qibt_avg]\n",
    "for value in Qibt_y['Mm3'].values:\n",
    "    Qibt_values.append(value)\n",
    "Qibt_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u3bzkaPsVK9qErUQJu61A795w+7ajXSillNs0WVGJXrducPSovQWSPIp3WILu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106222e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Qibt']=Qibt_values\n",
    "from matplotlib import pyplot as plt\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(df['P-ET-dS'].values,label='P-ET-dS')\n",
    "ax.plot(df['Qoutlet'].values+df['Qibt'].values,label='Qoutlet+Qibt')\n",
    "ax.legend()\n",
    "ax.set_xticklabels(df['year'].values)\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Flows [MCM/year]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise (optional +++)\n",
    "- Extract dS timeseries for Awash (Given: shapefile, GRACE MASCON)\n",
    "- Derive yearly dS of Awash\n",
    "- Estimate the water balance residual (P-ET-dS) of Awash for a chosen year (or all the years with available GRACE data). "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}