Example1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejecutar cr2 carga Variables mensuales:\n",
    "\n",
    "- prec = 'Precipitacion' \n",
    "- caud = 'Caudal'\n",
    "- temp = 'Temperatura'\n",
    "- tmax = 'Temperatura max'\n",
    "- tmin = 'Temperatura min'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Equivalente a lo siguiente\n",
    "# from cr2 import *\n",
    "# prec = Cr2('p')\n",
    "# caud = Cr2('q')\n",
    "# temp = Cr2('t')\n",
    "# tmin = Cr2('tmin')\n",
    "# tmax = Cr2('tmax')\n",
    "\n",
    "%run cr2.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__doc__', '__init__', '__module__', 'busca', 'df', 'filename', 'get_df', 'get_meta', 'iname', 'kname', 'meta', 'open_sources', 'plot_annual', 'plot_month', 'plot_simple', 'units', 'var', 'varname']\n"
     ]
    }
   ],
   "source": [
    "### Listados de métodos para objeto 'prec'\n",
    "print dir(prec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Buscar Estacion por 'nombre'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>codigo_estacion</th>\n",
       "      <th>institucion</th>\n",
       "      <th>fuente</th>\n",
       "      <th>nombre</th>\n",
       "      <th>altura</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "      <th>codigo_cuenca</th>\n",
       "      <th>nombre_cuenca</th>\n",
       "      <th>codigo_sub_cuenca</th>\n",
       "      <th>nombre_sub_cuenca</th>\n",
       "      <th>inicio_observaciones</th>\n",
       "      <th>fin_observaciones</th>\n",
       "      <th>cantidad_observaciones</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>05410007</td>\n",
       "      <td>DGA</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>Los Andes</td>\n",
       "      <td>820</td>\n",
       "      <td>-32.8336</td>\n",
       "      <td>-70.6</td>\n",
       "      <td>54</td>\n",
       "      <td>Rio Aconcagua</td>\n",
       "      <td>541</td>\n",
       "      <td>Aconcagua Medio</td>\n",
       "      <td>1971-08-01 00:00:00</td>\n",
       "      <td>2015-12-01 00:00:00</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>320029</td>\n",
       "      <td>DMC</td>\n",
       "      <td>dmc_anuarios</td>\n",
       "      <td>Los Andes Comisaria</td>\n",
       "      <td>819</td>\n",
       "      <td>-32.835</td>\n",
       "      <td>-70.6075</td>\n",
       "      <td>50</td>\n",
       "      <td>Costeras Quilimari-Petorca</td>\n",
       "      <td>500</td>\n",
       "      <td>Costeras Quilimari - Petorca</td>\n",
       "      <td>1979-02-01 00:00:00</td>\n",
       "      <td>2002-08-01 00:00:00</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0   codigo_estacion institucion        fuente               nombre altura  \\\n",
       "289        05410007         DGA       dga_web            Los Andes    820   \n",
       "896          320029         DMC  dmc_anuarios  Los Andes Comisaria    819   \n",
       "\n",
       "0     latitud  longitud codigo_cuenca               nombre_cuenca  \\\n",
       "289  -32.8336     -70.6            54               Rio Aconcagua   \n",
       "896   -32.835  -70.6075            50  Costeras Quilimari-Petorca   \n",
       "\n",
       "0   codigo_sub_cuenca             nombre_sub_cuenca inicio_observaciones  \\\n",
       "289               541               Aconcagua Medio  1971-08-01 00:00:00   \n",
       "896               500  Costeras Quilimari - Petorca  1979-02-01 00:00:00   \n",
       "\n",
       "0      fin_observaciones cantidad_observaciones  \n",
       "289  2015-12-01 00:00:00                    513  \n",
       "896  2002-08-01 00:00:00                    270  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec.busca('Andes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graficos base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GREREpBMGYkREREQ6YSBGREREpBMGYkREREQ6YSBGREREpBMGYkREREQ6YSBGREREpBMG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\nu63tJJHP53Ho0CHs2bMH165dQ0VFBdra2lBeXm56ebNK7Xa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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8d2358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico simple\n",
    "prec.plot_simple(289)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1OOcca/f3I102YcIEHThwQHXr1lW5cuV05513ht7sO3XqpEcffVR33XWXLr74Yt1+++3a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\n/Q/iojijEH58iAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa968d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafo precipitacion mensual promedio\n",
    "prec.plot_month(289)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAEDI8nsQu+SSS7RmzRqv5//2t7/5HP/oo49W+SBjAACAUNV82rRp02w3UVfTp09XTW3H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\nh0oNJ3o6efKkXnrpJW3atEmXXHKJJk6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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa571be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico precipitacion anual\n",
    "prec.plot_annual(289)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acceso directo a los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1037 entries, 1930-01-01 to 2016-05-01\n",
      "Columns: 1150 entries, 1 to 1150\n",
      "dtypes: float64(1150)\n",
      "memory usage: 9.1 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>1141</th>\n",
       "      <th>1142</th>\n",
       "      <th>1143</th>\n",
       "      <th>1144</th>\n",
       "      <th>1145</th>\n",
       "      <th>1146</th>\n",
       "      <th>1147</th>\n",
       "      <th>1148</th>\n",
       "      <th>1149</th>\n",
       "      <th>1150</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>38.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.9</td>\n",
       "      <td>...</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>129.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>71.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>20.4</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>33.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>152.2</td>\n",
       "      <td>92.7</td>\n",
       "      <td>101.2</td>\n",
       "      <td>89.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.2</td>\n",
       "      <td>123.1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0            1     2     3     4     5     6     7     8     9     10    ...   \\\n",
       "Date                                                                     ...    \n",
       "2016-01-01   38.4   6.9  11.0   0.0   4.2   9.8  41.2   NaN   NaN  24.9  ...    \n",
       "2016-02-01  129.8   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "2016-03-01    2.2   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "2016-04-01   33.8   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "2016-05-01   15.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "\n",
       "0           1141   1142  1143   1144  1145  1146   1147   1148  1149  1150  \n",
       "Date                                                                        \n",
       "2016-01-01   4.6    6.7  22.2    1.4   0.6   NaN    6.8    7.4  12.5   NaN  \n",
       "2016-02-01   0.0    0.0  24.3    0.0   0.0   NaN   42.6   42.3  45.1  71.6  \n",
       "2016-03-01   0.0    0.0  44.8    0.0  19.4   NaN   16.4   21.6  20.4  33.6  \n",
       "2016-04-01  98.0  152.2  92.7  101.2  89.4   NaN  174.2  123.1  11.0  43.0  \n",
       "2016-05-01   NaN    NaN   NaN    NaN   NaN   NaN    NaN    NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 1150 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print prec.df.info()\n",
    "prec.df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acceso directo a los metadatos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 14 entries, codigo_estacion to cantidad_observaciones\n",
      "Columns: 1150 entries, 1 to 1150\n",
      "dtypes: object(1150)\n",
      "memory usage: 125.9+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>1141</th>\n",
       "      <th>1142</th>\n",
       "      <th>1143</th>\n",
       "      <th>1144</th>\n",
       "      <th>1145</th>\n",
       "      <th>1146</th>\n",
       "      <th>1147</th>\n",
       "      <th>1148</th>\n",
       "      <th>1149</th>\n",
       "      <th>1150</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>codigo_estacion</th>\n",
       "      <td>01000005</td>\n",
       "      <td>01200002</td>\n",
       "      <td>01200003</td>\n",
       "      <td>01201005</td>\n",
       "      <td>01201010</td>\n",
       "      <td>01201003</td>\n",
       "      <td>01001005</td>\n",
       "      <td>01110001</td>\n",
       "      <td>01202009</td>\n",
       "      <td>01020014</td>\n",
       "      <td>...</td>\n",
       "      <td>330021</td>\n",
       "      <td>330019</td>\n",
       "      <td>330031</td>\n",
       "      <td>330030</td>\n",
       "      <td>370033</td>\n",
       "      <td>420004</td>\n",
       "      <td>430002</td>\n",
       "      <td>430004</td>\n",
       "      <td>510005</td>\n",
       "      <td>530005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institucion</th>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>DGA</td>\n",
       "      <td>...</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DMC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuente</th>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>...</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "      <td>dmc_web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre</th>\n",
       "      <td>Visviri</td>\n",
       "      <td>Villa Industrial (Tacora)</td>\n",
       "      <td>Humapalca</td>\n",
       "      <td>Rio Caracarani En Humapalca</td>\n",
       "      <td>Alcerreca</td>\n",
       "      <td>Rio Lluta En Alcerreca</td>\n",
       "      <td>Caquena</td>\n",
       "      <td>Puquios</td>\n",
       "      <td>Pacollo</td>\n",
       "      <td>Cotacotani</td>\n",
       "      <td>...</td>\n",
       "      <td>Pudahuel Santiago</td>\n",
       "      <td>Eulogio Sanchez Tobalaba Ad.</td>\n",
       "      <td>Juan Fernandez Estacion Meteorologica</td>\n",
       "      <td>Santo Domingo Ad.</td>\n",
       "      <td>Maria Dolores Los Angeles Ad.</td>\n",
       "      <td>Chaiten Ad.</td>\n",
       "      <td>Futaleufu Ad.</td>\n",
       "      <td>Alto Palenaad.</td>\n",
       "      <td>Teniente Gallardo Puerto Natales Ad.</td>\n",
       "      <td>Fuentes Martinez Porvenir Ad.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>altura</th>\n",
       "      <td>4080</td>\n",
       "      <td>4080</td>\n",
       "      <td>3980</td>\n",
       "      <td>3908</td>\n",
       "      <td>3990</td>\n",
       "      <td>3550</td>\n",
       "      <td>4400</td>\n",
       "      <td>3750</td>\n",
       "      <td>4185</td>\n",
       "      <td>4550</td>\n",
       "      <td>...</td>\n",
       "      <td>482</td>\n",
       "      <td>650</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>120</td>\n",
       "      <td>10</td>\n",
       "      <td>347</td>\n",
       "      <td>281</td>\n",
       "      <td>69</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitud</th>\n",
       "      <td>-17.595</td>\n",
       "      <td>-17.7719</td>\n",
       "      <td>-17.835</td>\n",
       "      <td>-17.8428</td>\n",
       "      <td>-17.9931</td>\n",
       "      <td>-18.0036</td>\n",
       "      <td>-18.0542</td>\n",
       "      <td>-18.1747</td>\n",
       "      <td>-18.1769</td>\n",
       "      <td>-18.1836</td>\n",
       "      <td>...</td>\n",
       "      <td>-33.3919</td>\n",
       "      <td>-33.4544</td>\n",
       "      <td>-33.6367</td>\n",
       "      <td>-33.655</td>\n",
       "      <td>-37.4028</td>\n",
       "      <td>-42.9303</td>\n",
       "      <td>-43.1889</td>\n",
       "      <td>-43.6117</td>\n",
       "      <td>-51.6672</td>\n",
       "      <td>-53.1875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitud</th>\n",
       "      <td>-69.4831</td>\n",
       "      <td>-69.7244</td>\n",
       "      <td>-69.7039</td>\n",
       "      <td>-69.6994</td>\n",
       "      <td>-69.6594</td>\n",
       "      <td>-69.6331</td>\n",
       "      <td>-69.2017</td>\n",
       "      <td>-69.7439</td>\n",
       "      <td>-69.5092</td>\n",
       "      <td>-69.2328</td>\n",
       "      <td>...</td>\n",
       "      <td>-70.7944</td>\n",
       "      <td>-70.5478</td>\n",
       "      <td>-78.835</td>\n",
       "      <td>-71.6142</td>\n",
       "      <td>-72.4225</td>\n",
       "      <td>-72.7008</td>\n",
       "      <td>-71.8525</td>\n",
       "      <td>-71.8053</td>\n",
       "      <td>-72.5289</td>\n",
       "      <td>-70.3203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codigo_cuenca</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>81</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>122</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre_cuenca</th>\n",
       "      <td>Altiplanicas</td>\n",
       "      <td>Rio Lluta</td>\n",
       "      <td>Rio Lluta</td>\n",
       "      <td>Rio Lluta</td>\n",
       "      <td>Rio Lluta</td>\n",
       "      <td>Rio Lluta</td>\n",
       "      <td>Altiplanicas</td>\n",
       "      <td>Quebrada De La Concordia</td>\n",
       "      <td>Rio Lluta</td>\n",
       "      <td>Altiplanicas</td>\n",
       "      <td>...</td>\n",
       "      <td>Rio Maipo</td>\n",
       "      <td>Rio Maipo</td>\n",
       "      <td>Costeras Quilimari-Petorca</td>\n",
       "      <td>Costeras Quilimari-Petorca</td>\n",
       "      <td>Rio Itata</td>\n",
       "      <td>Cuencas E Islas Entre R.Bueno Y R. Puelo</td>\n",
       "      <td>Cuencas E Islas Entre R.Bueno Y R. Puelo</td>\n",
       "      <td>Cuencas E Islas Entre R.Bueno Y R. Puelo</td>\n",
       "      <td>Costeras Entre Seno Andrew Y R. Hollemberg E I...</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codigo_sub_cuenca</th>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>100</td>\n",
       "      <td>111</td>\n",
       "      <td>120</td>\n",
       "      <td>102</td>\n",
       "      <td>...</td>\n",
       "      <td>573</td>\n",
       "      <td>573</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>810</td>\n",
       "      <td>1040</td>\n",
       "      <td>1040</td>\n",
       "      <td>1040</td>\n",
       "      <td>1229</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre_sub_cuenca</th>\n",
       "      <td>Entre Limite Peru-Bolivia Y Rio Lauca</td>\n",
       "      <td>Rio Lluta Alto</td>\n",
       "      <td>Rio Lluta Alto</td>\n",
       "      <td>Rio Lluta Alto</td>\n",
       "      <td>Rio Lluta Alto</td>\n",
       "      <td>Rio Lluta Alto</td>\n",
       "      <td>Entre Limite Peru-Bolivia Y Rio Lauca</td>\n",
       "      <td>Quebrada De La Concordia</td>\n",
       "      <td>Rio Lluta Alto</td>\n",
       "      <td>Rio Lauca</td>\n",
       "      <td>...</td>\n",
       "      <td>Mapocho Bajo</td>\n",
       "      <td>Mapocho Bajo</td>\n",
       "      <td>Costeras Quilimari - Petorca</td>\n",
       "      <td>Costeras Quilimari - Petorca</td>\n",
       "      <td>Nuble Alto</td>\n",
       "      <td>Costeras Entre Rio Bueno Y Rio Maullin</td>\n",
       "      <td>Costeras Entre Rio Bueno Y Rio Maullin</td>\n",
       "      <td>Costeras Entre Rio Bueno Y Rio Maullin</td>\n",
       "      <td>Costeras Entre Rio Serrano Y Rio Hollenberg</td>\n",
       "      <td>Costeras Del Estrecho Magallanes Entre Fronter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inicio_observaciones</th>\n",
       "      <td>1968-05-01 00:00:00</td>\n",
       "      <td>1975-01-01 00:00:00</td>\n",
       "      <td>1971-12-01 00:00:00</td>\n",
       "      <td>2013-06-01 00:00:00</td>\n",
       "      <td>1971-01-01 00:00:00</td>\n",
       "      <td>2001-12-01 00:00:00</td>\n",
       "      <td>1970-01-01 00:00:00</td>\n",
       "      <td>1975-05-01 00:00:00</td>\n",
       "      <td>1978-11-01 00:00:00</td>\n",
       "      <td>1961-04-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>1966-02-01 00:00:00</td>\n",
       "      <td>1954-11-01 00:00:00</td>\n",
       "      <td>1963-04-01 00:00:00</td>\n",
       "      <td>1966-08-01 00:00:00</td>\n",
       "      <td>1952-01-01 00:00:00</td>\n",
       "      <td>1965-11-01 00:00:00</td>\n",
       "      <td>1954-01-01 00:00:00</td>\n",
       "      <td>1961-12-01 00:00:00</td>\n",
       "      <td>1999-01-01 00:00:00</td>\n",
       "      <td>1985-04-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin_observaciones</th>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>1981-05-01 00:00:00</td>\n",
       "      <td>1999-05-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2008-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cantidad_observaciones</th>\n",
       "      <td>552</td>\n",
       "      <td>474</td>\n",
       "      <td>526</td>\n",
       "      <td>25</td>\n",
       "      <td>531</td>\n",
       "      <td>160</td>\n",
       "      <td>493</td>\n",
       "      <td>69</td>\n",
       "      <td>243</td>\n",
       "      <td>625</td>\n",
       "      <td>...</td>\n",
       "      <td>590</td>\n",
       "      <td>572</td>\n",
       "      <td>625</td>\n",
       "      <td>590</td>\n",
       "      <td>325</td>\n",
       "      <td>394</td>\n",
       "      <td>682</td>\n",
       "      <td>587</td>\n",
       "      <td>116</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 1150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         1     \\\n",
       "0                                                               \n",
       "codigo_estacion                                      01000005   \n",
       "institucion                                               DGA   \n",
       "fuente                                                dga_web   \n",
       "nombre                                                Visviri   \n",
       "altura                                                   4080   \n",
       "latitud                                               -17.595   \n",
       "longitud                                             -69.4831   \n",
       "codigo_cuenca                                              10   \n",
       "nombre_cuenca                                    Altiplanicas   \n",
       "codigo_sub_cuenca                                         100   \n",
       "nombre_sub_cuenca       Entre Limite Peru-Bolivia Y Rio Lauca   \n",
       "inicio_observaciones                      1968-05-01 00:00:00   \n",
       "fin_observaciones                         2016-05-01 00:00:00   \n",
       "cantidad_observaciones                                    552   \n",
       "\n",
       "                                             2                    3     \\\n",
       "0                                                                        \n",
       "codigo_estacion                          01200002             01200003   \n",
       "institucion                                   DGA                  DGA   \n",
       "fuente                                    dga_web              dga_web   \n",
       "nombre                  Villa Industrial (Tacora)            Humapalca   \n",
       "altura                                       4080                 3980   \n",
       "latitud                                  -17.7719              -17.835   \n",
       "longitud                                 -69.7244             -69.7039   \n",
       "codigo_cuenca                                  12                   12   \n",
       "nombre_cuenca                           Rio Lluta            Rio Lluta   \n",
       "codigo_sub_cuenca                             120                  120   \n",
       "nombre_sub_cuenca                  Rio Lluta Alto       Rio Lluta Alto   \n",
       "inicio_observaciones          1975-01-01 00:00:00  1971-12-01 00:00:00   \n",
       "fin_observaciones             2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "cantidad_observaciones                        474                  526   \n",
       "\n",
       "                                               4                    5     \\\n",
       "0                                                                          \n",
       "codigo_estacion                            01201005             01201010   \n",
       "institucion                                     DGA                  DGA   \n",
       "fuente                                      dga_web              dga_web   \n",
       "nombre                  Rio Caracarani En Humapalca            Alcerreca   \n",
       "altura                                         3908                 3990   \n",
       "latitud                                    -17.8428             -17.9931   \n",
       "longitud                                   -69.6994             -69.6594   \n",
       "codigo_cuenca                                    12                   12   \n",
       "nombre_cuenca                             Rio Lluta            Rio Lluta   \n",
       "codigo_sub_cuenca                               120                  120   \n",
       "nombre_sub_cuenca                    Rio Lluta Alto       Rio Lluta Alto   \n",
       "inicio_observaciones            2013-06-01 00:00:00  1971-01-01 00:00:00   \n",
       "fin_observaciones               2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "cantidad_observaciones                           25                  531   \n",
       "\n",
       "                                          6     \\\n",
       "0                                                \n",
       "codigo_estacion                       01201003   \n",
       "institucion                                DGA   \n",
       "fuente                                 dga_web   \n",
       "nombre                  Rio Lluta En Alcerreca   \n",
       "altura                                    3550   \n",
       "latitud                               -18.0036   \n",
       "longitud                              -69.6331   \n",
       "codigo_cuenca                               12   \n",
       "nombre_cuenca                        Rio Lluta   \n",
       "codigo_sub_cuenca                          120   \n",
       "nombre_sub_cuenca               Rio Lluta Alto   \n",
       "inicio_observaciones       2001-12-01 00:00:00   \n",
       "fin_observaciones          2016-01-01 00:00:00   \n",
       "cantidad_observaciones                     160   \n",
       "\n",
       "                                                         7     \\\n",
       "0                                                               \n",
       "codigo_estacion                                      01001005   \n",
       "institucion                                               DGA   \n",
       "fuente                                                dga_web   \n",
       "nombre                                                Caquena   \n",
       "altura                                                   4400   \n",
       "latitud                                              -18.0542   \n",
       "longitud                                             -69.2017   \n",
       "codigo_cuenca                                              10   \n",
       "nombre_cuenca                                    Altiplanicas   \n",
       "codigo_sub_cuenca                                         100   \n",
       "nombre_sub_cuenca       Entre Limite Peru-Bolivia Y Rio Lauca   \n",
       "inicio_observaciones                      1970-01-01 00:00:00   \n",
       "fin_observaciones                         2016-01-01 00:00:00   \n",
       "cantidad_observaciones                                    493   \n",
       "\n",
       "                                            8                    9     \\\n",
       "0                                                                       \n",
       "codigo_estacion                         01110001             01202009   \n",
       "institucion                                  DGA                  DGA   \n",
       "fuente                                   dga_web              dga_web   \n",
       "nombre                                   Puquios              Pacollo   \n",
       "altura                                      3750                 4185   \n",
       "latitud                                 -18.1747             -18.1769   \n",
       "longitud                                -69.7439             -69.5092   \n",
       "codigo_cuenca                                 11                   12   \n",
       "nombre_cuenca           Quebrada De La Concordia            Rio Lluta   \n",
       "codigo_sub_cuenca                            111                  120   \n",
       "nombre_sub_cuenca       Quebrada De La Concordia       Rio Lluta Alto   \n",
       "inicio_observaciones         1975-05-01 00:00:00  1978-11-01 00:00:00   \n",
       "fin_observaciones            1981-05-01 00:00:00  1999-05-01 00:00:00   \n",
       "cantidad_observaciones                        69                  243   \n",
       "\n",
       "                                       10    \\\n",
       "0                                             \n",
       "codigo_estacion                    01020014   \n",
       "institucion                             DGA   \n",
       "fuente                              dga_web   \n",
       "nombre                           Cotacotani   \n",
       "altura                                 4550   \n",
       "latitud                            -18.1836   \n",
       "longitud                           -69.2328   \n",
       "codigo_cuenca                            10   \n",
       "nombre_cuenca                  Altiplanicas   \n",
       "codigo_sub_cuenca                       102   \n",
       "nombre_sub_cuenca                 Rio Lauca   \n",
       "inicio_observaciones    1961-04-01 00:00:00   \n",
       "fin_observaciones       2016-01-01 00:00:00   \n",
       "cantidad_observaciones                  625   \n",
       "\n",
       "                                              ...                          \\\n",
       "0                                             ...                           \n",
       "codigo_estacion                               ...                           \n",
       "institucion                                   ...                           \n",
       "fuente                                        ...                           \n",
       "nombre                                        ...                           \n",
       "altura                                        ...                           \n",
       "latitud                                       ...                           \n",
       "longitud                                      ...                           \n",
       "codigo_cuenca                                 ...                           \n",
       "nombre_cuenca                                 ...                           \n",
       "codigo_sub_cuenca                             ...                           \n",
       "nombre_sub_cuenca                             ...                           \n",
       "inicio_observaciones                          ...                           \n",
       "fin_observaciones                             ...                           \n",
       "cantidad_observaciones                        ...                           \n",
       "\n",
       "                                       1141                          1142  \\\n",
       "0                                                                           \n",
       "codigo_estacion                      330021                        330019   \n",
       "institucion                             DMC                           DMC   \n",
       "fuente                              dmc_web                       dmc_web   \n",
       "nombre                    Pudahuel Santiago  Eulogio Sanchez Tobalaba Ad.   \n",
       "altura                                  482                           650   \n",
       "latitud                            -33.3919                      -33.4544   \n",
       "longitud                           -70.7944                      -70.5478   \n",
       "codigo_cuenca                            57                            57   \n",
       "nombre_cuenca                     Rio Maipo                     Rio Maipo   \n",
       "codigo_sub_cuenca                       573                           573   \n",
       "nombre_sub_cuenca              Mapocho Bajo                  Mapocho Bajo   \n",
       "inicio_observaciones    1966-02-01 00:00:00           1954-11-01 00:00:00   \n",
       "fin_observaciones       2016-04-01 00:00:00           2016-04-01 00:00:00   \n",
       "cantidad_observaciones                  590                           572   \n",
       "\n",
       "                                                         1143  \\\n",
       "0                                                               \n",
       "codigo_estacion                                        330031   \n",
       "institucion                                               DMC   \n",
       "fuente                                                dmc_web   \n",
       "nombre                  Juan Fernandez Estacion Meteorologica   \n",
       "altura                                                     35   \n",
       "latitud                                              -33.6367   \n",
       "longitud                                              -78.835   \n",
       "codigo_cuenca                                              50   \n",
       "nombre_cuenca                      Costeras Quilimari-Petorca   \n",
       "codigo_sub_cuenca                                         500   \n",
       "nombre_sub_cuenca                Costeras Quilimari - Petorca   \n",
       "inicio_observaciones                      1963-04-01 00:00:00   \n",
       "fin_observaciones                         2016-04-01 00:00:00   \n",
       "cantidad_observaciones                                    625   \n",
       "\n",
       "                                                1144  \\\n",
       "0                                                      \n",
       "codigo_estacion                               330030   \n",
       "institucion                                      DMC   \n",
       "fuente                                       dmc_web   \n",
       "nombre                             Santo Domingo Ad.   \n",
       "altura                                            75   \n",
       "latitud                                      -33.655   \n",
       "longitud                                    -71.6142   \n",
       "codigo_cuenca                                     50   \n",
       "nombre_cuenca             Costeras Quilimari-Petorca   \n",
       "codigo_sub_cuenca                                500   \n",
       "nombre_sub_cuenca       Costeras Quilimari - Petorca   \n",
       "inicio_observaciones             1966-08-01 00:00:00   \n",
       "fin_observaciones                2016-04-01 00:00:00   \n",
       "cantidad_observaciones                           590   \n",
       "\n",
       "                                                 1145  \\\n",
       "0                                                       \n",
       "codigo_estacion                                370033   \n",
       "institucion                                       DMC   \n",
       "fuente                                        dmc_web   \n",
       "nombre                  Maria Dolores Los Angeles Ad.   \n",
       "altura                                            120   \n",
       "latitud                                      -37.4028   \n",
       "longitud                                     -72.4225   \n",
       "codigo_cuenca                                      81   \n",
       "nombre_cuenca                               Rio Itata   \n",
       "codigo_sub_cuenca                                 810   \n",
       "nombre_sub_cuenca                          Nuble Alto   \n",
       "inicio_observaciones              1952-01-01 00:00:00   \n",
       "fin_observaciones                 2016-04-01 00:00:00   \n",
       "cantidad_observaciones                            325   \n",
       "\n",
       "                                                            1146  \\\n",
       "0                                                                  \n",
       "codigo_estacion                                           420004   \n",
       "institucion                                                  DMC   \n",
       "fuente                                                   dmc_web   \n",
       "nombre                                               Chaiten Ad.   \n",
       "altura                                                        10   \n",
       "latitud                                                 -42.9303   \n",
       "longitud                                                -72.7008   \n",
       "codigo_cuenca                                                104   \n",
       "nombre_cuenca           Cuencas E Islas Entre R.Bueno Y R. Puelo   \n",
       "codigo_sub_cuenca                                           1040   \n",
       "nombre_sub_cuenca         Costeras Entre Rio Bueno Y Rio Maullin   \n",
       "inicio_observaciones                         1965-11-01 00:00:00   \n",
       "fin_observaciones                            2008-04-01 00:00:00   \n",
       "cantidad_observaciones                                       394   \n",
       "\n",
       "                                                            1147  \\\n",
       "0                                                                  \n",
       "codigo_estacion                                           430002   \n",
       "institucion                                                  DMC   \n",
       "fuente                                                   dmc_web   \n",
       "nombre                                             Futaleufu Ad.   \n",
       "altura                                                       347   \n",
       "latitud                                                 -43.1889   \n",
       "longitud                                                -71.8525   \n",
       "codigo_cuenca                                                104   \n",
       "nombre_cuenca           Cuencas E Islas Entre R.Bueno Y R. Puelo   \n",
       "codigo_sub_cuenca                                           1040   \n",
       "nombre_sub_cuenca         Costeras Entre Rio Bueno Y Rio Maullin   \n",
       "inicio_observaciones                         1954-01-01 00:00:00   \n",
       "fin_observaciones                            2016-04-01 00:00:00   \n",
       "cantidad_observaciones                                       682   \n",
       "\n",
       "                                                            1148  \\\n",
       "0                                                                  \n",
       "codigo_estacion                                           430004   \n",
       "institucion                                                  DMC   \n",
       "fuente                                                   dmc_web   \n",
       "nombre                                            Alto Palenaad.   \n",
       "altura                                                       281   \n",
       "latitud                                                 -43.6117   \n",
       "longitud                                                -71.8053   \n",
       "codigo_cuenca                                                104   \n",
       "nombre_cuenca           Cuencas E Islas Entre R.Bueno Y R. Puelo   \n",
       "codigo_sub_cuenca                                           1040   \n",
       "nombre_sub_cuenca         Costeras Entre Rio Bueno Y Rio Maullin   \n",
       "inicio_observaciones                         1961-12-01 00:00:00   \n",
       "fin_observaciones                            2016-04-01 00:00:00   \n",
       "cantidad_observaciones                                       587   \n",
       "\n",
       "                                                                     1149  \\\n",
       "0                                                                           \n",
       "codigo_estacion                                                    510005   \n",
       "institucion                                                           DMC   \n",
       "fuente                                                            dmc_web   \n",
       "nombre                               Teniente Gallardo Puerto Natales Ad.   \n",
       "altura                                                                 69   \n",
       "latitud                                                          -51.6672   \n",
       "longitud                                                         -72.5289   \n",
       "codigo_cuenca                                                         122   \n",
       "nombre_cuenca           Costeras Entre Seno Andrew Y R. Hollemberg E I...   \n",
       "codigo_sub_cuenca                                                    1229   \n",
       "nombre_sub_cuenca             Costeras Entre Rio Serrano Y Rio Hollenberg   \n",
       "inicio_observaciones                                  1999-01-01 00:00:00   \n",
       "fin_observaciones                                     2016-04-01 00:00:00   \n",
       "cantidad_observaciones                                                116   \n",
       "\n",
       "                                                                     1150  \n",
       "0                                                                          \n",
       "codigo_estacion                                                    530005  \n",
       "institucion                                                           DMC  \n",
       "fuente                                                            dmc_web  \n",
       "nombre                                      Fuentes Martinez Porvenir Ad.  \n",
       "altura                                                                 27  \n",
       "latitud                                                          -53.1875  \n",
       "longitud                                                         -70.3203  \n",
       "codigo_cuenca                                                         128  \n",
       "nombre_cuenca                                            Tierra Del Fuego  \n",
       "codigo_sub_cuenca                                                    1280  \n",
       "nombre_sub_cuenca       Costeras Del Estrecho Magallanes Entre Fronter...  \n",
       "inicio_observaciones                                  1985-04-01 00:00:00  \n",
       "fin_observaciones                                     2016-04-01 00:00:00  \n",
       "cantidad_observaciones                                                341  \n",
       "\n",
       "[14 rows x 1150 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print prec.meta.info()\n",
    "prec.meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    552.000000\n",
       "mean      24.184239\n",
       "std       41.947654\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        1.500000\n",
       "75%       30.000000\n",
       "max      227.200000\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec.df[1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "codigo_estacion                                        01000005\n",
       "institucion                                                 DGA\n",
       "fuente                                                  dga_web\n",
       "nombre                                                  Visviri\n",
       "altura                                                     4080\n",
       "latitud                                                 -17.595\n",
       "longitud                                               -69.4831\n",
       "codigo_cuenca                                                10\n",
       "nombre_cuenca                                      Altiplanicas\n",
       "codigo_sub_cuenca                                           100\n",
       "nombre_sub_cuenca         Entre Limite Peru-Bolivia Y Rio Lauca\n",
       "inicio_observaciones                        1968-05-01 00:00:00\n",
       "fin_observaciones                           2016-05-01 00:00:00\n",
       "cantidad_observaciones                                      552\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec.meta[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo con otras variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>codigo_estacion</th>\n",
       "      <th>institucion</th>\n",
       "      <th>fuente</th>\n",
       "      <th>nombre</th>\n",
       "      <th>altura</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "      <th>codigo_cuenca</th>\n",
       "      <th>nombre_cuenca</th>\n",
       "      <th>codigo_sub_cuenca</th>\n",
       "      <th>nombre_sub_cuenca</th>\n",
       "      <th>inicio_observaciones</th>\n",
       "      <th>fin_observaciones</th>\n",
       "      <th>cantidad_observaciones</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>04320001</td>\n",
       "      <td>DGA</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>Rio Elqui En Algarrobal</td>\n",
       "      <td>760</td>\n",
       "      <td>-29.9953</td>\n",
       "      <td>-70.5867</td>\n",
       "      <td>43</td>\n",
       "      <td>Rio Elqui</td>\n",
       "      <td>432</td>\n",
       "      <td>R. Elqui Medio</td>\n",
       "      <td>1930-01-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0   codigo_estacion institucion   fuente                   nombre altura  \\\n",
       "147        04320001         DGA  dga_web  Rio Elqui En Algarrobal    760   \n",
       "\n",
       "0     latitud  longitud codigo_cuenca nombre_cuenca codigo_sub_cuenca  \\\n",
       "147  -29.9953  -70.5867            43     Rio Elqui               432   \n",
       "\n",
       "0   nombre_sub_cuenca inicio_observaciones    fin_observaciones  \\\n",
       "147    R. Elqui Medio  1930-01-01 00:00:00  2016-04-01 00:00:00   \n",
       "\n",
       "0   cantidad_observaciones  \n",
       "147                    804  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caud.busca('Algarrobal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rio Lluta' 'Altiplanicas' 'Rio San Jose'\n",
      " 'Costeras R.San Jose-Q.Camarones' 'Q. Rio Camarones'\n",
      " 'Costeras R.Camarones-Pampa Del Tamarugal' 'Pampa Del Tamarugal' 'Rio Loa'\n",
      " 'Salar De Atacama' 'Endorreicas Entre Frontera Y Vertiente' 'Rio Copiapo'\n",
      " 'Rio Huasco' 'Rio Los Choros' 'Rio Elqui' 'Costeras R.Elqui-R.Limari'\n",
      " 'Rio Limari' 'Rio Choapa' 'Costeras R.Choapa-R.Quilimari'\n",
      " 'Costeras Entre R.Choapa Y R.Quilimari' 'Rio Petorca' 'Rio Quilimari'\n",
      " 'Rio Ligua' 'Rio Aconcagua' 'Costeras Ligua-Aconcagua' 'Rio Maipo'\n",
      " 'Costeras Aconcagua-Maipo' 'Rio Rapel' 'Costeras Rapel-E. Nilahue'\n",
      " 'Rio Mataquito' 'Rio Maule' 'Costeras Mataquito-Maule'\n",
      " 'Costeras Maule-Limite Octava R.' 'Rio Itata'\n",
      " 'Costeras E Islas Entre Rio Itata Y Rio Bio-Bio' 'Rio Bio-Bio'\n",
      " 'Costeras E Islas Entre Rios Bio-Bio Y Carampangue' 'Rio Carampangue'\n",
      " 'Rio Lebu' 'Costeras Lebu-Paicavi' 'Rio Imperial'\n",
      " 'Costeras E Islas Entre R. Paicavi Y Limite Reg.' 'Rio Tolten'\n",
      " 'Rio Valdivia' 'Rio Bueno' 'Cuencas E Islas R. Bueno - R. Puelo'\n",
      " 'Cuencas E Islas Entre R.Bueno Y R. Puelo' 'Rio Puelo'\n",
      " 'Islas Chiloe Y Circundantes' 'Costeras Entre R.Puelo Y R.Yelcho'\n",
      " 'Rio Yelcho' 'Rio Palena Y Costeras Limite Decima Region'\n",
      " 'Costeras E Islas R. Palena - R. Aisen' 'Rio Aisen'\n",
      " 'Costeras E Islas R Aisen R Baker C. Gral. Martinez' 'Rio Baker'\n",
      " 'Rio Pascua'\n",
      " 'Islas Entre Limite Region Y Canal Ancho Y Estrecho De La Concepcion'\n",
      " 'Costeras Entre Seno Andrew Y R. Hollemberg E Islas Al Oriente'\n",
      " 'Costeras Seno Andrew  R Hollemberg'\n",
      " 'Costeras E Islas Entre R Hollemberg Golfo Alte. Laguna Blanca'\n",
      " 'Costeras E Islas R Hollemberg  Laguna Blanca' 'Vertiente Del Atlantico'\n",
      " 'Tierra Del Fuego' 'Costeras L Blanca E Magallanes'\n",
      " 'Costeras Entre Lag.  Blanca(Inc) Seno Otway Canal Jeronimo Y Magallanes'\n",
      " 'Islas Al Sur Del Canal Beagle Y Territorio Antartico']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>codigo_estacion</th>\n",
       "      <th>institucion</th>\n",
       "      <th>fuente</th>\n",
       "      <th>nombre</th>\n",
       "      <th>altura</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "      <th>codigo_cuenca</th>\n",
       "      <th>nombre_cuenca</th>\n",
       "      <th>codigo_sub_cuenca</th>\n",
       "      <th>nombre_sub_cuenca</th>\n",
       "      <th>inicio_observaciones</th>\n",
       "      <th>fin_observaciones</th>\n",
       "      <th>cantidad_observaciones</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>01502008</td>\n",
       "      <td>DGA</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>Rio Camarones En Chilpe</td>\n",
       "      <td>1565</td>\n",
       "      <td>-18.9431</td>\n",
       "      <td>-69.6197</td>\n",
       "      <td>15</td>\n",
       "      <td>Q. Rio Camarones</td>\n",
       "      <td>150</td>\n",
       "      <td>Rio Camarones Antes Junta Quebrada De Chiza</td>\n",
       "      <td>2005-12-01 00:00:00</td>\n",
       "      <td>2014-02-01 00:00:00</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>01502002</td>\n",
       "      <td>DGA</td>\n",
       "      <td>dga_web</td>\n",
       "      <td>Rio Camarones En Conanoxa</td>\n",
       "      <td>710</td>\n",
       "      <td>-19.0444</td>\n",
       "      <td>-69.9842</td>\n",
       "      <td>15</td>\n",
       "      <td>Q. Rio Camarones</td>\n",
       "      <td>150</td>\n",
       "      <td>Rio Camarones Antes Junta Quebrada De Chiza</td>\n",
       "      <td>1958-05-01 00:00:00</td>\n",
       "      <td>2015-12-01 00:00:00</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  codigo_estacion institucion   fuente                     nombre altura  \\\n",
       "31        01502008         DGA  dga_web    Rio Camarones En Chilpe   1565   \n",
       "32        01502002         DGA  dga_web  Rio Camarones En Conanoxa    710   \n",
       "\n",
       "0    latitud  longitud codigo_cuenca     nombre_cuenca codigo_sub_cuenca  \\\n",
       "31  -18.9431  -69.6197            15  Q. Rio Camarones               150   \n",
       "32  -19.0444  -69.9842            15  Q. Rio Camarones               150   \n",
       "\n",
       "0                             nombre_sub_cuenca inicio_observaciones  \\\n",
       "31  Rio Camarones Antes Junta Quebrada De Chiza  2005-12-01 00:00:00   \n",
       "32  Rio Camarones Antes Junta Quebrada De Chiza  1958-05-01 00:00:00   \n",
       "\n",
       "0     fin_observaciones cantidad_observaciones  \n",
       "31  2014-02-01 00:00:00                     63  \n",
       "32  2015-12-01 00:00:00                    419  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# caud.meta.T # Listado de todas las estaciones\n",
    "print caud.meta.T.nombre_cuenca.unique()\n",
    "caud.busca('Rio Camarones', 'nombre_cuenca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sou3bt2vTpk2SpMcee0zvvvuuEhMTdejQIU2YMCHLefbs2VMjR47UiRMndOLECY0YMUJP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\nFzPx+uL1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5c8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "caud.plot_month(31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYg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\nAoQqAAAAC/w/JrjTlzQ1H0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa92eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "caud.plot_simple(31)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acceso directo a datos desde DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xbe1a470>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mKbY6KaZxu0FCaMrfrLJx/KTtMU8VXZSTcRtG95y9Ebi68mzW9ZS5EYMq+s7N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\niIiIof8fVWs+dSc33l4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4e9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = temp.df.loc[:,:10].plot(kind='box')\n",
    "_ = ax.set_xticklabels(temp.meta.loc['nombre',:10].values, rotation=90)\n",
    "ax.set_ylabel('%s %s'%(temp.var, temp.units))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc0083c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WqxxujuocN0tZOWSaMNLHq14KnACMBqZKGlVUZiIwIiJGAtOA6emhzcB5ETEa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa6681d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmin.df.loc[:,2].dropna().plot(kind='kde', title=tmin.meta[2].nombre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc0c4400>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa668518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmax.df.loc[:,2].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xa91d320>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Umj/qG9/4JldddT379j3BX/6yhb17u+jouNp8LUZVY0JilJ1yJPPL9hq51i0ppGjW9u3b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\nwzAMoyBMSAzDMIyC+P8BNfAG7KfRdvwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa6685f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "caud.df.plot(294,311, kind='scatter')\n",
    "plt.xlabel(caud.meta[294].nombre)\n",
    "plt.ylabel(caud.meta[311].nombre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Ver Computational tools de Pandas](http://pandas.pydata.org/pandas-docs/stable/computation.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xaaa1160>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BA7B48WKtc69fv45u3bohLS0Nrq6u6NOnD1566SVMmzYNALBu3TqsW7fOJG8444XqGIZh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\nAP8Dy+9WjGlEREYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa902240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(facecolor='w')\n",
    "prec.df[280].dropna().plot(ax=ax, style='x')\n",
    "# Progressive quantile 90%\n",
    "prec.df[280].dropna().expanding().quantile(.9).plot(ax=ax)\n",
    "# Progressive averages\n",
    "prec.df[280].dropna().expanding().mean().plot(ax=ax, title=tmin.meta[2].nombre)\n",
    "plt.legend(['prec','q90','mean'])\n",
    "ax.set_ylabel('%s %s'%(prec.var, prec.units))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x6de3080>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PwNixouQvvVR8+t26JRW9NaHbVGIxmQfo1y/z+lWrxELdf39JQnGjdWsJl8yV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\n4SuKohQIqvAVRVEKBFX4iqIoBcL/A6mvUWc/WplJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa718080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Media movil de 12 meses\n",
    "ax = prec.df[280].dropna().rolling(window=12).mean().plot(style='.--')\n",
    "ax.set_ylabel('%s %s'%(prec.var, prec.units))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}