PruebasDatos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_excel('~/Documents/GitHub/Estancia/DatosJuntos.xlsx','Hoja1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtdirrafaga</th>\n",
       "      <th>dtdirviento</th>\n",
       "      <th>dtprecipitacion</th>\n",
       "      <th>dtraprafaga</th>\n",
       "      <th>dtrapviento</th>\n",
       "      <th>dttempaire</th>\n",
       "      <th>dthumrelativa</th>\n",
       "      <th>dtradsolar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>165990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>160.788242</td>\n",
       "      <td>120.878877</td>\n",
       "      <td>0.014416</td>\n",
       "      <td>3.319929</td>\n",
       "      <td>1.275757</td>\n",
       "      <td>18.197867</td>\n",
       "      <td>52.726996</td>\n",
       "      <td>197.491668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>109.657447</td>\n",
       "      <td>109.988876</td>\n",
       "      <td>0.198840</td>\n",
       "      <td>2.358366</td>\n",
       "      <td>0.905286</td>\n",
       "      <td>5.891570</td>\n",
       "      <td>26.073773</td>\n",
       "      <td>312.815085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>165.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>288.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dtdirrafaga    dtdirviento  dtprecipitacion    dtraprafaga  \\\n",
       "count  166657.000000  166657.000000    166657.000000  166657.000000   \n",
       "mean      160.788242     120.878877         0.014416       3.319929   \n",
       "std       109.657447     109.988876         0.198840       2.358366   \n",
       "min         0.000000       0.000000         0.000000       0.000000   \n",
       "25%        64.000000      23.000000         0.000000       1.600000   \n",
       "50%       165.000000      66.000000         0.000000       3.000000   \n",
       "75%       250.000000     215.000000         0.000000       4.900000   \n",
       "max       360.000000     360.000000        14.800000      22.500000   \n",
       "\n",
       "         dtrapviento     dttempaire  dthumrelativa     dtradsolar  \n",
       "count  166657.000000  166657.000000  166657.000000  165990.000000  \n",
       "mean        1.275757      18.197867      52.726996     197.491668  \n",
       "std         0.905286       5.891570      26.073773     312.815085  \n",
       "min         0.000000      -0.200000       0.000000      -1.000000  \n",
       "25%         0.700000      14.300000      33.000000      -1.000000  \n",
       "50%         1.200000      17.400000      54.000000       2.000000  \n",
       "75%         1.800000      22.400000      73.000000     288.000000  \n",
       "max         8.900000      38.900000     100.000000    1388.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Datee              datetime64[ns]\n",
       "dtdirrafaga                 int64\n",
       "dtdirviento                 int64\n",
       "dtprecipitacion           float64\n",
       "dtraprafaga               float64\n",
       "dtrapviento               float64\n",
       "dttempaire                float64\n",
       "dthumrelativa               int64\n",
       "dtradsolar                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtdirrafaga</th>\n",
       "      <th>dtdirviento</th>\n",
       "      <th>dtprecipitacion</th>\n",
       "      <th>dtraprafaga</th>\n",
       "      <th>dtrapviento</th>\n",
       "      <th>dttempaire</th>\n",
       "      <th>dthumrelativa</th>\n",
       "      <th>dtradsolar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>166657.000000</td>\n",
       "      <td>165990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>160.788242</td>\n",
       "      <td>120.878877</td>\n",
       "      <td>0.014416</td>\n",
       "      <td>3.319929</td>\n",
       "      <td>1.275757</td>\n",
       "      <td>18.197867</td>\n",
       "      <td>52.726996</td>\n",
       "      <td>197.491668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>109.657447</td>\n",
       "      <td>109.988876</td>\n",
       "      <td>0.198840</td>\n",
       "      <td>2.358366</td>\n",
       "      <td>0.905286</td>\n",
       "      <td>5.891570</td>\n",
       "      <td>26.073773</td>\n",
       "      <td>312.815085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>165.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>288.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dtdirrafaga    dtdirviento  dtprecipitacion    dtraprafaga  \\\n",
       "count  166657.000000  166657.000000    166657.000000  166657.000000   \n",
       "mean      160.788242     120.878877         0.014416       3.319929   \n",
       "std       109.657447     109.988876         0.198840       2.358366   \n",
       "min         0.000000       0.000000         0.000000       0.000000   \n",
       "25%        64.000000      23.000000         0.000000       1.600000   \n",
       "50%       165.000000      66.000000         0.000000       3.000000   \n",
       "75%       250.000000     215.000000         0.000000       4.900000   \n",
       "max       360.000000     360.000000        14.800000      22.500000   \n",
       "\n",
       "         dtrapviento     dttempaire  dthumrelativa     dtradsolar  \n",
       "count  166657.000000  166657.000000  166657.000000  165990.000000  \n",
       "mean        1.275757      18.197867      52.726996     197.491668  \n",
       "std         0.905286       5.891570      26.073773     312.815085  \n",
       "min         0.000000      -0.200000       0.000000      -1.000000  \n",
       "25%         0.700000      14.300000      33.000000      -1.000000  \n",
       "50%         1.200000      17.400000      54.000000       2.000000  \n",
       "75%         1.800000      22.400000      73.000000     288.000000  \n",
       "max         8.900000      38.900000     100.000000    1388.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "667"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(data1['dtradsolar']).values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data1.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad61270fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dtdirrafaga\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad64597e48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axs = plt.subplots(2,4, sharey=True, sharex=True)\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dtdirrafaga\", ax=axs[0][0])\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dtdirviento\", ax=axs[0][1])\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dtprecipitacion\", ax=axs[0][2])\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dtraprafaga\", ax=axs[0][3])\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dtrapviento\",ax=axs[1][0])\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dttempaire\", ax=axs[1][1])\n",
    "data1.plot(kind=\"scatter\", x=\"dtradsolar\", y=\"dthumrelativa\", ax=axs[1][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.01952e+05, 9.97300e+03, 7.34600e+03, 6.12500e+03, 4.94600e+03,\n",
       "        4.24900e+03, 3.54300e+03, 3.20300e+03, 3.08100e+03, 3.42900e+03,\n",
       "        3.99000e+03, 3.71900e+03, 3.44900e+03, 3.54700e+03, 2.88500e+03,\n",
       "        9.38000e+02, 2.35000e+02, 3.90000e+01, 8.00000e+00]),\n",
       " array([-1.00000000e+00,  7.21052632e+01,  1.45210526e+02,  2.18315789e+02,\n",
       "         2.91421053e+02,  3.64526316e+02,  4.37631579e+02,  5.10736842e+02,\n",
       "         5.83842105e+02,  6.56947368e+02,  7.30052632e+02,  8.03157895e+02,\n",
       "         8.76263158e+02,  9.49368421e+02,  1.02247368e+03,  1.09557895e+03,\n",
       "         1.16868421e+03,  1.24178947e+03,  1.31489474e+03,  1.38800000e+03]),\n",
       " <a list of 19 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ceil redondea hacia arriba\n",
    "k=int(np.ceil(1+np.log2(166657))) #uso de regla de sturges para calcular\n",
    "plt.hist(data1['dtradsolar'], bins = k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    166657.000000\n",
       "mean        196.701261\n",
       "std         312.437383\n",
       "min          -1.000000\n",
       "25%          -1.000000\n",
       "50%           2.000000\n",
       "75%         286.000000\n",
       "max        1388.000000\n",
       "Name: dtradsolar, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot muestra datos del describe, como son media, mediana, moda, etc.\n",
    "plt.boxplot(data1['dtradsolar'])\n",
    "data1['dtradsolar'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the plot space upon which to plot the data\n",
    "fig, ax = plt.subplots(figsize = (10,10))\n",
    "\n",
    "# add the x-axis and the y-axis to the plot\n",
    "ax.plot(data1['Datee'], \n",
    "        data1['dtradsolar'], \n",
    "        color = 'red')\n",
    "\n",
    "# rotate tick labels\n",
    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
    "\n",
    "# set title and labels for axes\n",
    "ax.set(xlabel=\"Date\",\n",
    "       ylabel=\"Solar Radiation\",\n",
    "       title=\"Solar radiation\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the plot space upon which to plot the data\n",
    "fig, ax= plt.subplots()\n",
    "\n",
    "# add the x-axis and the y-axis to the plot\n",
    "ax.plot(data1.index.values, \n",
    "        data1['dtradsolar'], \n",
    "        color = 'green')\n",
    "\n",
    "# rotate tick labels\n",
    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
    "\n",
    "# set title and labels for axes\n",
    "ax.set(xlabel=\"Date\",\n",
    "       ylabel=\"Solar Radiation\",\n",
    "       title=\"Solar Radiation\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datee</th>\n",
       "      <th>dtradsolar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 00:20:00</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 00:30:00</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 00:40:00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Datee  dtradsolar\n",
       "2 2014-01-01 00:20:00        -1.0\n",
       "3 2014-01-01 00:30:00        -1.0\n",
       "4 2014-01-01 00:40:00         0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[2:5][['Datee','dtradsolar']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datee</th>\n",
       "      <th>dtdirrafaga</th>\n",
       "      <th>dtdirviento</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 00:10:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 00:20:00</td>\n",
       "      <td>180</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Datee  dtdirrafaga  dtdirviento\n",
       "1 2014-01-01 00:10:00            0            0\n",
       "2 2014-01-01 00:20:00          180           11"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.iloc[1:3, 0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   NaT\n",
       "2013-01-02   NaT\n",
       "2013-01-03   NaT\n",
       "2013-01-04   NaT\n",
       "2013-01-05   NaT\n",
       "2013-01-06   NaT\n",
       "2013-01-07   NaT\n",
       "2013-01-08   NaT\n",
       "2013-01-09   NaT\n",
       "2013-01-10   NaT\n",
       "2013-01-11   NaT\n",
       "2013-01-12   NaT\n",
       "2013-01-13   NaT\n",
       "2013-01-14   NaT\n",
       "2013-01-15   NaT\n",
       "2013-01-16   NaT\n",
       "2013-01-17   NaT\n",
       "2013-01-18   NaT\n",
       "2013-01-19   NaT\n",
       "2013-01-20   NaT\n",
       "2013-01-21   NaT\n",
       "2013-01-22   NaT\n",
       "2013-01-23   NaT\n",
       "2013-01-24   NaT\n",
       "2013-01-25   NaT\n",
       "2013-01-26   NaT\n",
       "2013-01-27   NaT\n",
       "2013-01-28   NaT\n",
       "2013-01-29   NaT\n",
       "2013-01-30   NaT\n",
       "              ..\n",
       "2013-12-02   NaT\n",
       "2013-12-03   NaT\n",
       "2013-12-04   NaT\n",
       "2013-12-05   NaT\n",
       "2013-12-06   NaT\n",
       "2013-12-07   NaT\n",
       "2013-12-08   NaT\n",
       "2013-12-09   NaT\n",
       "2013-12-10   NaT\n",
       "2013-12-11   NaT\n",
       "2013-12-12   NaT\n",
       "2013-12-13   NaT\n",
       "2013-12-14   NaT\n",
       "2013-12-15   NaT\n",
       "2013-12-16   NaT\n",
       "2013-12-17   NaT\n",
       "2013-12-18   NaT\n",
       "2013-12-19   NaT\n",
       "2013-12-20   NaT\n",
       "2013-12-21   NaT\n",
       "2013-12-22   NaT\n",
       "2013-12-23   NaT\n",
       "2013-12-24   NaT\n",
       "2013-12-25   NaT\n",
       "2013-12-26   NaT\n",
       "2013-12-27   NaT\n",
       "2013-12-28   NaT\n",
       "2013-12-29   NaT\n",
       "2013-12-30   NaT\n",
       "2013-12-31   NaT\n",
       "Freq: D, Name: Datee, Length: 365, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.Datee[pd.date_range('20130101', '20131231')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = data1[data1['dtradsolar']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = df2[(df2['Datee']>'2014-01-01') & (df2['Datee']< '2014-12-31')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fad65af39e8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_plot['dtradsolar'].tail(300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fad64058320>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_plot['dthumrelativa'].tail(300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datee</th>\n",
       "      <th>dtdirrafaga</th>\n",
       "      <th>dtdirviento</th>\n",
       "      <th>dtprecipitacion</th>\n",
       "      <th>dtraprafaga</th>\n",
       "      <th>dtrapviento</th>\n",
       "      <th>dttempaire</th>\n",
       "      <th>dthumrelativa</th>\n",
       "      <th>dtradsolar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2014-01-01 07:20:00</td>\n",
       "      <td>360</td>\n",
       "      <td>289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2014-01-01 07:30:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2014-01-01 07:40:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2014-01-01 07:50:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2014-01-01 08:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Datee  dtdirrafaga  dtdirviento  dtprecipitacion  \\\n",
       "44 2014-01-01 07:20:00          360          289              0.0   \n",
       "45 2014-01-01 07:30:00            0            0              0.0   \n",
       "46 2014-01-01 07:40:00            0            0              0.0   \n",
       "47 2014-01-01 07:50:00            0            0              0.0   \n",
       "48 2014-01-01 08:00:00            0            0              0.0   \n",
       "\n",
       "    dtraprafaga  dtrapviento  dttempaire  dthumrelativa  dtradsolar  \n",
       "44          1.6          0.6         9.3              0         3.0  \n",
       "45          0.0          0.0         9.3              0        11.0  \n",
       "46          0.0          0.0         9.5              0        12.0  \n",
       "47          0.0          0.0         9.5              0        21.0  \n",
       "48          0.0          0.0         9.7              0        31.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modelo lineal\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept      184.116002\n",
       "dtdirrafaga      0.078272\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = smf.ols(formula=\"dtradsolar~dtdirrafaga\", data = data1).fit()\n",
    "lm.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El modelo lineal predictivo seria: 16.984368 + 0.003158 * dtradsolar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept      0.000000e+00\n",
       "dtdirrafaga    3.368115e-29\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mostrar los p-valores para garantizar que no son  0\n",
    "lm.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007546854449795859"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checar R²\n",
    "lm.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000748689553379922"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R² ajustado\n",
    "lm.rsquared_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>dtradsolar</td>    <th>  R-squared:         </th>  <td>   0.001</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.001</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   125.9</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 17 Mar 2019</td> <th>  Prob (F-statistic):</th>  <td>3.37e-29</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:33:09</td>     <th>  Log-Likelihood:    </th> <td>-1.1938e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>166657</td>      <th>  AIC:               </th>  <td>2.388e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>166655</td>      <th>  BIC:               </th>  <td>2.388e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>  184.1160</td> <td>    1.358</td> <td>  135.597</td> <td> 0.000</td> <td>  181.455</td> <td>  186.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dtdirrafaga</th> <td>    0.0783</td> <td>    0.007</td> <td>   11.219</td> <td> 0.000</td> <td>    0.065</td> <td>    0.092</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>39297.178</td> <th>  Durbin-Watson:     </th> <td>   0.176</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>73544.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.537</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 4.071</td>   <th>  Cond. No.          </th> <td>    345.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             dtradsolar   R-squared:                       0.001\n",
       "Model:                            OLS   Adj. R-squared:                  0.001\n",
       "Method:                 Least Squares   F-statistic:                     125.9\n",
       "Date:                Sun, 17 Mar 2019   Prob (F-statistic):           3.37e-29\n",
       "Time:                        15:33:09   Log-Likelihood:            -1.1938e+06\n",
       "No. Observations:              166657   AIC:                         2.388e+06\n",
       "Df Residuals:                  166655   BIC:                         2.388e+06\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept     184.1160      1.358    135.597      0.000     181.455     186.777\n",
       "dtdirrafaga     0.0783      0.007     11.219      0.000       0.065       0.092\n",
       "==============================================================================\n",
       "Omnibus:                    39297.178   Durbin-Watson:                   0.176\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            73544.976\n",
       "Skew:                           1.537   Prob(JB):                         0.00\n",
       "Kurtosis:                       4.071   Cond. No.                         345.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resumen de la regresion lineal\n",
    "lm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         184.116002\n",
       "1         184.116002\n",
       "2         198.205008\n",
       "3         195.856841\n",
       "4         184.116002\n",
       "5         205.249511\n",
       "6         204.310244\n",
       "7         184.116002\n",
       "8         203.605794\n",
       "9         203.527522\n",
       "10        207.597679\n",
       "11        184.116002\n",
       "12        188.812338\n",
       "13        184.116002\n",
       "14        184.116002\n",
       "15        184.116002\n",
       "16        186.464170\n",
       "17        184.116002\n",
       "18        188.655793\n",
       "19        192.256317\n",
       "20        184.116002\n",
       "21        184.116002\n",
       "22        184.116002\n",
       "23        184.116002\n",
       "24        184.116002\n",
       "25        184.116002\n",
       "26        204.623333\n",
       "27        184.116002\n",
       "28        200.553176\n",
       "29        184.116002\n",
       "             ...    \n",
       "166627    194.056579\n",
       "166628    194.526212\n",
       "166629    199.613909\n",
       "166630    190.847416\n",
       "166631    186.464170\n",
       "166632    194.447940\n",
       "166633    188.342704\n",
       "166634    186.307625\n",
       "166635    188.890610\n",
       "166636    186.229353\n",
       "166637    185.994536\n",
       "166638    189.281971\n",
       "166639    209.397941\n",
       "166640    207.989040\n",
       "166641    203.762338\n",
       "166642    199.926998\n",
       "166643    195.152390\n",
       "166644    196.874380\n",
       "166645    204.466789\n",
       "166646    195.387207\n",
       "166647    187.168620\n",
       "166648    201.414171\n",
       "166649    205.484328\n",
       "166650    185.681447\n",
       "166651    210.102391\n",
       "166652    189.360243\n",
       "166653    184.116002\n",
       "166654    198.752914\n",
       "166655    195.778568\n",
       "166656    195.308935\n",
       "Length: 166657, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predic_RS = lm.predict(pd.DataFrame(data1['dtdirrafaga']))\n",
    "Predic_RS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "DFTest = df_plot.head(100)\n",
    "FC = [\"dtdirrafaga\", \"dtdirviento\", \"dtprecipitacion\", \"dtraprafaga\", \"dtrapviento\", \"dttempaire\", \"dthumrelativa\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data1[FC]\n",
    "Y = data1[\"dtradsolar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.36018481062794\n",
      "[ 3.00386362e-02 -5.03249875e-02  1.70838831e+01  2.03631409e+01\n",
      " -3.49084652e+01  2.41627738e+00  5.93768163e-01]\n"
     ]
    }
   ],
   "source": [
    "print(lm.intercept_)\n",
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dtdirrafaga', 0.030038636236787996),\n",
       " ('dtdirviento', -0.050324987504394424),\n",
       " ('dtprecipitacion', 17.08388306486039),\n",
       " ('dtraprafaga', 20.363140895814816),\n",
       " ('dtrapviento', -34.90846523326029),\n",
       " ('dttempaire', 2.4162773778188242),\n",
       " ('dthumrelativa', 0.5937681630032094)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(FC,lm.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00893479239182604"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1[\"Predict_RSolar\"] = 99.36018481062794 + data1[\"dtdirrafaga\"] * 0.030038636236787996 + data1[\"dtdirviento\"] * (-0.050324987504394424) + data1[\"dtprecipitacion\"] * 17.08388306486039 + data1['dtraprafaga'] * 20.363140895814816 + data1['dtrapviento'] * (-34.90846523326029) + data1['dttempaire'] * 2.4162773778188242 + data1['dthumrelativa'] * 0.5937681630032094"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datee</th>\n",
       "      <th>dtdirrafaga</th>\n",
       "      <th>dtdirviento</th>\n",
       "      <th>dtprecipitacion</th>\n",
       "      <th>dtraprafaga</th>\n",
       "      <th>dtrapviento</th>\n",
       "      <th>dttempaire</th>\n",
       "      <th>dthumrelativa</th>\n",
       "      <th>dtradsolar</th>\n",
       "      <th>Predict_RSolar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.972725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 00:10:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.972725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 00:20:00</td>\n",
       "      <td>180</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>143.256737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 00:30:00</td>\n",
       "      <td>150</td>\n",
       "      <td>71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>135.746706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 00:40:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.972725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Datee  dtdirrafaga  dtdirviento  dtprecipitacion  dtraprafaga  \\\n",
       "0 2014-01-01 00:00:00            0            0              0.0          0.0   \n",
       "1 2014-01-01 00:10:00            0            0              0.0          0.0   \n",
       "2 2014-01-01 00:20:00          180           11              0.0          1.7   \n",
       "3 2014-01-01 00:30:00          150           71              0.0          1.5   \n",
       "4 2014-01-01 00:40:00            0            0              0.0          0.0   \n",
       "\n",
       "   dtrapviento  dttempaire  dthumrelativa  dtradsolar  Predict_RSolar  \n",
       "0          0.0        10.6              0         0.0      124.972725  \n",
       "1          0.0        10.6              0         0.0      124.972725  \n",
       "2          0.6        10.5              0        -1.0      143.256737  \n",
       "3          0.6        10.7              0        -1.0      135.746706  \n",
       "4          0.0        10.6              0         0.0      124.972725  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nuevos datos INIFAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Prec</th>\n",
       "      <th>Temt</th>\n",
       "      <th>Vel.V</th>\n",
       "      <th>Dir.V</th>\n",
       "      <th>Hum.R</th>\n",
       "      <th>Rad.G</th>\n",
       "      <th>Et</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01 00:15:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>232.1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01 00:30:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>235.2</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01 00:45:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>225.1</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01 01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245.7</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fecha  Prec  Temt  Vel.V  Dir.V  Hum.R  Rad.G   Et\n",
       "0 2013-01-01 00:00:00   0.0  13.6    1.0  235.5   80.0    0.0  NaN\n",
       "1 2013-01-01 00:15:00   0.0  13.5    0.1  232.1   81.0    0.0  NaN\n",
       "2 2013-01-01 00:30:00   0.0  13.7    1.4  235.2   81.0    0.0  NaN\n",
       "3 2013-01-01 00:45:00   0.0  13.7    0.1  225.1   82.0    0.0  0.0\n",
       "4 2013-01-01 01:00:00   0.0  13.6    0.2  245.7   83.0    0.0  NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwdata = pd.read_excel('BD_GTO19.xlsx', skiprows=4)\n",
    "nwdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fecha', 'Prec', 'Temt', 'Vel.V', 'Dir.V', 'Hum.R', 'Rad.G', 'Et'], dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata = nwdata.columns\n",
    "idata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prec</th>\n",
       "      <th>Temt</th>\n",
       "      <th>Vel.V</th>\n",
       "      <th>Dir.V</th>\n",
       "      <th>Hum.R</th>\n",
       "      <th>Rad.G</th>\n",
       "      <th>Et</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>192652.000000</td>\n",
       "      <td>192652.000000</td>\n",
       "      <td>192652.000000</td>\n",
       "      <td>192652.000000</td>\n",
       "      <td>192652.000000</td>\n",
       "      <td>192652.000000</td>\n",
       "      <td>192652.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.013669</td>\n",
       "      <td>17.149964</td>\n",
       "      <td>3.483889</td>\n",
       "      <td>128.957871</td>\n",
       "      <td>54.095208</td>\n",
       "      <td>234.243626</td>\n",
       "      <td>0.043751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.202328</td>\n",
       "      <td>7.538828</td>\n",
       "      <td>4.855469</td>\n",
       "      <td>107.301537</td>\n",
       "      <td>27.326581</td>\n",
       "      <td>334.088118</td>\n",
       "      <td>0.144126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>84.200000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>235.500000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>459.325000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.200000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1343.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Prec           Temt          Vel.V          Dir.V  \\\n",
       "count  192652.000000  192652.000000  192652.000000  192652.000000   \n",
       "mean        0.013669      17.149964       3.483889     128.957871   \n",
       "std         0.202328       7.538828       4.855469     107.301537   \n",
       "min         0.000000      -1.000000       0.000000       0.000000   \n",
       "25%         0.000000      12.900000       0.000000      45.600000   \n",
       "50%         0.000000      17.400000       1.000000      84.200000   \n",
       "75%         0.000000      22.800000       5.600000     235.500000   \n",
       "max        25.200000      36.400000      37.000000     360.000000   \n",
       "\n",
       "               Hum.R          Rad.G             Et  \n",
       "count  192652.000000  192652.000000  192652.000000  \n",
       "mean       54.095208     234.243626       0.043751  \n",
       "std        27.326581     334.088118       0.144126  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%        33.000000       0.000000       0.000000  \n",
       "50%        56.000000       0.000000       0.000000  \n",
       "75%        77.000000     459.325000       0.000000  \n",
       "max       100.000000    1343.600000       1.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwdata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "nwdata = nwdata.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha    datetime64[ns]\n",
       "Prec            float64\n",
       "Temt            float64\n",
       "Vel.V           float64\n",
       "Dir.V           float64\n",
       "Hum.R           float64\n",
       "Rad.G           float64\n",
       "Et              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwdata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prec</th>\n",
       "      <th>Temt</th>\n",
       "      <th>Vel.V</th>\n",
       "      <th>Dir.V</th>\n",
       "      <th>Hum.R</th>\n",
       "      <th>Rad.G</th>\n",
       "      <th>Et</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prec</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007469</td>\n",
       "      <td>0.026314</td>\n",
       "      <td>0.005695</td>\n",
       "      <td>0.092521</td>\n",
       "      <td>-0.040795</td>\n",
       "      <td>-0.013951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temt</th>\n",
       "      <td>-0.007469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.432780</td>\n",
       "      <td>0.366453</td>\n",
       "      <td>-0.186027</td>\n",
       "      <td>0.615790</td>\n",
       "      <td>0.292979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vel.V</th>\n",
       "      <td>0.026314</td>\n",
       "      <td>0.432780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034391</td>\n",
       "      <td>-0.254797</td>\n",
       "      <td>0.368494</td>\n",
       "      <td>0.198421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dir.V</th>\n",
       "      <td>0.005695</td>\n",
       "      <td>0.366453</td>\n",
       "      <td>0.034391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.083176</td>\n",
       "      <td>0.224993</td>\n",
       "      <td>0.109782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hum.R</th>\n",
       "      <td>0.092521</td>\n",
       "      <td>-0.186027</td>\n",
       "      <td>-0.254797</td>\n",
       "      <td>-0.083176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.357917</td>\n",
       "      <td>-0.188470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rad.G</th>\n",
       "      <td>-0.040795</td>\n",
       "      <td>0.615790</td>\n",
       "      <td>0.368494</td>\n",
       "      <td>0.224993</td>\n",
       "      <td>-0.357917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.403722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Et</th>\n",
       "      <td>-0.013951</td>\n",
       "      <td>0.292979</td>\n",
       "      <td>0.198421</td>\n",
       "      <td>0.109782</td>\n",
       "      <td>-0.188470</td>\n",
       "      <td>0.403722</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Prec      Temt     Vel.V     Dir.V     Hum.R     Rad.G        Et\n",
       "Prec   1.000000 -0.007469  0.026314  0.005695  0.092521 -0.040795 -0.013951\n",
       "Temt  -0.007469  1.000000  0.432780  0.366453 -0.186027  0.615790  0.292979\n",
       "Vel.V  0.026314  0.432780  1.000000  0.034391 -0.254797  0.368494  0.198421\n",
       "Dir.V  0.005695  0.366453  0.034391  1.000000 -0.083176  0.224993  0.109782\n",
       "Hum.R  0.092521 -0.186027 -0.254797 -0.083176  1.000000 -0.357917 -0.188470\n",
       "Rad.G -0.040795  0.615790  0.368494  0.224993 -0.357917  1.000000  0.403722\n",
       "Et    -0.013951  0.292979  0.198421  0.109782 -0.188470  0.403722  1.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwdata.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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