{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ext = pd.read_csv(r'C:\\Users\\ivan_pinar\\Dropbox\\Creación de MOCs\\MOC Master Python Análisis de Datos\\Datasets\\9.2\\temp_hum_ext.csv',encoding = \"ISO-8859-1\",delimiter=',')"
   ]
  },
  {
   "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>Estación</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Temperatura (ºC)</th>\n",
       "      <th>Velocidad del viento (km/h)</th>\n",
       "      <th>Dirección del viento</th>\n",
       "      <th>Racha (km/h)</th>\n",
       "      <th>Dirección de racha</th>\n",
       "      <th>Precipitación (mm)</th>\n",
       "      <th>Presión (hPa)</th>\n",
       "      <th>Tendencia (hPa)</th>\n",
       "      <th>Humedad (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Azuaga</td>\n",
       "      <td>Badajoz</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9</td>\n",
       "      <td>Oeste</td>\n",
       "      <td>14</td>\n",
       "      <td>Noroeste</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Badajoz</td>\n",
       "      <td>Badajoz</td>\n",
       "      <td>11.1</td>\n",
       "      <td>3</td>\n",
       "      <td>Oeste</td>\n",
       "      <td>8</td>\n",
       "      <td>Noroeste</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Badajoz Aeropuerto</td>\n",
       "      <td>Badajoz</td>\n",
       "      <td>14.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Oeste</td>\n",
       "      <td>11</td>\n",
       "      <td>Oeste</td>\n",
       "      <td>0.0</td>\n",
       "      <td>995.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Barcarrota</td>\n",
       "      <td>Badajoz</td>\n",
       "      <td>8.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Este</td>\n",
       "      <td>10</td>\n",
       "      <td>Este</td>\n",
       "      <td>0.0</td>\n",
       "      <td>974.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Brozas</td>\n",
       "      <td>Cáceres</td>\n",
       "      <td>13.3</td>\n",
       "      <td>13</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>23</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Estación Provincia  Temperatura (ºC)  \\\n",
       "0              Azuaga   Badajoz              12.6   \n",
       "1             Badajoz   Badajoz              11.1   \n",
       "2  Badajoz Aeropuerto   Badajoz              14.4   \n",
       "3          Barcarrota   Badajoz               8.4   \n",
       "4              Brozas   Cáceres              13.3   \n",
       "\n",
       "   Velocidad del viento (km/h) Dirección del viento  Racha (km/h)  \\\n",
       "0                            9                Oeste            14   \n",
       "1                            3                Oeste             8   \n",
       "2                            5                Oeste            11   \n",
       "3                            5                 Este            10   \n",
       "4                           13             Nordeste            23   \n",
       "\n",
       "  Dirección de racha  Precipitación (mm)  Presión (hPa)  Tendencia (hPa)  \\\n",
       "0           Noroeste                 0.0            NaN              NaN   \n",
       "1           Noroeste                 0.0            NaN              NaN   \n",
       "2              Oeste                 0.0          995.7              0.9   \n",
       "3               Este                 0.0          974.3              0.4   \n",
       "4           Nordeste                 0.0            NaN              NaN   \n",
       "\n",
       "   Humedad (%)  \n",
       "0         62.0  \n",
       "1         90.0  \n",
       "2         71.0  \n",
       "3         94.0  \n",
       "4         61.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ext.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ext[\"Humedad (%)\"].dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ext[\"Temperatura (ºC)\"].dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x216519f41d0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=df_ext, x=\"Temperatura (ºC)\", y=\"Humedad (%)\",scatter=True, color='blue', order=1, label=\"Orden 1\")\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2164e360c18>"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.regplot(data=df_ext, x=\"Temperatura (ºC)\", y=\"Humedad (%)\", scatter=True, order=2, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pendiente, intercepto, r_value, p_value, std_err = stats.linregress(df_ext[\"Temperatura (ºC)\"],df_ext[\"Humedad (%)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x216508f8ef0>"
      ]
     },
     "execution_count": 59,
     "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": [
    "sns.regplot(data=df_ext, x=\"Temperatura (ºC)\", y=\"Humedad (%)\", scatter=True, order=1, color='blue', label='order 1',line_kws={'label':\"y={0:.1f}x+{1:.1f}\".format(pendiente,intercepto)})\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_act = pd.read_excel(r'C:\\Users\\ivan_pinar\\Dropbox\\Creación de MOCs\\MOC Master Python Análisis de Datos\\Datasets\\9.2\\Datos población activa.xlsx',sheet_name=\"Total Histórico\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Periodo</th>\n",
       "      <th>Total Activos</th>\n",
       "      <th>Periodo num</th>\n",
       "      <th>Periodo num_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020T1</td>\n",
       "      <td>22994.2</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>202001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019T4</td>\n",
       "      <td>23158.8</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>201910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019T3</td>\n",
       "      <td>23088.7</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>201907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019T2</td>\n",
       "      <td>23035.5</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>201904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019T1</td>\n",
       "      <td>22825.4</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>201901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Periodo  Total Activos Periodo num  Periodo num_int\n",
       "0  2020T1        22994.2  2020-01-01           202001\n",
       "1  2019T4        23158.8  2019-10-01           201910\n",
       "2  2019T3        23088.7  2019-07-01           201907\n",
       "3  2019T2        23035.5  2019-04-01           201904\n",
       "4  2019T1        22825.4  2019-01-01           201901"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_act.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Crear una regresión lineal de orden 1 con eje x “Periodo num_int” / eje y “Total Activos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21651ab7b38>"
      ]
     },
     "execution_count": 65,
     "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": [
    "sns.regplot(data=df_act, x=\"Periodo num_int\", y=\"Total Activos\",scatter=True, color='blue', label='order 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Añadir la fórmula de la ecuación de esta regresión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pendiente, intercepto, r_value, p_value, std_err = stats.linregress(df_act[\"Periodo num_int\"],df_act[\"Total Activos\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21651a4c198>"
      ]
     },
     "execution_count": 67,
     "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": [
    "sns.regplot(data=df_act, x=\"Periodo num_int\", y=\"Total Activos\", scatter=True, order=1, color='blue', label='order 1',line_kws={'label':\"y={0:.1f}x+{1:.1f}\".format(pendiente,intercepto)})\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#¿cuál sería el número de personas activas estimadas en 202201 (1er trimestre de 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_202201 = 1.8*202201-340650.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23311.70000000001"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_202201"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}