Encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count_bikes</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>laborables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.110847</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday year month weathersit       temp  windspeed  casual  \\\n",
       "0        1  01-01-2018    0     1          2  14.110847  10.749882     331   \n",
       "1        2  02-01-2018    0     1          2  14.902598  16.652113     131   \n",
       "2        3  03-01-2018    0     1          1   8.050924  16.636703     120   \n",
       "3        4  04-01-2018    0     1          1   8.200000  10.739832     108   \n",
       "4        5  05-01-2018    0     1          1   9.305237  12.522300      82   \n",
       "\n",
       "   registered  count_bikes  dia_semana  laborables  \n",
       "0         654          985           0           0  \n",
       "1         670          801           1           1  \n",
       "2        1229         1349           2           1  \n",
       "3        1454         1562           3           1  \n",
       "4        1518         1600           4           1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle(\"Datos/bikes_eda_limpio.pkl\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=violet>Estandarización."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>temp</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count_bikes</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>laborables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.110847</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>726</td>\n",
       "      <td>10.420847</td>\n",
       "      <td>23.458911</td>\n",
       "      <td>247</td>\n",
       "      <td>1867</td>\n",
       "      <td>2114</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>10.386653</td>\n",
       "      <td>10.416557</td>\n",
       "      <td>644</td>\n",
       "      <td>2451</td>\n",
       "      <td>3095</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>10.386653</td>\n",
       "      <td>8.333661</td>\n",
       "      <td>159</td>\n",
       "      <td>1182</td>\n",
       "      <td>1341</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>10.489153</td>\n",
       "      <td>23.500518</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>8.849153</td>\n",
       "      <td>10.374682</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>730 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant       temp  windspeed  casual  registered  count_bikes  \\\n",
       "0          1  14.110847  10.749882     331         654          985   \n",
       "1          2  14.902598  16.652113     131         670          801   \n",
       "2          3   8.050924  16.636703     120        1229         1349   \n",
       "3          4   8.200000  10.739832     108        1454         1562   \n",
       "4          5   9.305237  12.522300      82        1518         1600   \n",
       "..       ...        ...        ...     ...         ...          ...   \n",
       "725      726  10.420847  23.458911     247        1867         2114   \n",
       "726      727  10.386653  10.416557     644        2451         3095   \n",
       "727      728  10.386653   8.333661     159        1182         1341   \n",
       "728      729  10.489153  23.500518     364        1432         1796   \n",
       "729      730   8.849153  10.374682     439        2290         2729   \n",
       "\n",
       "     dia_semana  laborables  \n",
       "0             0           0  \n",
       "1             1           1  \n",
       "2             2           1  \n",
       "3             3           1  \n",
       "4             4           1  \n",
       "..          ...         ...  \n",
       "725           4           1  \n",
       "726           5           0  \n",
       "727           6           0  \n",
       "728           0           1  \n",
       "729           1           1  \n",
       "\n",
       "[730 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericas = df.select_dtypes(include=np.number)\n",
    "numericas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iniciamos el método para escalar\n",
    "\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.110847</td>\n",
       "      <td>10.749882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.902598</td>\n",
       "      <td>16.652113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        temp  windspeed\n",
       "0  14.110847  10.749882\n",
       "1  14.902598  16.652113"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericas.drop([\"count_bikes\", 'instant', 'dia_semana', 'laborables','casual','registered'], axis = 1, inplace = True)\n",
    "\n",
    "numericas.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ajustamos nuestros datos.  \n",
    "\n",
    "scaler.fit(numericas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformamos los datos\n",
    "\n",
    "X_escaladas = scaler.transform(numericas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.827613</td>\n",
       "      <td>-0.387833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.722069</td>\n",
       "      <td>0.748899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp  windspeed\n",
       "0 -0.827613  -0.387833\n",
       "1 -0.722069   0.748899"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# por último convertiremos el array que nos devuelve en un dataframe. \n",
    "\n",
    "numericas_estandar = pd.DataFrame(X_escaladas, columns = numericas.columns)\n",
    "numericas_estandar.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "numericas_estandar.rename(columns={'temp':'temp_es', 'windspeed':'windspeed_es'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chequeamos la media y la desviación estándar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La media de la columna temp_es es:  -2.5307001547620006e-16\n",
      "La desviación estándar de la columna temp_es es: 1.0006856360078737\n",
      "-------------------------------------------------------------\n",
      "La media de la columna windspeed_es es:  7.786769706960002e-17\n",
      "La desviación estándar de la columna windspeed_es es: 1.000685636007874\n",
      "-------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for columna in numericas_estandar.columns:\n",
    "    print(f'La media de la columna {columna} es:  {numericas_estandar[columna].mean()}')\n",
    "    print(f'La desviación estándar de la columna {columna} es: {numericas_estandar[columna].std()}')\n",
    "    print(\"-------------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.827613</td>\n",
       "      <td>-0.387833</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.722069</td>\n",
       "      <td>0.748899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp_es  windspeed_es\n",
       "0 -0.827613     -0.387833\n",
       "1 -0.722069      0.748899"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericas_estandar.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unimos el df con las columnas estandarizadas al original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count_bikes</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>laborables</th>\n",
       "      <th>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.110847</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.827613</td>\n",
       "      <td>-0.387833</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.722069</td>\n",
       "      <td>0.748899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.635432</td>\n",
       "      <td>0.745931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday year month weathersit       temp  windspeed  casual  \\\n",
       "0        1  01-01-2018    0     1          2  14.110847  10.749882     331   \n",
       "1        2  02-01-2018    0     1          2  14.902598  16.652113     131   \n",
       "2        3  03-01-2018    0     1          1   8.050924  16.636703     120   \n",
       "3        4  04-01-2018    0     1          1   8.200000  10.739832     108   \n",
       "4        5  05-01-2018    0     1          1   9.305237  12.522300      82   \n",
       "\n",
       "   registered  count_bikes  dia_semana  laborables   temp_es  windspeed_es  \n",
       "0         654          985           0           0 -0.827613     -0.387833  \n",
       "1         670          801           1           1 -0.722069      0.748899  \n",
       "2        1229         1349           2           1 -1.635432      0.745931  \n",
       "3        1454         1562           3           1 -1.615560     -0.389769  \n",
       "4        1518         1600           4           1 -1.468226     -0.046477  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estandarizado = pd.concat([df, numericas_estandar], axis =1)\n",
    "df_estandarizado.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropeamos las columnas originales y dejamos las estandarizadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estandarizado.drop(['temp', 'windspeed'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count_bikes</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>laborables</th>\n",
       "      <th>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.722069</td>\n",
       "      <td>0.748899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.635432</td>\n",
       "      <td>0.745931</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday year month weathersit  casual  registered  count_bikes  \\\n",
       "0        1  01-01-2018    0     1          2     331         654          985   \n",
       "1        2  02-01-2018    0     1          2     131         670          801   \n",
       "2        3  03-01-2018    0     1          1     120        1229         1349   \n",
       "3        4  04-01-2018    0     1          1     108        1454         1562   \n",
       "4        5  05-01-2018    0     1          1      82        1518         1600   \n",
       "\n",
       "   dia_semana  laborables   temp_es  windspeed_es  \n",
       "0           0           0 -0.827613     -0.387833  \n",
       "1           1           1 -0.722069      0.748899  \n",
       "2           2           1 -1.635432      0.745931  \n",
       "3           3           1 -1.615560     -0.389769  \n",
       "4           4           1 -1.468226     -0.046477  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estandarizado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estandarizado = df_estandarizado.astype({'dia_semana':'category', 'laborables':'category'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant            int64\n",
       "dteday            object\n",
       "year            category\n",
       "month           category\n",
       "weathersit      category\n",
       "casual             int64\n",
       "registered         int64\n",
       "count_bikes        int64\n",
       "dia_semana      category\n",
       "laborables      category\n",
       "temp_es          float64\n",
       "windspeed_es     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estandarizado.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separamos en dos dataset para hacer un modelo para los usuarios casuales y otro para los registrados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>dteday</th>\n",
       "      <th>year</th>\n",
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       "      <th>weathersit</th>\n",
       "      <th>temp_es</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
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       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month weathersit   temp_es  windspeed_es  casual  \\\n",
       "0  01-01-2018    0     1          2 -0.827613     -0.387833     331   \n",
       "1  02-01-2018    0     1          2 -0.722069      0.748899     131   \n",
       "2  03-01-2018    0     1          1 -1.635432      0.745931     120   \n",
       "3  04-01-2018    0     1          1 -1.615560     -0.389769     108   \n",
       "4  05-01-2018    0     1          1 -1.468226     -0.046477      82   \n",
       "\n",
       "  dia_semana laborables  \n",
       "0          0          0  \n",
       "1          1          1  \n",
       "2          2          1  \n",
       "3          3          1  \n",
       "4          4          1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual = df_estandarizado[[\"dteday\",\"year\",\"month\",\"weathersit\",\"temp_es\",\"windspeed_es\",\"casual\", \"dia_semana\",\"laborables\"]]\n",
    "df_casual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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",
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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>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
       "      <th>registered</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>laborables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.827613</td>\n",
       "      <td>-0.387833</td>\n",
       "      <td>654</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.722069</td>\n",
       "      <td>0.748899</td>\n",
       "      <td>670</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.635432</td>\n",
       "      <td>0.745931</td>\n",
       "      <td>1229</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>1454</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>1518</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month weathersit   temp_es  windspeed_es  registered  \\\n",
       "0  01-01-2018    0     1          2 -0.827613     -0.387833         654   \n",
       "1  02-01-2018    0     1          2 -0.722069      0.748899         670   \n",
       "2  03-01-2018    0     1          1 -1.635432      0.745931        1229   \n",
       "3  04-01-2018    0     1          1 -1.615560     -0.389769        1454   \n",
       "4  05-01-2018    0     1          1 -1.468226     -0.046477        1518   \n",
       "\n",
       "  dia_semana laborables  \n",
       "0          0          0  \n",
       "1          1          1  \n",
       "2          2          1  \n",
       "3          3          1  \n",
       "4          4          1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_registered = df_estandarizado[[\"dteday\",\"year\",\"month\",\"weathersit\",\"temp_es\",\"windspeed_es\",\"registered\", \"dia_semana\",\"laborables\"]]\n",
    "df_registered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=violet> Encoding df_casual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>dia_semana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_dia_sem= {4:0, 2:1, 3:1, 5:1, 6:2, 1:3, 0:4 }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arian\\AppData\\Local\\Temp\\ipykernel_11928\\2086087014.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_casual['dia_sem_encoded'] = df_casual['dia_semana'].map(map_dia_sem)\n"
     ]
    }
   ],
   "source": [
    "df_casual['dia_sem_encoded'] = df_casual['dia_semana'].map(map_dia_sem)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arian\\AppData\\Local\\Temp\\ipykernel_11928\\954347097.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_casual.drop('dia_semana', axis=1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df_casual.drop('dia_semana', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
       "      <th>casual</th>\n",
       "      <th>laborables</th>\n",
       "      <th>dia_sem_encoded</th>\n",
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       "  <tbody>\n",
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       "      <td>-0.827613</td>\n",
       "      <td>-0.387833</td>\n",
       "      <td>331</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>131</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.635432</td>\n",
       "      <td>0.745931</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month weathersit   temp_es  windspeed_es  casual  \\\n",
       "0  01-01-2018    0     1          2 -0.827613     -0.387833     331   \n",
       "1  02-01-2018    0     1          2 -0.722069      0.748899     131   \n",
       "2  03-01-2018    0     1          1 -1.635432      0.745931     120   \n",
       "3  04-01-2018    0     1          1 -1.615560     -0.389769     108   \n",
       "4  05-01-2018    0     1          1 -1.468226     -0.046477      82   \n",
       "\n",
       "  laborables  dia_sem_encoded  \n",
       "0          0                4  \n",
       "1          1                3  \n",
       "2          1                1  \n",
       "3          1                1  \n",
       "4          1                0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>laborables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df_casual['laborables'], prefix_sep='_', prefix='laborables', dtype= int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casual = pd.concat([df_casual, dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casual.drop('laborables', axis=1, inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
       "      <th>casual</th>\n",
       "      <th>dia_sem_encoded</th>\n",
       "      <th>laborables_0</th>\n",
       "      <th>laborables_1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.745931</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month weathersit   temp_es  windspeed_es  casual  \\\n",
       "0  01-01-2018    0     1          2 -0.827613     -0.387833     331   \n",
       "1  02-01-2018    0     1          2 -0.722069      0.748899     131   \n",
       "2  03-01-2018    0     1          1 -1.635432      0.745931     120   \n",
       "3  04-01-2018    0     1          1 -1.615560     -0.389769     108   \n",
       "4  05-01-2018    0     1          1 -1.468226     -0.046477      82   \n",
       "\n",
       "   dia_sem_encoded  laborables_0  laborables_1  \n",
       "0                4             1             0  \n",
       "1                3             0             1  \n",
       "2                1             0             1  \n",
       "3                1             0             1  \n",
       "4                0             0             1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>weathersit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_weathersit = {1:3, 2:2, 3:1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casual['weathersit_encoded'] = df_casual['weathersit'].map(map_weathersit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casual.drop('weathersit', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
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       "      <th>dia_sem_encoded</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.635432</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month   temp_es  windspeed_es  casual  dia_sem_encoded  \\\n",
       "0  01-01-2018    0     1 -0.827613     -0.387833     331                4   \n",
       "1  02-01-2018    0     1 -0.722069      0.748899     131                3   \n",
       "2  03-01-2018    0     1 -1.635432      0.745931     120                1   \n",
       "3  04-01-2018    0     1 -1.615560     -0.389769     108                1   \n",
       "4  05-01-2018    0     1 -1.468226     -0.046477      82                0   \n",
       "\n",
       "   laborables_0  laborables_1 weathersit_encoded  \n",
       "0             1             0                  2  \n",
       "1             0             1                  2  \n",
       "2             0             1                  3  \n",
       "3             0             1                  3  \n",
       "4             0             1                  3  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>month\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_month={1:1, 2:2, 12:3, 3:4, 11:4, 4:5, 10:5, 5:6, 6:7, 9:7, 7:7, 8:7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casual['month_encoded'] = df_casual['month'].map(map_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casual.drop('month', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.635432</td>\n",
       "      <td>0.745931</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year   temp_es  windspeed_es  casual  dia_sem_encoded  \\\n",
       "0  01-01-2018    0 -0.827613     -0.387833     331                4   \n",
       "1  02-01-2018    0 -0.722069      0.748899     131                3   \n",
       "2  03-01-2018    0 -1.635432      0.745931     120                1   \n",
       "3  04-01-2018    0 -1.615560     -0.389769     108                1   \n",
       "4  05-01-2018    0 -1.468226     -0.046477      82                0   \n",
       "\n",
       "   laborables_0  laborables_1 weathersit_encoded  month_encoded  \n",
       "0             1             0                  2              1  \n",
       "1             0             1                  2              1  \n",
       "2             0             1                  3              1  \n",
       "3             0             1                  3              1  \n",
       "4             0             1                  3              1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = [\"year\"]\n",
    "\n",
    "df_encoded = pd.DataFrame()\n",
    "\n",
    "\n",
    "for columna in lista:\n",
    "    df_dummies = pd.get_dummies(df_casual[columna], prefix_sep = \"_\", prefix = columna, dtype = int)\n",
    "\n",
    "    df_encoded = pd.concat([df_encoded, df_dummies], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
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       "      <th>windspeed_es</th>\n",
       "      <th>casual</th>\n",
       "      <th>dia_sem_encoded</th>\n",
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       "      <th>weathersit_encoded</th>\n",
       "      <th>month_encoded</th>\n",
       "      <th>year_0</th>\n",
       "      <th>year_1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2018</td>\n",
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       "      <td>02-01-2018</td>\n",
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       "      <td>131</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>120</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year   temp_es  windspeed_es  casual  dia_sem_encoded  \\\n",
       "0  01-01-2018    0 -0.827613     -0.387833     331                4   \n",
       "1  02-01-2018    0 -0.722069      0.748899     131                3   \n",
       "2  03-01-2018    0 -1.635432      0.745931     120                1   \n",
       "3  04-01-2018    0 -1.615560     -0.389769     108                1   \n",
       "4  05-01-2018    0 -1.468226     -0.046477      82                0   \n",
       "\n",
       "   laborables_0  laborables_1 weathersit_encoded  month_encoded  year_0  \\\n",
       "0             1             0                  2              1       1   \n",
       "1             0             1                  2              1       1   \n",
       "2             0             1                  3              1       1   \n",
       "3             0             1                  3              1       1   \n",
       "4             0             1                  3              1       1   \n",
       "\n",
       "   year_1  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual = pd.concat([df_casual, df_encoded], axis = 1)\n",
    "df_casual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>temp_es</th>\n",
       "      <th>windspeed_es</th>\n",
       "      <th>casual</th>\n",
       "      <th>dia_sem_encoded</th>\n",
       "      <th>laborables_0</th>\n",
       "      <th>laborables_1</th>\n",
       "      <th>weathersit_encoded</th>\n",
       "      <th>month_encoded</th>\n",
       "      <th>year_0</th>\n",
       "      <th>year_1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>-0.827613</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>-0.722069</td>\n",
       "      <td>0.748899</td>\n",
       "      <td>131</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday   temp_es  windspeed_es  casual  dia_sem_encoded  laborables_0  \\\n",
       "0  01-01-2018 -0.827613     -0.387833     331                4             1   \n",
       "1  02-01-2018 -0.722069      0.748899     131                3             0   \n",
       "\n",
       "   laborables_1 weathersit_encoded  month_encoded  year_0  year_1  \n",
       "0             0                  2              1       1       0  \n",
       "1             1                  2              1       1       0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual.drop(lista, axis = 1, inplace=True)\n",
    "df_casual.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=violet> Encoding df_registered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>dia_semana\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_dia_semr= {4:3, 2:2, 3:3, 5:3, 6:2, 1:1, 0:1 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arian\\AppData\\Local\\Temp\\ipykernel_11928\\1420231186.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_registered['dia_sem_encoded'] = df_registered['dia_semana'].map(map_dia_semr)\n"
     ]
    }
   ],
   "source": [
    "df_registered['dia_sem_encoded'] = df_registered['dia_semana'].map(map_dia_semr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arian\\AppData\\Local\\Temp\\ipykernel_11928\\3980960110.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_registered.drop('dia_semana', axis=1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df_registered.drop('dia_semana', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>dteday</th>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>1454</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>1518</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month weathersit   temp_es  windspeed_es  registered  \\\n",
       "0  01-01-2018    0     1          2 -0.827613     -0.387833         654   \n",
       "1  02-01-2018    0     1          2 -0.722069      0.748899         670   \n",
       "2  03-01-2018    0     1          1 -1.635432      0.745931        1229   \n",
       "3  04-01-2018    0     1          1 -1.615560     -0.389769        1454   \n",
       "4  05-01-2018    0     1          1 -1.468226     -0.046477        1518   \n",
       "\n",
       "  laborables  dia_sem_encoded  \n",
       "0          0                1  \n",
       "1          1                1  \n",
       "2          1                2  \n",
       "3          1                3  \n",
       "4          1                3  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_registered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>laborables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummiesr = pd.get_dummies(df_registered['laborables'], prefix_sep='_', prefix='laborables', dtype= int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_registered = pd.concat([df_registered, dummiesr], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_registered.drop('laborables', axis=1, inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>1454</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
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       "      <td>1518</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month weathersit   temp_es  windspeed_es  registered  \\\n",
       "0  01-01-2018    0     1          2 -0.827613     -0.387833         654   \n",
       "1  02-01-2018    0     1          2 -0.722069      0.748899         670   \n",
       "2  03-01-2018    0     1          1 -1.635432      0.745931        1229   \n",
       "3  04-01-2018    0     1          1 -1.615560     -0.389769        1454   \n",
       "4  05-01-2018    0     1          1 -1.468226     -0.046477        1518   \n",
       "\n",
       "   dia_sem_encoded  laborables_0  laborables_1  \n",
       "0                1             1             0  \n",
       "1                1             0             1  \n",
       "2                2             0             1  \n",
       "3                3             0             1  \n",
       "4                3             0             1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_registered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>weathersit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_weathersitr = {1:3, 2:2, 3:1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_registered['weathersit_encoded'] = df_registered['weathersit'].map(map_weathersitr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_registered.drop('weathersit', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.615560</td>\n",
       "      <td>-0.389769</td>\n",
       "      <td>1454</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>1518</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year month   temp_es  windspeed_es  registered  dia_sem_encoded  \\\n",
       "0  01-01-2018    0     1 -0.827613     -0.387833         654                1   \n",
       "1  02-01-2018    0     1 -0.722069      0.748899         670                1   \n",
       "2  03-01-2018    0     1 -1.635432      0.745931        1229                2   \n",
       "3  04-01-2018    0     1 -1.615560     -0.389769        1454                3   \n",
       "4  05-01-2018    0     1 -1.468226     -0.046477        1518                3   \n",
       "\n",
       "   laborables_0  laborables_1 weathersit_encoded  \n",
       "0             1             0                  2  \n",
       "1             0             1                  2  \n",
       "2             0             1                  3  \n",
       "3             0             1                  3  \n",
       "4             0             1                  3  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_registered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=orange>month\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_monthr={1:1, 2:2, 3:3, 12:4, 11:6, 4:5, 10:7, 5:6, 6:8, 9:8, 7:7, 8:8}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_registered['month_encoded'] = df_registered['month'].map(map_monthr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_registered.drop('month', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1454</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>1518</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year   temp_es  windspeed_es  registered  dia_sem_encoded  \\\n",
       "0  01-01-2018    0 -0.827613     -0.387833         654                1   \n",
       "1  02-01-2018    0 -0.722069      0.748899         670                1   \n",
       "2  03-01-2018    0 -1.635432      0.745931        1229                2   \n",
       "3  04-01-2018    0 -1.615560     -0.389769        1454                3   \n",
       "4  05-01-2018    0 -1.468226     -0.046477        1518                3   \n",
       "\n",
       "   laborables_0  laborables_1 weathersit_encoded  month_encoded  \n",
       "0             1             0                  2              1  \n",
       "1             0             1                  2              1  \n",
       "2             0             1                  3              1  \n",
       "3             0             1                  3              1  \n",
       "4             0             1                  3              1  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_registered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = [\"year\"]\n",
    "\n",
    "df_encoded = pd.DataFrame()\n",
    "\n",
    "\n",
    "for columna in lista:\n",
    "    df_dummies = pd.get_dummies(df_registered[columna], prefix_sep = \"_\", prefix = columna, dtype = int)\n",
    "\n",
    "    df_encoded = pd.concat([df_encoded, df_dummies], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1454</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.468226</td>\n",
       "      <td>-0.046477</td>\n",
       "      <td>1518</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday year   temp_es  windspeed_es  registered  dia_sem_encoded  \\\n",
       "0  01-01-2018    0 -0.827613     -0.387833         654                1   \n",
       "1  02-01-2018    0 -0.722069      0.748899         670                1   \n",
       "2  03-01-2018    0 -1.635432      0.745931        1229                2   \n",
       "3  04-01-2018    0 -1.615560     -0.389769        1454                3   \n",
       "4  05-01-2018    0 -1.468226     -0.046477        1518                3   \n",
       "\n",
       "   laborables_0  laborables_1 weathersit_encoded  month_encoded  year_0  \\\n",
       "0             1             0                  2              1       1   \n",
       "1             0             1                  2              1       1   \n",
       "2             0             1                  3              1       1   \n",
       "3             0             1                  3              1       1   \n",
       "4             0             1                  3              1       1   \n",
       "\n",
       "   year_1  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_registered = pd.concat([df_registered, df_encoded], axis = 1)\n",
    "df_registered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>dteday</th>\n",
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      "text/plain": [
       "       dteday   temp_es  windspeed_es  registered  dia_sem_encoded  \\\n",
       "0  01-01-2018 -0.827613     -0.387833         654                1   \n",
       "1  02-01-2018 -0.722069      0.748899         670                1   \n",
       "\n",
       "   laborables_0  laborables_1 weathersit_encoded  month_encoded  year_0  \\\n",
       "0             1             0                  2              1       1   \n",
       "1             0             1                  2              1       1   \n",
       "\n",
       "   year_1  \n",
       "0       0  \n",
       "1       0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_registered.drop(lista, axis = 1, inplace=True)\n",
    "df_registered.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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       "      <th>casual</th>\n",
       "      <th>dia_sem_encoded</th>\n",
       "      <th>laborables_0</th>\n",
       "      <th>laborables_1</th>\n",
       "      <th>weathersit_encoded</th>\n",
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       "      <td>01-01-2018</td>\n",
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      "text/plain": [
       "       dteday   temp_es  windspeed_es  casual  dia_sem_encoded  laborables_0  \\\n",
       "0  01-01-2018 -0.827613     -0.387833     331                4             1   \n",
       "1  02-01-2018 -0.722069      0.748899     131                3             0   \n",
       "\n",
       "   laborables_1 weathersit_encoded  month_encoded  year_0  year_1  \n",
       "0             0                  2              1       1       0  \n",
       "1             1                  2              1       1       0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casual.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casual.to_csv(\"./Datos/casual_encoded.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_registered.to_csv(\"./Datos/registered_encoded.csv\")"
   ]
  }
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