limpieza-fecha4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import re\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10,8)\n",
    "pd.options.display.max_rows=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</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",
       "    </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>6</td>\n",
       "      <td>0</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",
       "    </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>0</td>\n",
       "      <td>0</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",
       "    </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>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",
       "    </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>2</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",
       "    </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>3</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday year month weekday workingday weathersit       temp  \\\n",
       "0        1  01-01-2018    0     1       6          0          2  14.110847   \n",
       "1        2  02-01-2018    0     1       0          0          2  14.902598   \n",
       "2        3  03-01-2018    0     1       1          1          1   8.050924   \n",
       "3        4  04-01-2018    0     1       2          1          1   8.200000   \n",
       "4        5  05-01-2018    0     1       3          1          1   9.305237   \n",
       "\n",
       "   windspeed  casual  registered  count_bikes  \n",
       "0  10.749882     331         654          985  \n",
       "1  16.652113     131         670          801  \n",
       "2  16.636703     120        1229         1349  \n",
       "3  10.739832     108        1454         1562  \n",
       "4  12.522300      82        1518         1600  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_pickle('Datos/bikes_eda.pkl')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730, 12)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104.28571428571429"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "730/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "735"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekdays=[0,1,2,3,4,5,6]* 105\n",
    "len(weekdays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "while len(weekdays)>730:\n",
    "    weekdays.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "730"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(weekdays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekdays[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dia_semana'] = weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "lista_2018 = [\"01-01-2018\", \"15-01-2018\", \"19-02-2018\", \"16-04-2018\", \"28-05-2018\", \"04-04-2018\",\n",
    "              \"03-09-2018\", \"08-10-2018\", \"12-11-2018\", \"22-11-2018\", \"25-12-2018\"]\n",
    "\n",
    "lista_2019 = [\"01-01-2019\", \"21-01-2019\", \"18-02-2019\", \"16-04-2019\", \"27-05-2019\", \"04-07-2019\", \n",
    "              \"02-09-2019\", \"14-10-2019\", \"11-11-2019\", \"28-11-2019\", \"25-12-2019\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_2018.extend(lista_2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['laborables'] = np.where(df['dia_semana'].isin([5,6]) | df['dteday'].isin(lista_2018), 0, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['workingday', 'weekday'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>02-01-2018</td>\n",
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       "      <td>1</td>\n",
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       "    <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": 43,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle('Datos/bikes_eda_limpio.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "  \n",
    "    Tuesday, January 1, 2019: New Year’s Day     \n",
    "    Monday, January 21, 2019: Martin Luther King, Jr.’s Birthday     \n",
    "    Monday, February 18, 2019: Washington’s Birthday     \n",
    "    Tuesday, April 16, 2019: DC Emancipation Day     \n",
    "    Monday, May 27, 2019: Memorial Day     \n",
    "    Thursday, July 4, 2019: Independence Day     \n",
    "    Monday, September 2, 2019: Labor Day     \n",
    "    Monday, October 14, 2019: Columbus Day     \n",
    "    Monday, November 11, 2019: Veterans Day     \n",
    "    Thursday, November 28, 2019: Thanksgiving Day     \n",
    "    Wednesday, December 25, 2019: Christmas Day   \n",
    "\n",
    "\n",
    "\n",
    "    Monday, January 1, 2018: New Year’s Day\n",
    "    Monday, January 15, 2018: Martin Luther King Jr.’s Birthday\n",
    "    Monday, February 19, 2018: Washington’s Birthday\n",
    "    Monday, April 16, 2018: DC Emancipation Day\n",
    "    Monday, May 28, 2018: Memorial Day\n",
    "    Wednesday, July 4, 2018: Independence Day\n",
    "    Monday, September 3, 2018: Labor Day\n",
    "    Monday, October 8, 2018: Columbus Day\n",
    "    Monday, November 12, 2018: Veterans Day*\n",
    "    Thursday, November 22, 2018: Thanksgiving Day\n",
    "    Tuesday, December 25, 2018: Christmas Day\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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