02-AmasandoDatos_Postclase.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amasando los datos\n",
    "\n",
    "El objetivo de este módulo es aprender a cargar datos, limpiarlos, y manipularlos. Es decir, cargar un archivo de texto básico, y aprender a hacer operaciones básicas (estadísticas), unir dos fuentes de datos, y operaciones mas complejas como el concepto de `split-apply-combine`. \n",
    "\n",
    "Para esto, usaremos una estructura llamada *Dataframe*.  Esta es la estructura básica de los paquetes para manejo de datos, para casi todos los lenguajes o software, y las instrucciones son muy similares entre unos y otros.  \n",
    "\n",
    "En Python, esto está incorporado dentro del paquete `Panda`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Panda tiene un nuevo tipo para almacenar datos, las `Series` que corresponden a una **columna** de datos, y los `Dataframe`, que son varias columnas de datos con los mismo indices.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero cargamos los datos (Ojo, los datos vienen con un registro \"NA\" para aquellos que no son validos):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "dataTrips = pd.read_csv(\"trips_new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2011-07-28T10:12:00.04</td>\n",
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       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
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       "      <td>2011-07-28T10:35:00.04</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>Registered</td>\n",
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       "      <td>Female</td>\n",
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       "    <tr>\n",
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       "      <td>2011-07-28T10:37:00.04</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>527</td>\n",
       "      <td>2012-10-01T20:24:00.04</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>226</td>\n",
       "      <td>2012-10-01T20:24:00.04</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01T20:28:00.04</td>\n",
       "      <td>26.0</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>535</td>\n",
       "      <td>2012-10-01T20:25:00.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01T20:34:00.04</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>429</td>\n",
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       "      <td>61.0</td>\n",
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       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>199</td>\n",
       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01T20:36:00.04</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552073 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date  start_station  \\\n",
       "0            8         9  2011-07-28T10:12:00.04           23.0   \n",
       "1            9       220  2011-07-28T10:21:00.04           23.0   \n",
       "2           10        56  2011-07-28T10:33:00.04           23.0   \n",
       "3           11        64  2011-07-28T10:35:00.04           23.0   \n",
       "4           12        12  2011-07-28T10:37:00.04           23.0   \n",
       "...        ...       ...                     ...            ...   \n",
       "552068  623507       527  2012-10-01T20:24:00.04           46.0   \n",
       "552069  623508       226  2012-10-01T20:24:00.04           25.0   \n",
       "552070  623509       535  2012-10-01T20:25:00.04           16.0   \n",
       "552071  623512       429  2012-10-01T20:29:00.04           61.0   \n",
       "552072  623517       199  2012-10-01T20:32:00.04           22.0   \n",
       "\n",
       "                      end_date  end_station bike_nr subscription_type  \\\n",
       "0       2011-07-28T10:12:00.04         23.0  B00468        Registered   \n",
       "1       2011-07-28T10:25:00.04         23.0  B00554        Registered   \n",
       "2       2011-07-28T10:34:00.04         23.0  B00456        Registered   \n",
       "3       2011-07-28T10:36:00.04         23.0  B00554        Registered   \n",
       "4       2011-07-28T10:37:00.04         23.0  B00554        Registered   \n",
       "...                        ...          ...     ...               ...   \n",
       "552068  2012-10-01T20:32:00.04         67.0  B00486            Casual   \n",
       "552069  2012-10-01T20:28:00.04         26.0  B00170        Registered   \n",
       "552070  2012-10-01T20:34:00.04         47.0  T01067        Registered   \n",
       "552071  2012-10-01T20:36:00.04         33.0  B00621        Registered   \n",
       "552072  2012-10-01T20:36:00.04         81.0  B00012        Registered   \n",
       "\n",
       "       zip_code  birth_date  gender  \n",
       "0         97217      1976.0    Male  \n",
       "1          2215      1966.0    Male  \n",
       "2          2108      1943.0    Male  \n",
       "3          2116      1981.0  Female  \n",
       "4         97214      1983.0  Female  \n",
       "...         ...         ...     ...  \n",
       "552068      NaN         NaN     NaN  \n",
       "552069    02118      1986.0    Male  \n",
       "552070    02113      1965.0    Male  \n",
       "552071    02135      1986.0    Male  \n",
       "552072    02116      1989.0    Male  \n",
       "\n",
       "[552073 rows x 11 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada columna es una serie de datos. Podemos pedir una columna específica usado dataframe.columna o dataframe['columna']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         B00468\n",
       "1         B00554\n",
       "2         B00456\n",
       "3         B00554\n",
       "4         B00554\n",
       "           ...  \n",
       "552068    B00486\n",
       "552069    B00170\n",
       "552070    T01067\n",
       "552071    B00621\n",
       "552072    B00012\n",
       "Name: bike_nr, Length: 552073, dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['bike_nr']\n",
    "dataTrips.bike_nr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           9\n",
       "1         220\n",
       "2          56\n",
       "3          64\n",
       "4          12\n",
       "         ... \n",
       "552068    527\n",
       "552069    226\n",
       "552070    535\n",
       "552071    429\n",
       "552072    199\n",
       "Name: duration, Length: 552073, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las series de datos pueden operarse como vectores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1          True\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "552068     True\n",
       "552069     True\n",
       "552070     True\n",
       "552071     True\n",
       "552072     True\n",
       "Name: duration, Length: 552073, dtype: bool"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration'] *100000\n",
    "dataTrips['duration']*dataTrips['duration']\n",
    "dataTrips['duration'] >100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y estas funciones también aplican a los Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos agregar una columna con un valor dado (o una lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>test2</th>\n",
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       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2011-07-28T10:12:00.04</td>\n",
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       "      <td>2011-07-28T10:12:00.04</td>\n",
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       "      <td>2011-07-28T10:21:00.04</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>1966.0</td>\n",
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       "      <td>Perro</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>56</td>\n",
       "      <td>2011-07-28T10:33:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:34:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Perro</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>2011-07-28T10:35:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:36:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Perro</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-07-28T10:37:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:37:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Perro</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>527</td>\n",
       "      <td>2012-10-01T20:24:00.04</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Perro</td>\n",
       "      <td>552068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>226</td>\n",
       "      <td>2012-10-01T20:24:00.04</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01T20:28:00.04</td>\n",
       "      <td>26.0</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Perro</td>\n",
       "      <td>552069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>535</td>\n",
       "      <td>2012-10-01T20:25:00.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01T20:34:00.04</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Perro</td>\n",
       "      <td>552070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>429</td>\n",
       "      <td>2012-10-01T20:29:00.04</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01T20:36:00.04</td>\n",
       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Perro</td>\n",
       "      <td>552071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>199</td>\n",
       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01T20:36:00.04</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Perro</td>\n",
       "      <td>552072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552073 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date  start_station  \\\n",
       "0            8         9  2011-07-28T10:12:00.04           23.0   \n",
       "1            9       220  2011-07-28T10:21:00.04           23.0   \n",
       "2           10        56  2011-07-28T10:33:00.04           23.0   \n",
       "3           11        64  2011-07-28T10:35:00.04           23.0   \n",
       "4           12        12  2011-07-28T10:37:00.04           23.0   \n",
       "...        ...       ...                     ...            ...   \n",
       "552068  623507       527  2012-10-01T20:24:00.04           46.0   \n",
       "552069  623508       226  2012-10-01T20:24:00.04           25.0   \n",
       "552070  623509       535  2012-10-01T20:25:00.04           16.0   \n",
       "552071  623512       429  2012-10-01T20:29:00.04           61.0   \n",
       "552072  623517       199  2012-10-01T20:32:00.04           22.0   \n",
       "\n",
       "                      end_date  end_station bike_nr subscription_type  \\\n",
       "0       2011-07-28T10:12:00.04         23.0  B00468        Registered   \n",
       "1       2011-07-28T10:25:00.04         23.0  B00554        Registered   \n",
       "2       2011-07-28T10:34:00.04         23.0  B00456        Registered   \n",
       "3       2011-07-28T10:36:00.04         23.0  B00554        Registered   \n",
       "4       2011-07-28T10:37:00.04         23.0  B00554        Registered   \n",
       "...                        ...          ...     ...               ...   \n",
       "552068  2012-10-01T20:32:00.04         67.0  B00486            Casual   \n",
       "552069  2012-10-01T20:28:00.04         26.0  B00170        Registered   \n",
       "552070  2012-10-01T20:34:00.04         47.0  T01067        Registered   \n",
       "552071  2012-10-01T20:36:00.04         33.0  B00621        Registered   \n",
       "552072  2012-10-01T20:36:00.04         81.0  B00012        Registered   \n",
       "\n",
       "       zip_code  birth_date  gender   test   test2  \n",
       "0         97217      1976.0    Male  Perro       0  \n",
       "1          2215      1966.0    Male  Perro       1  \n",
       "2          2108      1943.0    Male  Perro       2  \n",
       "3          2116      1981.0  Female  Perro       3  \n",
       "4         97214      1983.0  Female  Perro       4  \n",
       "...         ...         ...     ...    ...     ...  \n",
       "552068      NaN         NaN     NaN  Perro  552068  \n",
       "552069    02118      1986.0    Male  Perro  552069  \n",
       "552070    02113      1965.0    Male  Perro  552070  \n",
       "552071    02135      1986.0    Male  Perro  552071  \n",
       "552072    02116      1989.0    Male  Perro  552072  \n",
       "\n",
       "[552073 rows x 13 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['test2'] = list(range(552073))\n",
    "dataTrips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y borrarlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2011-07-28T10:12:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:12:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>220</td>\n",
       "      <td>2011-07-28T10:21:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:25:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>56</td>\n",
       "      <td>2011-07-28T10:33:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:34:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>2011-07-28T10:35:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:36:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-07-28T10:37:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:37:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>527</td>\n",
       "      <td>2012-10-01T20:24:00.04</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>226</td>\n",
       "      <td>2012-10-01T20:24:00.04</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01T20:28:00.04</td>\n",
       "      <td>26.0</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>535</td>\n",
       "      <td>2012-10-01T20:25:00.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01T20:34:00.04</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>429</td>\n",
       "      <td>2012-10-01T20:29:00.04</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01T20:36:00.04</td>\n",
       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>199</td>\n",
       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01T20:36:00.04</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552073 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date  start_station  \\\n",
       "0            8         9  2011-07-28T10:12:00.04           23.0   \n",
       "1            9       220  2011-07-28T10:21:00.04           23.0   \n",
       "2           10        56  2011-07-28T10:33:00.04           23.0   \n",
       "3           11        64  2011-07-28T10:35:00.04           23.0   \n",
       "4           12        12  2011-07-28T10:37:00.04           23.0   \n",
       "...        ...       ...                     ...            ...   \n",
       "552068  623507       527  2012-10-01T20:24:00.04           46.0   \n",
       "552069  623508       226  2012-10-01T20:24:00.04           25.0   \n",
       "552070  623509       535  2012-10-01T20:25:00.04           16.0   \n",
       "552071  623512       429  2012-10-01T20:29:00.04           61.0   \n",
       "552072  623517       199  2012-10-01T20:32:00.04           22.0   \n",
       "\n",
       "                      end_date  end_station bike_nr subscription_type  \\\n",
       "0       2011-07-28T10:12:00.04         23.0  B00468        Registered   \n",
       "1       2011-07-28T10:25:00.04         23.0  B00554        Registered   \n",
       "2       2011-07-28T10:34:00.04         23.0  B00456        Registered   \n",
       "3       2011-07-28T10:36:00.04         23.0  B00554        Registered   \n",
       "4       2011-07-28T10:37:00.04         23.0  B00554        Registered   \n",
       "...                        ...          ...     ...               ...   \n",
       "552068  2012-10-01T20:32:00.04         67.0  B00486            Casual   \n",
       "552069  2012-10-01T20:28:00.04         26.0  B00170        Registered   \n",
       "552070  2012-10-01T20:34:00.04         47.0  T01067        Registered   \n",
       "552071  2012-10-01T20:36:00.04         33.0  B00621        Registered   \n",
       "552072  2012-10-01T20:36:00.04         81.0  B00012        Registered   \n",
       "\n",
       "       zip_code  birth_date  gender  \n",
       "0         97217      1976.0    Male  \n",
       "1          2215      1966.0    Male  \n",
       "2          2108      1943.0    Male  \n",
       "3          2116      1981.0  Female  \n",
       "4         97214      1983.0  Female  \n",
       "...         ...         ...     ...  \n",
       "552068      NaN         NaN     NaN  \n",
       "552069    02118      1986.0    Male  \n",
       "552070    02113      1965.0    Male  \n",
       "552071    02135      1986.0    Male  \n",
       "552072    02116      1989.0    Male  \n",
       "\n",
       "[552073 rows x 11 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#del dataTrips['test']\n",
    "del dataTrips['test2']\n",
    "dataTrips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     8,      9,     10, ..., 623509, 623512, 623517])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['id'].index\n",
    "dataTrips['id'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También podemos pedir un subconjunto de elementos, como si fuera una matriz. Por ejemplo, las 10 primeras filas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>220</td>\n",
       "      <td>2011-07-28T10:21:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:25:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>56</td>\n",
       "      <td>2011-07-28T10:33:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:34:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>2011-07-28T10:35:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:36:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-07-28T10:37:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:37:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  duration              start_date  start_station  \\\n",
       "1   9       220  2011-07-28T10:21:00.04           23.0   \n",
       "2  10        56  2011-07-28T10:33:00.04           23.0   \n",
       "3  11        64  2011-07-28T10:35:00.04           23.0   \n",
       "4  12        12  2011-07-28T10:37:00.04           23.0   \n",
       "\n",
       "                 end_date  end_station bike_nr subscription_type zip_code  \\\n",
       "1  2011-07-28T10:25:00.04         23.0  B00554        Registered     2215   \n",
       "2  2011-07-28T10:34:00.04         23.0  B00456        Registered     2108   \n",
       "3  2011-07-28T10:36:00.04         23.0  B00554        Registered     2116   \n",
       "4  2011-07-28T10:37:00.04         23.0  B00554        Registered    97214   \n",
       "\n",
       "   birth_date  gender  \n",
       "1      1966.0    Male  \n",
       "2      1943.0    Male  \n",
       "3      1981.0  Female  \n",
       "4      1983.0  Female  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['id']\n",
    "dataTrips.iloc[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['id'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O las filas que cumplen con una condición. Por ejemplo, los viajes de la bicicleta B00468."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "552068    False\n",
       "552069    False\n",
       "552070    False\n",
       "552071    False\n",
       "552072    False\n",
       "Name: bike_nr, Length: 552073, dtype: bool"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['bike_nr']=='B00468'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y seleccionar solo las filas que cumplen esa condicion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": false
   },
   "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2011-07-28T10:12:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:12:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>128</td>\n",
       "      <td>397</td>\n",
       "      <td>2011-07-28T13:25:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T13:32:00.04</td>\n",
       "      <td>44.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2143</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>939</td>\n",
       "      <td>3924</td>\n",
       "      <td>2011-07-29T18:17:00.04</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2011-07-29T19:23:00.04</td>\n",
       "      <td>42.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>1046</td>\n",
       "      <td>507</td>\n",
       "      <td>2011-07-29T21:40:00.04</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2011-07-29T21:49:00.04</td>\n",
       "      <td>36.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1077</td>\n",
       "      <td>955</td>\n",
       "      <td>2011-07-30T02:42:00.04</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2011-07-30T02:58:00.04</td>\n",
       "      <td>41.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549617</th>\n",
       "      <td>620699</td>\n",
       "      <td>712</td>\n",
       "      <td>2012-10-01T08:09:00.04</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2012-10-01T08:21:00.04</td>\n",
       "      <td>35.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551173</th>\n",
       "      <td>622434</td>\n",
       "      <td>297</td>\n",
       "      <td>2012-10-01T17:05:00.04</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2012-10-01T17:10:00.04</td>\n",
       "      <td>43.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02043</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551428</th>\n",
       "      <td>622720</td>\n",
       "      <td>767</td>\n",
       "      <td>2012-10-01T17:38:00.04</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2012-10-01T17:51:00.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551721</th>\n",
       "      <td>623084</td>\n",
       "      <td>428</td>\n",
       "      <td>2012-10-01T18:32:00.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01T18:39:00.04</td>\n",
       "      <td>59.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>03051</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551789</th>\n",
       "      <td>623165</td>\n",
       "      <td>520</td>\n",
       "      <td>2012-10-01T18:48:00.04</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2012-10-01T18:57:00.04</td>\n",
       "      <td>57.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>862 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date  start_station  \\\n",
       "0            8         9  2011-07-28T10:12:00.04           23.0   \n",
       "100        128       397  2011-07-28T13:25:00.04           23.0   \n",
       "847        939      3924  2011-07-29T18:17:00.04           44.0   \n",
       "941       1046       507  2011-07-29T21:40:00.04           42.0   \n",
       "969       1077       955  2011-07-30T02:42:00.04           36.0   \n",
       "...        ...       ...                     ...            ...   \n",
       "549617  620699       712  2012-10-01T08:09:00.04           45.0   \n",
       "551173  622434       297  2012-10-01T17:05:00.04           35.0   \n",
       "551428  622720       767  2012-10-01T17:38:00.04           43.0   \n",
       "551721  623084       428  2012-10-01T18:32:00.04           16.0   \n",
       "551789  623165       520  2012-10-01T18:48:00.04           59.0   \n",
       "\n",
       "                      end_date  end_station bike_nr subscription_type  \\\n",
       "0       2011-07-28T10:12:00.04         23.0  B00468        Registered   \n",
       "100     2011-07-28T13:32:00.04         44.0  B00468        Registered   \n",
       "847     2011-07-29T19:23:00.04         42.0  B00468            Casual   \n",
       "941     2011-07-29T21:49:00.04         36.0  B00468            Casual   \n",
       "969     2011-07-30T02:58:00.04         41.0  B00468            Casual   \n",
       "...                        ...          ...     ...               ...   \n",
       "549617  2012-10-01T08:21:00.04         35.0  B00468        Registered   \n",
       "551173  2012-10-01T17:10:00.04         43.0  B00468        Registered   \n",
       "551428  2012-10-01T17:51:00.04         16.0  B00468        Registered   \n",
       "551721  2012-10-01T18:39:00.04         59.0  B00468        Registered   \n",
       "551789  2012-10-01T18:57:00.04         57.0  B00468        Registered   \n",
       "\n",
       "       zip_code  birth_date  gender  \n",
       "0         97217      1976.0    Male  \n",
       "100        2143      1986.0  Female  \n",
       "847         NaN         NaN     NaN  \n",
       "941         NaN         NaN     NaN  \n",
       "969         NaN         NaN     NaN  \n",
       "...         ...         ...     ...  \n",
       "549617    02215      1978.0  Female  \n",
       "551173    02043      1948.0    Male  \n",
       "551428    02116      1969.0  Female  \n",
       "551721    03051      1977.0    Male  \n",
       "551789    02118      1973.0    Male  \n",
       "\n",
       "[862 rows x 11 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips[dataTrips['bike_nr']=='B00468']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver el numero de registros y columnas del DataFrame (`.shape`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(862, 11)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.shape\n",
    "dataTrips[dataTrips['bike_nr']=='B00468'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un aspecto clave de los DataFrame es que cada columna tiene un *typo* (`dtype` en Python) asociado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     int64\n",
       "duration               int64\n",
       "start_date            object\n",
       "start_station        float64\n",
       "end_date              object\n",
       "end_station          float64\n",
       "bike_nr               object\n",
       "subscription_type     object\n",
       "zip_code              object\n",
       "birth_date           float64\n",
       "gender                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cambiaremos las columnas a *categorias*. Por ejemplo, la columna *gender* tiene dos categorías: *Male* y *Female* (o `NaN` si no se conoce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['gender']= dataTrips['gender'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Male\n",
       "1           Male\n",
       "2           Male\n",
       "3         Female\n",
       "4         Female\n",
       "           ...  \n",
       "552068       NaN\n",
       "552069      Male\n",
       "552070      Male\n",
       "552071      Male\n",
       "552072      Male\n",
       "Name: gender, Length: 552073, dtype: category\n",
       "Categories (2, object): ['Female', 'Male']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['gender']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Posiblemente al leer también tiene errores. Por ejemplo, la estación de inicio la reconoció como un número (*float*). Lo corregimos como categoría."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['start_station']=dataTrips['start_station'].astype('category')\n",
    "dataTrips['end_station']=dataTrips['end_station'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         23.0\n",
       "1         23.0\n",
       "2         23.0\n",
       "3         23.0\n",
       "4         23.0\n",
       "          ... \n",
       "552068    46.0\n",
       "552069    25.0\n",
       "552070    16.0\n",
       "552071    61.0\n",
       "552072    22.0\n",
       "Name: start_station, Length: 552073, dtype: category\n",
       "Categories (95, float64): [3.0, 4.0, 5.0, 6.0, ..., 95.0, 96.0, 97.0, 98.0]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['start_station']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otras columnas (como *start_date* y *end_date*) son del tipo Fecha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['start_date']=dataTrips['start_date'].astype('datetime64[ns]')\n",
    "dataTrips['end_date']=dataTrips['end_date'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['subscription_type']=dataTrips['subscription_type'].astype('category')\n",
    "dataTrips['zip_code']=dataTrips['zip_code'].astype('category')\n",
    "dataTrips['bike_nr']=dataTrips['bike_nr'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            int64\n",
       "duration                      int64\n",
       "start_date           datetime64[ns]\n",
       "start_station              category\n",
       "end_date             datetime64[ns]\n",
       "end_station                category\n",
       "bike_nr                    category\n",
       "subscription_type          category\n",
       "zip_code                   category\n",
       "birth_date                  float64\n",
       "gender                     category\n",
       "dtype: object"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y, por ejemplo, las duraciones deberían ser `float`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['duration']=dataTrips['duration'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            int64\n",
       "duration                    float64\n",
       "start_date           datetime64[ns]\n",
       "start_station              category\n",
       "end_date             datetime64[ns]\n",
       "end_station                category\n",
       "bike_nr                    category\n",
       "subscription_type          category\n",
       "zip_code                   category\n",
       "birth_date                  float64\n",
       "gender                     category\n",
       "dtype: object"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.520730e+05\n",
       "mean     1.694982e+03\n",
       "std      4.436507e+04\n",
       "min      0.000000e+00\n",
       "25%      4.110000e+02\n",
       "50%      6.870000e+02\n",
       "75%      1.204000e+03\n",
       "max      1.199446e+07\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora, transformemos las duraciones (que están en segundos) en minutos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['duration']/60\n",
    "dataTrips['duration']=dataTrips['duration']/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.150000\n",
       "1         3.666667\n",
       "2         0.933333\n",
       "3         1.066667\n",
       "4         0.200000\n",
       "            ...   \n",
       "552068    8.783333\n",
       "552069    3.766667\n",
       "552070    8.916667\n",
       "552071    7.150000\n",
       "552072    3.316667\n",
       "Name: duration, Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>birth_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>552073.000000</td>\n",
       "      <td>552073.000000</td>\n",
       "      <td>352960.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>312921.005660</td>\n",
       "      <td>28.249699</td>\n",
       "      <td>1976.29481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>180091.585665</td>\n",
       "      <td>739.417819</td>\n",
       "      <td>11.00360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1932.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>154977.000000</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1969.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>315388.000000</td>\n",
       "      <td>11.450000</td>\n",
       "      <td>1979.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>468523.000000</td>\n",
       "      <td>20.066667</td>\n",
       "      <td>1985.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>623517.000000</td>\n",
       "      <td>199907.633333</td>\n",
       "      <td>1995.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id       duration    birth_date\n",
       "count  552073.000000  552073.000000  352960.00000\n",
       "mean   312921.005660      28.249699    1976.29481\n",
       "std    180091.585665     739.417819      11.00360\n",
       "min         8.000000       0.000000    1932.00000\n",
       "25%    154977.000000       6.850000    1969.00000\n",
       "50%    315388.000000      11.450000    1979.00000\n",
       "75%    468523.000000      20.066667    1985.00000\n",
       "max    623517.000000  199907.633333    1995.00000"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funciones cualquiera pueden ser aplicadas a cada elemento con la función `.apply(<funcion>)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cuadrado(x):\n",
    "    return x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.150000\n",
       "1         3.666667\n",
       "2         0.933333\n",
       "3         1.066667\n",
       "4         0.200000\n",
       "            ...   \n",
       "552068    8.783333\n",
       "552069    3.766667\n",
       "552070    8.916667\n",
       "552071    7.150000\n",
       "552072    3.316667\n",
       "Name: duration, Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0.022500\n",
       "1         13.444444\n",
       "2          0.871111\n",
       "3          1.137778\n",
       "4          0.040000\n",
       "            ...    \n",
       "552068    77.146944\n",
       "552069    14.187778\n",
       "552070    79.506944\n",
       "552071    51.122500\n",
       "552072    11.000278\n",
       "Name: duration, Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration'].apply(cuadrado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0.022500\n",
       "1         13.444444\n",
       "2          0.871111\n",
       "3          1.137778\n",
       "4          0.040000\n",
       "            ...    \n",
       "552068    77.146944\n",
       "552069    14.187778\n",
       "552070    79.506944\n",
       "552071    51.122500\n",
       "552072    11.000278\n",
       "Name: duration, Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration'].apply(lambda x: x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fijemonos que tenemos varias columnas con datos faltantes.  Algunos no son importantes (*zip_code*, *birth_date* para los usuarios no registrados) pero otros pueden ser errores (*start_station* y *end_station*).  Podemos eliminar las final con datos faltantes `NaN` usando las manipulaciones anteriores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ejemplo, podemos ver si un dato es `NaN` o no, con el comando `isna()`.  Esto hay que aplicarlo sobre ***CADA*** registro de la columna, y con eso podemos filtrar los datos que queremos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dataTrips['end_station'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2312</th>\n",
       "      <td>2549</td>\n",
       "      <td>23.533333</td>\n",
       "      <td>2011-07-31 15:48:00.040</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2011-07-31 16:12:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00427</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8859</th>\n",
       "      <td>9726</td>\n",
       "      <td>177657.850000</td>\n",
       "      <td>2011-08-06 16:02:00.040</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00137</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14522</th>\n",
       "      <td>15931</td>\n",
       "      <td>169941.283333</td>\n",
       "      <td>2011-08-12 00:38:00.040</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00542</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17391</th>\n",
       "      <td>19168</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>2011-08-13 19:14:00.040</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2011-08-13 19:35:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00028</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17417</th>\n",
       "      <td>19201</td>\n",
       "      <td>29.383333</td>\n",
       "      <td>2011-08-13 19:25:00.040</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2011-08-13 19:55:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00526</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2129</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17418</th>\n",
       "      <td>19202</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>2011-08-13 19:25:00.040</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2011-08-13 19:55:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00178</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2129</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27081</th>\n",
       "      <td>30170</td>\n",
       "      <td>155675.883333</td>\n",
       "      <td>2011-08-21 22:24:00.040</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00483</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28396</th>\n",
       "      <td>31618</td>\n",
       "      <td>154337.116667</td>\n",
       "      <td>2011-08-22 20:42:00.040</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00569</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47735</th>\n",
       "      <td>54771</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>2011-09-09 14:18:00.040</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2011-09-09 14:30:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>? (0x67D8478F)</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49934</th>\n",
       "      <td>57202</td>\n",
       "      <td>127094.000000</td>\n",
       "      <td>2011-09-10 18:45:00.040</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00488</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53928</th>\n",
       "      <td>61664</td>\n",
       "      <td>123171.516667</td>\n",
       "      <td>2011-09-13 12:08:00.040</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00119</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62892</th>\n",
       "      <td>71569</td>\n",
       "      <td>114341.233333</td>\n",
       "      <td>2011-09-19 15:18:00.040</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00218</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79147</th>\n",
       "      <td>89693</td>\n",
       "      <td>97114.116667</td>\n",
       "      <td>2011-10-01 14:25:00.040</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00470</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89333</th>\n",
       "      <td>101036</td>\n",
       "      <td>86312.083333</td>\n",
       "      <td>2011-10-09 02:27:00.040</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00273</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109702</th>\n",
       "      <td>123700</td>\n",
       "      <td>64140.150000</td>\n",
       "      <td>2011-10-24 11:59:00.040</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00405</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02186</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123173</th>\n",
       "      <td>138535</td>\n",
       "      <td>42614.150000</td>\n",
       "      <td>2011-11-08 09:45:00.050</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2011-12-08 00:00:00.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00261</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140610</th>\n",
       "      <td>157775</td>\n",
       "      <td>5.433333</td>\n",
       "      <td>2012-03-15 09:27:00.040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-03-15 09:33:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00139</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02465</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152607</th>\n",
       "      <td>172567</td>\n",
       "      <td>7.716667</td>\n",
       "      <td>2012-03-29 11:59:00.040</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2012-03-29 12:07:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00102</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02109</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155141</th>\n",
       "      <td>175582</td>\n",
       "      <td>22.766667</td>\n",
       "      <td>2012-04-01 10:37:00.040</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2012-04-01 11:00:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00082</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155165</th>\n",
       "      <td>175615</td>\n",
       "      <td>1.866667</td>\n",
       "      <td>2012-04-01 10:58:00.040</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2012-04-01 11:00:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00234</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155303</th>\n",
       "      <td>175800</td>\n",
       "      <td>12.966667</td>\n",
       "      <td>2012-04-01 12:47:00.040</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2012-04-01 13:00:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00153</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155306</th>\n",
       "      <td>175804</td>\n",
       "      <td>11.383333</td>\n",
       "      <td>2012-04-01 12:48:00.040</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2012-04-01 13:00:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00313</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258169</th>\n",
       "      <td>295244</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>2012-06-05 13:44:00.040</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2012-06-05 14:01:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00611</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397936</th>\n",
       "      <td>450219</td>\n",
       "      <td>11.416667</td>\n",
       "      <td>2012-08-08 11:08:00.040</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2012-08-08 11:20:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00057</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02476</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461122</th>\n",
       "      <td>521516</td>\n",
       "      <td>6.650000</td>\n",
       "      <td>2012-08-30 17:24:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-08-30 17:31:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T01124</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02141</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463503</th>\n",
       "      <td>524144</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>2012-08-31 14:10:00.040</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2012-08-31 14:23:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00095</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02141</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464516</th>\n",
       "      <td>525251</td>\n",
       "      <td>15.483333</td>\n",
       "      <td>2012-08-31 18:39:00.040</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2012-08-31 18:55:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00625</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474598</th>\n",
       "      <td>536471</td>\n",
       "      <td>9.583333</td>\n",
       "      <td>2012-09-04 14:57:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-04 15:07:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00514</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02134</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475288</th>\n",
       "      <td>537218</td>\n",
       "      <td>787.433333</td>\n",
       "      <td>2012-09-04 21:13:00.040</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2012-09-05 10:20:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00505</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02114</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475934</th>\n",
       "      <td>537923</td>\n",
       "      <td>7244.966667</td>\n",
       "      <td>2012-09-05 12:38:00.040</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2012-09-10 13:23:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00071</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476042</th>\n",
       "      <td>538039</td>\n",
       "      <td>51.433333</td>\n",
       "      <td>2012-09-05 13:50:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-05 14:41:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00267</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02134</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478421</th>\n",
       "      <td>540722</td>\n",
       "      <td>1688.283333</td>\n",
       "      <td>2012-09-06 10:10:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-07 14:18:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00267</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02141</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479086</th>\n",
       "      <td>541453</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>2012-09-06 15:11:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-06 15:22:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00206</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02134</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487631</th>\n",
       "      <td>551171</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>2012-09-09 16:23:00.040</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2012-09-09 16:29:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00413</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489115</th>\n",
       "      <td>552819</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>2012-09-10 08:30:00.040</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2012-09-10 08:51:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T01195</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02134</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494822</th>\n",
       "      <td>559232</td>\n",
       "      <td>2064.116667</td>\n",
       "      <td>2012-09-11 22:43:00.040</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2012-09-13 09:07:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00607</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02141</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498726</th>\n",
       "      <td>563628</td>\n",
       "      <td>344.683333</td>\n",
       "      <td>2012-09-13 08:12:00.040</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-09-13 13:57:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T01277</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511929</th>\n",
       "      <td>578554</td>\n",
       "      <td>8.416667</td>\n",
       "      <td>2012-09-17 08:48:00.040</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2012-09-17 08:56:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00397</td>\n",
       "      <td>Registered</td>\n",
       "      <td>70065</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521043</th>\n",
       "      <td>588616</td>\n",
       "      <td>8.166667</td>\n",
       "      <td>2012-09-20 08:45:00.040</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2012-09-20 08:53:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T01217</td>\n",
       "      <td>Registered</td>\n",
       "      <td>70065</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521966</th>\n",
       "      <td>589644</td>\n",
       "      <td>5852.533333</td>\n",
       "      <td>2012-09-20 14:28:00.040</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-09-24 16:01:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00157</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524955</th>\n",
       "      <td>592950</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>2012-09-21 13:30:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-21 13:30:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527040</th>\n",
       "      <td>595290</td>\n",
       "      <td>33.133333</td>\n",
       "      <td>2012-09-22 08:36:00.040</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2012-09-22 09:09:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00347</td>\n",
       "      <td>Registered</td>\n",
       "      <td>20912</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533037</th>\n",
       "      <td>602091</td>\n",
       "      <td>9.766667</td>\n",
       "      <td>2012-09-24 08:46:00.040</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2012-09-24 08:56:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T01119</td>\n",
       "      <td>Registered</td>\n",
       "      <td>70065</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537326</th>\n",
       "      <td>606913</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>2012-09-25 16:12:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-25 16:12:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T01168</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539896</th>\n",
       "      <td>609840</td>\n",
       "      <td>5.983333</td>\n",
       "      <td>2012-09-26 12:26:00.040</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2012-09-26 12:32:00.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>20912</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id       duration              start_date start_station  \\\n",
       "2312      2549      23.533333 2011-07-31 15:48:00.040          21.0   \n",
       "8859      9726  177657.850000 2011-08-06 16:02:00.040          52.0   \n",
       "14522    15931  169941.283333 2011-08-12 00:38:00.040          36.0   \n",
       "17391    19168      20.900000 2011-08-13 19:14:00.040          39.0   \n",
       "17417    19201      29.383333 2011-08-13 19:25:00.040          53.0   \n",
       "17418    19202      29.333333 2011-08-13 19:25:00.040          53.0   \n",
       "27081    30170  155675.883333 2011-08-21 22:24:00.040          35.0   \n",
       "28396    31618  154337.116667 2011-08-22 20:42:00.040          24.0   \n",
       "47735    54771      11.500000 2011-09-09 14:18:00.040          47.0   \n",
       "49934    57202  127094.000000 2011-09-10 18:45:00.040          32.0   \n",
       "53928    61664  123171.516667 2011-09-13 12:08:00.040          36.0   \n",
       "62892    71569  114341.233333 2011-09-19 15:18:00.040          42.0   \n",
       "79147    89693   97114.116667 2011-10-01 14:25:00.040          42.0   \n",
       "89333   101036   86312.083333 2011-10-09 02:27:00.040          49.0   \n",
       "109702  123700   64140.150000 2011-10-24 11:59:00.040          36.0   \n",
       "123173  138535   42614.150000 2011-11-08 09:45:00.050          20.0   \n",
       "140610  157775       5.433333 2012-03-15 09:27:00.040          16.0   \n",
       "152607  172567       7.716667 2012-03-29 11:59:00.040          48.0   \n",
       "155141  175582      22.766667 2012-04-01 10:37:00.040          38.0   \n",
       "155165  175615       1.866667 2012-04-01 10:58:00.040           4.0   \n",
       "155303  175800      12.966667 2012-04-01 12:47:00.040          24.0   \n",
       "155306  175804      11.383333 2012-04-01 12:48:00.040          24.0   \n",
       "258169  295244      16.900000 2012-06-05 13:44:00.040          54.0   \n",
       "397936  450219      11.416667 2012-08-08 11:08:00.040          74.0   \n",
       "461122  521516       6.650000 2012-08-30 17:24:00.040           NaN   \n",
       "463503  524144      12.750000 2012-08-31 14:10:00.040          90.0   \n",
       "464516  525251      15.483333 2012-08-31 18:39:00.040          38.0   \n",
       "474598  536471       9.583333 2012-09-04 14:57:00.040           NaN   \n",
       "475288  537218     787.433333 2012-09-04 21:13:00.040          42.0   \n",
       "475934  537923    7244.966667 2012-09-05 12:38:00.040          31.0   \n",
       "476042  538039      51.433333 2012-09-05 13:50:00.040           NaN   \n",
       "478421  540722    1688.283333 2012-09-06 10:10:00.040           NaN   \n",
       "479086  541453      10.300000 2012-09-06 15:11:00.040           NaN   \n",
       "487631  551171       5.816667 2012-09-09 16:23:00.040          88.0   \n",
       "489115  552819      21.600000 2012-09-10 08:30:00.040          18.0   \n",
       "494822  559232    2064.116667 2012-09-11 22:43:00.040          91.0   \n",
       "498726  563628     344.683333 2012-09-13 08:12:00.040          25.0   \n",
       "511929  578554       8.416667 2012-09-17 08:48:00.040          98.0   \n",
       "521043  588616       8.166667 2012-09-20 08:45:00.040          98.0   \n",
       "521966  589644    5852.533333 2012-09-20 14:28:00.040          22.0   \n",
       "524955  592950       0.133333 2012-09-21 13:30:00.040           NaN   \n",
       "527040  595290      33.133333 2012-09-22 08:36:00.040          57.0   \n",
       "533037  602091       9.766667 2012-09-24 08:46:00.040          98.0   \n",
       "537326  606913       0.066667 2012-09-25 16:12:00.040           NaN   \n",
       "539896  609840       5.983333 2012-09-26 12:26:00.040          94.0   \n",
       "\n",
       "                      end_date end_station         bike_nr subscription_type  \\\n",
       "2312   2011-07-31 16:12:00.040         NaN          B00427            Casual   \n",
       "8859   2011-12-08 00:00:00.050         NaN          B00137            Casual   \n",
       "14522  2011-12-08 00:00:00.050         NaN          B00542            Casual   \n",
       "17391  2011-08-13 19:35:00.040         NaN          B00028            Casual   \n",
       "17417  2011-08-13 19:55:00.040         NaN          B00526        Registered   \n",
       "17418  2011-08-13 19:55:00.040         NaN          B00178        Registered   \n",
       "27081  2011-12-08 00:00:00.050         NaN          B00483            Casual   \n",
       "28396  2011-12-08 00:00:00.050         NaN          B00569            Casual   \n",
       "47735  2011-09-09 14:30:00.040         NaN  ? (0x67D8478F)            Casual   \n",
       "49934  2011-12-08 00:00:00.050         NaN          B00488            Casual   \n",
       "53928  2011-12-08 00:00:00.050         NaN          B00119            Casual   \n",
       "62892  2011-12-08 00:00:00.050         NaN          B00218            Casual   \n",
       "79147  2011-12-08 00:00:00.050         NaN          B00470            Casual   \n",
       "89333  2011-12-08 00:00:00.050         NaN          B00273            Casual   \n",
       "109702 2011-12-08 00:00:00.050         NaN          B00405        Registered   \n",
       "123173 2011-12-08 00:00:00.050         NaN          B00261            Casual   \n",
       "140610 2012-03-15 09:33:00.040         NaN          B00139        Registered   \n",
       "152607 2012-03-29 12:07:00.040         NaN          B00102        Registered   \n",
       "155141 2012-04-01 11:00:00.040         NaN          B00082            Casual   \n",
       "155165 2012-04-01 11:00:00.040         NaN          B00234        Registered   \n",
       "155303 2012-04-01 13:00:00.040         NaN          B00153            Casual   \n",
       "155306 2012-04-01 13:00:00.040         NaN          B00313            Casual   \n",
       "258169 2012-06-05 14:01:00.040         NaN          B00611            Casual   \n",
       "397936 2012-08-08 11:20:00.040         NaN          B00057        Registered   \n",
       "461122 2012-08-30 17:31:00.040         NaN          T01124        Registered   \n",
       "463503 2012-08-31 14:23:00.040         NaN          B00095        Registered   \n",
       "464516 2012-08-31 18:55:00.040         NaN          B00625        Registered   \n",
       "474598 2012-09-04 15:07:00.040         NaN          B00514        Registered   \n",
       "475288 2012-09-05 10:20:00.040         NaN          B00505        Registered   \n",
       "475934 2012-09-10 13:23:00.040         NaN          B00071        Registered   \n",
       "476042 2012-09-05 14:41:00.040         NaN          B00267        Registered   \n",
       "478421 2012-09-07 14:18:00.040         NaN          B00267        Registered   \n",
       "479086 2012-09-06 15:22:00.040         NaN          B00206        Registered   \n",
       "487631 2012-09-09 16:29:00.040         NaN          B00413            Casual   \n",
       "489115 2012-09-10 08:51:00.040         NaN          T01195        Registered   \n",
       "494822 2012-09-13 09:07:00.040         NaN          B00607        Registered   \n",
       "498726 2012-09-13 13:57:00.040         NaN          T01277        Registered   \n",
       "511929 2012-09-17 08:56:00.040         NaN          B00397        Registered   \n",
       "521043 2012-09-20 08:53:00.040         NaN          T01217        Registered   \n",
       "521966 2012-09-24 16:01:00.040         NaN          B00157            Casual   \n",
       "524955 2012-09-21 13:30:00.040         NaN          B00456        Registered   \n",
       "527040 2012-09-22 09:09:00.040         NaN          B00347        Registered   \n",
       "533037 2012-09-24 08:56:00.040         NaN          T01119        Registered   \n",
       "537326 2012-09-25 16:12:00.040         NaN          T01168        Registered   \n",
       "539896 2012-09-26 12:32:00.040         NaN          B00456        Registered   \n",
       "\n",
       "       zip_code  birth_date  gender  \n",
       "2312        NaN         NaN     NaN  \n",
       "8859        NaN         NaN     NaN  \n",
       "14522       NaN         NaN     NaN  \n",
       "17391       NaN         NaN     NaN  \n",
       "17417      2129      1981.0  Female  \n",
       "17418      2129      1980.0    Male  \n",
       "27081       NaN         NaN     NaN  \n",
       "28396       NaN         NaN     NaN  \n",
       "47735       NaN         NaN     NaN  \n",
       "49934       NaN         NaN     NaN  \n",
       "53928       NaN         NaN     NaN  \n",
       "62892       NaN         NaN     NaN  \n",
       "79147       NaN         NaN     NaN  \n",
       "89333       NaN         NaN     NaN  \n",
       "109702    02186      1971.0    Male  \n",
       "123173      NaN         NaN     NaN  \n",
       "140610    02465      1953.0    Male  \n",
       "152607    02109      1985.0  Female  \n",
       "155141      NaN         NaN     NaN  \n",
       "155165    02118      1987.0  Female  \n",
       "155303      NaN         NaN     NaN  \n",
       "155306      NaN         NaN     NaN  \n",
       "258169      NaN         NaN     NaN  \n",
       "397936    02476      1958.0  Female  \n",
       "461122    02141      1985.0    Male  \n",
       "463503    02141      1985.0    Male  \n",
       "464516    02130         NaN    Male  \n",
       "474598    02134      1986.0    Male  \n",
       "475288    02114      1984.0  Female  \n",
       "475934    02135      1978.0  Female  \n",
       "476042    02134      1986.0    Male  \n",
       "478421    02141      1985.0    Male  \n",
       "479086    02134      1986.0    Male  \n",
       "487631      NaN         NaN     NaN  \n",
       "489115    02134      1986.0    Male  \n",
       "494822    02141      1985.0    Male  \n",
       "498726    02215      1984.0    Male  \n",
       "511929    70065      1988.0    Male  \n",
       "521043    70065      1988.0    Male  \n",
       "521966      NaN         NaN     NaN  \n",
       "524955    97214         NaN    Male  \n",
       "527040    20912      1988.0    Male  \n",
       "533037    70065      1988.0    Male  \n",
       "537326    97214         NaN    Male  \n",
       "539896    20912      1988.0    Male  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips[dataTrips['end_station'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "finalData = dataTrips.dropna(subset=['start_station', 'end_station'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2011-07-28 10:21:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>2011-07-28 10:33:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>2011-07-28 10:35:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26.0</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>2012-10-01 20:25:00.040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>2012-10-01 20:29:00.040</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552020 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8  0.150000 2011-07-28 10:12:00.040          23.0   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040          23.0   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040          23.0   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040          23.0   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040          23.0   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040          46.0   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040          25.0   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040          16.0   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040          61.0   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040          22.0   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040        23.0  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered     2215   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered     2108   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered     2116   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040        67.0  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040        26.0  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040        47.0  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040        33.0  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040        81.0  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender  \n",
       "0           1976.0    Male  \n",
       "1           1966.0    Male  \n",
       "2           1943.0    Male  \n",
       "3           1981.0  Female  \n",
       "4           1983.0  Female  \n",
       "...            ...     ...  \n",
       "552068         NaN     NaN  \n",
       "552069      1986.0    Male  \n",
       "552070      1965.0    Male  \n",
       "552071      1986.0    Male  \n",
       "552072      1989.0    Male  \n",
       "\n",
       "[552020 rows x 11 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(552020, 11)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analicemos ahora las duraciones. Hagamos un histograma de sus valores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(finalData['duration'], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    552020.000000\n",
       "mean         25.841395\n",
       "std         477.967443\n",
       "min           0.000000\n",
       "25%           6.850000\n",
       "50%          11.450000\n",
       "75%          20.066667\n",
       "max      199907.633333\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['duration'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Que pasó?  Hay outliers en los datos... Limitemonos entonces a aquellos datos con duraciones menores a 1 dia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(finalData['duration']> 24*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.150000\n",
       "1         3.666667\n",
       "2         0.933333\n",
       "3         1.066667\n",
       "4         0.200000\n",
       "            ...   \n",
       "552068    8.783333\n",
       "552069    3.766667\n",
       "552070    8.916667\n",
       "552071    7.150000\n",
       "552072    3.316667\n",
       "Name: duration, Length: 551611, dtype: float64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData[finalData['duration']<24*60]['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(finalData[finalData['duration']<24*60]['duration'], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mas razonable. Actualicemos los datos solo a aquellos viajes que duran menos de 24 horas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2011-07-28 10:21:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>2011-07-28 10:33:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>2011-07-28 10:35:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26.0</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>2012-10-01 20:25:00.040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>2012-10-01 20:29:00.040</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8  0.150000 2011-07-28 10:12:00.040          23.0   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040          23.0   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040          23.0   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040          23.0   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040          23.0   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040          46.0   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040          25.0   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040          16.0   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040          61.0   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040          22.0   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040        23.0  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered     2215   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered     2108   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered     2116   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040        67.0  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040        26.0  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040        47.0  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040        33.0  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040        81.0  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender  \n",
       "0           1976.0    Male  \n",
       "1           1966.0    Male  \n",
       "2           1943.0    Male  \n",
       "3           1981.0  Female  \n",
       "4           1983.0  Female  \n",
       "...            ...     ...  \n",
       "552068         NaN     NaN  \n",
       "552069      1986.0    Male  \n",
       "552070      1965.0    Male  \n",
       "552071      1986.0    Male  \n",
       "552072      1989.0    Male  \n",
       "\n",
       "[551611 rows x 11 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData = finalData[finalData['duration']<24*60]\n",
    "finalData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usemos las fechas. Estas pertenecen al tipo `DateTime`, por lo que podemos hacerles preguntas a ese registro, como el día de la semana de cada fecha, o el mes, etc.   <br>\n",
    "\n",
    "Partamos analizando las fechas en que se realizan los viajes, y los dias de semana que corresponden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Thursday\n",
       "1         Thursday\n",
       "2         Thursday\n",
       "3         Thursday\n",
       "4         Thursday\n",
       "            ...   \n",
       "552068      Monday\n",
       "552069      Monday\n",
       "552070      Monday\n",
       "552071      Monday\n",
       "552072      Monday\n",
       "Name: start_date, Length: 551611, dtype: object"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['start_date'].dt.day_name()\n",
    "#.dt.day_name()\n",
    "#finalData['start_date'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-212-9527532ece51>: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",
      "  finalData['dia_semana'] = finalData['start_date'].dt.day_name()\n"
     ]
    }
   ],
   "source": [
    "finalData['dia_semana'] = finalData['start_date'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-216-7ae7986174cd>: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",
      "  finalData['dia_semana']=finalData['dia_semana'].astype('category')\n"
     ]
    }
   ],
   "source": [
    "finalData['dia_semana']=finalData['dia_semana'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(finalData['start_date'].dt.dayofweek, bins=7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(finalData['start_date'].dt.month, bins=range(1,13))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(finalData['start_date'].dt.hour, bins=range(1,25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp=finalData[finalData['start_date'].dt.dayofweek>=5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(tmp['start_date'].dt.hour, bins=range(1,25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tambien podemos agregar una columna a la tabla. Por ejemplo, agreguemos el mes de cada viaje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-229-07735d57d753>: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",
      "  finalData['start_month'] = finalData['start_date'].dt.month\n"
     ]
    },
    {
     "data": {
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       "<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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>start_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>8</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
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       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2011-07-28 10:21:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>2011-07-28 10:33:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>11</td>\n",
       "      <td>1.066667</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>Registered</td>\n",
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       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>1986.0</td>\n",
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       "    <tr>\n",
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       "      <td>8.916667</td>\n",
       "      <td>2012-10-01 20:25:00.040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>2012-10-01 20:29:00.040</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8  0.150000 2011-07-28 10:12:00.040          23.0   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040          23.0   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040          23.0   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040          23.0   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040          23.0   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040          46.0   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040          25.0   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040          16.0   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040          61.0   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040          22.0   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040        23.0  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered     2215   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered     2108   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered     2116   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040        67.0  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040        26.0  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040        47.0  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040        33.0  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040        81.0  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender dia_semana  start_month  \n",
       "0           1976.0    Male   Thursday            7  \n",
       "1           1966.0    Male   Thursday            7  \n",
       "2           1943.0    Male   Thursday            7  \n",
       "3           1981.0  Female   Thursday            7  \n",
       "4           1983.0  Female   Thursday            7  \n",
       "...            ...     ...        ...          ...  \n",
       "552068         NaN     NaN     Monday           10  \n",
       "552069      1986.0    Male     Monday           10  \n",
       "552070      1965.0    Male     Monday           10  \n",
       "552071      1986.0    Male     Monday           10  \n",
       "552072      1989.0    Male     Monday           10  \n",
       "\n",
       "[551611 rows x 13 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['start_month'] = finalData['start_date'].dt.month\n",
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>23.0</td>\n",
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       "      <td>B00554</td>\n",
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       "      <td>9.0</td>\n",
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       "      <td>46.0</td>\n",
       "      <td>T01216</td>\n",
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       "      <td>Monday</td>\n",
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       "      <th>552051</th>\n",
       "      <td>623488</td>\n",
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       "      <td>2012-10-01 20:15:00.040</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2012-10-01 20:22:00.040</td>\n",
       "      <td>35.0</td>\n",
       "      <td>B00234</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02111</td>\n",
       "      <td>1974.0</td>\n",
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       "      <td>10</td>\n",
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       "      <td>2012-10-01 20:22:00.040</td>\n",
       "      <td>96.0</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>T01088</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02139</td>\n",
       "      <td>1983.0</td>\n",
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       "      <td>10</td>\n",
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       "      <td>2012-10-01 20:22:00.040</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>52.0</td>\n",
       "      <td>B00026</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02199</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88226 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "            id   duration              start_date start_station  \\\n",
       "3           11   1.066667 2011-07-28 10:35:00.040          23.0   \n",
       "4           12   0.200000 2011-07-28 10:37:00.040          23.0   \n",
       "6           14   0.400000 2011-07-28 10:47:00.040          23.0   \n",
       "7           15   0.116667 2011-07-28 10:48:00.040          23.0   \n",
       "8           16   0.133333 2011-07-28 11:01:00.040          23.0   \n",
       "...        ...        ...                     ...           ...   \n",
       "552048  623484  12.616667 2012-10-01 20:13:00.040           9.0   \n",
       "552051  623488   7.300000 2012-10-01 20:15:00.040          40.0   \n",
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       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered     2116   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered    97214   \n",
       "6      2011-07-28 10:47:00.040        23.0  B00554        Registered     2140   \n",
       "7      2011-07-28 10:48:00.040        23.0  B00554        Registered     2140   \n",
       "8      2011-07-28 11:01:00.040        23.0  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552048 2012-10-01 20:26:00.040        46.0  T01216        Registered    02115   \n",
       "552051 2012-10-01 20:22:00.040        35.0  B00234        Registered    02111   \n",
       "552052 2012-10-01 20:18:00.040        38.0  T01078        Registered    02114   \n",
       "552062 2012-10-01 20:31:00.040        80.0  T01088        Registered    02139   \n",
       "552065 2012-10-01 20:24:00.040        52.0  B00026        Registered    02199   \n",
       "\n",
       "        birth_date  gender dia_semana  start_month  \n",
       "3           1981.0  Female   Thursday            7  \n",
       "4           1983.0  Female   Thursday            7  \n",
       "6           1971.0  Female   Thursday            7  \n",
       "7           1971.0  Female   Thursday            7  \n",
       "8           1983.0  Female   Thursday            7  \n",
       "...            ...     ...        ...          ...  \n",
       "552048      1982.0  Female     Monday           10  \n",
       "552051      1974.0  Female     Monday           10  \n",
       "552052      1978.0  Female     Monday           10  \n",
       "552062      1983.0  Female     Monday           10  \n",
       "552065      1988.0  Female     Monday           10  \n",
       "\n",
       "[88226 rows x 13 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=finalData[finalData['gender']==\"Female\"]\n",
    "tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comentario técnico:\n",
    "Cuando los índices son enteros y pedimos `data[3]`, puede ser consuso entender si esto es el indice 3 o la posición 3. Para evitar confuciones, están las funciones `.loc()` y `.iloc()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "5    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series(['a', 'b', 'c','d'], index=[1, 3, 5,8])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[3]\n",
    "data.loc[3]  #busque el index 3 y retorne su valor\n",
    "data.iloc[3] # busque la posicion 3 del vector y retorne su valor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pregunta: ¿Como desplegar la primera linea de los datos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   id  duration              start_date start_station                end_date  \\\n",
       "0   8  0.150000 2011-07-28 10:12:00.040          23.0 2011-07-28 10:12:00.040   \n",
       "1   9  3.666667 2011-07-28 10:21:00.040          23.0 2011-07-28 10:25:00.040   \n",
       "2  10  0.933333 2011-07-28 10:33:00.040          23.0 2011-07-28 10:34:00.040   \n",
       "\n",
       "  end_station bike_nr subscription_type zip_code  birth_date gender  \\\n",
       "0        23.0  B00468        Registered    97217      1976.0   Male   \n",
       "1        23.0  B00554        Registered     2215      1966.0   Male   \n",
       "2        23.0  B00456        Registered     2108      1943.0   Male   \n",
       "\n",
       "  dia_semana  start_month  \n",
       "0   Thursday            7  \n",
       "1   Thursday            7  \n",
       "2   Thursday            7  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.iloc[0]  # Como si fuera una matriz, fila 0\n",
    "finalData.head(3)  # los primeros \"n\" datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analizando los datos. \n",
    " Ok, tenemos los datos ya limpios. Esto es generalmente muy tedioso,\n",
    " pero vimos que tenemos herramientas para poder hacerlo de forma un \n",
    " poco mas sencilla. \n",
    "  Ahora queremos empezar a hacer algunos análisis. Para esto, utilizaremos\n",
    " algunas funciones propias de los `Dataframes`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La estructura de datos de cada columna permite aplicar algunas funciones \"agregadoras\" básicas, por ejemplo, ver promedio `.mean()`, contar `.count()`, sumar `.sum()`, mínimo `.min()`, máximo `.max()`, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.616271490839257"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "como vimos antes, también podemos usar `describe()` para mostrar algunas de las estadísticas más comunes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero: ¿si quisieramos tener la duración promedio por género?  Esto implica que deberíamos **separar** el conjunto en  grupos (uno por cada género), **aplicar** la función (`.mean()` en este caso) y **combinar** los resultados en una tabla final."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este concepto, conocido como `split-apply-combine` es clave en el análisis de datos. En Panda, esto se hace con el comando `.groupby(<como agrupar>)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x14416cb80>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    13.477725\n",
       "Male      11.143362\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "Casual        39.560557\n",
       "Registered    11.722582\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('subscription_type')['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos agrupar por mas de una columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type  gender\n",
       "Casual             Female          NaN\n",
       "                   Male            NaN\n",
       "Registered         Female    13.477725\n",
       "                   Male      11.143362\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(['subscription_type','gender'])['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O incluso por una columna externa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3          True\n",
       "4          True\n",
       "          ...  \n",
       "552068    False\n",
       "552069     True\n",
       "552070    False\n",
       "552071     True\n",
       "552072     True\n",
       "Name: birth_date, Length: 551611, dtype: bool"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=finalData['birth_date']>1980\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birth_date\n",
       "False    25.712564\n",
       "True     11.647627\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(tmp)['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>start_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2011-07-28 10:21:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>2011-07-28 10:33:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>2011-07-28 10:35:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26.0</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>2012-10-01 20:25:00.040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>2012-10-01 20:29:00.040</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8  0.150000 2011-07-28 10:12:00.040          23.0   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040          23.0   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040          23.0   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040          23.0   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040          23.0   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040          46.0   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040          25.0   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040          16.0   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040          61.0   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040          22.0   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040        23.0  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered     2215   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered     2108   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered     2116   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040        67.0  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040        26.0  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040        47.0  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040        33.0  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040        81.0  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender dia_semana  start_month  \n",
       "0           1976.0    Male   Thursday            7  \n",
       "1           1966.0    Male   Thursday            7  \n",
       "2           1943.0    Male   Thursday            7  \n",
       "3           1981.0  Female   Thursday            7  \n",
       "4           1983.0  Female   Thursday            7  \n",
       "...            ...     ...        ...          ...  \n",
       "552068         NaN     NaN     Monday           10  \n",
       "552069      1986.0    Male     Monday           10  \n",
       "552070      1965.0    Male     Monday           10  \n",
       "552071      1986.0    Male     Monday           10  \n",
       "552072      1989.0    Male     Monday           10  \n",
       "\n",
       "[551611 rows x 13 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pregunta**: ¿Cuál es la duración promedio de los viajes para cada tipo de suscriptor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'duration', 'start_date', 'start_station', 'end_date',\n",
       "       'end_station', 'bike_nr', 'subscription_type', 'zip_code', 'birth_date',\n",
       "       'gender', 'dia_semana', 'start_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "Casual        39.560557\n",
       "Registered    11.722582\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('subscription_type')['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "Casual        39.560557\n",
       "Registered    11.722582\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('subscription_type')['duration'].apply(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PREGUNTA 1**: ¿Cual es la duración promedio por género?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    13.477725\n",
       "Male      11.143362\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PREGUNTA 2**: ¿Cual es el total de minutos por género?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    1.189086e+06\n",
       "Male      2.979078e+06\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female     88226\n",
       "Male      267341\n",
       "Name: duration, dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PREGUNTA 3**: ¿Cuál es la duración promedio para cada día de la semana? (usemos la fecha de inicio del viaje)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_date\n",
       "0    19.364533\n",
       "1    17.174780\n",
       "2    17.526173\n",
       "3    16.690964\n",
       "4    19.379387\n",
       "5    30.778098\n",
       "6    31.385722\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aux=finalData['start_date'].dt.dayofweek\n",
    "finalData.groupby(aux)['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_date\n",
       "Friday       19.379387\n",
       "Monday       19.364533\n",
       "Saturday     30.778098\n",
       "Sunday       31.385722\n",
       "Thursday     16.690964\n",
       "Tuesday      17.174780\n",
       "Wednesday    17.526173\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(finalData['start_date'].dt.day_name())['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PREGUNTA 4**: ¿Cuál es la duración promedio para cada mes del año? (usemos la fecha de término del viaje)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "end_date\n",
       "3     20.545315\n",
       "4     18.493682\n",
       "5     18.611862\n",
       "6     17.358136\n",
       "7     20.134779\n",
       "8     24.697308\n",
       "9     21.833481\n",
       "10    28.366187\n",
       "11    21.398513\n",
       "12    16.875000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(finalData['end_date'].dt.month)['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pregunta**: ¿Cuál es la proporción de usuarios `Casual` para cada estación de inicio? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35540262975176345"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(finalData['subscription_type']==\"Casual\")/len(finalData['subscription_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra opción es que nosotros hagamos una función que entregue esta proporción. Por ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "def propCasual(data):\n",
    "    numero_casual = sum(data['subscription_type']==\"Casual\")\n",
    "    numero_total = len(data['subscription_type'])\n",
    "    return numero_casual/numero_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35540262975176345"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propCasual(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station\n",
       "3.0     0.289539\n",
       "4.0     0.290126\n",
       "5.0     0.353937\n",
       "6.0     0.351005\n",
       "7.0     0.438072\n",
       "          ...   \n",
       "94.0    0.571984\n",
       "95.0    0.358974\n",
       "96.0    0.376518\n",
       "97.0    0.710623\n",
       "98.0    0.594796\n",
       "Length: 95, dtype: float64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_station').apply(propCasual)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ejercicio**: La empresa tiene una tarifa distinta para aquellos viajes que duran mas de media hora.   Por eso, Calcule la proporción de viajes desde cada estación de inicio (*start_station*) que duran mas de 30 minutos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def propViajes30min(data):\n",
    "    numero_largos = sum(data['duration']>30)\n",
    "    numero_total = len(data['duration'])\n",
    "    return numero_largos/numero_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12154036087025095"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propViajes30min(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station\n",
       "3.0     0.085264\n",
       "4.0     0.076041\n",
       "5.0     0.099347\n",
       "6.0     0.130794\n",
       "7.0     0.153276\n",
       "          ...   \n",
       "94.0    0.124514\n",
       "95.0    0.069597\n",
       "96.0    0.080972\n",
       "97.0    0.256410\n",
       "98.0    0.161710\n",
       "Length: 95, dtype: float64"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_station').apply(propViajes30min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station\n",
       "26.0    0.038899\n",
       "14.0    0.042330\n",
       "19.0    0.049587\n",
       "57.0    0.049974\n",
       "39.0    0.056280\n",
       "          ...   \n",
       "93.0    0.266667\n",
       "74.0    0.276288\n",
       "37.0    0.336530\n",
       "15.0    0.352891\n",
       "92.0    0.607595\n",
       "Length: 95, dtype: float64"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_station').apply(propViajes30min).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos ahora que queremos rescatar los dos días mas utilizados, por cada tipo de usuario, o por estación. Esto requeriría crea una propia función, que nos entregue los dos dias. Pero esto sabemos hacerlo!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-334-6e6a0d869054>: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",
      "  finalData['start_day_name'] = finalData['start_date'].dt.day_name()\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_station</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2011-07-28 10:21:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>2011-07-28 10:33:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>2011-07-28 10:35:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26.0</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>2012-10-01 20:25:00.040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47.0</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>2012-10-01 20:29:00.040</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33.0</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81.0</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8  0.150000 2011-07-28 10:12:00.040          23.0   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040          23.0   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040          23.0   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040          23.0   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040          23.0   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040          46.0   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040          25.0   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040          16.0   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040          61.0   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040          22.0   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040        23.0  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered     2215   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered     2108   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered     2116   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040        67.0  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040        26.0  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040        47.0  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040        33.0  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040        81.0  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender dia_semana  start_month start_day_name  \n",
       "0           1976.0    Male   Thursday            7       Thursday  \n",
       "1           1966.0    Male   Thursday            7       Thursday  \n",
       "2           1943.0    Male   Thursday            7       Thursday  \n",
       "3           1981.0  Female   Thursday            7       Thursday  \n",
       "4           1983.0  Female   Thursday            7       Thursday  \n",
       "...            ...     ...        ...          ...            ...  \n",
       "552068         NaN     NaN     Monday           10         Monday  \n",
       "552069      1986.0    Male     Monday           10         Monday  \n",
       "552070      1965.0    Male     Monday           10         Monday  \n",
       "552071      1986.0    Male     Monday           10         Monday  \n",
       "552072      1989.0    Male     Monday           10         Monday  \n",
       "\n",
       "[551611 rows x 14 columns]"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['start_day_name'] = finalData['start_date'].dt.day_name()\n",
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_day_name\n",
       "Friday       83601\n",
       "Monday       82261\n",
       "Saturday     76894\n",
       "Sunday       73956\n",
       "Thursday     80061\n",
       "Tuesday      77222\n",
       "Wednesday    77616\n",
       "Name: start_day_name, dtype: int64"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resumenDias = finalData.groupby('start_day_name')['start_day_name'].count()\n",
    "finalData.groupby('start_day_name')['start_day_name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_day_name\n",
       "Friday       83601\n",
       "Monday       82261\n",
       "Thursday     80061\n",
       "Wednesday    77616\n",
       "Tuesday      77222\n",
       "Saturday     76894\n",
       "Sunday       73956\n",
       "Name: start_day_name, dtype: int64"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_day_name')['start_day_name'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_day_name\n",
       "Friday       83601\n",
       "Monday       82261\n",
       "Saturday     76894\n",
       "Sunday       73956\n",
       "Thursday     80061\n",
       "Tuesday      77222\n",
       "Wednesday    77616\n",
       "Name: start_day_name, dtype: int64"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_day_name')['start_day_name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_day_name\n",
       "Friday       83601\n",
       "Monday       82261\n",
       "Thursday     80061\n",
       "Wednesday    77616\n",
       "Tuesday      77222\n",
       "Saturday     76894\n",
       "Sunday       73956\n",
       "Name: start_day_name, dtype: int64"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_day_name')['start_day_name'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_day_name\n",
       "Friday    83601\n",
       "Monday    82261\n",
       "Name: start_day_name, dtype: int64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_day_name')['start_day_name'].count().sort_values(ascending=False).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Friday', 'Monday'], dtype='object', name='start_day_name')"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_day_name')['start_day_name'].count().sort_values(ascending=False).head(2).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Friday', 'Monday']"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(finalData.groupby('start_day_name')['start_day_name'].count().sort_values(ascending=False).head(2).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Friday', 'Monday']"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(resumenDias.sort_values(ascending=False).head(2).index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pomgamos todo esto en una función:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "def best2days(x):\n",
    "    resumenDias = x.groupby('start_day_name')['start_day_name'].count()\n",
    "    return list(resumenDias.sort_values(ascending=False).head(2).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Friday', 'Monday']"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best2days(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "Casual         [Saturday, Sunday]\n",
       "Registered    [Thursday, Tuesday]\n",
       "dtype: object"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('subscription_type').apply(best2days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ejercicio:** encuentre el tipo de suscripción más común para cada estación de partida.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sub_mas_comun(x):\n",
    "    num_casual = sum(x['subscription_type']==\"Casual\")\n",
    "    num_reg = sum(x['subscription_type']==\"Registered\")\n",
    "    if num_casual > num_reg:\n",
    "        return \"Casual\"\n",
    "    else:\n",
    "        return \"Registered\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Registered'"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_mas_comun(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station\n",
       "3.0     Registered\n",
       "4.0     Registered\n",
       "5.0     Registered\n",
       "6.0     Registered\n",
       "7.0     Registered\n",
       "           ...    \n",
       "94.0        Casual\n",
       "95.0    Registered\n",
       "96.0    Registered\n",
       "97.0        Casual\n",
       "98.0        Casual\n",
       "Length: 95, dtype: object"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_station').apply(sub_mas_comun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station\n",
       "3.0     [Registered]\n",
       "4.0     [Registered]\n",
       "5.0     [Registered]\n",
       "6.0     [Registered]\n",
       "7.0     [Registered]\n",
       "            ...     \n",
       "94.0        [Casual]\n",
       "95.0    [Registered]\n",
       "96.0    [Registered]\n",
       "97.0        [Casual]\n",
       "98.0        [Casual]\n",
       "Length: 95, dtype: object"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sub_mas_comun(x):\n",
    "    resumenDias = x.groupby('subscription_type')['subscription_type'].count()\n",
    "    return list(resumenDias.sort_values(ascending=False).index[0:1])\n",
    "sub_mas_comun(finalData)\n",
    "finalData.groupby('start_station').apply(sub_mas_comun)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos hacer que retorne un DataFrame, de esta forma el resultado será la union de todos estos dataframe, generando una nueva tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cree un nuevo `DataFrame` de bicicletas, donde cada fila tenga:\n",
    "- El id de cada bicicleta `bike_nr`\n",
    "- la duración promedio de cada bicicleta\n",
    "- la desviación estándar de las duraciones de cada bicicleta\n",
    "- el número de viajes realizados en cada bicicleta\n",
    "\n",
    "Para esto, podemos hacer que el resultado de la función a aplicar sobre cada grupo sea un `Dataframe`, por lo que al combinarlo tendremos el dataframe pedido.   Para definir un DataFrame usamos el comando `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "def statsBike(x):\n",
    "    prom = x['duration'].mean()\n",
    "    dev = x['duration'].std()\n",
    "    num = x['duration'].count()\n",
    "    return [prom,dev,num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bike_nr\n",
       "? (0x241EC230)     [33.170138888888886, 60.41472501744495, 192]\n",
       "? (0x67D8478F)      [25.73062015503876, 40.00103415496366, 129]\n",
       "? (0xA533D104)     [38.82852112676056, 107.23705791448033, 142]\n",
       "B00000              [11.863333333333337, 7.971571648392464, 45]\n",
       "B00001            [18.051804215791353, 32.528162361042305, 933]\n",
       "                                      ...                      \n",
       "T01447            [19.926090342679128, 22.907668786529175, 214]\n",
       "T01448             [17.29666666666667, 27.434640408401332, 315]\n",
       "T01449            [19.712345679012348, 37.349238093496325, 243]\n",
       "T01450             [17.281904761904762, 42.33884965262123, 280]\n",
       "b00225             [17.541197497765864, 38.80963265532772, 746]\n",
       "Length: 885, dtype: object"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('bike_nr').apply(statsBike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "def statsBikeDF(x):\n",
    "    prom = x['duration'].mean()\n",
    "    dev = x['duration'].std()\n",
    "    num = x['duration'].count()\n",
    "    return pd.DataFrame({'mean_duration': [prom], 'stddev_duration': [dev], 'num_trips': [num]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   mean_duration  stddev_duration  num_trips\n",
       "0      21.616271        58.056289     551611"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "statsBikeDF(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B00145'"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['bike_nr'].mode().iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>214</td>\n",
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       "    <tr>\n",
       "      <th>T01448</th>\n",
       "      <th>0</th>\n",
       "      <td>17.296667</td>\n",
       "      <td>27.434640</td>\n",
       "      <td>315</td>\n",
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       "    <tr>\n",
       "      <th>T01449</th>\n",
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       "      <td>37.349238</td>\n",
       "      <td>243</td>\n",
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       "      <td>746</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean_duration  stddev_duration  num_trips\n",
       "bike_nr                                                    \n",
       "? (0x241EC230) 0      33.170139        60.414725        192\n",
       "? (0x67D8478F) 0      25.730620        40.001034        129\n",
       "? (0xA533D104) 0      38.828521       107.237058        142\n",
       "B00000         0      11.863333         7.971572         45\n",
       "B00001         0      18.051804        32.528162        933\n",
       "...                         ...              ...        ...\n",
       "T01447         0      19.926090        22.907669        214\n",
       "T01448         0      17.296667        27.434640        315\n",
       "T01449         0      19.712346        37.349238        243\n",
       "T01450         0      17.281905        42.338850        280\n",
       "b00225         0      17.541197        38.809633        746\n",
       "\n",
       "[885 rows x 3 columns]"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes= finalData.groupby('bike_nr').apply(statsBikeDF)\n",
    "bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.04894736842105"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes['mean_duration'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>315</td>\n",
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       "      <th>882</th>\n",
       "      <td>T01449</td>\n",
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       "      <td>746</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bike_nr  level_1  mean_duration  stddev_duration  num_trips\n",
       "0    ? (0x241EC230)        0      33.170139        60.414725        192\n",
       "1    ? (0x67D8478F)        0      25.730620        40.001034        129\n",
       "2    ? (0xA533D104)        0      38.828521       107.237058        142\n",
       "3            B00000        0      11.863333         7.971572         45\n",
       "4            B00001        0      18.051804        32.528162        933\n",
       "..              ...      ...            ...              ...        ...\n",
       "880          T01447        0      19.926090        22.907669        214\n",
       "881          T01448        0      17.296667        27.434640        315\n",
       "882          T01449        0      19.712346        37.349238        243\n",
       "883          T01450        0      17.281905        42.338850        280\n",
       "884          b00225        0      17.541197        38.809633        746\n",
       "\n",
       "[885 rows x 5 columns]"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes = bikes.reset_index()\n",
    "bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "del bikes['level_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>27.434640</td>\n",
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       "    <tr>\n",
       "      <th>882</th>\n",
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       "      <td>37.349238</td>\n",
       "      <td>243</td>\n",
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       "      <th>883</th>\n",
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       "      <td>38.809633</td>\n",
       "      <td>746</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bike_nr  mean_duration  stddev_duration  num_trips\n",
       "0    ? (0x241EC230)      33.170139        60.414725        192\n",
       "1    ? (0x67D8478F)      25.730620        40.001034        129\n",
       "2    ? (0xA533D104)      38.828521       107.237058        142\n",
       "3            B00000      11.863333         7.971572         45\n",
       "4            B00001      18.051804        32.528162        933\n",
       "..              ...            ...              ...        ...\n",
       "880          T01447      19.926090        22.907669        214\n",
       "881          T01448      17.296667        27.434640        315\n",
       "882          T01449      19.712346        37.349238        243\n",
       "883          T01450      17.281905        42.338850        280\n",
       "884          b00225      17.541197        38.809633        746\n",
       "\n",
       "[885 rows x 4 columns]"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ejercicio (BONUS)**: Incluya las siguientes variables para cada fila:\n",
    "- número de viajes que parten y terminan en un día diferente\n",
    "- estación mas común de inicio de un viaje \n",
    "- estación mas común de término de un viaje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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