Clase2-Amasando_Datos_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": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3146: 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(\"Data/trips_new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        text-align: right;\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>97217.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
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       "      <td>9</td>\n",
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       "      <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>2215.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</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>2108.0</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>2116.0</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>97214.0</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       97217.0      1976.0    Male  \n",
       "1        2215.0      1966.0    Male  \n",
       "2        2108.0      1943.0    Male  \n",
       "3        2116.0      1981.0  Female  \n",
       "4       97214.0      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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.duration"
   ]
  },
  {
   "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": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['bike_nr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1          True\n",
       "2         False\n",
       "3          True\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']> 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                72\n",
       "1              1980\n",
       "2               560\n",
       "3               704\n",
       "4               144\n",
       "            ...    \n",
       "552068    328588189\n",
       "552069    140912808\n",
       "552070    333577315\n",
       "552071    267486648\n",
       "552072    124079883\n",
       "Length: 552073, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']*dataTrips['id']"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['sinrelevancia']='chao'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "      <th>sinrelevancia</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>97217.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\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>2215.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2108.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2116.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</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>97214.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>552068</th>\n",
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       "      <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>chao</td>\n",
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       "    <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",
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       "      <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>chao</td>\n",
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       "    <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>chao</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",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552073 rows × 12 columns</p>\n",
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      "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 sinrelevancia  \n",
       "0       97217.0      1976.0    Male          chao  \n",
       "1        2215.0      1966.0    Male          chao  \n",
       "2        2108.0      1943.0    Male          chao  \n",
       "3        2116.0      1981.0  Female          chao  \n",
       "4       97214.0      1983.0  Female          chao  \n",
       "...         ...         ...     ...           ...  \n",
       "552068      NaN         NaN     NaN          chao  \n",
       "552069    02118      1986.0    Male          chao  \n",
       "552070    02113      1965.0    Male          chao  \n",
       "552071    02135      1986.0    Male          chao  \n",
       "552072    02116      1989.0    Male          chao  \n",
       "\n",
       "[552073 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>23.0</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>chao</td>\n",
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       "      <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",
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       "      <td>Male</td>\n",
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       "      <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",
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       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
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       "      <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>chao</td>\n",
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       "      <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>chao</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 sinrelevancia   test2  \n",
       "0       97217.0      1976.0    Male          chao       0  \n",
       "1        2215.0      1966.0    Male          chao       1  \n",
       "2        2108.0      1943.0    Male          chao       2  \n",
       "3        2116.0      1981.0  Female          chao       3  \n",
       "4       97214.0      1983.0  Female          chao       4  \n",
       "...         ...         ...     ...           ...     ...  \n",
       "552068      NaN         NaN     NaN          chao  552068  \n",
       "552069    02118      1986.0    Male          chao  552069  \n",
       "552070    02113      1965.0    Male          chao  552070  \n",
       "552071    02135      1986.0    Male          chao  552071  \n",
       "552072    02116      1989.0    Male          chao  552072  \n",
       "\n",
       "[552073 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\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>2215.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2108.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2116.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</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>97214.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</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",
       "    </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",
       "      <td>chao</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>chao</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>chao</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>chao</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>chao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552073 rows × 12 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 sinrelevancia  \n",
       "0       97217.0      1976.0    Male          chao  \n",
       "1        2215.0      1966.0    Male          chao  \n",
       "2        2108.0      1943.0    Male          chao  \n",
       "3        2116.0      1981.0  Female          chao  \n",
       "4       97214.0      1983.0  Female          chao  \n",
       "...         ...         ...     ...           ...  \n",
       "552068      NaN         NaN     NaN          chao  \n",
       "552069    02118      1986.0    Male          chao  \n",
       "552070    02113      1965.0    Male          chao  \n",
       "552071    02135      1986.0    Male          chao  \n",
       "552072    02116      1989.0    Male          chao  \n",
       "\n",
       "[552073 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#del dataTrips['test']\n",
    "del dataTrips['test2']\n",
    "dataTrips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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', 'sinrelevancia'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=552073, step=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                   623512\n",
       "duration                                429\n",
       "start_date           2012-10-01T20:29:00.04\n",
       "start_station                          61.0\n",
       "end_date             2012-10-01T20:36:00.04\n",
       "end_station                            33.0\n",
       "bike_nr                              B00621\n",
       "subscription_type                Registered\n",
       "zip_code                              02135\n",
       "birth_date                           1986.0\n",
       "gender                                 Male\n",
       "sinrelevancia                          chao\n",
       "Name: 552071, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.loc[552071]"
   ]
  },
  {
   "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": 25,
   "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>sinrelevancia</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>97217.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\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>2215.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2108.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2116.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</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>97214.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>2011-07-28T10:39:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:39:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-07-28T10:47:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:47:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>2011-07-28T10:48:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T10:48:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>2011-07-28T11:01:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28T11:01:00.04</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17</td>\n",
       "      <td>1108</td>\n",
       "      <td>2011-07-28T11:55:00.04</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2011-07-28T12:13:00.04</td>\n",
       "      <td>40.0</td>\n",
       "      <td>B00550</td>\n",
       "      <td>Registered</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>1055</td>\n",
       "      <td>2011-07-28T11:55:00.04</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2011-07-28T12:13:00.04</td>\n",
       "      <td>40.0</td>\n",
       "      <td>B00580</td>\n",
       "      <td>Registered</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "5   13        19  2011-07-28T10:39:00.04           23.0   \n",
       "6   14        24  2011-07-28T10:47:00.04           23.0   \n",
       "7   15         7  2011-07-28T10:48:00.04           23.0   \n",
       "8   16         8  2011-07-28T11:01:00.04           23.0   \n",
       "9   17      1108  2011-07-28T11:55:00.04           47.0   \n",
       "10  18      1055  2011-07-28T11:55:00.04           47.0   \n",
       "\n",
       "                  end_date  end_station bike_nr subscription_type zip_code  \\\n",
       "0   2011-07-28T10:12:00.04         23.0  B00468        Registered  97217.0   \n",
       "1   2011-07-28T10:25:00.04         23.0  B00554        Registered   2215.0   \n",
       "2   2011-07-28T10:34:00.04         23.0  B00456        Registered   2108.0   \n",
       "3   2011-07-28T10:36:00.04         23.0  B00554        Registered   2116.0   \n",
       "4   2011-07-28T10:37:00.04         23.0  B00554        Registered  97214.0   \n",
       "5   2011-07-28T10:39:00.04         23.0  B00456        Registered   2021.0   \n",
       "6   2011-07-28T10:47:00.04         23.0  B00554        Registered   2140.0   \n",
       "7   2011-07-28T10:48:00.04         23.0  B00554        Registered   2140.0   \n",
       "8   2011-07-28T11:01:00.04         23.0  B00554        Registered  97214.0   \n",
       "9   2011-07-28T12:13:00.04         40.0  B00550        Registered   1867.0   \n",
       "10  2011-07-28T12:13:00.04         40.0  B00580        Registered   1867.0   \n",
       "\n",
       "    birth_date  gender sinrelevancia  \n",
       "0       1976.0    Male          chao  \n",
       "1       1966.0    Male          chao  \n",
       "2       1943.0    Male          chao  \n",
       "3       1981.0  Female          chao  \n",
       "4       1983.0  Female          chao  \n",
       "5       1951.0    Male          chao  \n",
       "6       1971.0  Female          chao  \n",
       "7       1971.0  Female          chao  \n",
       "8       1983.0  Female          chao  \n",
       "9       1994.0    Male          chao  \n",
       "10      1956.0    Male          chao  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.loc[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['id'][3]"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1          True\n",
       "2         False\n",
       "3          True\n",
       "4          True\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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['bike_nr']=='B00554'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 31,
     "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": 32,
   "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>sinrelevancia</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>97217.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2143.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</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",
       "      <td>chao</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",
       "      <td>chao</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",
       "      <td>chao</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",
       "    </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",
       "      <td>chao</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",
       "      <td>chao</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",
       "      <td>chao</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",
       "      <td>chao</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",
       "      <td>chao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>862 rows × 12 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 sinrelevancia  \n",
       "0       97217.0      1976.0    Male          chao  \n",
       "100      2143.0      1986.0  Female          chao  \n",
       "847         NaN         NaN     NaN          chao  \n",
       "941         NaN         NaN     NaN          chao  \n",
       "969         NaN         NaN     NaN          chao  \n",
       "...         ...         ...     ...           ...  \n",
       "549617    02215      1978.0  Female          chao  \n",
       "551173    02043      1948.0    Male          chao  \n",
       "551428    02116      1969.0  Female          chao  \n",
       "551721    03051      1977.0    Male          chao  \n",
       "551789    02118      1973.0    Male          chao  \n",
       "\n",
       "[862 rows x 12 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.loc[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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(552073, 12)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(862, 12)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips[dataTrips['bike_nr']=='B00468'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              8\n",
       "100          128\n",
       "847          939\n",
       "941         1046\n",
       "969         1077\n",
       "           ...  \n",
       "549617    620699\n",
       "551173    622434\n",
       "551428    622720\n",
       "551721    623084\n",
       "551789    623165\n",
       "Name: id, Length: 862, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.loc[dataTrips['bike_nr']=='B00468','id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>sinrelevancia</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>97217.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</td>\n",
       "    </tr>\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>2215.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2108.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>chao</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>2116.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</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>97214.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>chao</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",
       "    </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",
       "      <td>chao</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>chao</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>chao</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>chao</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>chao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552073 rows × 12 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 sinrelevancia  \n",
       "0       97217.0      1976.0    Male          chao  \n",
       "1        2215.0      1966.0    Male          chao  \n",
       "2        2108.0      1943.0    Male          chao  \n",
       "3        2116.0      1981.0  Female          chao  \n",
       "4       97214.0      1983.0  Female          chao  \n",
       "...         ...         ...     ...           ...  \n",
       "552068      NaN         NaN     NaN          chao  \n",
       "552069    02118      1986.0    Male          chao  \n",
       "552070    02113      1965.0    Male          chao  \n",
       "552071    02135      1986.0    Male          chao  \n",
       "552072    02116      1989.0    Male          chao  \n",
       "\n",
       "[552073 rows x 12 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2011-07-28T10:12:00.04\n",
       "1         2011-07-28T10:21:00.04\n",
       "2         2011-07-28T10:33:00.04\n",
       "3         2011-07-28T10:35:00.04\n",
       "4         2011-07-28T10:37:00.04\n",
       "                   ...          \n",
       "552068    2012-10-01T20:24:00.04\n",
       "552069    2012-10-01T20:24:00.04\n",
       "552070    2012-10-01T20:25:00.04\n",
       "552071    2012-10-01T20:29:00.04\n",
       "552072    2012-10-01T20:32:00.04\n",
       "Name: start_date, Length: 552073, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['start_date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un aspecto clave de los DataFrame es que cada columna tiene un *tipo* (`dtype` en Python) asociado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
       "sinrelevancia         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si no es capaz de identificar un tipo básico (como `int64` o `float64`), entonces asume que es cualquier cosa, o sea, tipo `object`."
   ]
  },
  {
   "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": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     552073\n",
       "unique       885\n",
       "top       B00145\n",
       "freq        1074\n",
       "Name: bike_nr, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['bike_nr'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     552073\n",
       "unique                    235082\n",
       "top       2012-09-14T17:11:00.04\n",
       "freq                          18\n",
       "Name: start_date, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['start_date'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "para cambiar un tipo usamos `.astype('<tipo>')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['gender'] = dataTrips['gender'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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               category\n",
       "sinrelevancia          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.dtypes"
   ]
  },
  {
   "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": 52,
   "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": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/pandas/io/formats/format.py:1403: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
      "  for val, m in zip(values.ravel(), mask.ravel())\n",
      "/usr/local/lib/python3.9/site-packages/pandas/io/formats/format.py:1403: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
      "  for val, m in zip(values.ravel(), mask.ravel())\n"
     ]
    },
    {
     "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": 53,
     "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": 54,
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2011-07-28 10:12:00.040\n",
       "1        2011-07-28 10:21:00.040\n",
       "2        2011-07-28 10:33:00.040\n",
       "3        2011-07-28 10:35:00.040\n",
       "4        2011-07-28 10:37:00.040\n",
       "                   ...          \n",
       "552068   2012-10-01 20:24:00.040\n",
       "552069   2012-10-01 20:24:00.040\n",
       "552070   2012-10-01 20:25:00.040\n",
       "552071   2012-10-01 20:29:00.040\n",
       "552072   2012-10-01 20:32:00.040\n",
       "Name: start_date, Length: 552073, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['start_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-0add0f73aa48>:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  dataTrips['start_date'].describe()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count                         552073\n",
       "unique                        235082\n",
       "top       2012-09-14 17:11:00.040000\n",
       "freq                              18\n",
       "first     2011-07-28 10:12:00.040000\n",
       "last      2012-10-01 20:32:00.040000\n",
       "Name: start_date, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['start_date'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dataTrips['sinrelevancia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "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": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['duration']=dataTrips['duration'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           9.0\n",
       "1         220.0\n",
       "2          56.0\n",
       "3          64.0\n",
       "4          12.0\n",
       "          ...  \n",
       "552068    527.0\n",
       "552069    226.0\n",
       "552070    535.0\n",
       "552071    429.0\n",
       "552072    199.0\n",
       "Name: duration, Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0 days 00:00:00\n",
       "1        0 days 00:04:00\n",
       "2        0 days 00:01:00\n",
       "3        0 days 00:01:00\n",
       "4        0 days 00:00:00\n",
       "               ...      \n",
       "552068   0 days 00:08:00\n",
       "552069   0 days 00:04:00\n",
       "552070   0 days 00:09:00\n",
       "552071   0 days 00:07:00\n",
       "552072   0 days 00:04:00\n",
       "Length: 552073, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['end_date']-dataTrips['start_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>9.0</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>97217.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>220.0</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>2215.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>56.0</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>2108.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>64.0</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>2116.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12.0</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>97214.0</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>527.0</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>226.0</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>535.0</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>429.0</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>199.0</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>552073 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8       9.0 2011-07-28 10:12:00.040          23.0   \n",
       "1            9     220.0 2011-07-28 10:21:00.040          23.0   \n",
       "2           10      56.0 2011-07-28 10:33:00.040          23.0   \n",
       "3           11      64.0 2011-07-28 10:35:00.040          23.0   \n",
       "4           12      12.0 2011-07-28 10:37:00.040          23.0   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507     527.0 2012-10-01 20:24:00.040          46.0   \n",
       "552069  623508     226.0 2012-10-01 20:24:00.040          25.0   \n",
       "552070  623509     535.0 2012-10-01 20:25:00.040          16.0   \n",
       "552071  623512     429.0 2012-10-01 20:29:00.040          61.0   \n",
       "552072  623517     199.0 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  97217.0   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered   2215.0   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered   2108.0   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered   2116.0   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered  97214.0   \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",
       "[552073 rows x 11 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utilizando los tipos\n",
    "Dado el tipo de una columna, podemos usar sus funciones. Por ejemplo, podemos usar `.describe()` para ver estadísticas básicas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "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": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['duration']/60\n",
    "dataTrips['duration']=dataTrips['duration']/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0 days 00:00:00\n",
       "1        0 days 00:04:00\n",
       "2        0 days 00:01:00\n",
       "3        0 days 00:01:00\n",
       "4        0 days 00:00:00\n",
       "               ...      \n",
       "552068   0 days 00:08:00\n",
       "552069   0 days 00:04:00\n",
       "552070   0 days 00:09:00\n",
       "552071   0 days 00:07:00\n",
       "552072   0 days 00:04:00\n",
       "Length: 552073, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['end_date']-dataTrips['start_date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para funciones mas complicadas, podemos usar una función cualquiera aplicandola a cada elemento con `.apply(<funcion>)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def raiz(x):\n",
    "    return np.sqrt(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raiz(64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.387298\n",
       "1         1.914854\n",
       "2         0.966092\n",
       "3         1.032796\n",
       "4         0.447214\n",
       "            ...   \n",
       "552068    2.963669\n",
       "552069    1.940790\n",
       "552070    2.986079\n",
       "552071    2.673948\n",
       "552072    1.821172\n",
       "Name: duration, Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration'].apply(raiz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(o mas corto, definiendo una alpha-function, como vimos la clase pasada)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.387298\n",
       "1         1.914854\n",
       "2         0.966092\n",
       "3         1.032796\n",
       "4         0.447214\n",
       "            ...   \n",
       "552068    2.963669\n",
       "552069    1.940790\n",
       "552070    2.986079\n",
       "552071    2.673948\n",
       "552072    1.821172\n",
       "Name: duration, Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips['duration'].apply(lambda x: np.sqrt(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3.098387e+00\n",
       "1         1.723369e+01\n",
       "2         9.660918e+00\n",
       "3         1.136075e+01\n",
       "4         5.366563e+00\n",
       "              ...     \n",
       "552068    1.847868e+06\n",
       "552069    1.210098e+06\n",
       "552070    1.861847e+06\n",
       "552071    1.667239e+06\n",
       "552072    1.135532e+06\n",
       "Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips.apply(lambda x : np.sqrt(x['duration'])*x['id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3.098387e+00\n",
       "1         1.723369e+01\n",
       "2         9.660918e+00\n",
       "3         1.136075e+01\n",
       "4         5.366563e+00\n",
       "              ...     \n",
       "552068    1.847868e+06\n",
       "552069    1.210098e+06\n",
       "552070    1.861847e+06\n",
       "552071    1.667239e+06\n",
       "552072    1.135532e+06\n",
       "Length: 552073, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(dataTrips['duration'])*dataTrips['id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datos faltantes"
   ]
  },
  {
   "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": "code",
   "execution_count": 89,
   "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>97217.0</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>2215.0</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>2108.0</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>2116.0</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>97214.0</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>552073 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  97217.0   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered   2215.0   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered   2108.0   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered   2116.0   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered  97214.0   \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",
       "[552073 rows x 11 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ejemplo, podemos ver si un dato es `NaN` o no, con el comando `isna()` o `isnull()` (son lo mismo).  Esto hay que aplicarlo sobre ***CADA*** registro de la columna, y con eso podemos filtrar los datos que queremos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_station</th>\n",
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       "      <th>gender</th>\n",
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       "  </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",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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>2129.0</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>2129.0</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",
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       "    <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    2129.0      1981.0  Female  \n",
       "17418    2129.0      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": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips[dataTrips['end_station'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 11)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips[dataTrips['end_station'].isna()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dataTrips['end_station'].isna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y los eliminamos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "finalData = dataTrips.dropna(subset=['start_station', 'end_station'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67.0</td>\n",
       "      <td>B00486</td>\n",
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       "      <td>16.0</td>\n",
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       "      <td>47.0</td>\n",
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       "            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",
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       "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  97217.0   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered   2215.0   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered   2108.0   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered   2116.0   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered  97214.0   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040        67.0  B00486            Casual      NaN   \n",
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       "552070 2012-10-01 20:34:00.040        47.0  T01067        Registered    02113   \n",
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       "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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(552020, 11)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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  97217.0   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered   2215.0   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered   2108.0   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered   2116.0   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered  97214.0   \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": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analicemos ahora las duraciones. Hagamos un histograma de sus valores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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": "markdown",
   "metadata": {},
   "source": [
    "¿Que pasó?  Hay outliers en los datos... Limitémonos entonces a aquellos datos con duraciones menores a 1 día."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\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: duration, Length: 552020, dtype: bool"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['duration'] > 24*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>97217.0</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>2215.0</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>2108.0</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>2116.0</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>97214.0</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  97217.0   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered   2215.0   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered   2108.0   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered   2116.0   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered  97214.0   \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": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData[finalData['duration']<24*60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(finalData['duration']>24*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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": 118,
   "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>97217.0</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>2215.0</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>2108.0</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>2116.0</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>97214.0</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  97217.0   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered   2215.0   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered   2108.0   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered   2116.0   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered  97214.0   \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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData = finalData[finalData['duration']<24*60]\n",
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    551611.000000\n",
       "mean         21.616271\n",
       "std          58.056289\n",
       "min           0.000000\n",
       "25%           6.850000\n",
       "50%          11.433333\n",
       "75%          20.050000\n",
       "max        1439.550000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['duration'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manejo de fechas"
   ]
  },
  {
   "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": 132,
   "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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3\n",
       "1         3\n",
       "2         3\n",
       "3         3\n",
       "4         3\n",
       "         ..\n",
       "552068    0\n",
       "552069    0\n",
       "552070    0\n",
       "552071    0\n",
       "552072    0\n",
       "Name: start_date, Length: 551611, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['start_date'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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": 130,
     "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": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-133-9527532ece51>: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": 135,
   "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: dia_semana, Length: 551611, dtype: object"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['dia_semana']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-136-7ae7986174cd>: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": 137,
   "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": 138,
   "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": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14432"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(finalData['start_date'].dt.month == 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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": 151,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>1953</td>\n",
       "      <td>39.666667</td>\n",
       "      <td>2011-07-31 00:03:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-31 00:42:00.040</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B00344</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>1954</td>\n",
       "      <td>4.916667</td>\n",
       "      <td>2011-07-31 00:07:00.040</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2011-07-31 00:12:00.040</td>\n",
       "      <td>36.0</td>\n",
       "      <td>B00158</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>1955</td>\n",
       "      <td>740.716667</td>\n",
       "      <td>2011-07-31 00:09:00.040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2011-07-31 12:30:00.040</td>\n",
       "      <td>30.0</td>\n",
       "      <td>B00077</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>1956</td>\n",
       "      <td>6.916667</td>\n",
       "      <td>2011-07-31 00:16:00.040</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2011-07-31 00:23:00.040</td>\n",
       "      <td>41.0</td>\n",
       "      <td>B00157</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>1957</td>\n",
       "      <td>133.150000</td>\n",
       "      <td>2011-07-31 00:16:00.040</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2011-07-31 02:29:00.040</td>\n",
       "      <td>41.0</td>\n",
       "      <td>B00505</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549281</th>\n",
       "      <td>620308</td>\n",
       "      <td>14.916667</td>\n",
       "      <td>2012-09-30 23:50:00.040</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2012-10-01 00:05:00.040</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B00565</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549282</th>\n",
       "      <td>620309</td>\n",
       "      <td>16.150000</td>\n",
       "      <td>2012-09-30 23:52:00.040</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2012-10-01 00:09:00.040</td>\n",
       "      <td>42.0</td>\n",
       "      <td>T01098</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549283</th>\n",
       "      <td>620310</td>\n",
       "      <td>28.116667</td>\n",
       "      <td>2012-09-30 23:55:00.040</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012-10-01 00:23:00.040</td>\n",
       "      <td>18.0</td>\n",
       "      <td>T01113</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02134</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549284</th>\n",
       "      <td>620311</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>2012-09-30 23:57:00.040</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2012-10-01 00:02:00.040</td>\n",
       "      <td>45.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549285</th>\n",
       "      <td>620312</td>\n",
       "      <td>3.850000</td>\n",
       "      <td>2012-09-30 23:59:00.040</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2012-10-01 00:03:00.040</td>\n",
       "      <td>45.0</td>\n",
       "      <td>B00587</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02446</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73956 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id    duration              start_date start_station  \\\n",
       "1760      1953   39.666667 2011-07-31 00:03:00.040          23.0   \n",
       "1761      1954    4.916667 2011-07-31 00:07:00.040          36.0   \n",
       "1762      1955  740.716667 2011-07-31 00:09:00.040           5.0   \n",
       "1763      1956    6.916667 2011-07-31 00:16:00.040           8.0   \n",
       "1764      1957  133.150000 2011-07-31 00:16:00.040           8.0   \n",
       "...        ...         ...                     ...           ...   \n",
       "549281  620308   14.916667 2012-09-30 23:50:00.040          50.0   \n",
       "549282  620309   16.150000 2012-09-30 23:52:00.040          55.0   \n",
       "549283  620310   28.116667 2012-09-30 23:55:00.040          22.0   \n",
       "549284  620311    4.350000 2012-09-30 23:57:00.040          55.0   \n",
       "549285  620312    3.850000 2012-09-30 23:59:00.040          55.0   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "1760   2011-07-31 00:42:00.040        32.0  B00344            Casual      NaN   \n",
       "1761   2011-07-31 00:12:00.040        36.0  B00158            Casual      NaN   \n",
       "1762   2011-07-31 12:30:00.040        30.0  B00077            Casual      NaN   \n",
       "1763   2011-07-31 00:23:00.040        41.0  B00157            Casual      NaN   \n",
       "1764   2011-07-31 02:29:00.040        41.0  B00505            Casual      NaN   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "549281 2012-10-01 00:05:00.040        32.0  B00565            Casual      NaN   \n",
       "549282 2012-10-01 00:09:00.040        42.0  T01098        Registered    02116   \n",
       "549283 2012-10-01 00:23:00.040        18.0  T01113        Registered    02134   \n",
       "549284 2012-10-01 00:02:00.040        45.0  B00468        Registered    02215   \n",
       "549285 2012-10-01 00:03:00.040        45.0  B00587        Registered    02446   \n",
       "\n",
       "        birth_date gender dia_semana  \n",
       "1760           NaN    NaN     Sunday  \n",
       "1761           NaN    NaN     Sunday  \n",
       "1762           NaN    NaN     Sunday  \n",
       "1763           NaN    NaN     Sunday  \n",
       "1764           NaN    NaN     Sunday  \n",
       "...            ...    ...        ...  \n",
       "549281         NaN    NaN     Sunday  \n",
       "549282      1991.0   Male     Sunday  \n",
       "549283      1976.0   Male     Sunday  \n",
       "549284      1987.0   Male     Sunday  \n",
       "549285      1985.0   Male     Sunday  \n",
       "\n",
       "[73956 rows x 12 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData[finalData['start_date'].dt.dayofweek == 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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['start_date'].dt.dayofweek == 3]['start_date'].dt.hour, bins=range(1,25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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['start_date'].dt.dayofweek == 6]['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": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-154-07735d57d753>: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": {
      "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",
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       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
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       "    <tr>\n",
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       "      <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",
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       "      <td>2215.0</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>2108.0</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
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       "    <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",
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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>97214.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
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       "      <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",
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       "      <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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       "    <tr>\n",
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       "      <td>16.0</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>Registered</td>\n",
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       "      <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  97217.0   \n",
       "1      2011-07-28 10:25:00.040        23.0  B00554        Registered   2215.0   \n",
       "2      2011-07-28 10:34:00.040        23.0  B00456        Registered   2108.0   \n",
       "3      2011-07-28 10:36:00.040        23.0  B00554        Registered   2116.0   \n",
       "4      2011-07-28 10:37:00.040        23.0  B00554        Registered  97214.0   \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": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['start_month'] = finalData['start_date'].dt.month\n",
    "finalData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comentario técnico:\n",
    "Cuando los índices son enteros y pedimos `data[3]`, puede ser confuso 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": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "5    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 155,
     "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": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 159,
     "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": 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>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>97217.0</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>2215.0</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>2108.0</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>2116.0</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>97214.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>2011-07-28 10:39:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:39:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2011-07-28 10:47:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:47:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>2011-07-28 10:48:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2011-07-28 10:48:00.040</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "3  11  1.066667 2011-07-28 10:35:00.040          23.0 2011-07-28 10:36:00.040   \n",
       "4  12  0.200000 2011-07-28 10:37:00.040          23.0 2011-07-28 10:37:00.040   \n",
       "5  13  0.316667 2011-07-28 10:39:00.040          23.0 2011-07-28 10:39:00.040   \n",
       "6  14  0.400000 2011-07-28 10:47:00.040          23.0 2011-07-28 10:47:00.040   \n",
       "7  15  0.116667 2011-07-28 10:48:00.040          23.0 2011-07-28 10:48:00.040   \n",
       "\n",
       "  end_station bike_nr subscription_type zip_code  birth_date  gender  \\\n",
       "0        23.0  B00468        Registered  97217.0      1976.0    Male   \n",
       "1        23.0  B00554        Registered   2215.0      1966.0    Male   \n",
       "2        23.0  B00456        Registered   2108.0      1943.0    Male   \n",
       "3        23.0  B00554        Registered   2116.0      1981.0  Female   \n",
       "4        23.0  B00554        Registered  97214.0      1983.0  Female   \n",
       "5        23.0  B00456        Registered   2021.0      1951.0    Male   \n",
       "6        23.0  B00554        Registered   2140.0      1971.0  Female   \n",
       "7        23.0  B00554        Registered   2140.0      1971.0  Female   \n",
       "\n",
       "  dia_semana  start_month  \n",
       "0   Thursday            7  \n",
       "1   Thursday            7  \n",
       "2   Thursday            7  \n",
       "3   Thursday            7  \n",
       "4   Thursday            7  \n",
       "5   Thursday            7  \n",
       "6   Thursday            7  \n",
       "7   Thursday            7  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.iloc[0]  # Como si fuera una matriz, fila 0\n",
    "finalData.head(8)  # 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": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.616271490839257"
      ]
     },
     "execution_count": 172,
     "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": {
     "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": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    13.477725\n",
       "Male      11.143362\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    13.477725\n",
       "Male      11.143362\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos agrupar por mas de una columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = finalData.groupby(['subscription_type','gender'])['duration'].mean()\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.477725009256641"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp['Registered','Female']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si prefieren el resultado como una tabla, podemos usar `reset_index()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>subscription_type</th>\n",
       "      <th>gender</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Casual</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casual</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Registered</td>\n",
       "      <td>Female</td>\n",
       "      <td>13.477725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Registered</td>\n",
       "      <td>Male</td>\n",
       "      <td>11.143362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subscription_type  gender   duration\n",
       "0            Casual  Female        NaN\n",
       "1            Casual    Male        NaN\n",
       "2        Registered  Female  13.477725\n",
       "3        Registered    Male  11.143362"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(['subscription_type','gender'])['duration'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O incluso por una columna externa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birth_date\n",
       "1932.0    137.127778\n",
       "1934.0     59.761111\n",
       "1938.0     16.671569\n",
       "1939.0     10.004878\n",
       "1940.0     10.152321\n",
       "1941.0     49.044444\n",
       "1942.0     15.702347\n",
       "1943.0     18.109359\n",
       "1944.0     22.426049\n",
       "1945.0     14.442955\n",
       "1946.0     15.726562\n",
       "1947.0     12.564659\n",
       "1948.0     12.217541\n",
       "1949.0     14.556532\n",
       "1950.0     14.132196\n",
       "1951.0     11.455912\n",
       "1952.0     13.815716\n",
       "1953.0     13.026895\n",
       "1954.0     13.574334\n",
       "1955.0     11.819292\n",
       "1956.0     13.628939\n",
       "1957.0     13.329296\n",
       "1958.0     12.670971\n",
       "1959.0     11.368420\n",
       "1960.0     12.348481\n",
       "1961.0     10.747483\n",
       "1962.0     10.899400\n",
       "1963.0     11.379984\n",
       "1964.0     11.769453\n",
       "1965.0     12.656773\n",
       "1966.0     10.996085\n",
       "1967.0     10.895413\n",
       "1968.0     11.709671\n",
       "1969.0     11.052782\n",
       "1970.0     12.126330\n",
       "1971.0     10.620023\n",
       "1972.0     11.991783\n",
       "1973.0     11.685062\n",
       "1974.0     11.594037\n",
       "1975.0     11.106560\n",
       "1976.0     13.639893\n",
       "1977.0     10.833483\n",
       "1978.0     10.924763\n",
       "1979.0     11.553320\n",
       "1980.0     11.266910\n",
       "1981.0     12.055930\n",
       "1982.0     11.370247\n",
       "1983.0     11.240753\n",
       "1984.0     11.936896\n",
       "1985.0     12.560582\n",
       "1986.0     11.441301\n",
       "1987.0     11.657824\n",
       "1988.0     11.452403\n",
       "1989.0     11.842564\n",
       "1990.0     11.470798\n",
       "1991.0     11.100876\n",
       "1992.0     11.114832\n",
       "1993.0     10.179277\n",
       "1994.0     12.074017\n",
       "1995.0     12.976974\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('birth_date')['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birth_date\n",
       "False    25.260456\n",
       "True     11.610514\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = finalData['birth_date'] > 1981\n",
    "finalData.groupby(tmp)['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pregunta**: ¿Cuál es la duración promedio de los viajes para cada tipo de suscriptor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "Casual        39.560557\n",
       "Registered    11.722582\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('subscription_type')['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PREGUNTA 1**: ¿Cuál es la duración promedio por género?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    13.477725\n",
       "Male      11.143362\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PREGUNTA 2**: ¿Cuál es el total de minutos por género?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    1.189086e+06\n",
       "Male      2.979078e+06\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].sum()"
   ]
  },
  {
   "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": 201,
   "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": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = finalData['start_date'].dt.day_name()\n",
    "finalData.groupby(tmp)['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(finalData['start_date'].dt.day_name())['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-208-eea2aa22fa85>: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']= finalData['start_date'].dt.day_name()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dia\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": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['dia']= finalData['start_date'].dt.day_name()\n",
    "finalData.groupby('dia')['duration'].mean()"
   ]
  },
  {
   "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": 209,
   "metadata": {},
   "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": 209,
     "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": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station\n",
       "3.0     4445\n",
       "4.0     8193\n",
       "5.0     4902\n",
       "6.0     9450\n",
       "7.0     4045\n",
       "        ... \n",
       "94.0     257\n",
       "95.0     273\n",
       "96.0     247\n",
       "97.0     273\n",
       "98.0     538\n",
       "Name: subscription_type, Length: 95, dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_station')['subscription_type'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 212,
   "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": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35540262975176345"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propCasual(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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": 214,
     "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": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "    "
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalData['start_day_name'] = finalData['start_date'].dt.day_name()\n",
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pongamos todo esto en una función:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best2days(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalData.groupby('bike_nr').apply(statsBike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "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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