Clase3-Amasando_Datos_postclase.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Leamos los datos (pero esta vez,  entreguemos los *types* en el comando)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips = pd.read_csv(\"data/trips_new.csv\", dtype={'id': 'int64', 'duration': 'float64', 'start_date': 'object', 'start_station': 'category', 'end_date': 'object', 'end_station': 'category', 'bike_nr': 'category', 'subscription_type': 'category', 'zip_code': 'category', 'birth_date': 'float64', 'gender': 'category'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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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",
       "      <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",
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       "      <th>0</th>\n",
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       "      <td>Female</td>\n",
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       "      <th>...</th>\n",
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       "      <td>67</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
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       "      <td>623509</td>\n",
       "      <td>535.0</td>\n",
       "      <td>2012-10-01T20:25:00.04</td>\n",
       "      <td>16</td>\n",
       "      <td>2012-10-01T20:34:00.04</td>\n",
       "      <td>47</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
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       "      <th>552071</th>\n",
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       "    <tr>\n",
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       "      <td>623517</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2012-10-01T20:32:00.04</td>\n",
       "      <td>22</td>\n",
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       "      <td>81</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
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       "<p>552073 rows × 11 columns</p>\n",
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       "            id  duration              start_date start_station  \\\n",
       "0            8       9.0  2011-07-28T10:12:00.04            23   \n",
       "1            9     220.0  2011-07-28T10:21:00.04            23   \n",
       "2           10      56.0  2011-07-28T10:33:00.04            23   \n",
       "3           11      64.0  2011-07-28T10:35:00.04            23   \n",
       "4           12      12.0  2011-07-28T10:37:00.04            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507     527.0  2012-10-01T20:24:00.04            46   \n",
       "552069  623508     226.0  2012-10-01T20:24:00.04            25   \n",
       "552070  623509     535.0  2012-10-01T20:25:00.04            16   \n",
       "552071  623512     429.0  2012-10-01T20:29:00.04            61   \n",
       "552072  623517     199.0  2012-10-01T20:32:00.04            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0       2011-07-28T10:12:00.04          23  B00468        Registered    97217   \n",
       "1       2011-07-28T10:25:00.04          23  B00554        Registered    02215   \n",
       "2       2011-07-28T10:34:00.04          23  B00456        Registered    02108   \n",
       "3       2011-07-28T10:36:00.04          23  B00554        Registered    02116   \n",
       "4       2011-07-28T10:37:00.04          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068  2012-10-01T20:32:00.04          67  B00486            Casual      NaN   \n",
       "552069  2012-10-01T20:28:00.04          26  B00170        Registered    02118   \n",
       "552070  2012-10-01T20:34:00.04          47  T01067        Registered    02113   \n",
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       "\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>Female</td>\n",
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       "      <td>Female</td>\n",
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       "      <th>...</th>\n",
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       "      <td>B00486</td>\n",
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       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
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       "    <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",
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       "      <td>B00621</td>\n",
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       "      <td>02135</td>\n",
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       "      <td>Male</td>\n",
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       "      <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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81</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",
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       "<p>552073 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8       9.0 2011-07-28 10:12:00.040            23   \n",
       "1            9     220.0 2011-07-28 10:21:00.040            23   \n",
       "2           10      56.0 2011-07-28 10:33:00.040            23   \n",
       "3           11      64.0 2011-07-28 10:35:00.040            23   \n",
       "4           12      12.0 2011-07-28 10:37:00.040            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507     527.0 2012-10-01 20:24:00.040            46   \n",
       "552069  623508     226.0 2012-10-01 20:24:00.040            25   \n",
       "552070  623509     535.0 2012-10-01 20:25:00.040            16   \n",
       "552071  623512     429.0 2012-10-01 20:29:00.040            61   \n",
       "552072  623517     199.0 2012-10-01 20:32:00.040            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040          67  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040          26  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040          47  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040          33  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040          81  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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTrips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terminamos de ajustar los datos: duración en minutos,  sin errores en las estaciones, y viajes menores a 24 horas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrips['duration']=dataTrips['duration']/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dataTrips['start_station'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dataTrips['end_station'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalData=dataTrips.dropna(subset=['start_station', 'end_station'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <th>gender</th>\n",
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       "  </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</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2011-07-28 10:21:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</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</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</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</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</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</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67</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</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26</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</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>22</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81</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   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040            23   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040            23   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040            23   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040            46   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040            25   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040            16   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040            61   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040          67  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040          26  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040          47  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040          33  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040          81  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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <th>birth_date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>552020.000000</td>\n",
       "      <td>552020.000000</td>\n",
       "      <td>352930.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>312916.785448</td>\n",
       "      <td>25.841395</td>\n",
       "      <td>1976.294223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>180084.792795</td>\n",
       "      <td>477.967443</td>\n",
       "      <td>11.003620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1932.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>154979.750000</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>315383.500000</td>\n",
       "      <td>11.450000</td>\n",
       "      <td>1979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>468506.250000</td>\n",
       "      <td>20.066667</td>\n",
       "      <td>1985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>623517.000000</td>\n",
       "      <td>199907.633333</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id       duration     birth_date\n",
       "count  552020.000000  552020.000000  352930.000000\n",
       "mean   312916.785448      25.841395    1976.294223\n",
       "std    180084.792795     477.967443      11.003620\n",
       "min         8.000000       0.000000    1932.000000\n",
       "25%    154979.750000       6.850000    1969.000000\n",
       "50%    315383.500000      11.450000    1979.000000\n",
       "75%    468506.250000      20.066667    1985.000000\n",
       "max    623517.000000  199907.633333    1995.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalData = finalData[finalData['duration']<24*60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2011-07-28 10:12:00.040</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1966.0</td>\n",
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       "    <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</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02108</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</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</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</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <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</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67</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</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26</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</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81</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   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040            23   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040            23   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040            23   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040            46   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040            25   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040            16   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040            61   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040          67  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040          26  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040          47  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040          33  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040          81  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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.616271490839257"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio:\n",
    "Hubway cobra basado en la duración del viaje. Usuarios *Casual* pagan `$0` si el viaje dura menos de 30 minutos, `$2` si dura entre 30 y 60 minutos, y `$6` para viajes entre 60 a 90 minutos. Por cada 30 minutos adicionales, se cobran `$8` adicionales, con un máximo de `$100`.   Un usuario *Registered* para el 75\\% de la tarifa solamente (con un máximo de `$75`, por lo tanto). \n",
    "\n",
    "Casual:\n",
    "- [0 - 30] : 0\n",
    "- [30 - 59] : 2\n",
    "- [60 - 89] : 6\n",
    "- [90 - 119] : 14 (6+8)\n",
    "- [120 - 149] : 22 (6+8+8)\n",
    "\n",
    "Hasta $100 máximo\n",
    "\n",
    "Registered: solo 75% del valor.\n",
    "\n",
    "\n",
    "**Ejercicio**: Agregue una columna `fee` que indique cuanto se cobró en cada viaje.\n",
    "\n",
    "**Ejercicio 2**: calcule fueron los ingresos por cada tipo de usuario, y también calcule los ingresos de cada año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(120 - 90)//30 +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pago(duracion):\n",
    "    if duracion <= 30:\n",
    "        costo = 0\n",
    "    elif duracion <= 60:\n",
    "        costo = 2\n",
    "    elif duracion <= 90:\n",
    "        costo = 6\n",
    "    else:\n",
    "        num_medias_horas_extras  = (duracion-90)//30 + 1\n",
    "        costo = 6 + 8*num_medias_horas_extras\n",
    "    \n",
    "    if costo >= 100:\n",
    "        costo = 100\n",
    "    \n",
    "    return costo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-4cddacfebcce>: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['payment'] = finalData['duration'].apply(pago)\n"
     ]
    }
   ],
   "source": [
    "finalData['payment'] = finalData['duration'].apply(pago)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pago(x):\n",
    "    duracion = x['duration']\n",
    "    tipo_subs = x['subscription_type']\n",
    "    \n",
    "    if duracion <= 30:\n",
    "        costo = 0\n",
    "    elif duracion <= 60:\n",
    "        costo = 2\n",
    "    elif duracion <= 90:\n",
    "        costo = 6\n",
    "    else:\n",
    "        num_medias_horas_extras  = (duracion-90)//30 + 1\n",
    "        costo = 6 + 8*num_medias_horas_extras\n",
    "    \n",
    "    if costo >= 100:\n",
    "        costo = 100\n",
    "        \n",
    "    if tipo_subs == \"Registered\":\n",
    "        costo = costo * 0.75\n",
    "    \n",
    "    return costo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-2d434b62b860>: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['payment'] = finalData.apply(pago, axis=1)\n"
     ]
    }
   ],
   "source": [
    "finalData['payment'] = finalData.apply(pago, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>payment</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</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</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>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 12 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   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040            23   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040            23   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040            23   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040            46   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040            25   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040            16   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040            61   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040          67  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040          26  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040          47  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040          33  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040          81  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender  payment  \n",
       "0           1976.0    Male      0.0  \n",
       "1           1966.0    Male      0.0  \n",
       "2           1943.0    Male      0.0  \n",
       "3           1981.0  Female      0.0  \n",
       "4           1983.0  Female      0.0  \n",
       "...            ...     ...      ...  \n",
       "552068         NaN     NaN      0.0  \n",
       "552069      1986.0    Male      0.0  \n",
       "552070      1965.0    Male      0.0  \n",
       "552071      1986.0    Male      0.0  \n",
       "552072      1989.0    Male      0.0  \n",
       "\n",
       "[551611 rows x 12 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "Casual        651780.0\n",
       "Registered     52692.0\n",
       "Name: payment, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('subscription_type')['payment'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "Casual        3.324662\n",
       "Registered    0.148191\n",
       "Name: payment, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('subscription_type')['payment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_date\n",
       "2011    392714.0\n",
       "2012    311758.0\n",
       "Name: payment, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(finalData['start_date'].dt.year)['payment'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type  start_date\n",
       "Casual             2011          370328.0\n",
       "                   2012          281452.0\n",
       "Registered         2011           22386.0\n",
       "                   2012           30306.0\n",
       "Name: payment, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(['subscription_type', finalData['start_date'].dt.year])['payment'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_date\n",
       "2011-07-28 10:12:00.040    0.0\n",
       "2011-07-28 10:21:00.040    0.0\n",
       "2011-07-28 10:33:00.040    0.0\n",
       "2011-07-28 10:35:00.040    0.0\n",
       "2011-07-28 10:37:00.040    0.0\n",
       "                          ... \n",
       "2012-10-01 20:22:00.040    0.0\n",
       "2012-10-01 20:24:00.040    0.0\n",
       "2012-10-01 20:25:00.040    0.0\n",
       "2012-10-01 20:29:00.040    0.0\n",
       "2012-10-01 20:32:00.040    0.0\n",
       "Name: payment, Length: 234953, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_date')['payment'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_date\n",
       "2011-07-28 10:12:00.040    0.0\n",
       "2011-07-28 10:21:00.040    0.0\n",
       "2011-07-28 10:33:00.040    0.0\n",
       "2011-07-28 10:35:00.040    0.0\n",
       "2011-07-28 10:37:00.040    0.0\n",
       "                          ... \n",
       "2012-10-01 20:22:00.040    0.0\n",
       "2012-10-01 20:24:00.040    0.0\n",
       "2012-10-01 20:25:00.040    0.0\n",
       "2012-10-01 20:29:00.040    0.0\n",
       "2012-10-01 20:32:00.040    0.0\n",
       "Name: payment, Length: 234953, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(finalData['start_date'])['payment'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repaso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "ea1c01ee-2466-4c36-91d0-98fe1e58b400.png": {
     "image/png": 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BJxiZQ+oJOgKOgCPgCDgCjoATDK8DjoAj4Ag4Ao6AI5A5Ak4wMofUE3QEHAFHwBFwBBwBJxheBxwBR8ARcAQcAUcgcwScYGQOqSfoCJQLAXYDSO4I8K1vfStw1BP+93//V6Mnn7V0k9fqSbNT4hoOfE8rsKhWFp2CZZbfkSyXVpWN5T9tGaWNZ+m229kJRruVmOfXEUiJQJJUJAXZP/3TP6VM4atopPM///M/SlLoQP/lX/5Fb5Amxz//8z/XTVjqykDJIyc7srwJRmVZ9Hbsu6saVvetbKxcOOcdaBO0FwJtjTKqFqwsuWdtqlq8dr7mBKOdS8/z7gj0gACC7h//+Ef429/+Fj766CMVet/5znfCD3/4w/C9732vhye/voUQ/Mtf/hJeeuml8OWXX4Y55pgj9OvXTyO89dZb4f333w+LLrpo6NOnT9dDPGOC3M5dNzvsB/j+8Y9/DJ999pl2JN/97ncV37y++9NPPw0vvvhi+OSTT8Kss84allpqqfDtb3+7w1Bt7nOs46ZM/vSnP2nZ/Nu//ZuWyw9+8IPmEu/haXsvbeLll18O//qv/xrmnXfesNhii3WRc3ucuJTlq6++qvf69+8faJudFpxgdFqJ+vc4AoIA5AJi8dhjj4W77747PP/880oQFlhggbD11luHHXfc8RtCrxI4E5iPPPJI2HPPPcMHH3wQ1l9//TBhwgRN++ijjw5Tp04N++67b+A3QpzAc7yfTrZTR2b2nW+//XY477zzwiuvvBL+/d//PaywwgrhyCOP1N8KRsZ/IBdDhgwJv/3tb8OPf/zjMGnSpLDwwgsr3vVqpjLOWimSo+5BhMGHej9t2jStt7PNNlvYdtttw9ChQ3PJJ+/lgDRcffXV4cADDwyQmT322COceuqpXZo+Xk7b+O///u/w61//Ohx00EHaboizySabdF45CigeHAFHoIMQEAEWhVzEK6+8Ms4///xROp4onX/8j//4jyhCL+633376tcSzwz6f/0W923X9v/7rv+J9990XZSSmz2+00UZRRuzx6aefjn379tVr6623nl7juT//+c/xhRde0GdkFNf1Hku/U87gJJ1EfPDBB+Ncc80VhUwpztLpx+nTp+tnggfBMOZs/xvGeuH//1SLZ89YPCGKcYkllohC3OJKK60UX3/9dU3f0kvGr0zP0ujUM9/7xRdfxF/96ldx4MCBWibgRL0XrV08/PDD9dOT5WK4VWJSiR3/V8ZNXrPff/3rX+OFF14YZVokzj777PHQQw/V8hFNV1c9IB3RCsbzzz9f2yX157jjjtP71Cl7t+WJ+JXvtnuc7d09xUnGb+VvWJcHR8AR6BAEEDYIqWeffVY7IATd3HPPHTfbbLMoo7e43XbbxRtuuKFLKPHZPEMwAWUCizOCEYE933zzRRmhx0033VTJhIzcVWDLqCtefPHF+k6Eq4ze4pprrhkXWWSReMkll2ja5KfTAljJaFVJHORNpimiTI9EmT6KDz300Ez48u2GMefKw7Cx6xbf/rcz13/zm99EUad3kZk33nij613EqwzVrlXG6YT/+U7q6jPPPKP1D8In04ARwrfzzjtH0STEKVOmKFbWifPdhq2dk1jYtcpzsp3YPa5xGMHg/ZD5Qw45ZKZ32rN///vfo2gG4xZbbBF32GEHJaqkZfcrfyf/T+YxGd/ywrkswQlGWUrC8+EIZIAAAufzzz+P1157rRKC//zP/4yiUo9ig6EkgJETWgYC2omPP/5Y/0fg8ZzMH8cPP/xQR4IIKiMY88wzj462Nt54YyUYCGnSpIOTuW6Nx2h6991315GjTMXoSA6By/1OCibswWrEiBGKM9ocSNWCCy4YJ06c2NXpgx94gzOja1HfR5lqUpz5bZ0BeBJPbAa0HLhHOfCOZDw0GEsuuaRivOKKK8bXXnstij2GxuUdxCXwXsqa9NBm2Xs6qRzsW6w8qI8nn3yyYiP2DKrFePTRR2cqC56hjVDfwee9997TsuF/0rG0wNSwA0swfPfddzUu/1Ne1a5R3y+44IIoMyFRbGTi8OHDtTx5Fs0fbY7Ae6gPXJ8xY4b+Jl+0Fb6DdHgPJJY8UmfIYzKQBnGIS5xku+VeGYLbYOQyI+eJOgKtR0AEStc88OTJk8PBBx8cvv/974e99947HH/88WoPgV0Ec/UigIKQg0A87CSYzxeCEe6//341Nlt++eXVVoN7zBUz7y+CL6yzzjrhF7/4hcblWRF6QVS8YcMNNwy33XZbkGkZtffA2PEnP/lJGDBggKa99tpra946wU4AnMEPI1fsLR544IEwcODAIII+PPfcc2GfffbReXfiSQeic/LMzS+zzDJBCF+444471D4FLMEFg1vpIMLPf/5zNUhcbrnl9Nqdd96plSgZTzQYYfDgwWpjIARDnxFip2U0yyyzKN6kKZ2Wlq10YkEISdhll126DG9bXzPzfyPlIWRLscdmSKYGwymnnKJYCRmYaaWTEAO1zXj44YfVSFk0fGGNNdbQA0NL7t9zzz3hySefDEIcw8orrxxIE/sXbJhkmjCANeWIMSdxttpqq4ChJuVMOf70pz/VtrfNNttoO7rrrrvUKFc0iZqeEA1N/9577w0yhaPGuuuuu24gT7RByxO2U+SDesN9yhZbHwJlS56IT1smT6uttprGw+aEQHsvNJSB5XgeHAFHIBsEGLkwir311ltVRcsUydJLL63TIoxwGfFwMJISoadqfVTJqN3nnHNOndsXgaSjQDGS03giBKNpMLDBYIT1+OOPd9kerLLKKqriFUIRxXJe1ffYfTD3LcI3nnbaaTpi472dEhhtMg0lZCCirRECF3faaSedKtl+++11tElZPPXUU3GhhRZSLQfnH/3oR4oRKnSwkc5IR9TYtGAvw1TL4osvHsVwc6Z4QuY0nhAY1WBQRthgSAej+IK3dDBRyKS+++abb9YyRIM1bNgwhZ38dGpAoyAGx1onwYI6yTQedY57lBcHWgdsk8Ae+wzwpo3wPxgT/5133ol77bWX4ocdhZAP1d6RLpoR0maqkakxuyaG06pFQCsybtw41WBQ/6kb0tnrO4jP9KGsyFLtyJgxY/QdMghQuyg0EMcee6xeY3pFyILWAfJJfZHVKPGBBx7QIkT7IcQirrrqqhqHNkz6sporjh07diatV5Fl7lMkRaLv73YEckCAjoSpi0GDBqkQRThhQyGW6moUiMDlwFYAAYgAQ0ChcmfOGkEFUZBRr6poEWSVBIMOkc4SAS0rS6JY7asthmg+utTDCN3TTz89yqisS/Wcw+e2PEnwRV0tI9hIB47g/9nPfhZHjhypHQlE680339R8MaUBCbHODCIn2g4ldti0yKhUVedGHCwethykQ4dGR4UhLSp7M/KkTCmv3/3ud/GJJ55QOxviykhaVf8TZZqG9CmjG2+8saPwryxwm1q46qqrtD6CF3WPaQ4C5cUBeZBVJUq4qd+yxFcJGeXH/6KpiBgmM93A1AbtgPqNEeYGG2ygBNAINNdEaxcXlCkxcIagMC0JwcB4k/KBuEAUKEcMc0lPlnnHUaNG6XSYrD7SaxigHnXUUV2kg3h8A8SE+sEAgXfwv2gl9VuYjsR+g+u0WdIkP9Qfvov6RACbIoMTjCLR93c7AjkggFBhhMNoB6GJELIRrixPjbKkUjUTGG8iKLknyyvV6AytBWQEAYngIi7GaIyMEHymwRC1rQpXhCijMkaLCGas5hldI3gZKZpwz+EzC0mS7wFfm2sHp2WXXVZJFEatfDudiUwr6bdjdAhZIB4dGZolOjmzoyAu5UQ8NBw8DymQ6aYIxhASrqHVIB4dB79JjzLDBoMygrRwjdE1Wo0TTjihKw6reizfhYCW40vtu9BMYGwMVhAtDDvNZsHiUGYXXXSRdvxoJujUrc6CHSulWAHy+9//Pu6///6aFnYUGGrKdFjcbbfduq4dcMABqumQJdp6jfYBgTeCQT4gCRA+NCKQDtohhEWmTZRMcI14kBDaDZrBk046Sa8RDy3JH/7wh3jppZdqno048V0YrKL5wICb9/L9EBzaKIMGM7B2gpFj5fOkHYHeigCCBUEkfjBUyDH9wegLoYTKnNEdHRYEI0kcbGSE4KMThITYdEgyXjWCgbEaafMs6n6EHHkwQd8JZUFnhfaHTohOhhHjQOnc+fbrrrtO/4dciX8K7dSZRjGCgcYBMkBnxVQGONEZXH755UowmBbhGlog0oO0GZ4WD4IBUTGCQXp0QuKHRK/xrptuuknzRifFCgXIJoG8d1ow8kAHK/5ZlCxXfjdtgTLDoFL8UyhO4InWiWlD6ikkGw0BS1ktHmUB2Ya4QEQw6OUaZOKMM85Qo0qm/7gGYUGDlSQYEIfDDjtM300b4p28hykXyhYyw7NJgoGRKtdM04HWhXaKhpG6hraQPDMNQh2g/ZKvW265RafpaN+0dZa9EpxgdFqN9+9xBEqAAIIX4cTZhCNCEI2DGBuq/QAW9kY8EFysWEC1z9QIQo5VEajXISm1NBiM0jhs5AfBuOyyy/T95KNTgnVodOyorxH6TIGcc845UQz7dPRMR8XomLiVBIP5dwiBGIcqxuB65plnzkQw0IigmUjGY6R61llnqQYDDQedC4SFeBAIlh6zTBZyc+KJJ0ZxKqW+HxjdWp47pQwqv4PvAwPqKhhApJnSoCO2b4dgQOx23XVXxR1t0fXXX6+rNsRoWUk2z7KUm/YiDrA0HlODrAqhLI444gi9RqcOsWBViGkcaFvYTyQJBtoPpjTo5CGGlBvtyqa2xo8fr//3RDCwlRKDU61X1DW+i7ygVaEOQFi4DvnnuznIC/knOMGorC3+vyPgCGSCAMLVNAjTp0+PsvpAhRKCjlEuc/dGMJi/RWBCMDBWRBCKC3BV6aclGEnVso24bfSYyQeVIBHrsBiR0pkj5CFtJuD5H1U26m2wp2Op1GCYWh6M6cCYi09OkUBYsK0gHiNqi4dK3ToqIxhonMgTRAZiQuey5ZZbqhaE8sNOpNPKoLIa8P0QCGx9mOYALzBHy8a300lDctEa4A+D+9hMXHPNNarJQ5NBJ42WQDzWzqTBoHzQNEC+IY08C8HAtogpDVmpote6IxjYcpA/yo3yoNww3iQvTNeQXk8Eg3yjRWTahzwyEIBgoBkhLfLMVBnLx7HTgfQyLYMGjcC7iwxug1Ek+v5uRyAnBBBMTIPQySFg6YgYnSHQULGzggSCgSoWwYVgwoqd+XpsKoiHcRlCGoJhUykIMgQr1xHSZoOB9iJJMFAtM0IjH0ULuSwh5lsYLTP6ZbQImaAzw+HYWmutpXiCiSzP1U6EjgU7CzoDpj7AF60DGgYwZloE7QMEw6ZI0DBhNIuhLitSLB4jdNKzKRJGwpQrgQ6LlQ/M86M9otPC/ma6EEtCJ5WBflDiD98GkQBXiDJYU6+xa6BOU/85IAloGcAT4gBhw3D27LPPVk0Az4wePVoJBtoo4pldRiMEAwNgvObSBjGoRltF+UAAIfPVbDCqTZFUEgw0M2i9+E7aGVNDRqIgWhxlCU4wylISng9HICME6NSxhj/33HPj7bffrmQCVTkdD50fVu10PCzrw4IdQcroipEcworOCdWr+F/QUTQGiwhkhCMdKULZll9CTgaKDQIEI6lGJg1Gi8QjficEOjKENwTLHDqhxcCYlYDWAeKA4Id0oOWAEGCUCcZ0BkxzEJ/fYAxJwCgzSTAsHktYk/EoU1tFwrMY4UJCCBgwQugoX96Pup/pAOoCoTcQDKYn0DYw2gcDs0+ATLPUGgKCp1lsNFjSi0aPzpsyA0+I2y9/+UslGNjXkAYEA00DBMPsXCAKpsGgXREPcoLdA3lgmSrXIJ+0DaY4TOPA9Bnp4FSNaTXicY37phHhGprFUbIyhPIjj2gqILQQKAiuGXnyrXjpxRAbUoomi3pSFpLhBEObn/9xBNofARvJYfCG4RqdDYITEsAZAYVdBXPKCC72GIFgINBQ8TNnbOSCuBiyMTJCQGIDAMGoXEVC2ozWIRi8F4t3BKu9D18NvIfAKLOdgxEMppFYjcM3siQQ1Tx4osFBjc51tDsQBCMYYAxWdAh0HsThTMfCCJeOgbKBiFBudCiUDfHovIjHO4gHYeF6UoPBPToZm7axEbrViXbGPU3e+U4Oysb8kVj9ByvwQFMByUALR/1Gw8C0EmSMes/0CNoBjDyTBIP2AsHAboZyhGBgg5EkmpQRxpZmg8E7aS+0Ke5R7rwTLRb71yS1ELwbmwrSw37GCAa+VShX02DQRrHBwM8NGiu85tLWeA/ljhaS9oZhL4SzDMEJRhlKwfPgCGSAgHUmCDlWJrAKhBEw5IDOS7wAqlqWERDEAcHFKI+OD5sJiAIjNkZyaC8QtMRDvcs8L50my/8QhHScLInkOXMyxPsxfkTAcx1BjCMgCIrlLYPPLDQJ+8bNN99chTq2FthKcJ0pKQwG0RRBPHCkBE5MkdDhgDUYgw3TIWgYGHXyLMTB5ujpkCgr4mGMiNGtbOutBI29SNBA8Q60SXSoPE9HhDYD3wh0ULwfDQr3yjKazbvgzNYEEsEqHab4wJA6TccOUcAdPn5d6IzplCF52CRRFmiSCGjiWBFC26HeY8/AVAsGnVyDlKClgCRgP8M18EabQccOyeYaBJCpFvIBwaEtkAfKgzbK6hTi0U6ZmrHpGkuPaRDiQmBpk7Q/6gKElOsYDNMeeZ42jiaEb6L9QXYJlH+RwV2Fy5DBgyPQKQiIMGHQEERYBXGGpe6spfMJMhrWrcSFaOh9ETy61boIoyCCUl2Ai9o/iI1AkA5O3SbLyErjinBVF8a4EpeOLay++urqElmIR5BONYjw1PiielYX2mzrLuRF88B7cW8sZEPTks6vraE2bNmmHrfQ0oGpe24ZLQfp4NQ1OK7V+U7pFNStM669hXgF6XCCjKKDCH91XS1Geeo+mrIAdyEr6vJZjDyDaKDC9OnT1a27dIa69TfASQem5SYqdi1TXFzLCFzfjRtx3MPLdEsQohGuuOIKdfve1oDXmXmr/0Ki1WU9uApJDmKEqS7rhUxoHRWNhLpXl45e67QQP3XHTVkQXzpvdcMtmocg5ETjCBkMYCxagyCkIdCWhMwo3qJFCDItFkS7FMQuJsjUoLYjXHfzHOnhKl5Ihn4RbVLIobosF4IfhISqq3GepfxE26Huw4W46HbzovXQLd6FLKlbeiGs2p5of2JLpXWFOkfbpd7xnaLxKLzNOcGoswJ7dEegXRBA2CYD/9MJmnCik4RgQEbY70I8IXZ1ZPYscQn2P7+NJHR3DSFtzyXj87tTQrVvt29L3qODA2OZF1eCwT4udAAE4nGAJx2XGHRq5wTBkFU+oa/scWEBTCExhGT6PCsjWn0OUiI2CEFGsUEcbQUZlWvcZFlYep16Bhuw4mx42beCld3nWhIXrief4beFnuo7cXqKy73k81aOyWvdvcfidPcO0rL0LA07J5+1a0WcnWAUgbq/0xHIGQEEj6hRVfghbEx4MlriN0QDgiG2BIHRMASDzk8c9XQJLeISiM+oztJgdJW8hqDmmr2HtDlMyJFOUpjn/Om5J1/57YwULfDdjE4JdHCMXis3J2PkSzwwIw5lVUkwZMWIjpq5B37WWVa+mzRkPj6IgaESREbXYmOgREPU/5oPKwf9p5f8qVb/DUcwrKyj4JvEmLZjddjqb7VrxOE6GPM8cblGHeCa/c87aQP8b+WRjGfvT6ZHXK7zLbQ/Amkk61u176x8T5FF7gSjSPT93Y5AAQiYgIVgMMoV48wgS1N1F0ibFjEhWED2OuqVaDDAGFU6KnJ22jQ1vZEuygOCARERD55B5viDuH3W6Rc6EItXDRieRf0uBn86DYMKfvTo0TqNRXwvx2qo+bVWIeAEo1VI+3scgZIgQKfEIUaBQYxBlWAw10snxZwz97xjaq6wDEMxlFWMZZWNEgswRrNg93kLv4knxoG6bXtfmRoRF+FqN5CMV5kj7kFAsCOQnUCDeGYN4gpbyQX3eiImlWn5/45AHgjkTjCo6BZcaBkStc+OW22MPEbzCHg9ax7DWimkxThtvMr3oaLH2A/5KksvlXSgXvfgCBSNQEsIhhELGpD9LvrDy/7+JFbJ32XPt+evfRCgXtE5EWiXzPd6+8y2/NJinDZeT7lDm8Hh5dgTSn6vlQjkSjCo7Fg4W6XHOMXVdrWLN4kbeLEEql7cEFgE7zBq492bY1g98bqSXy1Ii3HaeNVyiszgeciFB0egLAjkSjBYD3/nnXfqMjhxBBLEC2AQZyBl+fbS5gO/A+Am/uqDOOdRq3DWcSNAahEN4nBALJxclLaIPWOOgCPgCHQ8ArkQDOvgcDbCMjgciohHNLWgxqFImo6y45Gv8oHgQhCvfrp2HstzLMpljwh17JIGN+Kg9rZlheJyuMqb/JIj4Ag4Ao6AI5AvArkSDHGTq5bpOJkRH+lh4sSJXUu0bISdHGXTOVona/f5fK6hArRr9gzXCHZd/2nTP8lvZGmbuLpVT3Q43cEjH17iDIPkJya/nTXUeGUUl7e6LA6vbttss41GJ14SX0sj+bxd87Mj4Ag4Ao6AI9AsArkSDEbirAGHYMjOfzOtAbeMo/K3zo9rlaSBe1wz4mHxLS7XiVNr6sDeV8azdfz2jRAM2YkyQNBwUyu7L3YRDL7TMOJb+N+ew5XzHXfcEWRjpPDZZ58F8VsfZEfNLnwsXvJ5IxicPRSPgJVR8Tlpzxx4PW7PcvNcdyYCLScY+FLH8xxqfLzOsbcBRoyo9HFZjP0BQgKbA+wPCIzKuY4BE46AsOfAeFQ2pdE1/EwDyAYxM3k4a6fiosNnmRl7FKCFYL8B2V1PfdInCQb+9cGCA09x4AZGYMJz4HrZZZcF2SRH8UN7cdBBB6l3RjDjGXse/FjKBs7sF5H0MNdO2HVCXitJRfL/5O9O+NasvyFJKJK/eU/l/1m/29NzBByBnhFoCcHA09zKK6+sI3E2mZEtaZUgcO3YY49VYiA7/4Wrr766a/MX7slOcWqDcNddd+lInA6VjZNkW9yA4xrZvU6nA7DvIM1KBzY9f3o57kIuMIbFc9/111+vJAC7C5zm4GaYKRJsMNhfgA2kcCEMnmgoIAe4eAYnvn3SpEnqIhjiRboQB/Y9IA5aDWxi0IagIcE5D8QMz39oOmRnTBXILpRbVy8gDxxJjVKSUNj91uWo/d5kGk2rt5Vnvsiutd/XeY4dgTZHQIRY5kEEpqYpu8LF/v37Y7moWztLpxn3339/3U54IdmGmO1qZSQd77vvvigdqe5tz1a1bD0rS1p1S122IJ4yZUqUUbpueSwujaN0yFF2kNMti2UHxyiGpLoNLy+1d2f+URknSD5F66Db+44fP16/XYSlnsWbYpQ9BhQ3cHn++efjjBkz4p577qk4sNUz2/+CkZAE3WoYnEaNGtWFk2h74qyzzqpbRcsOi1FWpOh2wlxjS1+2exYNkqax/vrr6/a+lqeMP9WT6wYBw1u0ebrdtmiYus6i0dOt0u1MO2n1Ye/m3Op313qf5e3LL7/Uts8W9DzDdfCkbYGvHd0UgV92BByBHBFoiQaDkThbFe+zzz7hjDPO0BE7Oweef/75Os2B73y0F8Q59NBDdYtpto5mG2L+32STTXQb4vvvv19H9LjUZQTOFACe644++mh1rSs4tY0tBnkV4adaip/+9Ke6HTZaCLZm5voNN9yg00hoM9A6CCELjzzyiE6bYM+CBmfChAnhySef1C2Cx40bp1NH55xzjk6ToJ1gWTDGokyPsP8B+x2gBWFPBKZk2NyK5bBML1EGu+22m77b19LnP2qw8qccfve736lGj5G2XacOcBC4VkTg/eSprBoApljZLn2WWWZReKi3HGg17LC827kIHP2djkBvRaAlBIPllnRw7Hdwyy236JLLsWPH6gZLTI3QsWEbwJJWrsuIPey3335qU8A1GZmHCy64IJx22mmazkknnaRTCUOGDAkse+XewIEDVRC3S+dIp4HdBKSJ7ZzZcIpvgGhNnz49bLfddvr9ZoMhmiCNT4eEPYpoNHRVjm2KxLQRmyWx5bZoidQuA3IBZhZ4J4agH330kZI4toSGmDCVAt7gTBwXxoZYPmcjDtjUiHZOd8KkXOkU7R5vLopY2FdbPSg6H5YfO5MvDnzqMGhhihD7Ihz5GcmwM5jad9jZ0vGzI+AI5ItASwjGq6++GkQ1r0aJdKQ77LBDkOkRHbVBOBASCFZG5nSy7733Xrj77ru1I0V7wTJNOmKICAaNdIYYJY4cOTKstdZa2qmyJJPQLkKE7xX1rtpU8F2MwhCUF154oa4eAZOnn35aV5Fgg7H44ovr0lM0G7fffrtuVPXpp58qbn369AlHHHFEgHBhh4G9BTgNHTpUCZuRBjZUuvnmm8Mvf/lLtePAmJR8sEW3TL/MFDffate7UwdzDrZJp7whzJQ/RI8OsSwdOpoV2plMx2nbJc9FB9o3xBy7JbBCE3r88cfrNWy02L6cg3xzQDR4xuSCnYv+Dn+/I9AbEMiVYIgNhnZ6LFOloSM4GWWgumfkjFC1ETdgIxi4TzwO/seoE/8Zb731lnaYrLSAdCBMMIqkU2VawASHncteeAhrCALbN7MDIgSMpalocJjKGCrkgDMajElCGpguQpCi4cC4E40GBAFtDwTjyCOP1GcgI2JzoQQjSRrQepx33nmaPoRiySWXVIwx/KQc0HaMGTNGr7ULhmUv42r5s7pNJ0n5QbTRMq2xxhrh8MMP1zpPnDIEpiAhPRhiY2TMyq8i6wa48H483J599tlKsiEYTDESxB5LV6YhG5AjRjRci1GG2uR56JUISKPNPEjnqWnKCFyNPEUoRLGViIsuuqgaFWJkKMspo6iFo3SeXYaNAwYMiMccc0wUQRuHDx8exf4iSgccMeSSKYEonWDEqBPDR4w9Zf5V0+F9GHa1U8AITQhGFPKkRq/ggwEs3yL+L6KsjFEjT87iOCuKpkdxkmWpUchEFC1PFJKmcTD4FJIVZUQcxe+Fpifq4yhaDU0Pw7cHH3wwYkCLkaeQsohRqGgyFE/ikibByq6dsGynvFpdpUyE9MXRo0dHjHpFe9VlkEicMhwYVtPWZIpTIS5DnsiDaETjBhtsEIUoR9H+RYzHZapV24loXbRtyJRrlJVWXUaflvd2qiueV0eg3RFgxJp5oDETWEUidhfa4S299NJRRshR/GCoQJWRR0QIyFSIrhqhg4VAyBSKCgUswrEMhziQnoz2IqstZFSiz8soPoqxoq4m4X67EQzyTCcjRpYqKGXUFUUzE2WJqQpMmRJR8iCGrxGidtZZZymOYsuiZEOW+0ZRrc9EMMTwNYqWQq+B57Bhw1TIch0iQ0cGyRDjWsUTcsd7nWBk3gS6TdDqqhEMVv5AmsXmRus+K0lsNQRxijwgo7Qx0aTp6hbaY5H5MZkgGlFdYSaaN11BhgwR4+U4bdq0KAblUbSdSt6QJQxOwBRCD/YeHAFHoHUItIRgiIoyrr766rocdbPNNtOOUvwzxFtvvTUiLGTKRJegMhJHg3HPPfdEsRWI1113XaQjJUAgHn74YdVaiKpJiQYdMqN2BEe7CQ/Ls/ikiGhu+CbIF5oENDcyf6yYiIt11WhccsklUaaZoqw0ieKES7UPfD/PGWlAIyIrTpQ0iIpYOwdIGctcZTpJCQajPvF7EcWOQ0d/aJcga0cddZTi3G44aqbb6A/4UpeNYMi0lxIMsUtSQs2nlKUMIBiyiknbKHmiky4yGHZoMNZbb70IwZBpnCjGylqff/3rX2tbQYuB3GBQAilyglFkqfm7ezMCuRIMRt6yh4Z2luLQSUfi4mVSyQEjbHEjHsXwMIodRsS3A50iB4KD+wg3hAaCBSGBqhaBQqcqRp1RjDy17LjP0U7BhCX+KU4//XT9XkgFGhq+Xea+FTc0GIwgUQNDyojDiBdfIXQA+LKAYIArHQCjOLHb0HgQEuKh6WCaBSJD+jzDVAsqcLHtUIIBaSG0G47tVOaGr2kp8Ody3HHHOcFIWYjWZoxg4ANm8803j7LcOorxc7z33nujLNvWKRTkCoMPWTXlBCMlvh7NEcgagVyNPFk9InPMupsqm3WJOjjgyZPVH3iiFFKh1/DTgD8G/DJgwCWjOzXWwhCRJZd46pTOU12JY9CJISSrKvCpsemmm6phIkak7Rpw8Y1PECFTasyJd038WIiGJ4jdihrZgRHLSi+//PLA8kaM7vC+iR8MjEW33XZbNdRkZQKrRFh5w5JUfGvgqXPrrbfWlTi8hyXBGIbyHlYK8H4MbzEUxYhOKlmhxnztWo618g2uBOoyB0aeGDvL9FfYYost1JgX3w5lwZ+VSNQzDIvLsAsyuAjJ0Dp7wAEHhMcee0yXug8aNEgNPMkvB0bMQtK1LWHwyWoSDD05CNRxD46AI5A/ArkQDMs2RAGfC0YYWP2AkGDPEZlPVStvGYUHsQ3QR1i2yWoROkbWuGPBzj2eEbWyrpjAqdbUqVPVIRUrJuhAyyKQ7bvrOZN3DvBghQwBUsH/kDGs4VmCi0U8GLAaBExxMMTKEn4TFxwRrqSFEOa6GMbq8xA5hC6dGuSC90BYKA8ICWSPlST878K3ntKrLy5lQ3CCUR9uFtvqNqTYCAarb7baaittC8gMDtpCkmDQdpxgGIp+dgRah0CuBMM+A8FgHZcJWe4lryF0+d9GGdy3uBAPlmziwVOMFXWDNEbbCJlk2jzTjgFCwGFLefltWFRiBD5c47uJx+gsGew6cezZZHy7bvF4p8VLpuO/s0cAzAlGMPBGiwaDJZeuwaiNt9XZSoIBdgxEIMgcSYKBRsgJRm1sPYYjkAcCuRIMEwic6cSsA0XAWkh2cHSYds86Qs6o8JlewWcEo3bU+fh0QM1v8Sy9dj3z7RwEwyn5P98JjuDD2UZkxEn+z/P8X5ke8ZPXSY/3VHueNDxkjwD4E/IiGKRv78iiXbTDFMlqq62m5Ay5ALlAg2HTJFxzgpF9PfYUHYG0CORKMNJmort4JjDZHZQtyJkSEUPFICtNdHdWnkOQenAE2gEB6/zzJhjWJuzcKDZZEQxrx8n8JH+nzR/pQIhNgyGGz0FWpznBSAugx3MEWoxAqQkGWCBUsOGQ3VN13wb27RCfGipoGIF7cATaBYE8CQb2OdjTMJ2ItoopAwhCMyELgmHfTP4gBxALpvU410syKgkGRp5oMGQliWo2bYrENRjNlLo/6whkh0DpCYZ9qgkqzhxOLgwZP7cLAlaH89BgYNDLviZo+1g5wcqrfffd9xs2OvVg1SzBsLaKUfdLL72kRssYG8tya12dQhuuh2SQXlKD0R3BwAaD97ASy6dI6ilxj+sIZItA2xAMBAuCmdGZk4tsK4Gn1hoE8iAYpEm7YDPALbfcUlcU0WmzhPm2225TuwTi1NORGxrNEAwjA6xYYr+VSbKfDiug6PTZSwgjbXFDrq9KmzdLs3KKpFKD4QTDStDPjkCxCLQNwQCmRgVlsRD72x2BrxCg/hKy0mCQHgd+UPBVIR5glUgwHYEPGXymsFkecSDm9YZmCAYDAvx8sOqLnX7RqrDsnGtMeYpH3zBhwgTVNJC/NCSDeEkNhtlgOMGot2Q9viPQGgTaimC0BhJ/iyOQDwJ0kIQsCQYdLpoBnHWJW3hdwo0PFPxAsDuu7HHSsL1SowSD7+QQ77Fhn332Cc8++6zaTe21115B9g1RB3KLLLKI7oKMNoNvSKOVdIKRT730VB2BvBDIlGCYAM0rs1mkm2aklMV70qbRDpjxLWXDLS2+ZYpnZZ0VweDbSJMpA7QXsqNoWHvttdVz7jvvvBPE/btORRCnlRoM3ofztylTpgTZ7VTtQDifeOKJqmnBfw3O4/DSK67SU2tYSNc1GGWq0Z4XR6BnBJomGDR6jmSo/D95r1W/LQ90jNY52tnyUPm/Xc/7nMTM8mnnvN+dNn3DrRKjyv/TpufxviID4JA1wZBdi8PgwYO1U5f9fYLsKBpkX46w4447qv0D5IL6VW/ZNaLBsLqNEzEc4+F5VzYxVKIjuyVrvoYMGaJEQTZ402mStPkjbScY3pIcgfZBoCmCYcLEPpf/Ccmz/bY4RZ2tw7SRnAlbO7cqX5WY2XsNp+7uW7y8z4YHgtwwS57t/RbP/vdzbQSsjLMiGKSHpoB9fOi0559//iC7s4ZHHnkkyO67qs249tpr1bMlcests0YJBnWHaRrbNwi39ieccILuiSM7IgfZsFDtRnCYd8011+g+ImnyRxwnGLXrmcdwBMqCQMMEg8ZOQFhiVGbBrtv/nKtdS97P67d1jPZ+yIVd47eRDd5fr/BtJM+WD/DC0K0y2H3O9rsyTt7/g0MSm0rMDD/y0QrM8v7eVqZvZZoFwbA6ktQUrLTSSqo1YPUInm8x8GT1hvmNSdb3NN/dDMFguezJJ5+sRIcN05geYdM9CAYb72Hsueaaa4ZbbrlF98Hhe2rVJ+I4wUhTch7HESgHAg0RDBNu7OqJIRe7phJo/Bx2n7NdR3jY/3oxxz/2LgzHGD0haNk0DAHLNetA7Uz8WsKt2ewaJjhCev7553WUaXh0h1uz72z0ednKPayzzjpq9U8a1XAzvOzc6Lt603NW3lkRDOrNe++9p503Xm4pM9zps5su9g19+/ZVD7gsX+XdlGM9oVGCwbtYngqpYCVLdwRj4MCB4eabb3aCUU+heFxHoI0QaIhgGIlgrvenP/2pbjNOx12mzgYhRz6XWWYZ3bdkqaWWUk0LRANPghxJskGZ5Zl/8kLHguBldMnW9Pb+stQXMCOP1iGwiRQqeLBiwyjya4d1VnliVhZcssoH+BKyIBikQ3ovv/xywLYBO4yVV1457L777uG5555T2wb8QRx22GF6UP8ou3pCowSDd9nKFla3WH3aeeedVYPBFAk7+G688cZq9Jl2i3pr0+4Ho55S9LiOQHEI1E0waOR2TJs2Ley55546imJLcDYaQniWIaCCfe2118K8884bRo8erepYpiW+/e1vqzYDomGdJp0lHWWenaURDDZuYy+VO+64Q0eYCy20UMs0O7XKBaGPx0U2iTr88MNVlf2Pf/xDcQKv7jDLE7daeW6n+7QbQlYEgzqFLwiWon7wwQddmjnewztmmWWWgGt9PHwaIawHr2YIhk3djBw5Utsg5z322CPcd999mifyAeG44IILNG/kuVY9Ig7f7ASjnlL0uI5AcQg0TDBo6E8//bSOnlD7n3baabrLKZ1UvSOlrD/fBO9+++2nneWxxx6rozsEFHs0MGIyF8J0mghfE8C1hFwjeTXBSL5YPoiwffDBB8OBBx6ovxtJM49nmO7CVwFqd5Y4brrppkp+cJLEAWZGNAwz8MoDszy+r+g0qQeEZgkG6XB8/vnn4brrrtPpEMoAsjrffPOFv/3tb+GVV17R+0ybXH/99amnIZIYNUIweJ68IROwBUGjQr2hXrGihPwedNBBOhhhaS1ElvjW/pLvr/xNPCcYlaj4/45AeRGoi2DQwO2goT/11FNKMDBaPPvss8OGG26o94skGCaEpk6dqkZldIgIMdTHBEZ1kAw0GdZhkl8EXF6dJXmiUzGCMWLEiPDQQw8F/AHwm/tFdtL2fnOMBMEYNmyYqrDJFzih1QA3IxtMmxhm4Fpk/nl/OwRwJmRBMKhLrNQ499xz1aFWX7G3wKhy0KBBukyVesVS1RVXXFHtMhZffHGtf2k6csOyGYJBGtgasboFssOup5BWVrWwcqR///5h3LhxATsMviWNzAA/4roGw0rIz45AuRFomGAgJJ988kndUMkIBsvO+J1GWOQJC0Lo17/+dcAnANMgdORY2NMp4uEwSTLoMM0ewzpJO2eVRwSjdSrYYDCSI3/YrzBdQn6zfme9eef9EAymvMgjI8/11ltP8YNYgBkkw4gZuNJZWYdVdP7r/d4i4mdFMMg7adHR4oablRjLLrtswNATWyMIIhpFPHviMfPMM8/ULc15xsorzfc3QzB41x//+EfNwxlnnKFygXqEZoMzPjq4Tn1KW3dI0wlGmpLzOI5AORCoi2CQZRo4hxEMRrqQCta8QzC4VyTBMCFEB84cLwIV9Sy7S0ImMHyDZLDpko3I0XJAMkzQ2TmrIiJP4AVO77777kwEA0+GYFaP4M8qX5YO+eObX3jhBZ0nx06EuXu8QoIZWNnulJCMVml+LH+dcgZngpFN7BQYxaP9w6B2kiwpBVsrj56+mzgQQtrf9OnTwxprrNFl2Mm27awmOf3009VjJkQWQ9B622ajBMPyTR5nyC6vp556qtpeMKVDm2OLdaYt2S+FOGnrPnH5BtdgGMJ+dgTKjUBdBMMaOI08STD4XVaCQceJm2KWqtJZIjTpLI1kVHaWFFdvJxhvvfVW2H777dUwlhEm2oskMeMapCzvqaVyN536c0f7IWRFMCCCbHJGx039hqRQJhgzs7qEJaB03gMGDNAlrPXmOAuCwTezXTt2IKx0YWoEo1RsReol1iZ/nGDUW5Ie3xEoBoG6CQaNHAHJaJxVJBhSIijKSjCAldE4KmRGT6x0se2cza4gz84SvAwzcLMpEmxEGFmWTYPB1AgEA2dIdExgBBkzYoY2g2um9aEDg5BlTcqKaQ75vpV6QMiCYOSb069Sb5ZgZJ1H8HMNRtaoenqOQH4I1E0waOAc7UIwEErseZAkGJAMtBhmi+EE46spkt/85jc6RYJaG+dMqN0hFOA0++yzd2EGwTDbFScY6RunE4z0WFWL6QSjGip+zREoLwINEQzTYGDkWXYNBmQIdT8Ot+gY6SiNYDAyR6vRaoJx1FFHqcOhsmkwkgRj880317lyyAVkzDQ/YGYaDDP0dA1GugbuBCMdTt3FcoLRHTJ+3REoJwKlIhgmgA2qRtTuJoTMyBOCwZwv+zEwGjeCwTSJaTAYjedlT0B+OEwtzhRJlgQjK8zA2ggGRoOVBAPczA7DjGOdYFhNTXe2srK60IyRZ7o3NhfLp0iaw8+fdgR6OwJNEQxsMNjzgE68WRsM0uCwgOqdo96AECed3kAw+E46K8gB380ZolQvMbNnqxEMSBnEAg0GHY5pfSBlTjDqq51OMOrDqzK2tW038qxExv93BMqJQCkIBoKDPS/wQGidHR0bI+V6gwmhWgTDVpLYaLzdNBiQC+xgwA133pAx6/TrJRmGuROMemtbffHBmeAajPpws9jWtp1gGCJ+dgTKjUChBAOBwUEnec8994QTTjhBl9wxdbHvvvuqwSH36TDTBhNCSYKx7bbbqg1GcoqknQkG3wipQNBOnjxZpzbQLmywwQZqnAlpQouRVpNBesR1gpG2ljUWD5wJPREMSGJZAlorNipjKewSSyyhmsFGtIpZfg/YUe+xX2IfFjyEMp1n9lU2lUf7pr2zDN00bZb3tO0iy3x7Wo5Ab0SgFATj448/Dpdccok636EQEA544TzvvPO6Osm0QqHTCQbfx4FTLLw4Pvzww10ajHnmmUcdL7HHA0KVeGlws3hOMPIVAeBMqEYw6CSvuOIK7RDRTqUpt3xzG8Kcc86pBIOdf3GKhcasHrKfdf7AD2ywEYJgPPbYY04wsgbZ03MEMkSgFAQDh0FoL/A+yIoOhMj6668frrzySp3/R7DY6KPWt5sQ6kQNBt/GgQtmdqHEQyLann79+qkzo9dff101NRdffHFYZZVVNG4a3EiTDs0JRq3a1dx9cCZUEgzsl1gWfNlll6lHWeKUgWD88Ic/7CIYjexlwndkGcAP2fDmm2/qhmkQDLyC4mDMNRhZIu1pOQLZIFAKgsFoHHfedJBs+44K9Ec/+pGq/xdbbLHUHSWQmBDqRIJh38cmUrh+fu6553SzKDQ9d999t2o0GHXaLq1g4QQjm4aSRSrdEQz2CqGjZFO+ejRPWeSppzTYOA2SwX457NTKVGaa+tRTms3cAz+IF55BwYxN1JxgNIOoP+sI5ItA4QSD0Rw7i9rGZGgupkyZoiPzs846q2vL8LSCrVMJBt/FgRto8GFTMv4HN/ayuP/++/X3F198octymXJK21mRjmsw8m1opA7OhKQG4/zzz9eNybC9QBtFOVg8jVzgH3ZrZUqElUPUJbQHRQfwIR+ffPKJkh00dXiddQ1G0SXj73cEvolAYQQDIcrx2WefhauuukqXu+KrgmWvEAuuY2PAaJx4aed+iYsA6jQNhuH16aefBubEmYM2bQUjzKeeeioMHjw4MN207rrrahwM3sCiFjkjbScY32wcWV8BZ0I1gsH+LnTkZSIYH374obY7limzGaDlP2tc6k0P/FhxhkyAYGy11VZOMOoF0eM7Ai1AoHCCgRA799xzVeU5cOBA3Ztj5MiRqv7cY489wtixY1XoggXCt1ZACHYywWCnzAkTJgQwmnvuuVWtjlEnG0mx58prr70WVl555XDDDTek3lDKCUatWpXNfeugkwSD3VQh1BtuuKFqotBkUH/LECD88847b7jwwgt1ypJ8p2mDeeYdDCHR1P+nn37ap0jyBNvTdgSaRKBwgvHGG2+opuK+++5TVeeIESMCx7333qvTI5dffrmuKrFOsNb3Eq+TCQYGnhCyMWPGKME47LDDAgcEAw0GBGOllVYKN954Y1hggQVcg1GrwrTwfncEgyku1PwYOdfSNrUwu+pcrW/fvuG6664Liy66aCtf3e27wBBbLTSbrKDyZardQuU3HIHCESiMYPDlCAtGIUOGDNGVEdgVYOzJqI6DjpLpAAw+iZtG+BKvkwkG8+KMKE888cRvEAzXYBTennrMAHWTUKnBgGCwEmLixImlWqbKFBt+MJjCLIMfDGvbrCJxPxg9VjW/6QiUAoFCCAaCggNnUXfeeWdgi3CELhbhyy23nDrQYXSCUEP4brTRRk4w/h8z5p5Zvjt8+PAw11xzhUMOOUQ1QKbBmCE7oa633no6GqaDAOdaam2L48tU822T4EzojmBMmjRJCYaVR765qZ2670VSGyOP4Qg4At0jUCjB+Otf/xoQqqj4MdjCUp0zxAOnPn369NHO86CDDlKtRBpDT4RzJ2owKEK+7csvvwy33XabanrwGYLGBx8iU6dODUOHDlXjN7anHz9+fJdhnhOM7htAK+84wWgObWvb7iq8ORz9aUegVQgUSjDYWfSUU05Ro0VG2wMGDNB5X64/8sgjOprD0PP0009XPOgo03SWnUwwAIIVIyxPxdht4403Vqdbd9xxRzjggAN0t1gIGTu2IpDTTiuBq2sw8m12TjCaw9cJRnP4+dOOQKsRKJRg/Pa3v9VlqZCJVVddNVx66aW68oEOlGkTVphsttlmAUNPfAQgYHo7wQCDd955J4wePVrtU3CExLJU3Cfj2XCZZZZRwsF0E0QrrdbHCUb+Tc8JRnMYO8FoDj9/2hFoNQKFEownnnhCR+IYLrKWHRfXLNN76aWXwn777Re4b4aeeBJEwNQakZsQ6jQ/GFYx+D6mkPg+NBUQCyNd+MVgyuToo4/W6RG7bs92dyZNJxjdoZPddXAmuA1GY5ha2/Ypksbw86ccgVYjUBjBwOPk7bffrkaK7HqIyp+17Yy6mSJhWgRfDrgKZ8+NNddc0wnG/9cOBC344QGVPUlYmoomY7vtttMdaHHYZKQhTYWyuD5FkgatxuOAMyEvgmHpWw7TEkyLX3nOysizMl/2nnrzRzrIBycYhqCfHYFyI1AYwUDIfvDBB7pxEVqL+eefX5ddIkQYob/33ns6FYCHQ/w50IFyr5ZQMiHUqRoMq058JxhiKIvXUww+8bjI9tT1BsPVCUa9yNUXH5wJeRAMqw+WI9pJmukxi1/tnAXBIF8YbFcLeAet1Z6Tz5GWE4wkIv7bESg3AoUQjDwhMSHU6QQDDPnWSgFd7VotvO0ZJxi1kGruPjgT8iAYkHL25+DMNGJyb5NGc50FwYAQsH8O52SwPFbW32Scyt/g5wSjEhX/3xEoLwKFEgwTuAZPUtgk7yWvW9zuziaEegPBAAPDiTM41YOVYWjPOsEwRPI5W1nlQTBeffVV3fUUmxy0WGuttVYYNWqUarasfOv9qiwIBvZV7PSLi/vkfiZzzDFHYGNDtG5p80c8Jxj1lqLHdwSKQ6BQgpHHZ5sQ6i0EIwsMTcA7wcgCze7TAGdClgSDNJmC+NWvfhXwf4IjNrQDrMq6+eabdUM8K9/uc1b9TrMEg/fiqRcX9kyHQjAIXGefkyuuuEJdkEMaahlv23NOMBRC/+MItAUCTjC+8x1VJzNfjZBrVAvQXWkjTDmsU8GAFR8VeCrF3fFxxx2no7I0Ara7dzR7nfzx3U4wmkWy5+fBmWB14S9/+Yu6xDdX4fV68rS6Bang2cMPP1w7cQgHXnBZ3s3Gd8RrpH41QzB4J9M17DGEC3t2AcaYG2d63INgsGmfbQOQRvPGc04weq5jftcRKBMCTjCcYKjAd4KRf7OkgyRkSTDocN9//31ddcVeJmxK9oc//CFgHH3yySdr506cRgw+myEYvJOVTmy6t++++6qRNu7tV1xxxfD3v/9dNzBcaqmlwnek/aUNTjDSIuXxHIFyIOAEwwmGE4wWtcWsCQbZpiNnR2I6b/ajwa6BZZwcBx98sO5MTLw0GgLiJUOjBIPv5GCFE9MgRxxxhO4xdM011ygB4l4jhIfn+F5fpposJf/tCJQXgcwJBqOzRoRHlhCRB2wwdtllFxVI2267rXq4REWLS/LZZptNR1D4i2AEhcV9EVMkuPZmigTB2UgHkBVm9v4XXnhB/WhgKLj55pvr5nNghiEemNHhJDFD3Z3HtFJW31W2dMCZkJUGg7RIE8+322+/Pf+qB1wc1U2ZMiVQ75mGsGmJeutYMwQDIvCnP/1J/bSwHUD//v1Vy4Kn2e9///uqYbG6oxlP8YdvdYKRAiiP4giUBIHMCMaZZ56pu54y/1sUwbCOkjMEA+dd/C4jwWBzsv33318t/+lwGpkjz6oOGW50THgCnSE7sjrByArdr9MBZ0IWBIO0OJhuuPXWW5VYLLzwwuHEE0/Uun/hhReGgQMHhuuvv16JoZXx17mp/asZgsH78GWDk7zLLrtMV7Pg7h9/Nxh9sscQZJWQlviQphOM2uXmMRyBsiCQGcEYO3as7omBECg6kAfcjA8ZMqTUGoxhw4apGrsWXq3ClL1hULWjwWAPmNVXX10N81yDUauE0t23csyKYNDZsvzzkksuUU0YbvXRWLDbLktUsXHALmOFFVZQMlIviW2UYIAG3wrBOPfccwMb8WGP8cc//lHP8847rxo4s4tyPVoM0nSCka6ueSxHoAwINE0w6CQ//vhj7ZCwXGdElXZEkicAjMIZvWHsNmjQoLDssstqZ1mGKZIjjzxSVdiLLLJIlxV9nlikTZvVCM8//7yW36abbhrWWGMNJxhpwUsRL2uCQXqsSoJMXHXVVWp/gc0DBAPtGB35mDFj1IU8HXO9msVmCAZwoM3E+dfnn3+uy1SvvvpqnTLB8RarW8hz3759U5MfJxgpKplHcQRKhEBTBAMtAQQDIzM6cgSYCdGiv5FR4pdffhlw6MMUSVkIBjuhQjBuueWS+Y41AABAAElEQVQWVRtj/1EGzCCFhhlz5E4wsq/BVs5ZaDDIHem9+OKLYejQoXpm6SflRnu86aab1NYBQ082vyNuKzUYhp59M+dXXnklHHrooep4i+WpaDc23nhj1UqkIT+k4RoMQ9bPjkD5EWiKYEybNk0JxrvvvqtGlOwXwqilyEBHiSBCq4JVPZ1l2WwwIBj33nuvagjWWWedwjGjvMANZ0hsMIc626dIsq/F1tlmQTBIi3SwNdpxxx3VoJL9aDDo5DpaAjx6YvzJtImR/3q0i81qMMgj8oAzB1490aiwQR/Lac8444yw5ZZbOsHIvqp5io5AKRBommCgikWIYMy17rrrphYWeX09goxRzmOPPRb22Wcf/V02gmGOtsDORpf1CP68sGM0jEbqzTffdCPPHECmbhKaJRjWYeO8avLkyeHAAw/UlVD9+vULfWXKAQdeTHVhn4GhJ6SxHpfc9unNEgzaoe2Nwjez6y/1/a677lJHW5deemn48Y9/nFq7Ym3bl6laCfnZESg3ApkQDBr+Oeeco6tI+F10Z4kww1MmHgT5XVaCYZ48y4AZ1ZRlqrvvvrsaefoqkuwbLuVMyIJg0Hl/+OGH4ayzzgp4AmUFCctBN9poIy2/0aNH6+qS5ZZbLlx55ZW6TJT31zNN0gzB4F04/Hr88cfDfPPNp0TjuuuuU++i5GGTTTZRHxksE09b/4nnUyTZ10tP0RHIC4FMCAaNHiGHcON3mvnUvD7IhFA7+MGAYBx77LGpBWyemEEK3Q9GXgh/lS51k9AswSAN0sLWAvfgd955p05Ros1YfPHFtWNn2fi4ceOUeDAtUc9UBOkTGiUYyACMvfHFgXbFtmVn2hKNBr4wTjvttMD0IN+RlvRY23YNxlfl438dgbIjkBnBMA1GmQjGzjvvXOopEtNggFlaIZtHhUJwQzB8L5I80P06TXAmZEEwSOfZZ58Nu+22m2oyVlllFdVU4FuCKRJWaJxwwgm62dnee++ty6Hr6cxJv1GCwXs+++wz3Wxt5MiRatPDte9+97uBpbQ4mGPqxup8Wo0nadBWnGBQOh4cgfIj4ASjQE+eTjDK30CyzCEdJCELgkFaLCvGdwmdLqulWJlBZ036eNFEw0EnPvfcc4cFF1yw7k9plGDwIvKHjQirppgqYUXXPPPMo9Ml5DUtqUhm2glGEg3/7QiUHwEnGE4wuqZoXIORb4PNkmDkm9OvUm+GYPSUP8OhXpLhBKMnVP2eI1A+BJxgOMFwgtGidmkdaxYaDMuypcn/lR223au8bs/WOmdBMCwPle9qJE+k5VMklUj6/45AeREoDcFICqJGhI9BbEIII0+3wTBUej6DGZi7BqNnnJq9a3U8S4LRbJ56ej4LgmHpWx2z/xs5W9t2G4xG0PNnHIHWI1A4wWBEwmHCFwjo7GwlSr1kw4RQbyAYfCtHJUaV/9eqVpaGE4xaSDV3H5wJvZFgNIfcV0+Dn2swskDS03AEWoNAKQgGAhfBYQHDNOskIRr22+73dDYh1OkEg+8EN86V+DSCGWk4weipZjV/j7IiOMFoDEtr267BaAw/f8oRaDUChRIMPICywyIe/vA6yBp59uaYa665dP0+y9r437QZacAxIdTpBAPssNIHsyTBACu2xcb3QNpgJMUJRlrEGovnBKMx3Owpa9tOMAwRPzsC5UagEIKBoOCAXFx88cXhwgsvVA0GWgw6SPZUYMndXnvtpTuhspEa8ZMdaXewmhDqZILBN86Q3WLHjh2ryxTxhkjgOksBcb7Ezrb8b74GusPLnnMNRk8IZXOP8iD0pMGAUJclzDbbbLq8lV1QqU+0zzT1Ka/8gx8Hy29Z4v3oo4+G1VdfXd3aMxhhp2QO3KKzB9H3vvc93Y+F/VnIt+U9jRzJ4husvEkr+U673tM1i8Oz/CauxU/es2vE8+AIlA2BQgkG6+PZ8Oi8887TETcCjdE3m26xxfOSSy6pzoK22WabujpLBGGnEwycLGHE+uqrr3Z5SqRygdlFF10UVltttbowQ1C5BiPf5mkdQzWCweZyl19+uWrsqL9l6Dj69OkTFlhggfCLX/xC65XtK5IvSt2nDn4ckGs8hLLfUFkJBmVIOVuA3DB4Iv9cp3xNM0tcDiNByThcJxDfDnverls6GtH/OAIlQqBQgvH+++/rXgp4AUWY4eFv6623DpMmTVLX44xK2E8El8eo/Gl4tQQvcWiUnUow7PumTp0adtllF51aWmyxxVRzwbTJ/PPPHw477DDtENLgRV20eE4w8m2Z4EyoJBjU/6222ipccsklNet3vjmcOXXqEgTj5z//uU5Zzny3uP/QYFDHIRgQ6S222EK9hJZFg0E50xYZJHEgs9CmcLDL7SeffKJEkv+Ji1t1pjuRd+yASxw8ofLbpkLR6hKfwD3ioO3CcysEo5ZcLK60/M29GYFSEAz2MWFDpBEjRuhungiOHXbYQbd3Zn+Tn/3sZ6rytI6wpwIjTqcSDL6NA+HC/hNsTIZQPfHEE8MGG2ygsCCIEFRug9FTLSnmHmVHqCQYTHUttdRSWueNSBeTw5nfevzxx6u78F133VUHAHR2ZQh//etfAxun/f73vw+rrrqq7rNSlikSkz/sSMz07z333KNEAAyZ1nn66ad1U7q11147DB8+XN2o33fffWH8+PFhzz33VLI0bdq0cP755+vu1NOnTw/XX3+9bm/PAGzeeecNp59+uu6Wi9t1iBYb2hGcZJShdnoekgiUhmDQcI444gjdYh3tw9ChQ5XZQzRcg/FVkRnBYFR0zTXXhH333Vc7pssuu0zPjGhsnjlZyLV+ky7CyTUYtZBq7n41gkFHwsZf2AkwYi1TYKRNfcIGytT7ReePegqOX3zxhZJoCAbanywJhrWHer+V5xjcoJmFBNx6661h0003Vc0DA6hhw4aFe++9NwwZMkTJJLZnaC6uvfbacNxxx4UjjzxStbgPPPBAgJCgsWDKk2nj+++/X7UVaCshodSXBx98MGy44YaBbe/r2ZW23u/y+I5AowiUhmCwXwIMf7311lPjxdtuu023mKbhIUBovGk6T2vknThFwrdxsAcF00gQsh/+8Ie69TWClg2kUGkjgNJgZZWGNJ1gGBr5ncGZkNRgQDDojKj/yy67rJabxcsvJ+lSvvvuu5VcrLjiitq5ke8yBKYUnn/+ee2c2eQtK4IB7nwjJKHeNgQuPM+BlgJtxPLLL6+DI8gZaUIgf/WrX+ngadtttw0XXHBBF8FAW3T00UeH/fbbT4nD4MGDw0ILLaTtHPKEXRUH33rWWWepdveYY45Rg1c2tkMDxrvrafdlKEvPQ2cjUAqCwRw0jJz5REZLH374odoS0OhoiDTMtOo/GlmnT5GADx0TAopv5UB7QQfFDpo/+clPtNamFTZg5gQj/4YOzoQkwUA7xxThJptsoh0IU1wWL/8c9fwGOjgIKxoyVnVZPen5qfzu8n4Opg0g10wlZGnkSdoYstpyeb6kHvsGnidAMJi+pNPHrgYNBIEpJiMYgwYN0tVzaDCY7mEgZQQDDQZaDmxLeB6bixtvvDEcddRRukKMaRFW4CED0GRyZioZOUB+PTgCpUFAGkXqIBU4SiOJMoKIUumjNIQoKry4+OKLxylTpkS7XytB4omQje+++2489NBDo3RuUUYMUZh6lJUkUTrLOOecc0aZo4wyl6np8kyaYHkQlWKUUWEUnxpR5i6jNNQ4efLkeNddd0URTFFWX0RZxRJlPjfKTo9RhIrmKe170uSFOPatYjcRRa0bX3/99SirYqJoHqLYTmgyYJE2kJ5sxx1FwxNHjRoV99lnH8Uf/ISIadofffRR13vTpGvfLKPCKKNVLQPZBjzKHHKUFQTx9ttvj7IkMMrOnVpm4rMkyjRNbpilyXM7xgFnDuoadU5U6VFGoVFIdZQRq14v03eJq/Ao8/vaVsqUL9ru+uuvH2VaIIrGM8oqtCgkKP7yl7+MormMMtUX33rrrSg7yqqcou0htwz/at/CPdEMqhwTewit58g5nuVemkA82rLYhsSDDz44yrRIlCXj+j9lTlpiO6Vtf//999ckZRoqyuqhKMvLoxAFrQNitxHF6D3KShmNIyQkisZS5ZkMxvSaEIwog4m46KKLqizg3XyjB0egTAgwIkgdrBJnTTCEbUXZwlmJgKy5jzKvGEWjERFwYmegnRmZTNPQLY+dSjAMB8qATh6ChEDq168fw6co/gr0f3BIS1wMVycYqZtCQxHBmaMawRBbIy1PKzeLW+RZ/ElE0YrFl156SetSspMuIl/UZ7B75ZVXlFhkTTAg0Ouss44ObhgEQKrp3CEG9bQlcCKPsspLiYKsjIu0LdosAzEGF2kIBgMjQncEY/To0UowGABQHk4wGmqW/lCOCJSGYMD2RV2sAkSMl7o6TFEzxocfflgbUJpGbg2tkwkG9SEp4N977z0dKUEwZGmhamsMhzR1h7gEJxhp0Go8jpVZdwQDDRfByqPxN2XzpBGMl19+OXX7y+bN1VOxOo0GA81F1gRDph7jSSedpAMbtErLLLOMavHQ2FFmHGnLhs4eLcoo0TKiRd17772VQKJBrSQYaF+qaTCcYFSvB361fRAoDcGQVSSq6qQBI0DE8ZCOyGUeOMoyVW3YaRi6CaFOJhgQLUZVaDA4mOpBXcpUU714UVVNaDrByLfhgjOHE4zGcLa2nQfBIEe0q48//linUsVAU6dskUtifB7F94ZOcabRZpAO2grK+U2Z4t1NphtNE8QUDoRDjDn1fUx3yioQJRhM9fCMTZE4wWisnvhT5UGgdASDuWlxIqWjBzpMVP+wfoSLazC+qjgIIQQT0yPMG4vfkLjmmmtGMepUQcb/afEiReISnGAoDLn9AWcOJxiNQQx2DDLyIhiWK8jBU089FcVTbhTDcyUa6667rtoi0d4oP2SRtRt7zs7kkWkNs7PB1gptyDPPPBPFADQussgiUQw4lcy8/fbb8dhjj1V7DVm22kUw0GhU2mBw7dxzz9XXYINhUyR33HGH5iXNAMzy6GdHoBUIFLqKBJfgWNCz7EqMOtX19YABA9SxFg5qsIgWYy5d5y02GpChmqtJiCMNv6M9ebI5HKsPWHHD8dBDD4UnnnhCf2+//fZqVc6qkjR4YW1s8dwPBmjkF8CZIJ2THkIStRxpA6wYEEM+XTFl5ZFfTtKlLDZQM+1FQr7SrkxK94b6YvF+2naem51ZGfEetjJgCShtTQxGdWUbq0PwP4OPClZeJVeZ2LNCGgJLfFl5QxosQ8Z/Bf4qSIdVIM8995z6uuB5PKUKOdCVMfjKeEBWkQwVP0CsoGMbBVaR3HDDDbrShGfFgFSXqbJslfydeeaZ6m+DPPsqkvrqlMfOGQFpFKmDVGAdQcDws1pFIg1GV5CwAkI2KFJjT+Y/ZWlXFN8OURqqsvM02gs+xPLYiVMkfBvz9CJsdJ6YVTfgxKobVt8wrfTiiy8qBsRNGyyuazDSItZYPHDmcA1G4/i1QoNB7qxNoInAiBINoZAKlU9oNtBGIANtysTKlvyxSm2NNdaICy64YBSSEcV3hcYnXaY0WQGGbRlTJbLMNsry07jWWmtFVohQN7BBY4oGzQaB94gzLtVOMp1CYIWMDMxURoonUM1vWhmpCfgfR6AFCBSqwYDNs8ETa70JsHg80vXt21d9OTCqkymSLlbOiKFWEMw6VoPBt4GRkAjFTYzvdF8D8BooTrY233xzHXESLw1WhqXFdw2GIZLPGZwJeWgwSFs6uS5tFOXfrLahGQ2GfSvf211dTBOH5y3YN+apwbB3ceZ95J02J8vL1U8JG7/JACvIlIduuMYmdcgs4pk2g/tieK0ePfFr0lfaJy79LT0hJeGdd97RODhYE6Khfi1wqIXjPCEheg+/QNyjXHGuh4dQ7rMpJHUI+SkGqOo+3PYpSebffzsCRSNQCMGwj6bhyAghsLcArn8RiDRWHGuZ+t/ipj2bEOpET56GAcIFvBBkYIiTMvBiWqSRYILPCUYj6KV/BpwJeREM0rVgHZ7938i5EYLBN9r38U7yQf3kbCEZx67T9vGe2VPgOep7qwgGebEy40yHjvtvpiQgCEzrMiWJB2I6fvsG+6bkt/B8tevJOP7bEeg0BAolGLXAbKRR8gxCqJMJRne4NYIXadlzTjC6Qzab6+BMsA44SxsMCCdz/xB2RtKMctkNtZlOrR6CYe2OtschBo56Ji+MzM02wOKhFSAOWEAsIMh00BavGuL2bCsJhuWDd4MlpP7JJ59U2zG8chLwnIttxI9//GMl+XxDNdyT10ivWrA4dt/+J25315JxqqXp1xyBohAoBcGwhlMJQiMNh7R6A8GohlkjeIE5afGsE4zKGpjt/1ZmWRMM0hX7Gd3/gh1GUcuLwyjdzZOO28q33q+pl2CI/YCq8l944YVw8803dxlGYhQp9ghdHSRkiPyKUz01glx44YWDOLYKK6ywwje0Hck88x207SIIBvmw8iMPkLmJEyfqLqiQOr5BvOoGWZKqBqDEhTg12iaT3+2/HYF2RaAUBCNL8EwI9UYNRqM4gpkTjEbRS/+cdVBZEgw6Ozp2cUEddtppJ9UKkCM2KBPX2YFdPImDdqDekJZg8F3YFUAsrrjiiiBGkYEVYnxn//799RrkgXwQj+3J2Tl0uuwpwjU64qWXXjqceuqpgW3MqYvVOmZr20URDPCzMuQ3dhHsjsoqEfHcqZoa7MZk+wMlVGgy+LZGsCd9D45AuyPgBENsPrBdQBggCLoTbo0WNAKJwzoVRphsWiTeSXXulk2OGu0AGs1T5XPkzwlGJSrZ/2+dk9WFZqdIrG6RDhuSyaoDtV9CjS++FnR0LasftP410smlIRiWB0b0o0aNUlLDNfJgBIPlt+wsyv/isEqnE8S3jU7jiGO4wLJrSBJGyhMmTNBND0mjkmRwjbZSJMGwWmH5I98YW7Px4PXXX68EaqWVVtKlpCyxx6aM0N20iaXnZ0egExFwguEEQzsgJxj5N286JULWBEM2DQzizEk7OHbwhMTSEdPh77HHHvqbDq7eUA/BYIUD/jxkeXgQN95BnD8F2dtDdxSFYKDBYGoETctQ8fEA4UHjIi60wymnnBJuuummIJsm6g6jaDHIf2Wey0QwwNLKkzMaG3ZFFUdYuhs0q0N23XVX9ZnBChLal5OMemugx297BKRxpA7S6DPxg5H6hQ1EtDy2ix8M8isdTgNfmt0j5IHgfjCyw7RaSuDMIaPeLi+Ptpsqm51JB6yPWXlUS6PyGnHZjEwMDXW3zb322kt9KrB5IH4U7J2Vz6X5P+1eJLwDr5XsFExenn32WfXPQB8sUx/qvZL3SSeseeI6e+awBwf7f4yWTbu4hltuvFmSXrU2wXUxDs3dk2cabJJxyBcBHxe4+WazRvzTgB+bpj3++ON1b5qWTN9/OwLtikCmGgxpWFVHHq1mYYwQscGQ3Qw1P3jEY906a8UZTWBhL069VBWLCrPIKRLU2iKgCpunlYqrxcMIizl0RrzTZW4cdfVqq62mmImgVMwY0bI23zBj+WEe00qtri+tep9hnbUGQ1zD63JJDDpll84gHXy48cYbw5ZbbhnQHlC/eXfllEOt706jwaiWhpAMrUficEptK0yDwTSKkAk1jsSzpWxPrnUMY0/ZdyPwPjxZyoZjVfPLN9BWyjBFUvndybKl/TDVg48ftDayw7F+33bbbad2Gjzr2oxKBP3/TkQgM4LB2vANNthA1b+Vqs1WA4cQkv1MwpAhQ1RQlZVg0BmMHDlS89jIHHkWuJpgNIKBytoJRhbIfjMNwzoLgkFaHDhlwo00KzXoyMaMGaPTFOIVMoiXSFXb9+nTpyESWy/BsDwxNbLnnnuq+3qMN41gQAxYznnLLbeEJZdcUjthbDOYHiE+BEm8ZKpDq2+i99WURFkJBvm18uU3DrCYJjrjjDOCbHim5ImVMsOHDw9Mn9DeK/2D8JwHR6CTEMiMYCDYzKCsDACJK19dMoZAKiPBgADRmSNwyhLoGMiPbDMd8FBIB4XWxzUY2ZSQdUBZEQzqNs6f0ASwAmPllVfWFRs4g4K40omz/wWaKN5dL4mtl2CAEnnqjmCw0gJSzV4b5A0PvthmoG2RqR1dXsson5Uo1bQtfEOZCYbVEvJJ/lkxw54j2KZge0LeaVOsMuGMQ0HKhKPa91p6fnYE2hWBpgkGKk3c4jIFwUgJBzr1CrKswaOBf/TRR0F2LlSV/qBBg4Jsl1yaKRKW6DG6WWCBBfQgv2UIbKpEJ8DSuk033TTIfgpOMDIsGCvnrAgG6aFtGjFihHqY3GijjXTlCMtA8cmAm+kTTjhBNXl0bvVqFrMmGKwWkR1CAxsZViMYdLiDBw/u2jqgEnq+tx0IBvm2sia/GOFOnjxZV5qg2cAnCBobDrHV0M/0KZPK0vb/OwGBpggGO3hCMFh6hnCgY7KGVTQ4CHHmP3HniwajLAQDF8MQDNTEYMZRlgBmkAzsU5xgZF8q1jayIBjkjvRwWIWtEZ03Pi9YRSJbeasdBvYy2DYcf/zx+jH1jpKzJhgzZswIYtSqO4AynTN+/PjAkk6WdzLFQ34hRmhj+LbK/HKtXQiG1R4rc9qVbGKmbsYZ+GDHBCFEm8Guq0yXQDKKHpxZvv3sCGSBQFMEAyMuCMbHH3+sRoGMSnD/W4ZGwrwngot53bJNkUAwcDOMzYrstKg+ACqFaRaFW28aLG/EMI29YXyKpF70ase3ziYLgkFa+GBAW4HdAmWGMSdePEmfdshv6hc2EPzmmXrqWdYEg63LsUlgKSf+L3BQtdZaa6nRJ75hZBWJ+oiBFJHXSjnCtXYjGNQK8k2gXNg0TVbKqEaDMkLze9BBB6n8dJ8ZCpP/6SAEmiYYzKnScMpi5GlC6JFHHtH19uStbATDHG2hLjYjz3oEf9b1D8x4P7u0oraFnPkqkqxRnrmjoV426miL8uKAVGCvcMQRR6jWCcdOq6yyiqrkscNAYzBgwAC1cUju5pn2y7ImGJ988kmQbcd1mgQtGZuEsVmYbDuutiJoNc477zz1owGRqJzSsbZdxlUktTAl7wTOTN9i2Mp3M73MRmk77rij4oHGlbaINrhImVDre/y+I5AKAanwqYM0+pp+MERwpk4vr4islRdDsjjPPPNEmYeOMiKKYlAWZR40igfBKJoXXUsvI6ooQlrX8OObgLzzjVkG0iNdMfhSPwf4CmBtvAiVKM6Rul3zn2UeaqVl3yx7kURxMR1lGW+UPRXixRdfHGV76iiun+Ojjz4axXgvynxylHlkXfOfF2a18tuu98GZo1k/GKRBHReNU5ROOkpHFKVzjmJIGMWDZhQ7mohfDa7LCDnK5lwN1bO0fjCS5UFdF6KqfjB4P34wxOBao3Dvqaeeiv369YsyNah5I/+LLrpolNF7FG2L1i3DKZkuv+27X3311SjOvPQZzkJK1KeGuEaPsjw9Uo/FUDmKAWyUqQlte+DVXbqV78nzf/JAwOfJQw89FGUpsfrMEMKlv7kmREzznIc8yvPbPG1HoBIBGHXqYA0cIUbDfUA6cZkWieKBL06ZMqVLeFpDLuqMAJcpCHU81C4EoyiseK8JMne0lbopNBTRyrhZgsHLSQvCt+6660aZv4+iuYiisdB8QZxlGi7KKDiKy3Al1sSnk60nNEIweA8Eg/zwfrF9UoLBdb5bRu9Rpkmi2FtEmbZRkgDZIJ6M6vW7qI/Vgn1DOxMMvovvsG8RbUwULaaSepnOVdKFszGZdla8wIy4HhyBdkQgkykSaQCBdfcYLfG7UrWZSpWSUSQpBM2Db3aWHlAwQx3ru6mmx6yRmOBMkA5Uj0anSEiDtHBohZEgtg3snnraaaep8SBTEbfddpvaODCvzxJQHKgRKOe0oZEpEvLFFBt77LBEE7usk08+WX102PdjhPrzn/9cjT1ZZsvUCEu2cQzG1AChWj55HvnSjlMklZgbFlxn07S7775b7VNYxcXScGygmPoSj6eKiRuAViLo/7cDAk4wCvbk6ZudtUMzySaP1qlkRTAwEqSDJl3zV0LHzP+soOIeIXmvni9phGCQvoy61fCbDc8wLsX+g1USFsgfPiLETbiuWsKzLnFqDUx4rlMIRhILfot2SW2gxo0bp/YZ4MMKm0MOOURtUlhtRtmCUTXyZen52REoEwJOMJxgaIeE0HINRr5Nkw6SkAXByDenX6XeKMGolTdwqNZJdnfd0utEgsG3Wb3gzKZxGMKy0gZjUFbWiD2ULuPFMJbgBqAKg/9pAwScYDjBcILRooZqHUmWBMPS5BMqO227V3k97ec2QzDs3dXyxbXkfctPrXzyTKdpMOzbDRMwwP07q+BY0isbpan2hykwpkz69++v/0MyKpfxJtPy345AGRAoJcFICp9aQqcSRBNCboNRiUz3/4MZOLsGo3uMsrhj9TpLgpFFvrpLoxmC0V2azVy3tt0JNhjd4ZCsI9iyyEou9WPCdBj2LCztZU+T7373u5qET5l0h6RfLwMCpSIYJkAYpViApdeam7W4nC2NTiQYfJsdEIJqIxi7D4bcJ14tksYzTjCStSif3+BMcILRGL7g18kaDEPF6gn/Y0cjy8TVZ8Z0cQuPvQp+fQ4++GB1BY9s9CkTQ87PZUOgNASDRoXg5YzBEwedHkZiHGmDCaFOIhh8k+GTJAuVoxeLA1b8tkC8amTE7hPXCYahkd/ZysQJRmMYg19vIBiGjrVLDD5xCY9jruSmaRiAsl8QBqC0bxtQ2PN+dgSKRqAUBIOGhNDF6vztt98O4tRJvRHScNgh8ic/+UlqLYYJoU4hGIYNghV8WIL46aefKunCA6C5F7bvtpUFLAU063zUqT0JH551gpF/UwRnghOMxrC2Om5TJI899pjuFIvXWeo4K1E42P0Xg0hWzyBDWMFiHTBvTpL0xnLSuqesztD+2TTtyiuv1E3TWNrKpmm2BJlvJbg2o3Vl42+qjUDhBMOEBkvWrrvuunDRRRfpZk00KPZWYNOtCy64QDtS4tYSDpZepxAMcIA0sIU61uW4gGbvF3ZiPeWUU9QVNN/MgdU5cXAfjWqVOfStt95aXTNDNsCuGn6Gq9tg1G4wzcQAZ0JPBIM6b+XRzLuyeJY6Qyd29dVXq68K6iIddVEBXMgDBANbBAgG254nCQZ5pt5DMCAd7U4wwNrqDb8ZXLCPEQagtFcGGJtsskk47LDDgjhV04GYG4CClIcyIFAowaDhcNAxsskWjoLYdZBGg3Bgd8XtttsunHTSSd12jpUgmhBqd4Jh2KCxYOdV9mhgx0zw4Z54T9WlbBtuuKFOJ+H3gM3dEDTEYZM3NB4IW3aoxN8GIzmerSQZds0JRmVtyvZ/cCZUIxh0krQBCAadaGUZZZuTdKmx3TtEVtzFK8Eg30UTDDDEFoF9fFhhAcHYYostVF6Yxs40GJ1CMKy0rJ0yfSzeTHUwdtVVV2k7X2655dQuY+ONN+7SalZOoVo6nEmLo8jyTObHf3cmAoUTDJzy3H///doJspvn3HPPHXbddVfd/IfKj+pv4YUXrtoxVisSGg0CulMIxgcffBDOPvts3doaLMALD5CLLbaYkg42uDKBg9CdOnWqkg8sze+5554g+1DoVvWXXXZZWGGFFaoKFRNcTjCq1ajsroEzoZJg4AWXTpIVA2UgFvbFffv2VYKBWp7fVk/sflFnNBgs2TSCwY6xDEqYHoFkJAkGhK2dp0gqMbY6xJlpUNl/RTeaxJsrU6aDBw9W8gUW1KVq9lc8y4Gc7ImEVL7b/3cE6kWgMIJhldw6UHZjRWPB7op0qLPMMku936LxreG0O8HgY/gWVKL33ntvkE2QAmSCrZ5lwzYlEeeff75u+Y724o477tD5WITKkCFDdNvrCRMmBNlQTZ31jBgxIuy3335dQiUJLu/hOScYSVSy/w3OBCMY7IZKGWK8Jxt+afmWaUQ5fvx4Jfi4rabDgsgWmT/Dz9oEHSwaDKYB0VbY9AgEA1kC6bApkmRHWiYS12gtszbL9OkTTzyhMlP2htLyGThwoGoyZeNCtdWqJBkQCzDEpqOvEEe0nR4cgTwQKJRg8EHspzBs2DB1LMOonLlF1MU0AoRZvcKAhtcJGgwrbL4HK3IECXYqhx9+uNphMEViBAN7i0svvVS3fpcdZJVc7L777uGGG24I+++/vwpZtmEfO3Zs1yg0iasJKycYhno+Z3AmGMHAUA8SeOqpp2qdTZaJ5cCesf9bebaVXLRDjiLzUvndtHE6RlZRDBo0SMkExMIOIxhJDQb4VsO4Mu12+d/KAyxmzJihMgA5gKMuZKnsIq0DNjMAhWjwDAMStJtozJiWXn755fV6keSxXTD3fNaHQOEEAzUnaj2MGFkxgkHZQgst1HCFpwF1EsFIFic2GNhYsC4+STDee+89JWa4F8bQa8yYMUrS2EAJN8N0FNiyyJb1Ve0wwAzB6wQjiXY+v8EagkEdhTS+8MILuukXZUg5QSaZBuM38TgTl+daHSrfWZbO2QYe2BchM+hM0WBg2MkBuaBTRYPBEnebIuk0gkF9SJYRGjHaPAMJbDTAgqk3NsSbb775ulbiMagbPnx4YGM1jGVlN1dt/04wWt3COv99hRIMBCnqfzpB7ApQ7WG0xFwiDaeRCs9znUYw+CY6Gzz7VSMYjF6OP/543aESVTsChtU3YIs9CyMaLM2ZS0fwkl6ys7D/nWDk3+CtflKe1H8ORpR//vOfVW1NJ4H6mjLjuhGOJMkgjVYEqxeteFeadxhBQC5wMP2BFgMtBVOqHEYuIBzcg1zYqgp7Ps272i2OlRX15cUXX1QDcFacUb8gYfjMWHvttdWg/vTTTw/YZIEhG6qxs63ZuTUic9sNK89v6xAolGCwymHKlCm6lTQrH9Zaay21WHeCMXMFQHhwvPHGG1UJBkZv2FiwigSCgQBh3hw1KPYYdFasNmE1AELYhJG9xf53gmGI5He2sjSCYQSCMoJYcNAWIBdoOOggitRi5IdEYykbSUDdD3FAQwGR4IA8QyyMXFTaX9izjb25/E9RtwiQUds0DQNiplBZDYQWE2xYlUf9gkz06dNHZQf2WQQnGAqD/8kIgUIJBoITwyRG2SxVXXXVVXWKZP7553cNRqKArVPqjmAwvXTCCSeEiRMnKsHAjgWNhWkwIHIYwjFqQegaobBX2P9OMAyR/M5WlhAMDggG5QOZQOhDNPjNwT2bKqHTIPB8bw6QBDpBDkgG2gsO6jVTIhANfnNAPiAhxOM5gp07GUNrz9QlVpWh0Zw2bZrWJfCAxBoWYLTuuuuq7Jhzzjm7rncyPv5trUOgMILBJ9IQnnrqqbDzzjuH119/PSyzzDJq9IbajnsIBkI9QoHnOnGKhO/qjmCwEgc/Gdhe9BWrcBxwsXQPW429995b8WMaitEMWJJWElP73wmGVrdc/4A1gToKwYBAoKWAZCQPIxeV2gt7PtdMljhx6i2HEQw6TKZB6CghFZANfnNwHRlCXKvvdi7xJ2aSNasn1B9kK/ZZGIASwMDaPL/RbmDsyQo+Anh5cASyQKAwgkEF56DyY4SEj30c+2B0xHwhgoOKbkfajyXN3kYwmLdnHhXDLaaXwG+PPfYIkydP1ikVRiZcYwUK+FQKEK4haJxgpK1ljccDa4LVU5sqMaIB2bBpEdNyEDf5XG/pJJMo2/fbt5tcgEAgK5JEw35zJn7ySKbZ6b+Rg9QrpkjwkMzAAyy4boH/0fqwco8VTdiweHAEskKgcIKBcSeq/WOOOUYr/o9+9CM1WMQgicrPqKQenxgmuDvBD4YVsnUw2FpAxtBMLLHEEqq12GCDDVSIPPzww2pvgRtxhMVRRx0Vxo0bp9j269dP466zzjpOMAzUAs/WWVpdNSJBZ2BaDdNwWGdgz5Dt5O8CP6Plr0YeWIBg8D9nSEblAbmwOPacnS2NTj5b3cIT8G233aYDDaahCcn6Yxhi5IkWFFstcPPgCGSBQGEEwzKPAGWp3kEHHaSe+ajcOMyhwsOmWWaFq2saRZqKbw2rUwkGHgxtmSpqz/XWW087Jbygjh49OlxzzTVq7IYdC450mMsHQ5aoYuAFPpWC1q65BsNqZb5n8CbYmTaQJBT85l7lkW+u2it1kwXUZeskucaRnBaxum7n9vrKxnNL3YGwPv3006oVfuaZZ1TLY3XNUjb8MJDdaaedlGQwteTBEcgCgcIJBg2BuWcIAR3kyy+/rP/TEFjHTaWnIyUeoZagIB7PdiLBwAaDaQ4MY221CN49rUPCngXNxXPPPafCBUHBHgWsKmGpWnf4gRm4OsFQiFryx+ozL+O31Vv7nTzXqvMtyXDJXgI+1jkaPpwhGNWulyz7uWcHfFjWjo8LlqvyP9oxC5UYcR2tKIbg2MB5cASyQKBwgsFHUPmZd8YeA0cxuL7FrgCbDPw5sLzKhEitjyatTiMYhhFzqWgvcJQDNhhyQjT4ZgIY4mAHt+EIF6abwA/BYQJFI1b84XnuO8GoACbnf63c7DX2P+fkb+4n67/ds+d6w9m+376dc7JOV/42TOw5+783nMEGGYgjLQw72acEt+p4AmYFCVpNyAZxOGyKDo0xy9oZkHhwBLJAoBQEgw8xwUEnyfIqKj6qTrMMT/ux1rg6SYNh3863sboAgcBIzazkk/f5TRwOLOmxqq8VSBdB7ASjFlL53Le6n0y92rXkff89MwLViES1azM/1bn/UX+YIoFMIEuRqxwQDJy6cTBgwWbLyAcEBHmBs0MPjkAWCJSGYPAx1tE182GkQYPqRIKRBpdKDCv/r5aGxXGCUQ2d1l6jLDw0jkBvJhX1ooac5ICIQD44c5g/jHrT8/iOQCUCpSIYZK6agK1HaPB8JxOMJD7d4ZImTrIiEJ+0nGAkUan923CrHdNjOALFIJCUBd3loDs50l18v+4IpEWgdAQjbca7i0eD6mSC0d13N3PdOkonGN2jmBTU/LaDaTwX0N3j5nfKiUCyPlfm0OtzJSL+f6MIOMEQ98LMO1Zb2tYoqMnnrCMyQyqWk7LSA78VOBU77rjjlBBhU1FUII+uwZgZfRPAVn6QVrtGWfE/ZYodDD4X8gr2zrzSb/d0vTNs9xLMNv/eXnrGs9XtxQmGE4wu25fersEwMsHZCAVkwv5nOTXGs+wZwv4v+BlZccUVdSUP8ZshiS4YexaM9d5ttSCtN38ev3kEvM00j6GlkFd7cYJRMME49thjtQNrpnOyStLI2RopFaw3EwwjEXbG6A0ygRU+HhBZ/vvss8/qVtjTp08P7P+y5JJL6jJAlgPzXKONlGc5qoXurleL2xuvJTFP/gaLyv97Iz6d+M3WJuyc/MZq15L3e/vvyjaR/D/5OyucMiUYuJllJMd0QxHBKhd5YBXJLrvsovnZdtttdSM1vNXNPvvs6ikUJ16s+2YHxqKmSPbff/+uKZI8CjdtGYAbBAePquxhMmPGDHU3vtpqq6lX0FlnnVUx+8EPfjATZkwP8Bx5LzL/ab+zp3hMd+BWGdf1fP+LL74Y8H6Ib5a33347/O1vf1PNBaSDQF3aYYcdwvjx47vqe70YgDuHaUus/ibP9pt3tpqE2rvr/S7ymkcgP5YnS9+mNvk/WQ+TeU7+tuf83J4IUP60Fw6C1Yfk2X5zv5VtJvnestQ58pTMF3iYzAafam0my7xnRjDOOuuswL4YFHwZCAY2DhAMwC0rwTjggAPU0x6YtbIhULEsWOWjUkEwcMuOky72M+ktBAMM0EhAFvABgK8AyIY5JDKsOFNO1O95551X98wZOnRoQ3XecKfs+W3/J9/F7+T15O/KeL3l/6TwAw+WVZrAtDNxrD1ZfDv3Fpw68Tut/jMYsN+V35m8nvxdGa83/Z+s+4Zdsq1Ye+Fsce3cLE6ZEAwa+amnnhoGDhzYbH4yeZ6K9eSTT4a99tpLK2IZCcaDDz6obtCHDRuma9CzKtBmAMQV+fHHHx/ef//9sNlmm4XVV1+9V2gwqC9oJiiTww47TKdDrOOnXJKCysoJF+w/+9nPwlJLLaX37Xoa/C093oFTuXfeeUdJDWlwzd5t8SrPad6RRRx7bz3flsV7e0rD8kLecMLHVuPsBkrA2BbyRxyOSiHaU7p+r9wIWF2kbeAYDGP5ZDvhfvL/or7G8mn1tKh8VHsvecLHSZ8+ffQ2bcWOyvZChCy+oWmCQQdJYS+++OJhjjnm0BFFFhmrBlA91/BUh0tt1PuDBg0Kyy67rHaWRU+R0JkceeSRusMhu8SiaqdhlCFAFHHRzqZom2yySRgwYECvIBhgj2DAiPP+++/XVT64Wa4UWNRrDjq0bbbZRu0v8JZKqKfOW3ljLHrLLbeEESNGKMGhQzQBpYkW/IfRDt9l2oCCs6OvBx/yBIa05X333Tfsuuuuih9lYSTDBKeRDB6up4zK8K2eh68QoMztoK8ZNWqUbinBFC1lmmwzyd+txo93I0MJ1MMy1TfaC+2ZgdGYMWOUmPM/bYa2Yu2GPPO/hWa/oSmCwZ4hEAy2EUfoktkiC9hA4UxBf/bZZ0p60GCUjWCwARH2H7bDaTLvRfymIoEZtgYQH/YwWWONNXoNwQBzGiEaBfZygQRiewEuVqf5TYfFCIClxiwz5plkg6xVdqTFwXOQYKZlRssmf9gBYROUfF+ttPK+j3Er34u9EgLIcMj7vT2lDz5gBxFG5mAHw1JvE5bIIFs6TJ7Jv5OMnhAt/z1rL5T7a6+9plPfGKRT/rSbMtRLULQ+hzqKXC9LmyFvaGiRbYssskg455xzdEM7DNnBj/Zih7UXzgS+pZnQFMGYNm2aEgwa+8477xyWX355NYSzzDWTsUaeTVY0rP7Z6AcAyzhFwo6oCEemcSjoogMVCaJ40kkn6aZIvckGw7BHgCEkUMGed955AbsiyAPXrG6BE2T18ssvDyussIJer6cRkg6HEYyLLroonHbaaTq9SEdJQ7d3Wb6KOmO0DZk6+eSTdSkuGp6iA7IF4nPiiSfqVBYbIQ4fPlyzxZSJ7V1kRIPy47AysnPR3+HvT4+AtRdIJNO4bMjGMnHkOoMg6mXRfQ55tD4HrTS2bAsttFDheaO+I2swWL/iiivC3HPPreYMLK/nOiQt2WbAkfbCmWebbS9NEwxWQpDRs88+O2y00UYqjMlgkYGKiJEnqlN+l5FgPPLII+HAAw8MxxxzTJFQzfRujDz33HNPJRq9jWBQh6krGHdOmTJF1bCoYxmFIMC4T4CwYp8yadKkLu1TPY3QhCXpsWIFgjF27Niw9dZba5q8ryyBVUN9+/YN1157bVhsscXKki0dxdJ20KCCG1pUygDBjrBEM4jgNE2GCUw+oJ6yKs0H9+KMGNm29gnBQK6zUdspp5wSdtxxR22bRREMKxoGIch08ka7+cUvfhGWXnrpwvMGfsgvZNpQMUjHBoOBDIMjApoW2oodtBXT/FlbsbN9az3nzAgGahcIBhWhSIJhAnzq1KmqVSkrwTBPnuYHo5lCrKfAq8UFM95vy1Tx89CbCAZ1FgFBh3/jjTeq4GKXyWWWWSb069cv3HTTTTrdBnY00EMPPVSNQcGtXsFm9ZN6iebvggsuCGeccUbYYostlGDQQZKfIuuD1ZHZZpstLLjgguHqq68OSyyxRCmEJdiwyokVWBAMpvJYWo1QhGDYgcCEDNocc1YjMsPGz61BgPaSbDNGMGifLCwYPHiw3q+3HWaZe/KH/ECmo8nHNmjy5MlKMLhXVN4MOwgG075ofrCTxJAdWwz6abMDhGhAzGlHEHPybPluRhZlTjAQnEUSDCoOeWgHPxjuKjzLZt5YWnRYTFGhdmfUgSYOLQYGrkwXoVKcMGFCOP/883XZKsbM2E2gmm1EePAM9ZP3QmguvPDCbxAM4jTTqBtD4ptPMRJLEoxGvvebqTZ+hfeDG1N5EIzHH388rL/++jonD5Ew3zbm38ZUv8kRWRlwbRyB3vckZc5Bm+Gg7NESQDDQYOy0006FE19KBYJhfY4RDAYo1FfrqIsoPbBLEgzaNG0HggGRsDZjxJxrtCXyTFuxo9G8Z04wALRIgmFCqLdu195IRQAzKlJv8+SJwEIw/OEPf9D5yXHjxqkQW2edddQuAk+d1GemSiAek2RaZM0111QiQidmuNWDOc8kCQYajDPPPHMmDUYj6daTh7Rx24FgrL322mH77bfXqRFWjJFnygaBaVoMG5E1KyzT4ubxskOAtsBBO00SDAykjWBwv+hOnPyZ1hwtQRk0GJQC8itJMGgje++9t2pX0PChpaS9oMngTFvhOn24tRfOjQYnGAV68nQNRqPVtrnnEEg0PDQXrBTBDmLixInauLCvYBkcBlrEI3DGev3cc89VB1s2rVWvULP0EJQcTJFAajAmTU6REK+ZRt0cOl8/XXaC8dhjjynhYxk6ZIL8ciBEIRiofRGWRjCsvMqA7dco+6+eEKAtcBjBMCNPJxg9ofb1vUqCAYkYKrYY2IfQZiAYtBfTZKC9oM3QVjiMZHydYn2/nGA4wdAGTEXqDRoMhBWNDoGFsEJ7cMMNN2iHxEj46KOP1pUT1smDC78hBExpsNxrnnnm6cKsnuZGOgQnGPWg9nVcKzubIoFgMJW11VZb6fwxwtIEJoLUCcbX2LXrLytzazNOMOoryUqCgaaCKSYcBGJzwXQOpByCAdFIajCcYFTB2iqkT5FUAaebS2DWGwgG38mB5uK3v/2tToPcfvvt2sBodPi+oJEZHmBiwa7Z/42cSYNgwhLCggaD6RfXYNRGFPwQmEmCgbfZLbfcUsmEEQwEphEM7DBcg1Eb27LGsDJPajB222039SHjUyS1S60awcAQ1QgG0zlJgmEaDKZIOo5gmAAGtqRwrw3j1zGsQvYGgpHE62sE6seOdDqdYFi9QFChqRktzq0eFNfgc801ly7NPfjgg7WTMiyq1T/Du9q9JP7d/bbnsyYYlm7lexvNp6WD4MnCyDOr/JFONYLBiie0FYzGIBkmMLnmBMNKsz3PVuZ5EYxk3Wy0vZAG+WsHGww0GBjG9u/fX9uMEQwGVhwdSzAQHBSSBRhUI8aiViE7nWDwnXRUyQYCdjQSM9AxLGudSYPnOnWKxLCifrFHDb5HONN5YgfDMkfmIwmNCplaGHPfyipLgkG7Ib1kvu09jEAaaUP2LVkQDPKSbNeWdqP11AmGIdg7ztQf6xuo5zZFgvavWQ0GaSedHDbT5zRLMMiLHbQN2m4yGA7Ja/yuFjcZB+ySRp4QDJb2VmowjJQbwchq5VVpbDAoIPZmAEhAw9CEj603WEH0BoJB46ACgZcFflvlsGu1zoZ5JxIM8OFgZ1RGGCNHjvw/9u731baq6gP4+it8U3qvom8Fo0ww0VDMFHyjgi8KFSILKSg1i5D7iCKFQb1QUCh/olga/fCFmsVFglBRNLQIAm8gSflPzGd8xuM4bM9zfqy19zp3rb3PmbDO3mevueaac8wxx/zOMccYs/v73/+egaPuuOOODNRTfLZIx/1otsx9dJbGAhjKIzyc/qqN1Y+Ek/EDNPm+XVj1rftYAGM3PrV1MYTm2qedi1skTvxd1GDQYliJ2VNm6Hmkwejb2/PMV31ufhgbYGyfc2pyHUoJdVwWYFT78HWNhZ3GrPuu7WmnvIt5PLMTwNhPg1FziDpVvRbL7ft9coCBwBhHmNXf/OY3CTIIxssuu2ypWAPVYZsMMLRRrIa33norw1pjhkqE6mc/+9lUFcvXhzkq36YBDIMLb4nB/4c//CG9Q0xOBhdPEBNT0a4PnYrGy36iszQGwNA2gNw2z8MPP5yRDQkSQlIwMJH6GD8KFDZ0Iq/2jQEwrDQFxMKvi0AHCLjwwgsTBBT/1Xt3+5RPu48Axm4U2rzfq8/HBhjKFW6cOyn5YNwwGL788su3gHpfaiprGYBR8smnRYJzoIBxWgYh+ksm1f3//ve/n6iS+4C08b5b8uyUAAMxe6eobAtCtqhwi4PEWpyn0SJWQIvgQy1Ckba637dA+UPYtnA5amHs1sKqtcW+aQsr/RbHhuc994ekqkOcitkiSFKLPfYWgUXaI4880oKZ2ksvvdTiDJUWgKZF/IMWroItVrctOjbf5/kxU7UxvA9aTAjtn//8Z4uTONsZZ5zR4jyFfNWQNlZ5AQZaGLi1mARaMFheyoxBku2rfH3aUm3+61//2iJGfYt97BaGVC0mrhbBp1oYQra//OUvLQwj27///e/srxiUB0azPnXeL482oXkE0GrhgtrOOeecFhNti0kteQAfS9X2/cob4753ufAanvvPf/7TYrumBaBucS5N8kefOikDz0S45BYnI0ItLYRNloMflOe66KKL2skYo5V/aBsCBLQ4d6WFxmfpMgIEp4xY5NPY922xgGih7s4q9eV/7dBvxu4VV1yR8sJnnBvTfv7zn7dYoLRYWDRjIyaPpA85hQ8853nXUVofClSfGy/6kozCk8eOHUvZ5H5f/qlWy09+/epXv2phDNxCy9XwpHliGf7wDB7705/+1AIYtNh+yHruVzdy4KOPPmqvvPJKC8PLFgeRtfPOO69F6PucYz2PbwN4tMdChp199tnt3HPPzfEUkXVbuJq2EydOVLN2/Ky2PvfccykTzBFhb9YieGDOhy+//HLOF+GGn3WJBUHKobHmw1kAjAhk1OLAohSSsQ+WgiP2iZKhUG1Ip1enbDLA0MZw0ctBFqvCFlqLFqrhBAahMk4wIE/fgVf03RSAUTwAQEYEzhz0hEgEyUraoIs81e4dR+YB/FjvHBNgxDkmCTDwQHhTtDjgr8X5HC00M8kXYV+Swq/ePaRZqwAM7yN0X3zxxRTixnWszLb4VF8AClLffpDvCGAM6cH1z1t9PhbAqPLiQMMWht5bc47FB7BqwSn15cnKOxRgKP9f//pXggmLYOPVIiE0KS1i8eQkL0+BEAtuCwn5LMJdQJHxvVc6Ahgh7K2M4+TGRJKf+tSnWuwf5+oc8EDkvhNldTYhtMkAAzNDnhjT9e1vf7vFgVTtiSeeaL/97W8TiRYt9mK+uleDaRMARvELARKnlLZQISZfXXnllS1OFMwmy1NtLhqcjs9671gAI8IltwIYhE1s+6RAijMRWhxO1kzqNFoffvhhNm/IOPLAKgDDu6w4acBoJmkl1a/4lFZsqDBHvyOAcTo4dT7vqD4fE2DgTRqucE1P2XDmmWfmnEMjEOcxLTXnDAUYKPz222/nIhFooOGzCHLRcNN2aztZQWsch4rmeKblsBiP85DanXfe2Wgm9kraSltzKDUYCIMAcQpdO378eG5pIC6VPzUTdSciy9M3FUNuIsAoWhDcTz31VA4KdLJ9QfBiRnmGpnpmnQFG0QYNqPsMSEDVds8tt9zSPvjggyRLtXUojcbI792ugwAYNFhhwJpj5Z133sntEYLr0ksvza2YeveQdiwLMLzLmAWAIkpprrwuuOCC1FisyqdHAGNID65/Xrykz8cCGCiizPfeey+3g21phNtms7C1BREHG+Z97+yblDcUYHgmogi3E7HFYSv6hRde2FoMbQcYp06dahEEMLUWQJG6GV995kV5DiXAQOBqvFUNlQ9bDmor+2sAx2Ox7yTf0M6Wf5MBhpUfprRCRbM47bK9//77uWJEL9eQVPnXFWCoP17S78DFd77znVx9A6px/PyWKn4ITQ4ib/XNQQGMCBSWYOIXv/hF2vhov9XOssBzVYBBi3TPPfckn1odstOyciTwihZD6OwZfXxkgzGEauudt/p8LIChPOPB/EDrB1TYfrBQAzYi6F3yZp/JuyirzKEAw7OeYSPGppFtoC3E7RoM9SDT4lC/BCBf+9rXWhh7pqw3Frx7r1Rz7KHTYCCMxhNC4c+c+0/sB6j4GfEQjjq+8u1FxMV78m86IROdtAAAFsdJREFUwGAYxEAUwLD3bnXIZoUhq3vSfoy3nWb+X0eAUfxhsFJv0lbQXDDwZcx0KtB/5Vls8xTf1cN1EACDUecll1ySqtPwkklhFMczb6l8vXdoWgVgGIO0Rt/85jdz35gxHQNbdbJtAiQb/0PqJe8RwBjai+udv/p8DIBRZeG9OBF5C/jGUQEtDjhMWyGazyE8ibryLwMw6llti+PUtwxOS4NR98llAIhcA9RvvfXWFucnJfAwhvZKhx5gnIoJgKEK4oVbXXvzzTfT2MbEabJA/CL0XoSse8VEm6zBgGDZF2A2e3K0PwyE7HPH2RqDV6w1oNYNYFRfWwHwVrjhhhsao1d7qvb7eWkYYCb0OST1dR0EwND/DNUADVsjeAGPsECv9w6lwbIAw3u8E8CIWCMpHK0U1U09zzrrrPboo48eAYyhHXII8+MjoHIsgGHssUmi2SMrgHJeR7y4ysDSFrTk3X2SfMsADM+VLKDdK4+WRYBBfkVQwGbRwE6DrLewZFtGm8E+Qzm71fXQA4xytyTMGCuGj3sSDhG5sjH0lHYj4HYGkG9TNRhFBwZAXAcjtkO6WkHdbA3QjJvpP/7xj6QX5uqTirbrBDCqn6nbGTXyoNB+E9n999/fuFtpv4E/l6TOrhIqq7qpLhp5Uq3SZN0U7sWAJ2EJfHJjRSO0qH7uS49VAIZ3ENTsQRgk01rQXhCQgJCxrf3qNIRPjzQYfXtvM/Lhj7EABoooj3s0Tw3Gx+wvzDGAMMARMYRSfso7hC+XARhVHwuk3QCG+vKGe/LJJ9MjLiIPbxlwW0TwkpPHtVPShkNrg0HQ8h2mzobO4pz69vvf/z47nUsOQcmAEfGGdPYmA4xiomIqn/akI2hUrg5NsAxA/Y4OfZK80roADPXFOxG8KScv+5NW7exRHnzwwbTAxi9929+HRmPkUe+quxXZmACjjDypfwkj4MIYEqdmGWNp7V0VYCij2qw/GNaJ3UKLoa8AZPf79lPlPbLBQNnDkarPx9BgoJjyaDuNDzYPAAaPJnMPvvQ7g8t6bx8qy3uQAKPq7T1i37ATAdQBJC7ppeXfqa6HEmAglIabIATIsdpyARoReTB95XW2yfLxxx8f3NmbDjDQDlNF9Le8TFTU4SYU6mf7i0MHCOZcB4ChXcCF4Gwsvk1YeIfdDjdd99Cn76S106A8qN/Uvep/UABDuxlSMm41hljH8+QYwg/V/lUBhn6gbSs+tWVS8W4EDPr1r389qF7VhiOAUT20+Z/V52MADGUph50fjZ9FicBTglb5pAGlFRC4TV782yfJe5AAw5g2jly0HbT+AHrF7mAoKqnH9qQNh06DUZ1nYuRaRxDan+U5wn//05/+dE4aDD15lVT+7cTb6X95DwPAwNAmU5dtJYY/AIZIcL/73e8GC260nDvA0LfabUBRudsSwDciVvJEqr6fI7hAX/Vz6bOxAMYDDzyQ/V4aDAJFtNivfOUrWwDjZz/7Wb63r8BUV2lVgFH9VXxqW8/+svFuT5l7ujx96yWvvj0CGP/XP4fhb/X5qgCj+Gwx+q2FibnGZG1h5n+2Dd/61re2xmofGiv7IAGG8YHvfRpLgizStLBbpLney2bEM4cWYHC/sXdu7wuKpNo9efJk++lPf5ouRFRYDFl0nqQj90vFkJto5Fltt/f+6quvZohXwaNMIMfCtReiFSFRhDh06EMvZVa+OQOMGly8ZESrxC/UhF/84hdTxVn9Lt9cU/XJ2ADDhM0Gh3vu++GuTCsoNLoVmm1GY6HoM4Q2qwAM7wMEbYFSSTPepmXjGkiQ2wPHx5K8fVK14Qhg9KHWZuSpPh8DYJikxZ4o7Z4FLW2vOYdXRgXcuvbaa/fcdthOWXUcA2AANxZM1113XWorvEe5bDDYjdDaCkopwBZZb8zfddddWZ3dxtChBhisY7nfICoPElsmCGXSpPKG0ERgpOmQdiNi3vz4jzwYaRMBhraZnOKAq1y9W7kzlqPpwXC0FwaK1IdWH5NsK+8cAYZ2FLhg/U19aeuMxfU111yTE1jdH9Lmavvp/FQ/11gAw2qsInkaQ+eff35qCAps8uvnqlvjaih9lgUY+oM619kgNJIs9V36DLgw5p999tkkfdGkTz/Ie6TB6EOpzclTfb4qwEARZdGiXXzxxcmHFmNAsN+BVq6q5Ci5Sgso4eX9kufHABjGm0XToheJMA4WkEAHJwiftJXmRvUE3L1/t3r6/VBqMDCMvTDEqnDXfnNhAoCDEcvnPve59tprr+1JxEUGQOxNBhgENyMkaj0MCfWaSAANky+3REyFDn1T5Z0bwKiBY0K28hAvBa8wCL7++uuTL7TV/WpD3zZPkU8dXWMBDBoAwgcP0PaZwPEEWyYTOlfd90OjIS1Dn2UAhvfoE3zIJsaqsPiUPchVV13VHosAegTyMnx6BDCm4Nzp3omf9PkYAEM5NL/sLciQG2+8MYGwsk+dOpVhEcw5tu/Epah379d6+VYFGBUHw1im1Sfnlcue6qbwDBO2Qd0AEFpAYKhA+l71M8amBBiTHNceBMkjZEN12sWJdl2gsTwmN7QVeRxzCKcuIpt1jlwPYdmFpWwXqt5ex+gqOzomn40T6vJ7TEZdrO7yaOgANHmUeXRkFwK5i07L94eBTx4nHermrWNyw6Zh5aQ+rujovMIlqgu1VheulV24HHV333131jG2iXq9S9tim6AL5NopK5gnj+uNQZFHdHuXpB19k2fkD+OhLmKSdDHY8ijzCHyWNIsJImkWg/ITNAu0fyA0U2910lafAS66OP2vi62RfN/VV1/dxXZA9qk8+m5Ie/vSZex81TfFC44yj1OEu7AK70It24VBcxfGZ735PIRaFyrTLoRmHoeOBvoqNFpd7NF2oQVM2nhvX/5abLP+PnbsWBeRYrvYksp69SnH+7QxNE6dMR4q3hzvAYS70F7kpa7yDek3+fV3HdceHmZdGPkmr4bg7YxtFxoY3yGUk57Fp1X3Ie9cpMfQ78W/nvPuanO1u+rh/8XfFv+v71WGz/rN89Umv29i0lZ0DHCQPBVbBV24O3fGTiw4uvAC6cWXylFGLF67AL7ZF6HByHHnnuPSYzuvC8+mlHXmnHD5H1T2n//8586cEwAgj4GvOWuvPqr2hblAF8A7x4lxq42xxZlHuBvfeF2b8fLx48e70MJ0EbjuE3yzU/+jXRiGdgFgssxYiHQRlLEzXxgz6mqcGzOu0DDmfOjdxbPFpzuVv99vkwCM/Sq1yv3qMOBkEwFG0UY7tyfMVEyx/d5e/ysLE80FYKhP9aPJJHy9c/I1OEJ92IUnQhdeCCkwDLhVBsBedBn7XvXZGABjsW5V7uJvvhcdAbBl0rIAY/Fd2+vmf3y6DCisZ9cBYFRd0WIRCPjd5bfi2+2/Lf6PVlVGfvm4PL/X83tNYPXMun6ihbauCjC0v+g6Nr2Uq37LAIw+/VL19g59bvKX0GU/eS/PoQUYxTw+Eao6vn5HxBqINZj8tleqZzcZYNSAKzoUjZYR2spAM2XMAWBU/2lj7It24WLZhcdIF2rNRN40F7Ra7tdAKzrM/VPbpDEBhrLQospeHCf4ocbUMrRZFWDsVDf1W4VPtXUdAIa2R0ySLuzHsg/CWyFXjKGOTw1krR61x+o57GlS+wJE+z+2v3JFaVKhAZI/XCizLHkjmm9qqmirjIPFfl+mr+f6TMmDMQCGNlZ5vqPZ4vjQFy6/udeXpspcFWBUvXx6f9XL/3jJp3EjLQKN/eqoPYcaYCCctL1D/b7bvXxglz+eQdRNBhiLTdfe/ZhsMf9O36uMqQFG9V3YKCTYAS4iVkJHON98881dRCxNlZ7+XTdwge7aJxEYrlW2SLKghT9Vtp9W5YcqdlWAUeX4LB5b/G3o9+KPOQMMdTQBUOVTxYf9WKqcad7CI64LO6fUyNna+frXv94BHFa+zzzzTBcGfF3YqHRh5J7bgeGGndtMJ0+e7MKNsosTNbuwQerioMMsnyrduKAqH6vPh/bJQeevPh8LYKgv+SGNOeesCjDUZ7d6uYcOZIY6FwDy+35JmYcWYOxHnGXuF0MeFoCxDI22P4NmGHdKgFH9RuCGd1EX8R26CDHdHY/9xjgwq4tIe7mnvq7gAs21UToIgJEFj/xnTIAxRtWKR+YMMPAnjcOPf/zjBMdf+tKX0g7Ell7EV0ietnUb5+Z04U6ctjNhrNfFwY7d7bff3oXXT/fKK6+kto7NSniGpZ1YxAxJLQZbMmPE1iAZd8UVV3Rxem7eQ+NNAxrV52MCjDF4cbEMdRwDYCyWOdb3qQEGodc7RWXTojcQUQb3CGSdoYh5NIilXvd7F3gAGasOm+imegDkyiLRTJrKiyQm3PSs4HnAmpr7WGgoMtaFY+nxW/VrVnRN/2iDa1UvktPV/DD6SndxXl3qrZ+mTMUDc46DgUa83shE1v/V5zEBZb87l4U3DZdDiQtxGPdloCdh7vHGH//4x/QE4oYo7oE4CN/73ve2YiSE5iuD63FZ5PL79ttvz6J/DoI3qs8DVOWcQ0YJYRDGxxlsby58uawXyUHQbLFM/DilF8kRwIizGzCvga0zMOyYqQYABuR6xL+anzNXKUeuS3MQ3OoxBcBAH7ThchkeRenbzcfboW3cHIt+BPS6J21xHQGM5XoS7fDBXAFG1Q/AMPHHlkbG8NHfLnz+4osv5th3QKEEYIQGIkNUO7hKPme0cMcWnVeZAMXjcWQCYBJeR/mb2ED/E1GOxRmpxd3UcmS5Xt37qaLpEcDYm0673T0CGLtRZonfMaPLIBVBUDwAA/W2225rjzzySHv66adbuL9mBExCysrA4VBTAYx77rkn61vApuo/1efpBhjara8EuwEmRJ0UMEogHOeMVF9uArjAztWv2oznTBI//OEPs82Oiy7f98o39ed2DYZ+mLJOxS9OC46tgYwL4FP8F9FLBfaKbYM8q0H8AIHIhFE2sS/WfQnR0vsR9HGE9ne/+92MAeKUTloIfT4EYIhtUyGrneXyWGg5yDMHXaGDiLYAhjNdHNblvZsyThaJXe3aC2BM2e4aD/qW5km/AZdkadV9sT2n87v345UpNRijuakyyGOgFA3asoAdax9paDnRibk/Gecx5L73XONgMFqMCWbLcnloO8fIj1aSvdt33303DdEi1PiBx8EI5s92s5aPs1O6n/zkJ2nQFmrhLoRyJyZK8VJZVI/R3inLKFprl2vRyDPOFOgeffTRNAiUbw576TwUQhWdPv2h8p+UT/UbuuCbCB6W9gxxJsPs4mBUHUNTmXEaYqu2u/TSSzP2TZxs28UklLFmyKTYEkmPEbGAxMP5/ve/n/ZGJ8OoUxwERp/yBEjqnnvuuS7ObepiqyTdtAOUdw899FB6WAXA6r785S8nbcrTYEo+H/PdRc8AETlmKg5GhM3u7rvvvoyDUXJizPcOLUsdGOt+9atfTW8gnm/iYExdN/QL8JNxMBgEr2UcDI2I80MyWNbQjjmI/OrDeltAER08R4DBaEugrYgrfxAkWKrMv/3tb903vvGNdAM04R1EoC194zJRcOFj4EZQEhgEcaz8uohCuTWZbQq40CHaLeFJVwEMYyci1ybAyAwz+cMdGMDQR2efffZW/aeunkmGu7IxhEcFKZtToK3ica6kEbo/PUIEh4uoqx1QBDww8hRkDXjoAzCef/757gc/+MGOAEO5yjemDgvA4AJ8//33p2yfmh+9H+1ff/31LqKDJsDgFSQ43RwSLxKGw4yL1w5gsPAXTTKMljqrT0hz6klBZ/OIuPfee7MucwMYViFWKQQjQaO+c1ixhlo5hZ7VURxGlatD0RBFeIuDdTLi22L004qQqO596l+CV3tDjZwTahw2lJMta3vg4jOf+Uz+v4m+/bsBDNobEyUPgjlNEKIk0mLgV67CU/Np0a8mbtEPyZwIrTw7gBFbIjkmeJTEVmhGNBUR1dgij2gnAOuwwUhvkwjr/v80GPIVCNkJYAAvTz31VAKXwwYw8IDFEC+aOcw5Fgw155jET5w4sRUMcMr50JhBH5FART4lv9cmkifEppNNTILAqLzGTJlMdIhqZWAS42pnoPIZN1lOHSr8gw8+SIEd54mkUAybg9msDA0SYdoNEALrC1/4wmihwvWJyySFXwAL4b+FxgZmuOhRIavDJoILY0L7JW100WCIUhqnjCYd8CdhVPky84R/TIbqI+w2MKnvpk41vq1g1Q3AoP2ZkwZD34bNQNaPZo4bqXgu4mIAHiWPgANBswAFWiIaCrExLD6os4EQ/EGWed4WCY0nIKpvjKECGIdli8TWEw1Q2OF03HgtftB7Dkk/Ob7BIiHO4En34rnUDZBVN6HBaVmE7K9gb+ZIdHTNKlR4AYxTcXaFypksplzlLApmaiGDG6CoAT0HgKGTrQjDMCujU4pQuVjvKQcKoQgAORsmTirNePdjaDC0z4U3qLftGYdV/Bb4o/2K48VTUJjINjVVPxM6rgIYP/rRj3L80BJMudrZTnc2OQTO8YhFUmekbM9zuv8HMEzSzqexmPn85z8/K4CBx9XN+Ca44zTMtJMQzwIgAAyA6TACTEEPTIdxamoyhMA3FpyJYS+fdpOWw8TFBoOWw7O2h5TLPoNWhAbMlqZ3z0kDNgZvlNzQ18YMgIE2zuCpOUeePhrUMeqzvYwa036vOccYpvEly+ZStzAgT3ljPqTBWAuA8cYbb6RRktC2AiE5HMbgn1pIGmgOtWF4qpMBjLkddhZHrqcAuemmm1IwTEWzGiAGqABG9jWpH8faIlG+/iAc9ImDvX75y18muLInKPiQvX73NxlcEEBFa211lQ2GCYL9CYNfE440lcDMl3/8x7aVVaKtRiCDkJ+KT1Wp6GfbIY6p7957771Z2WAUr7/zzjvZl7QTABr7FXwODAELDD/1ObANePjddg8thMMGHWQo8Nbll1+exqEmB3votktuueWWNGy0eKL1AGQcnshuyfun7J9F3hnre9F0EWCQmXjApwPLjKUpx4s6usg3/UqTz1DdmJlD3dQBT5G9tJFrs0UCYPCEkDB/HCE7Fl+tXA7tCmCBMecIMBioWa1Qi84lEdjCGRN8Yxh5lnBgxRzBgDI6p60hYY+9h/0OhgdAamKdCy0Ooh7oIRnwrgIYjDyFkqZCn1JQbm+z1Q4hafVMwzSXZBVr7LDan+NpqjSBwDrQ4LRmdkVU05L+dV8baH7RFY39b0Xuu+0f5/B4xqm4eIXGQpnuRzyMXMg5rdY2sK1F2hL8NSf+GYNfSoYUwCCbbJHYyhUtlWyfQ1JPEYiNY/1mzACPc0gW/QAqjdjaGXkCGCYIoZ2teAyGKdV0OtpF+GBE3+cIMNQv4nPkSgfNplp5oI9EMBXAGMNNVbn4wuqLRw/+sHIjIPEM2x1bMYcFXKBx0Vp/uwpgWFnQGNmrr62IOUwUtu+AQS53LOLnMLbxi4mZRmCObqr6Waq+ru/F5353Vf/67h6ZWb/VM4v/D/lN3k1JRZ/tAIMGJwIVpjbHvannHH0odDutCoDx5JNPppvqlHVDO6ncVGnH1hZgWIWJg1GDZSoGL4Y8Ooukfw+gGWE21lkkeMDFu0g/WGnQKJmkrDwBPxoL751SMPSn0Dg5a8DvBDB4FD0edilzAhhWxdxU7fPrO/WfCgjrAe/HV3M+i6TqaWKp/kazRQDh97rvngtPVL66X/8rU7sX8/jN/343lraDEfc3IVWfo5f2lgYDOOcVwdNJHrSaKlV/WTTa9gUwIqhjAoyp64Y/2IbEEQwpd083wPhfAAAA//+ZsCr4AABAAElEQVTsnQfYLEWV98tvo6uuWRAVLooimMiogGRBVLIEQYKSVESyKJJzXMALkuSSBAXJKJkLK4hkWAERlYsI4uKiwhrWTfWd32H/Q9O3e6anp+ednnmrnqffnrdDddWpc07969SpUy+JlkLFxKP/+7//G/7nf/4n/Pd//3e44447wuc+9zm/9k//9E9hzTXX9N9/9Vd/VTHH5h9TGf/5n/85bLbZZl6eDTfcMLz3ve8NL3/5y8NrX/va8JrXvCa88pWvDP/4j/8Y/uEf/iH83d/9XaDM/+///b/wkpe8xI+mSkZ5OKAZxxNPPBG+9KUvhVtuuSXsuOOOYZ999vEy8u1RJcpHvf/lX/4lfPrTnw5z5swJH//4x8MHPvABp9mrXvUqp9mrX/3qF9Hsb/7mb+aiGXnBG3/4wx/C9ddfH4466qjwox/9KLz73e8Ou+22W9h4442dHjw3Sj7pRWv4nNRku1Bnknjh97//ffja174Wjj322LD22muHM888M/z93/+904f2GHWivRdYYIFw/vnnh3e+851eribp0W/9oB/t8vOf/zx8/vOfDz/4wQ/CBz/4QefVl73sZS7byDf8inwj79BTfKqyt4G2/dZ9uj6vNkenIDc/+9nPwhZbbBGQnUMOOSR88pOfbAVfUr7vf//73ue87nWvC9/85jfDe97znpGXDXn5y1/+Er773e863V7xileETTfdNLzrXe8KyAxlRc6RGY6//du/9f7wr//6r+fS7XV48CXWgAlgJIDRCMCAmTl+97vfOUMDOukMllpqqbDLLrt4J8p9UpvBBSKBwsh2SPpdR8j0jkQtAQxRpL8z9IN/EsDoj27j/LTaPAGMeq2IvCSAUY92hW+JIZMFo5A8hReh2aAWDDKm4/y3f/u3cOGFF4bjjz8+PP3002G55ZYLu+++e1hllVX8Ph11E511YUUauvjcc8+FH//4x261YQTPCBhBpdyDjH6hMykBjHoNJdlOAKMe/cbxLbV5Ahj1Wi8BjHp0K31LDJkARimJ5roBzeoCDHW45PHrX/86nHvuueGEE04If/7zn8NKK63k4IKpFhgdq4Wen6sQLblAOR999FE3v/7Xf/2XmxOXXXZZnxrCbEg96wIN3iUlgFGvsaEf7ZMARj36jeNbavMEMOq13rQHGGKgLPnohHRkr1f5rfwmGWBQx24dNfdJ3Z7J0lL59euDQYfLN3gf35JZs2aFr3/96/4//jg777xzWHzxxb1T0LPZ77btN/XgAGDgL/K9730vvOUtbwkbbLCBT+9QF+b0xZs6V60HeZOaBBjkiRJR3uSvcg1qKRq2D4bKTHmrJNU1AYwq1Jr7GfiENChfzJ3z8K6ozZsEGMozW2rJTFVezL5LfpQv+WBkqfL875H7YNA4JJ35TSMjDJz7FQYxz6QBDNUrS6e8RYB7OqAjqQoNeYfn+gEYOAMJNOAUeuKJJ4YzzjjDHYdwEN11113DO97xjvCf//mfPsVA/tmyP1+6dv6lPnvssYf7kVBmpkhw2Npkk03Chz/84fC2t73NnaG4l2+DbjVS/ZsEGHxPAIP8oTOHrvdTPn8p86cOwKAMKg9ZUZa8xYdndOhzek7/F52VdwIYRdTpfU0AgyezfNL7zdE9oTZvGmBQI/JWgh6iDzLTTyKfOgBDdcuXo0gWeEbPUzY9w7lbok7T1gcDgmFKZ877P/7jP7yBadyXvvSl3lGxugMC9tPgaoRJAhhS2NCCDpv/+U0nn6WNmJBn6MRQ7HkP+iJm5D3yqwowaB9G8nz7kUceCThznnPOOWGeeeYJm2++efjsZz8b3vSmN7lA9AsQi8o31dfowAAYV1xxhYMo6IMCwQObaZ9PfOITfn7DG97Q8c/ItkNZecmH1BTAIL8//vGP4Te/+Y2v2mFKB9An73DaifanbeukfgAGZZGC5jdl4Uz7S45VBq5zoPh4B9qpnN34hXd4PgEMUbL6mfZ49tlnXW7hC7UNvFGXP6p/vf6TavOmAAb8Q1+jPof84T/0GXIDr8KD3fgwXxvyqAMwyEe6QLJDWZBhUrZddB/drjIjM/nn/ELmD+9NO4ABgThYynjDDTeEyy+/3BUljQRxWWrGMpqVTJmzPE4Ez9Ct9Cf5QtRJAxgIxa9+9atwzz33+BItltguv/zy4c1vfrPTEmaEfk8++aQv38PBEjpi1seSAA2zDJslIDTjXhWAQSeLIJLfQw89FI4++mh36mSJE1MJrBZh6S+C0E+7Zcszqt/QgQQNDz300HDttdd2aEZduI9CgK7rrbee1/f973+/A2LAXq+k/KVUBlmmCo8Dzm+66SafmqLzgEdQUCzDps1XX3115xHKVtb23crcL8CgXgAeAABLk+GB17/+9V4OeEZ8xvVf/OIX4bbbbnNeBpguueSSYcaMGV7+srLyPvVOAKNbq734HjTjAITifA3QYEk0AwD4Anr226G++AvD/U9tPijAEB3oc6655hq3TiI/AuXINEvpV1xxxbDIIos4H1atGXnXARh0/I899pjzM7oAHYMMLLrooq5jJQfkTzmZhr733nvDn/70J9f76HaWY5P0bL7MtO+0BRhPPfVUOPLIIz0WAEwOegSV0fAgyo997GNh7733doJLEPIEzP8vhpwUgAHjAixmz57tQIwzzLjwwgv7Sg06EWhDwpoAPRl58wxxAD7ykY+4PwEKvIwJoRn3qgAMxQ6hE95///3DRRdd5MIIqIDh6SzoZNQhU66y73KvjYmO+v7773enVcoOfUjwKB24eBFlQIyVddZZJyyzzDI966l8BgUY5MPBcuBTTz3VZYRyIjOMTqU4F1tssXDAAQd4fBrK32879AMwoBkxCoh9ctlllzl4QLGxRJn/3/jGNzrd4GfAx+GHH+4ADmUJTwFOmVZjQFFWVuoM7RPAcBJV+iOa/fKXvwxf+cpXwtVXX+06Yd111/XOlIGBZBX+7pdHKhVigIdU/iYABnL3r//6r+Hggw8Op5xyistzts/hN9O7++23nwN0vl2FHjxXB2Dg60Wb3HrrreGZZ57x/o+BNTplq622cvBA3gBygAWDOfo1QNJCCy3kDug77LCDx6+gnEVlRV5GCTBQVJWTFTYaIaMVOFonEq2zi4b2onV20Rziou73ypDnrLGjdVLRljBGQ9LROqZogbGiBWaKq622WjSmjzb6ifvuu68/yztVkspw4403RlNqnq8F5YnGUNGCn0QTsGgBwuJPfvKTaAAn2sgvmnKMhhD7+k6VsvCM6mpMEk3xx5/+9Kdx/fXXj2ZejwceeKBnAy3ySe/ZyCMaaPC6WAcSDYD5YSg3GhL3/Cm7reCIBsaiMVk0K4O3iSmPaCPHaMFoonVG/mwRHXXNOtVoQCTaCDhuueWW8eSTT462KiReeeWV0YIaRbNYREPRnpcxun+D8tB+fNcENFqn5O/zbX6P42GjAqex6UWQxYsOU8JeX+pqoCqab0Y888wz881X+D905qC94Dm1mYGCaAHInD94Ue1RmMn/3Ydnfvvb38ZjjjnGZQU629RNPOuss+JnPvMZLxv5cg2+65Vn0bdow/e9733e7rxfxKe8xz0biUVTis5v8KkBsQitDHi5nPMMusMsF3Hbbbd1foHO6A94lG994Qtf6FpW5YHsoiOgP2dbEh1PP/30eMkll0T40oCyf8eUtuspZI9v8z7HdEqi2eOPPx6322475xXaZd55541f/OIXI3rSpgucJ6FRWRuPimYqP/JCn4OOgicXWGAB103cr1JmnkPubLDm9UZvoYPRc9Zxx5VXXtn5lT7HQHlfvELe8JhZ452uZgnxcvYqmwHtuPTSS0ezNkYbDETkFX1jA5f47W9/28tAmQ1Qx4022sjLh243q7W3ozmgx2984xtd+y1oYyA+mvXK86fOtLsNTLw/pA+hP7TBaTSrd7SBqcsg3+Vd6jBIGomTpxXYR2AsazQF6ZEM8db/8pe/HLbeemtH2aYkHXkxAp9lqxMYjfNeEUrLAmSeMaKM/RSJaMRUB8s+iaZoghUefvhhHym+/e1v9+umYB0933XXXWGbbbbxKJwsCyUiJ9YFRo+Yy4lLgbWDfBmpZJPo2suCYZ2AjzZpC0asF198sa8aMYXv7UK0VCJ1YqLnPqN92kv5Z7/Zxt8qJ6MJE3C3YlB++IkzdONMvfC/gJ6MvKG3dfA9q0T+JBNcP7Ay1YnkST4cTItYxxr23HNPj8iH7wsWC0Y7+MJg0TIFFr7zne+4SZx65Nu+W6GpkynyrpE8VRYDzsEUl1sX4FOmbrCwYMGwjj/MN998bua9+eabvWxYLoiNAv1wDsYDn9GbgbWA5YV882XlGnUYlgVD+XejyTjeg2ZYHG1AEwyA+tQIo2IsF1iMiOyInkWnMIonid9HXV+1iYEfl5m6kTzJB7lDn2I9Q+7oc7DC4rxNpEv0J8+sscYaPp2U578yWpA35etnFQnvIL+33357R0eeffbZ/l2swUzBYmWhPJTNgJBbKCkb1tKZM2e6ZZspHaKGonPJk3bLJtp+WlswzCzqqM3mBKM5Czp6soZyhMrIhpGdmYSMdr1HdnoGJD7uFgzVBeRu4MGR6g9/+MNo5ninF1Yj0Cfp3//936OBMEeoIPCvfvWrbmVgZMdIEkvOYYcd5mgU2uSTMaFf6mXBsOBTPhoVysX6Y51XXHXVVX0UCqLGKsNoCSsXibzH7ZCVyYS1MxKHjljVGAHY1F087bTT3AIh2nlle/wRHZq0YGDdMoUSbQ457rXXXj6SMoARzTfELS20DSMTfbtHEV90ux8LBrxw1VVXxQsuuCBaGPy44IILerkYoWGpJGFROOKII5ymBjiiAWenoZmJ/dn555/f+ZyylvEp14dlweC7sjRi9Zmkg1GwdaBuOYKXORjFw+OMim1ZuVuBHn30UZddaNGGJF5oyoIBL2IpQ2boc2z1m4/w4Vn0pPoc8V8VOohv+rVgZOmLvrSp7Yj1zwZwbnWG/7C47LTTTl5eZAp9SxthBaftsAAaaHf5NjCSzdJ/cy1ZMGwvBpyOmINlRMOKBHwJcLwxZvDrRi0HZnmElkVr/OY5a/Cxt2AU1YtRIk6UxGfABwPLBssmQeUg2oMOOshHnOxvwkoHfDG2335792sBAbPnBfTL0xCaca2XBYMRrXwwTDn5CNMUVXjwwQfdQoK1hGTmRl9JQhRPHMlA4VVHA/l6T/X/0IIRMvvFMHKgfpSfekNzHORMuP03z5oi8md4rlfieRL5cTRpwWAEg/UPKwKWK3iB0SiWLEauRe3eq7xVLBj5PKgjjsjQyICmW1AuvfRSt2DgpKb5b+aQsV7iqMwIjJgp7Iug/XnIp4hPke1hWTCY277vvvuCTfd0/BLy9RvH/6ElvGbgL2BBgq6iL1YMfsOPtDfxaz71qU+5FQl/qnwbTHX9KRttjpxRxiYtGPQ5BspdprGcYdmgzuhY69Q7NOpVZ8pI+fqxYJCn3uNdfEOw5LF3k8ogiyBlwXcGJ1Se4T66nNV7BsrdIZ09WaBTXg9xLVkwjNbMx370ox+NpmgiIxlTNj5XBFozxnKEZg3SMxlBffQzCRYMVdYY0OfsbXrEaQRvZi0Y5sAVbdM5R7SmuKNNXfjztjrHfSCYq8a/BURMgkbZpP+rWjDw5wAVg7o5eB+kzdwl5WIUgC/HLLOqMGplxM7Bc20/4DUsGFiKTFh9nt9Mx86XjHIoP89Qb9qln6S6D8OCwcjHpiPcD4KRDtYH5txNIfclP9n6VLVg6B3ogQXgzjvvdAskfJq1YDCKNgc2H41p5IV1Er8RRtPoAEaXZQn68Y1hWDDIm3wNsLsvEfPh+NlMwgFvyC+GNskfBv7d2iWLxlvf+tZoHZjzeFlbTNV1tfkwLBjwG3KOFR2/BqyAWAuwwEpWq9STZ+v4YJC3ba0QbUAdzVnTLSjInA0K3aeJfPF7s0jCrouwStIf8g6WYtoRXxraioReyieuTXsLBkgM5MUolzPzg6Yk3cPWnFvcM77qCMwIPHEWDGM0r5Mxl68IMafLF1kwuM5qm29961vuXYz1x8Cae+mz8yDz3czdEcabZabQKDsy0f/9WDAYHcuKQfnIj7XlLDnGLwCfEEZEWFDY1Za5fNqX59pszYAWWIqI5Ikvg00xBDzuoZ8paW8HE2zn0ywNudYrkTfJhL5xCwY0ZTRKmyA/WAOgPX4ZLBUl9Vvefi0Yqh90w4LBUlR8QGTBwFcHCwUrTQxg+BzzEkss4b48+Fxh7ULeGaUVJfKH14ZhwSBvVmsRiZZ5cdpoEhL1ot0NmPnon5V7/K+24rfkEt1rna5bMfDJoA1pE+UxCnrwbdqc8tMmTVkwsPgiL9SfehvoDzNsVRirOvAjQ9a5x9ErUUbKV8eCwYoqvjfHgvtRBiwS+5tfCNfQsTbo89/UG98LfPHQ4eaAH2wqvGP1289WvkCnvG7l2igtGK1x8sRxEOcwGpltmBF2HJBQSFvZkh2YoGpjQ9RJWaYKc8PAHDAZHV8RwMBBFhMopucigAHgwInIrAtzKQzyhrZ1AQbvIvzQnTPgAicqm5N3IWFagc4O5z0prLwg9BLiqboPLTDl4wwHT+IAhkmS69RNSqlOeciDRD4cTU6R4BgGfenQ6SBpAzoLHNdQmtSFdqoiQ6pbvwCD96hjN4BhlrZg1sUXAQymdCgnChXFav4theUkb3hsWAAD2piVz6d2aJ9+aCWate0snsNxGUdvHLOpF9c5Sw5paxxy0ROY220k3Yr6q82HATCQCWSGwQ/gAPAF8GWqnkGRgFevNqWMdQEG/RxLzZEZZBadAJBgg8i11lrLp+yY7n7Mpu2QbQAG07UAYabCNa3YVoABo1VOJtxuosQ83NQy1ayTJ06JmH8w+TBFYg3szmqYrPm2CX3PsqqMkzRForpjwsXBED2enyKxkWrpFIkJUrT5eTfjQUDyyyb93+8UCaZ+2kTvc8ZUaMLmS6toW6a7TCB8OaGNZN0hlXd4pq0J/mZ5MImyUqcqvNerPtCHYxhTJMiLKRyXS/OFiQbOO3xiAcMqy0+2Dv1OkfAu9bv77rsrTZGwpBQHZZv/9ikS6+SiAehsEV70m7zhm2FMkehD4me11aScMfvLydNAspvcbdDmssnUGiZ3lk1SX2gAj/J71Iky0ObDmCLByfOkk07yPoelqizPhyYf/OAHO1MUVeSeMqIj6jh58i51o48zy4U73DKdxdQNy4cfeOCBaPGFvFy2Wi3OmTPHy2s+Vi7fOKYed9xx3kxFZeXaKKdIWgcwIAjzTrYE0AEGPgVm+ndmr9IpiSGnE8BglQD+DzYiccU+y3wfUNzEsMAfAsGxpYzOhNCHI5v0/6AAgzzJS8qJcrFSAH8M5rRpS1YKEUsDgdRz2bK05Tf1gN+aLCN5ij4olSbiYORXkQCO8MlhThd+QImiPFWffujbNMCg3fEVAiDj10J8FXx0squdiINDWYuUpeowTIBBm8Obk3LAD/hezbGOySzB3lHha4E84qtjFuJoS4o7NOf5Jnm+H34relZtPkyAAa/BU8TCQGbwQWEwpG8XlSt7jefqAAzeg9a8q36PuDEMrIlzBFAHeOAXxDWbTozmUO/+buhz5GgBiwdiVo3SspJvAhhGKJxs6IwgiHlyR1sj7wSkU1LQkQQwXrBgMELVMlUc5Wz6wx0SWabKsj+cLlmaCmOyfJSgWWUCw3VSEwCDfCQ4CA9Ah+VXNqfbKR+OVLQxSoP25mhTovyUSXRpqmzkJ9o0BTDokFE0WDAItAbNcQa2qQa/jlzZfLN/t186DwIwZsyY4coahallqgQGwzrJCJplqvw20280r/2OrOOgDI2KZF3XhwkwmmrrtuQDzWh3LBjm5+KWImgPfxB8CfkUT7YJWIh+avNhAQz0IvSx6eH4oQ99yHnWNjOcEoABjwvQATJsmsQD0wFyABgWSt8HYzh9IuP0hbayzUEHgbd4jqBehC8QnUQ3nanbtAYYmEQhFKaeQw45xCOKsVYehYkiwlxF/AUIyNEridCTaMEg2pqFsnV6ATBk+oZRbf7Oo8Hh9Y6g2BydI19oiJJHgKBNUScjujYFMNRGfEsjQcyvCIr5M/gKB0yAlB/m57miDkX5TMoZOnOgyJsEGMgPViqsAygprESK9od51UIR+3f7pfEgAIPRMeAWD3gBDHhh9uzZXjYsa4BO4hDAn+abE1daaaWuHvzQjjokgFFdIkQzAAYRjaUbiKjKPWSvjcBCNVT5mwQYDHDU52ABRK/aElVfcYd1h9UaWA5IfL9X4hl4u+oUiZ5nVRURsNG7gAniFzGdjQ43/wu37gEAidbJNfpELJP8T/RPVgeaI65H3ywrK+07rQEG8/Q0NoRFGdHxQEgUDqMgGr4fARBDTiLAQLHCeNALgEHYc3Xito7a51FR3DAjS7CgISby/fbbrysNJURNAwwxPe1HYkoAqwroHOEwB6s4y6Zz8LvhmX7a2TMcsz/QmYN6NgEwFLgKfqCtGXmtsMIKvtwOHsD/ZT9re76lb/dDskEABqZbysUyVaZG+D7ggN8sRdUyUPwumPfGLG2OwV48ni1KyiMBjCLqlF+DbuY8GG31jndk/I/eYPTcL+gs/8pw7qjNmwQY8B+8SYAxBrC2SuxFfY6mFPl2lcRzVQGGaM9Sfyz2yAnB8PCvQCcix+h2lq6q7gywCYnPPcoMeEdmABlsf8FzHEVpWgMM9gLBnI8ToA4UDiZ9pkiwZNBxdiNgnqhqlEkEGCBeC4Lj5mVGIpo7RUlQ70dtjTTCA+MB0og1AIDrta6bd0nDABjkqzaB2REsfEMoP85MrOPGOZFOQwqP5yYxQQeOpgAGtMRJDZM3a/iRHSKN0u7ElGH6BBkjqY37oWtdgIG1ilEgVhQsbgBLvk+7Uncc17a0PSAAFfApe0uw5xBAk2fKysr1ZMHopwXnfhb60hmOC5hXmzcFMJAH/BfU33BGbgDjdOL0OfAh3y3jwzxVea4fgAEPM10IwEBGkDMGhFjxARtY9Zj2pq14FgsElg4LSufPUl4GaTjm6rmyspLHKC0YI1mmavNJvkzKKu5R/1iWag3kS1GN2L7PA/tnsLeFNSaPV14yxfNG7Ilapkr9qZcxky9nMsDgSxBZtmQddIdGPMcyJ5ZcsT2zdTzBrEKdfTIMtfPIXIm8uTfIMtWyvLMfM2b35Zm0D9EeWcpKVFKbxvGdcy0wlG/bbaNvr5OZ2LOvj/1v8bLoUHeZKoQgL+skgoHKYIGtfAdb2sAUpkfANetfZ0dInq/SPjyXTabIeu5Fkn2e35RLPAi/GoAIZlXxWDa6zxn+hE951kZxwUaSnWfKykre8M4wlqlSpklOoh30Q97KaNw2Gqjc1tG67qgbB4N6kRd9DvJCn0OeZgnwfTyIFWOWA4+WyXOkqjTiefKqGgdDz5s1z5emEsXTAJT3e7Z5ou9Kzfd5jjJwtsFXMEtGsGB//izLadnLh+Xpeo538on25l2iEhMTiRga7D3Dvj+ELEA+kXP6XQ6zkvhycXgE/cv3q9Ih/23+HxnAyBYGAhUliEPl+uloyIv3JikORpY2WVrlG557HPnr0ANBKkt6Z9gAg+/zLQk2AsZ6bjYWs1GDr0knLC7his2E7sXtVu6y+rT1OnUnNQEwsnVUvtlr+g68UJeGdQCGypAtUxE/5q/xHu90k3Xuw8sJYIjKk39WmzcBMLLUyvJn9jr8RepHZsirH4CR/V5ROSiDOneeFQ3KZKNIlvQN8prWAANlq0YVUXSmkcuIqmfyZzXGJAIM6ga9OKvjyDOXOi9dh369hEX5TQXAoL1UD8plpkLf7ZPojXwfi4xN8wQLKR3MZFhaz3y7j8P/1JukNhrEgqH6lslPGX/ovSrnugCDeqJwSUXl4D4yz8FvPdNL1vVeAhhVWm8ynlGbNwUwlF9Zn1PHukOe/QKMsnJIFjhnk77BmaTneskM9ZzWAENEFOFEPF3v96yGm0SA0S8tqj4PzWDYqQIYlEvtxBkBwLxI+N7Zs2e76Y6Q0WzUhSmP8NekXsLkD7X4D3UlNQkwhlndugBjWGUSzySAMSwKty9ftXlTACNbQ8ljvjPPPlPlN/n0CzDy+ZJHlXL0W+YEMPKUHvB/GgCiJoBRnZBi7qkEGCodbUWHSzJHTwcZ7MpKyGbm7m1pXTCHUN8bgHL2ssYo3zaepRwSwKjXOpLtBDDq0W8c31KbDwNgNEUPyjgowGiqLPl8EsDIU2TA/8WQCWBUJyQ0m2oLRrZ0ajPAAw5P7APCxmx0JLbSwLfytuXL7oTEezxXBe1nv9GG39STlABGvdYQnySAUY9+4/iW2jwBjHqtlwBGPbqVviWGHBeAwY54lHmU5n++P0qAQWNSBjpeysHqA4tS6jsGWpS6zqZdbMJksR46AGOUNCtlwC43qCMpAYwuROpyC/qhMBPA6EKkCbulNk8Ao17DThzAQHmO2oxNGQAYm2++uSskHAZZ8srueRYvwJclsdMkS/pY5sOySMpMh0UH1+ToGAHhUKfCyokvfelLvtyInWIBGDDBKDtLyked2ToYv4c5tnWwxS8IFvzFacbyJYsU6cuZsjTDN6JpmolOlMnCiftSVpZYUT62kGZXVpbnainrqHmtH7GnTiTVMe/kOWvWrM5UUJM82E8Zs8/S5iwhPe+883yXSco9SnpDPwEMZIelhixvhVdZcodsc8CvyDfyzrbb4lPJWBtom6Vz+l1OAbV5N4Axav1J6Smf+hyWfloArMCS01GXDfpNhJPnMcccE9ZYYw1XnqNSQlLgnHEaBGDwu80Ag620KWMblJ4FQAq2X8FIAQbCCj0QWDy62c7YgkkF2/DOp08Q2p133tm3ldZSVoEc3m1zol6kbgCDdehtSShKAQzi0lB+ddKjKiO0w4LBcuYEMEbVClP3XXiOTjoPMCzIXDj44IM9psMoO3HJNGUAYBBrApDLFK/tEzJSgKGyEWOKQdpWW201nnEwqIjtf+CBP1SpqWPB4i/dcccdYZNNNnFl3laAYXtHhD322KO4AiO4ajv1Bdv2feQAg6rDR3QmdGgoE9sEK7CUFasGQXFYymobNnWWsvLcqDu/Xk0m2SgCGPiYnH766a0AmqqHhZkPMyxgF6OxBRdc0NtE90Z5BmDYHkbB9lhxK9vaa6+dLBijbJAhfhuZKQIYzz77bDj00EPDBhts0Aq+pIy2H5T3OVj+sPotvPDCrSgbAMP2SQlMMY9doC06JObMt912W4/CqE5hiDzXM2sa27Z8DxZT3kfBbQMYFqo22L4ADsgspLNHZOxZqSE/gAUFa4HFwA+2wY5H1sT8jJl5KqdIstWUcuGMkDBiZSkrtGNq6xOf+ETYeuut3RQ5DktZywCGhfT2KTxGP9QD/m1D2n333X00ts022wQLaeym1lGXCz6lc7Hdg4Nt2BUsJHkAnKUpklG3zHC+Lx0gC4ZtQuZWAgs/7yNyVpi1pc9hgEafQyeO7OCgTrlHOfCBfpSB6KUYAZjiHptInrfffnuw3d08XDGdEPPiNPYoEwoIohJGmTluzFUADNvvoBU+GL/85S8DAIOlmEwlMQ3QlqSRAm1pm6oFi30/UoAhulAuKRFCBVsM/3DppZd6eGz8RFjKuvLKK3v0T9oegW7DlJPKrzNlI1EXDviTujASEy+0qdyEL6Y8TNu0qVySb/QNAMM2q0oAQ0w2YWfaWnoJmQFg2H5MPoDEzwYfm1EDcskG8gL4Rf+0pT8UO+CHQdmY9sSyz/RN60OFC2CwNwYjHBocJmhDwqrCCIdgQW0CGDh5AjCuuuoq3yuCmPJtodlzzz3nZj7aD8vKcsst1wqAQXlQNNCJjpiw4sxxMooldoZtVBS++MUvurkU8ySJ5yT4fqEFf8oAhm2wFJiOWGqppUY62smTCBCMEqITR7YZCY2apnwfRc4UKJa2ZMHIt9pk/V8EMLD0Pfzwwy4z6PdRAwxRHH6kz2HQOMOmFrGytqVsDGYY3KIfsWCMBcBAyJkiISgSjneY1LEcjNokBFPazqDBtqr2jqZtUySsIsEJlc29mEsepRlNnR6Km2klOmqEZFSrSCSsRWfKKjDGpkVMlZx88sm+IgfT31bmxGS7dPpSVoX8HXWHmK2HaE0dOLKrSLAYsfkbUyQ8N0oZUplZEmw7G7tvCE6eyDbAbVQJunAA0nfZZRc3+6ZVJKNqjan5Lu2dtWBoszPi5eDDxsKCNvQ5lFF9DmDcdjoNCy20UCt0O/0L/koHHHCADxTGZooEgEEjk44//vhg291ODdf1+ApMSfwE5uhR5G0EGOyKh6Pi3nvv3aM2U3dbq0hwomsjwBAl1EHTziytZZrhyiuv9NE1Cgfgxi6ymE95pg2dNWWnLCSVXwCDuVHM/GeeeWarpsyYXmQVyfnnn99xWBs1YEOR08mkZarOShP/B5kpAhiY+7H8oePbkCgjFn3Kg9ywgSO7s7Yh4b927bXXulP82Dl5AjAgLs4tbVqmqiVDMGgbAQYWDHwHiINBhzOqTlCdHh0HnTXLVNtqwcgKK+UGmWOpeOqpp9ySgVDjqLrooov6MsaPfexjPsXDe6MceavconUeYBx77LEO6L7xjW90QNGoO3LKzGodAAbTUUzlQe9R0hH6oWuI0wLAuO2221IcDDHXhJ7V5vAeciMLBivLsBKwMoLro+JLyTR8qT4HPwec5ZmGaEPZABgMwIhxNLYAg1EY22xD6FE1NjImhqSxN9tsMy9PGwEGFgyU5D777ONlbkOHMoq9SAbRi7Q1Agw4wxqAzwCd9N133+1zjQA4FBD+DTwDjUcF5MSbnCkzhywYAAxWQmDBwHGxLSm72RkAA9keNf1oczoZ2jbFwWgLpwyvHNLneYCB7BxyyCEu3zwzar6kfAwa6XOygbZGXTZkdiICbSWAUSxkMBiHOpV8JE8ARhsUN53vuAEMKA5toR9nkDpmyhNPPDFcffXVvqpk/fXXd6sMkVwV/XNUyogyksQLWYBBLAcAhqZ12gA4swADcy/lHxXtoJvaOoUKhxrTI6nNE8Co194JYNSjW+lbYshxsmAkgFHanJVvQEM6bqxnTPHgE3TRRRcFVsbgj8GIFx8hon/CI6OwsvFdUgIYlZv1RQ9KthPAeBFZJvoftXkCGPWaOQGMenQrfUsMmQBGKYnmugHNxtWCka0M9RDIYCkrEShZysqSNlZDsEIGiwZOWNQXkDGVlgLKR0oAI9tq1X9LthPAqE6zcX9SbZ4ARr2WTACjHt1K3xJDJoBRSqK5bkCzSQAYVIy60IGTWD6dXcpKVFLW0HMQxneql7JSNlICGE6Gvv9IthPA6Jt0Y/uC2jwBjHpNmABGhm4wkw5dpuPrZ95XDDmJAIO6kThDl6LRNwyVT2XP6jnlN44+GKpD/kwnDi2oG/E9iDGBEyjXmSph9RNBmuTz0A+P5b9V9X+1X5MAgzqWtTl1KuKRquWt64OheoqvispQVuaiZ1Ve8uO9BDBEkck/q82HCTDgKfEsFIUH+9EHvEv5kpPn3Pz4EiPO873W3PfmusKjNAYKEoIqDgbXBnXyVN58lMYlT/3upnT8ocwf5TNJAIM66YAWoofOmeo73YquF13Te+TN/UkCGNSNesGnBK9i74LTTjvNp01wtMVpkTgkrN7AskH9h+2XQXlITQMM8pO8kD/yI3BBvbq1Pc+XpX4BhnhU/KTv6pz9DuUtul50Te+RL+8lgCGKTP5ZbT4MgJHnV/4nSXaqUpf3mgAY8LbkAjmgHEp8AznPJp7JP5e9z2/ym/arSNSwEOOPf/yjx0zHwx4T9hve8AZfcthN8WSJSl7kM0kAg/roYKUEjAbz4bAIjZS4ToRLns3Si46TsLXZa3qHMzTj3qQBDNVN9CA4z+WXX+5Ag6WsdKBEoiWyHeHG5ZORFewsnQb9LT6nnTgGXUVCHr/5zW88XDqh8YloCD+w1p2lufPNN5/zSFm796pPPwBDckeeKFuUGtegKeHGs2XgPnwqvlM5KDt8Wpb0jQQwyig0edfV5sMAGOgF8qfPQRbV58w777ze51SlJnk0ATCQZ+QGWUEWtIEj5SB/5Duf8s/l71PHBDCsgSAe69vZt54tuZ955hlfVrjSSiu5ObtqfAAx5KQADDEvnSORNolQCm1wVMRhkRDOPENi9QRBkXgW5oRRuYeDIxsEMVLn/6yy5z1dm0SAQf1ICC/ChqBieWPK5JprrnEew4rBbsCLLbZYz6Ws0Er0ytPx+S+V/+U9EmXhGARgUBdABYCJIHfkhc8J4AjeINjYRz7ykbDeeut5eGC+2295+wUYgN/f/va3Tt977rnHywPvEb6dUO7Un3I/8sgjvp01z2etRm9/+9udT1GaRUnvJ4BRRJ3JvKY2bxpgkC/8R5+DDKH7CN5FP0Ofw9YXVeWFvAYBGLxP//foo4/6tgfwPz5ibOSovOF5wn1TJmSc68gOMsNzZWnUAIOCVk5W2GiEjIaIoqG+OHv27GgBeKIRI37ve9+Lul85Q3uQd0wxRiNetFgA0Yjrh41kou1IFzfaaCO/z3McvZLKcOONN8Y3vvGNcZ555om2RDGecsop0VYVRIuPEK2DibZJVrQIkNE642ibJ0Vr4GhKv9I3epUhe5/ykK8xs9fDdgOMBgyiWWaiRaLzR7lflHgX2piyjravSlx88cWjMZgf0NzAmJeXspM/bWAgwu9DRwMZfhgDRotwWfot0dVi6ccll1wy2oY40TqFaPt8RAMs0aLARQtqFH/84x/HJ598MpogRhuBDo1mRbRo4pragbMJc7QtlaNZLqKN+OMqq6wSL7nkkmgbFnl7wef5pLbknmiWf6bb/7zDQXvBczZtEy1UfDRLVNx44429rXm/V97cpw62UibaTqyglmjKJtrIKxpIcrmBTywKZ7TYGp5frzyLyo382S7E0XxYPI9ufGrLgaOFI4427RRtm+oOn9oGbtGmpPx96IbesCiHnftZPrWtt7vSgDqQB7JrfjQRHcHZliTH008/3dvPBhbROotoYDsaEPfvIRtqszp0KKJNujY1FFCbIy/wDjoKnoS30U3cL+PLshLyDvmh0yzab7QOu9PnGKh2WSTPqrzCc/DYDTfc4DJoETy9nFXKpmeQEQu86HJhe5l4n6V76NtjjjnG7yHnHMiNgfZo2yKUVdOvUw909YUXXuh6hn7HVtPFU0891ftDG2R5f2igPz799NPRBioug031hyMHGBDAlhFGi+HuBETZ0slBONvQKH7nO99xQkHsKonnUCbjDjDEXICgvfbay4EC4AGlattnR5gYhc5zMAPCZ3tGeGcJ4yGEK6ywQrTNoKKNzl3ZQr8iOurapAMM1R96UWebXoi2j0lcYoklXPhsc69ovkTeIaIw9Jzeg1cBIPArQq/r/qPCH77JQb5NAAyzFrjiQeGgGGlnOlgL3ub/mwOrK1B4o9+y8nxVgAFdUFC2A6/zJoANOUYJmiOtg1vqjVxCN2gO+EWRIuvwKQfyzuClrKzKIwEMJ9G0+KM2bwpgKD/41SI8e5+DTgUI0+fsuuuuDlTLeLCI6ORZF2AgOwwi0eU2pelgB7ljUEy+3GcgYhtk+j2bboy2c3k0y2A060XcY489iorUucb70xJgiHiMfLAumGkqohCXWWaZaFuZe4PRaFKOHYr1+CEGmhSAQSdoJvCIFYLRIZ0ho1MsGKBP6kuHBfKcOXOmo1vufetb34o2neIo3fYYcXqWkY48SNMBYFBP0Qy6wX9mIo02neDgzfbfcIR/7733OgigU0RIOVByN910U7RwwG4JIx+uV008r283BTCOPPJIt2BYeGIHFpSFETx8AvAAYD7++ONexH7KygtVAQZ1wiJkex3ED3/4w04/LIdYVpZeemm3eqnetguml5NRo0VXdeuY7SnifApw47myxD3aIwGMMgpN3nW1eRMAg7yQAfU5DNQAwoBgdCn6gIMOuZ9EvnUAhuqGfJovWGcQCfDGmqH7ts163GabbRy8I09nnXWWW2/OO++8eOedd3YtKvWd1gDD5sDceoEywsR72GGHdQjbr0KE0mqUcQcYqgsMP2fOHO/YNL0CrQQwoBHMDRA56KCDHOWuuOKKPlJkNAg9eiU9M10AhugB7aAR9KNTxgIAwEDAzS8jXnfddS6ceu7BBx/0KTumkBg50Db98Ch05uC9pgGG+V3Er3zlK543U4BMlaBAsSpgNdC3Vfcq534ABkoM/kHhmQOtT5PkAQZ0xhTM6Iyy2Y69nSkM8WC3cvFMAhjdKDR599TmTQIMwDDT1OpzmH7Qd/qRZ1Gbd+sCDKwXTHUzVTtjxgx3OcACnbVgUF7bbdmt07YZpesslZNvd0s8N60BBsoIcz+jcsz6KEcUMA2m0WM3AubviVEmAWBk6waj4AOx1lpruWDkAQa+EZj3UNzvec97fF6aDpIRI3Tsxoi6N90ABvSl7vAbiemogw8+OJqDpFsz6KRnzZrl4I05fcyUmPYZ9Sy//PIRwMf7EnbPpMsfntX3mgYYTJFYbI9oe7G4bwlmX3whsGrpu12KVnirKsDQy6IFZWCOPA8woDPK0lbtdEaOzAVjFWK6h/vkUZa4lwBGGXUm87ravAmAAYXIzxYRRFum7n0OVjSslYP2Of0CDNVrjg0eLfCfD64Z4DBVKAuGyotlD/8kgAc+ibPN95H3ACfSXWWtP60BBsoCYmnuCaVNRynClhGt23U13CQBDOiEgGEaxikpa8GgvjCZLEFMMwEyOAM09t13346JnGeLkq5PR4ABPcQzCCOjfZzHEGhoiKMwPjCHH364O7/KIYx5UDpv3mkDwAD4ACiQIXx1sGjsueeeXh/KpzYuav+ya/0ADPKXkmbQUAQwKAdWIJSo+BSwxvw39MUK162saqc0RVLWYpN3XW3eBMBQXrOtz6ETR5aXW245d5aGctyvk3ivDsDAsnDFFVc4uGCa5uKLL3afJAEMyQLytNBCC7lex3qKvABG5ErQrdzkMW0tGCAwiIoiwyEMj3AcWiAYhKmTeJcOeZIAhujRDWCwSuSII47w0eHqq6/uq1QQIObCmTqBJmWMqOvTFWCIz6ARUybw5S233BI32WQT5006b3gUKxs05aBjxGzJyBv6iYbKq+is5+iIm7ZgqEyUk84bnwy8xZn6qStL/QAM6qv6dQMYzDezcgYrxsorr+zOqNAVpzVWLUmpltGPNkoAo4g6k3kNnqLNmwIY5MNqMeQXHyVbkuqO21CvigwXUZn3+gEYPA+f29JTlwOmZRkI4ivHoFAAQ8+hl5nqBIQA3PFXlAM1skYqk3GuT1uAQWNfeumlPjKksVE4dJQibFFj9rrGuzDkdAIYMBEHHSPLbplKOeCAA7yTwZqBox8m/jK6cp003QEGNIAWmp5jCgTnKniTTpAz1iMBDby4GYGU0ZX8sonnOIYBMLBasMzbtqr3+WXaHY9zQJJAO9/uJ/ULMMibbxQBDO6JT1F4OCVjmmapsFadrLnmmm5xUT6cs4m8E8DIUmTyf6vNmwQYOHUzhYg8Y6lEZ5L6lQ9Rn/f6ARjIAYsXvv3tb7ulEeCAzOCUz/QsUyE77LCDAx90BU6p6Gacoc8444y4xhprOMBgWnS33XZzuSorO9+algCDxkFZ3Hzzzb7sBqXNChKWD0kRqQH7OYshpxvAgEaiGzRg3g6FTYdIrBLWaHOdZ/JJzJkAxvOUgUYotEfNX4CVOygiRgzwaBZg0AHjjIUSKKJrEZ2h9TAAhlaRIFPETQFUUm6mTVgDz3e5108aBsDg+1k+ZdSGyRfaEucFHuzGpwlg9NOC4/+s+LYpgAH/2J4hPqiF51iVMWfOnA5P1qEYZawKMMTbfJPYNwwE8D386le/6itJiFOBhQKZYMn8Y4891pEH3mUQSVwipkp4DrDB0vmyhKxNW4ABwXC4UQApRoSYr2ACTNWceaafxPO8N90ABvWGyRFEGIrOceutt/YOEVQ82+YdeaaoIxSNE8B4fhQDjXCOxS8AUyrTDwIXAAwddOCA4l6BqMS/0JljGAADhYPPBXLDSo2ddtrJy2whwyNLWcvaXmUrOjcNMFT/LJ8y3YH5FxoTE4MpnbKych3ZTlMkRa01mdfU5k0BDPJDz2lhAbFvbBPEgfucqgCDVqIMAGuAhcIzMK3Jb+kZBjSUEb2NvrCovW71gA7ICPeYumU6HN8l5es/Mn+mLcAQ44DQMEOjYDANffKTn3RzPp0kaA3i9pOU7yQCDKw7cvLEC1pxMFC6YkJoBjMScIlVEHSCxNAAcEAbjnzStQQwnhd+TJIELVtwwQWdfowyEHhoKbDBmf9xAiVOCYJcBN6ytBb9mwQYRx11lAMeLBgsU5UCotNGWeHbQJRLvg2f9JMGARgsudMIMeu4jSKGP+FT6Ez0WeiMcsXiRvRN0SlfVtUhAYw8ZSb3f7V5UwADGcUPiJgt8Cf+DraNQiTWBDxZt8/pF2DwPQYELJclyCTWDFYIInMAB9sCwgPPMd0tfSHZQWawdGgKlEEFCVrl07QFGCIIQIJonShIGpwzHrJ48mPaxdyrZ/PEK/pfDDmpAIN5djo3pj2I/kaCwUDEmNSgm+0aGjfYYANnVByIiNkAcxYxIO/r+nQHGNABYWauk3lR5mm12gGAkQUX/M/BM7QJzp5ZWvo/uT/kr2+gMAcNFc43ATeUC8WEomR+mXlZ/he4ZOUG3+0FgHLF9TwYZVW10Kj+zCczNaPvC2CgvFnCStwBANxJJ53kJl7kHksLTsrKg/LmE9eSBSNPlcn+X23eBMCAUuSnKQOsfvAeK6623357D51NX4Qe1LP+o8cf8uwHYJAdfIz8ogMI0Y0Vgu0ysEwAeogqCtgGhGPlx1eR0Ob0iTiXU24GD7anUlfZntYAA0LTOJh0ieGAs5cUN+gMpcMSQZ7hqJJ4jsabRICBXwURJ6ER0x7sq0LCYQ7nPkaBdHjQkd8IDsi4Vwch2iaA8b8O1uggGV0QXY/RBWZI5kQxpzJywNIGfwp8MNrAYasXn+q+RiSDAgwUEJ0y/MCoB6cvlI4cPFFWgE2S2tj/qfinrgXjrrvuiguYtztlWnbZZTtLz1Gk+++/v087waPwKnwKTRlRMrIUjYqKyL0EMIooM7nX1OZNAQwoRZ6AXtvQ7EV9Do7SyA+rnPSc/+jxh/z6BRhFWTJIFMBQJE/KyW9WAzIdi7UPeQccsRKLvpPvcxSlUQOMl1Aom1OulHjUKuI7QZqg+66JFtzHr9noOZiJ03/byKVSfjykPI2QwYLuhOuvvz7w27xsfctcdgG1JTy+ixzPGnLrmrfym5TdVEUj6mVmtQCd2VHVFHiwvRuCjbR9V00LquXbkNsGWN4+xpDBfFuCBWYJFkzGaVZGO9HV5vaCKfowZ86cYFEWfZc+E7pgHY23hXVgviumdQy+66B1IL6zH/mW5d21sVp8E5qYcPrOtNaR+7bo1kEG6Gv+GX7YCMR3DzXA4DswfvnLX+7Qo6hq5EkiXw4Dhr6r67HHHhvMChJsY7JgoMVlohs9yYfDnLuCzR8HA5e+cykyyfvWYQezIAQL2x1sqqSzDX23PIvKS3vDZ2ZtCDYl5980a0nRo51rlMum8gK0sMBl4V3velcwX5Zglkmvr43EgoWx9x2BeZYt5c3RLthql2Br/T2fsnLyPPon7abaIffE/1Cbw9vIDLufWmAq56VDDjkk2JR6Jb7MEkp5WuccbGl0sMFosNWL3ufA8+S/3377ZV/p+pv8KJ85jwbbRsB53TbWDLbktLRsKgNnEjyP3NCfoudtYBNsJaDrm+OOOy6YZaWzS7ZF/XTdbFE9XbeTR5nMIC9m4fZdyqmXAftgwMTl0gZJXlbqjI7nsMGJ63YDMR1dVpZ3V6L8382RAwzKAYE4zHQVDMUFm2N1hWkjRW8kGqpqJckHok4SwBCNDCUHG+W5cqaTnzFjRmcbbEP4LiR0gjA7AMNG3M4k0KMb6BODJoDxvFRAD5QZPFfWoUJTtks306YrAQTTphN60pkvDAowxA+Uge8/ats8A8gpLwrERjfeccMjSlXlR89zrgMweA85tiW+AZ5EhgEOgFHJOIodoMb/AAzAEL+r8CnPJIABladHEl80CTCgHPmS4FWz3Hrnzm86WbMidAZl/lCPP+TVL8AoytKWy/p27Qw8APTm9OzggAEffaIFAXT9Aug3/zqXdb5dpqP4BvIy7QGGCFGkYPpVjGLISQMYYkgJBv/naZO9x33RAnCRf5b7SjzH/QQwRJEXQC88yUHK0hChhq5Zmmfvv5DTC7/0bBMA44VcX1CW+Wt8hzJ2U0DZd/K/6wIM8lFd+Z2nS/Ye90XfbiCY53iPZxPAgBrTI6nNmwYYUE+ynee7PL/2ojRlbAJg8B3JRrYMupYvB+XPl73omQQw/o8qEAylmE1S5Nlr3X6LIScRYFA36AOdYMBs5yHaiRnz93vRjOcTwOhGpRfuQWMd0J3f8ClmxW5JbdMUwCA/tXv+u+KNrKLKP9Pr/7oAg3IxbUTi+9BF5aC8KGMlrqusulZ2Vn0TwCij0ORdV5sPA2BArSL5qdPnNAEwqKtkQ2XgGvqCczbpfvZa0W/qlwBGAWVEUCmmgkcKL/EeRJ1EgFFY4QYuQjPonABGA8TskoV4uimA0eVTjdyqCzAa+XhBJpLtBDAKiDOhl9TmwwIYWbJJD2avVfnNe00AjF7f4jukfvrEBDB6UbXP+2LIBDCqE06ClQBGdZrVeVIKIgGMOtRLUyT1qDbeb0mfTwXAqEspyjgVAKNO+RLAqEO1Lu+IIRPA6EKk3C1oliwYOaIM4V/oTEoAox5xJdvJglGPfuP4lto8AYx6rZcARj26lb4lhhwXgGEx6Dtz+KWVGvINaJYAxpCJbNlDZ1ICGE6Gvv9ItgUwLCCaL9djSTVL7izuix+sBLBgRYEl1izb1XJq5q1J/ZiY+y5keqFRCqjNE8Don6zQjmNifDBs4xX3f+jl2do/qaq9ATFJ8sHYfPPN/feGG27oy45QOCghlvGhgFhCp5gOcjRD+TSpgNTI6lRYovelL33JlyPZRllhn3328TJK+VWrabNPUUbqzBLhFAejWdpmcxN/iheK4mBY4ClXCk3yYLYM/fxGTrJxMCj3qGSbckM/ZBuAYcGHQhnAwHcE+QZ0JIDRT4u371m1eR5gsGSTOBjEnWgDX1IGBrX0OfQxxMEg/tAoywbtOAhvYBukhS233LIwDgZyLlCO/mHJfeviYBAsiMA+VGiUnSUiQqPecsst3tgopLYCDJSkRY1zpTnqDkUAw/aF8bgK0z3Q1jBULbIh/oRHswBjnXXWCbNmzXLh1nPDKEM/eRIcSwDDopWOHAhTdgEM2+XWAYbtGutB4Rg8oCg5EsDop5Xb/SyyQJtnAQYdJQDj4IMP9gBtbehzKJ/6HACGbdng8TRGXTa+jwXD9i/pAAyCkxHrQ1Y/5CULMAAZTQ24Gwm0RSVsf4Gw8sort6KzRGTuvPNOj1jWZoBhoaiD7RvRGgm3kOIBq0qK5DmcJkFOSIALAYyZM2cGwDmAjki2owaa2ZoTMRCAcfbZZ4cFF1wwe2ukv7Fg7L777h7RdrnllvMoqCjLLMDAOpksGCNtpkY+jszkAcZWW23lEXQBGLZZWMfiJ/lq5MN9ZsK3LUS+RxYVwLDdwb1sfWbV6OPoEywYRCvFuoJcYPUBYGC9p6wCGIAMphNbBTDoJBmJEeqXiJtE7xulBQOC0tiEXT3jjDPcRNo2C4btcxEI7W27nIZVVlmls/a5Uc7qMzPoRgjsiy++2KOo2q6tgdHhdA0V3if5Kj0uBZgFGCeccIKDcyL32SZGLuAo1DYkQnyjdAhbPO+883ZiW4yybPApUUsvuugij1yL3iTuJgAAQABJREFUDCHfKEtZLyizAAbKMvlgjLLFBvs2MpMHGFgw2E4CebG9bnyEDl+MIlE+fZtom2eddZYD26233trBOdaDUfWHKhtn2zAtnHPOOS7PAI08wGBKkYPpEY5WWDBsZ8Swww47eFhgCfEoGrnomzAlwX5sN1FXQIRxboMPBnHmbXfTYLte+jwXdIMB2pAoB2gXRmMPi+WXXz4BjAYbRu0sgMFeIlgtGIllQYWe49PZ3w0WpVJWfFvKs9ILU/wQoAILhu0c7AADYKGDkOnyr0LGUJiqi85TXNz0uRoUgAeRDckMe5EAMGyH4E5ukhGdOzfSj7ko8MY3vtH3WrHduF0+sF5wAMg5ABeyYCAnOubKqOKFgadIABhsFMPGWuwrQAc1SgGWUmSPBqZJUDSMcNoCMHDyxIJhO6H6hjOUC+EZJc3EK8xrMo+IQAMwUN7JgiHqNHOGP6UsGd3cc889bmmD9iTkB2CsOWee5Z2s8sz+bqZUxblQBviSEVgb+JMyUHedkW1GsGyoBpgAXACOUZTwLddQmBr88F4b6lFM7XS1iALifeQAfmSvpVNOOcV1O/eQF65LXtBdHHqvKM9hXuPbbeMz8Txnpj1ZjMHAG/mR5QKZ4X9kRRYM1UPv16HbwACD3d9QiHj04uQ56ikSiABz4WHO9A2/2zZFolUk0M62o28NwGBUsP3224fHHnts2u+mWkeYqrwDP6KEUJjIDb/ZNI3Nvzhj1eDMgSxJgUppir+rfGvQZygraRAFM2gZsu9nFR4WCUZaUooACo3C+I3/xUtf+lJXmE15xGfLkn5PDQXgQQ6BcuQB2WA6989//nNHZpg2439AuwCH3p2akr6wyinLp1P17W7foTwMEpADZES+SZIXZAiZ4eCZrMVPdemWf7d7jQAMGpJtxEFG/KZQo0woY0bieMvCmG0FGHjCaxXJKOfpaCva7IEHHvBlquzOmVaRDIeDkQ8BDBQhIAKggWIUsJCyzAMMlYg8pmOSsuMMwOBAIQIksFagKFGeAhdaoqrpEb0/HWk3rnWG17Myk7XuSV4A5ZIZAXJkS++Oa92bKLd4nv4FORDQkJwIjPM/MsQzWYBBGcijbmoEYKAwWUWy5pprOnqkkKNIMBTEoDysSf7Upz7lTNZGgPH973/fV2wQBwMQNEgjNkFrvi+AkSwYTVC0OA8pveyIjJEXW0UDKDhrJCZlybN6rzjX6XUVXkXHAC4AEZh0ARkCGlzTdZ6RYoVKo5az6dVSg9cWvidlQTnyAaBAXrKyI+uFZEvvDl6K8c4BnpfMICvIBrLCGVDBIZmRXAFEJCs616FCYwADCwYAg0Yd1WhcBIDBxiWSZ5sCbaW9SMRBwzsLKGQVJqMtwAQKEyXJwf+M1uBlntV7wyvZ+OQs5SdlyIiL6ZLswTUOAIaep4aDKMvxodDklFQggTNygKwgF7L6ycqHzOg6z0lmJocS9WsCz3MgL5IZZAWwIZkRIEdWeEbv8NVBZKZxgEHDUsBRJTFiAhjVWwCawUQJYFSnWd0nswpT4CGrNKUkuZYdiem9ut+dpPek/KQMAREcAhUCFrJeZBVk9vck0WSS6wLv65BMSGYExCUvnOmDSElmXuAKyYxAhmQEkJGVHWSqSUCeAIaZh0BxEF6EbVIJ5QUjGyqcSJ7JgvGCEEyXX+IJzhptoRilJKVEdS8pyrk5AxmVMuQsxSlQof+lWMmhSbmeu0TpyrAoIP7PyotkJCs3khcBjGGVZxzzlRxIbiQnOiMv/JaM5M9165wARgIYjvRhqGTBqCtG/b2XVZhZpZlVkPzmXvbZ/r4y2U/Dr1KCeaCRHSjoOT072VSZ3NpJDrJyAcgokhmooOcnlyL91SzL/5IXDar1f15Wsu/097UXnm4VwBBTDFIx8oDp0hTJC43c6xc0g+YJYPSiVHP3xevkmFWa4t/s/ezv5kow3jlldQS/ObLAgtrpun6Pd42nd+klA9lzXm64p/vTm1rFtZfMSC6yZ2RHKfucrtU9twZgCIlSEVU8W+mqFYTBJhVgSIA4i0ZiBtFHAgYNyp7RszorvwQwRJGpOautis66NjUlGe+vSAay/C6epma6P961TKXPywT/64A6+fuJYsUUkDxkz/qtN/L/63q/51YBDExeMAmVk/mm3wrx/iQCDOpEyjZ89jf3JGxcFx11zj/L80p6JgEMUWRqztA9m/L/Z++l33NTQHybv5Pl9ezv/HPp//GjQFZG9Fv6jtqU8cT41XQ4Jc7SJ0u3rJxkfw9ailYADDrP3/72t+Hhhx/2JXp4uC5ouze+5S1v6ZthIOCkAQzqQ71YYaCljDjkEFgIL2AS9zlwelLgGdY3E0iFJUjcK2Mc3UsAY1Bxqv8+bZBNaqv89ewz6fcLgDtPJ9Ev0WgyKZBvb2pJmxddn0wK1K9VGZ2GITMjBRgwAwed5jXXXOObPrHsiBCmbGiz7bbbOhX7qTj5TRLAoD4Ai2eeeSbccccd4dZbbw3ss8KmNUQqZR8GnoFGRLbjme9+97seSpd48yuuuKJHWAVk8EwRLfV+Ahj1hXZYb9I2KZVToIify59Od7IUkNxnr4377yQvvVtwKmWmFQCDfRhOPPHEcNBBBzl1GJlvuummYebMmT5V0g9BYLBJARjUhUh19957b7jwwgsdOPz85z93Gr397W8Pxx57bFhrrbU8XgIghPDoX/nKV/x5rEBMOb3tbW/zpbAbb7xxxwkuz4JSNAlg5CmT/k8UmBwKqPPlrAPdylHH321yKJNqMiwKtAJgzJkzxzvBiy++2EOYYsVg5M3+9a997Ws7I/QqREBwJgFgSAGwqQ9RUk844QSnDdex+AAwuL7qqqv6tAjbwO+3337h/PPP911tl1xySQ/9DW3ZFRVaasopr0zIEyWTAEYVDkvPJAqMBwWQa5J0ieRcoIIBCFOqJKZa83rBb6Q/iQIDUGCkAINyw/T33Xdf2HrrrcMvfvGL8I53vMN9MRh5n3322Z0pgKrMT36TBDB+//vfh29/+9vh8ssvD0sssUS4/vrrfRpk4YUXdtDBDrYAsptuuilstdVWPk2y9tpruyXjpJNOcssQ/iwHHnhg2GyzzZw2ONBmkxRPAhhZqrTjN22TUjkF6CxTeoEC0n8vXAkOHNCJAhRYO5977rnwq1/9KhD4Dz+tVVZZxSOhZt9LvxMFBqXASAEGwgDT33jjjd754XvxsY99LMiSccQRR4R11123sFMsq7gEbBLiYIg+WDE4oA9bvUOfLMD43e9+F84666yw++67hze84Q1h1113Ddttt10477zzwk477RRe/epX+zbsBxxwgAO6PFjjO8mCUcZRw78O/TlSGowCWbCR/T1YruP1tngJQKEol0yzPv300+Gpp54KWDqxajLV+thjj7le+ehHP+rTrdIL05V249XS41HakQEMCQIrHjDf77LLLuG9732vd4iHH354ePbZZ/0anSbCkh91l5GXfCfBgpGvH3Vilc0ee+wRvve9770IYPz6178Oxx13XACQYa3Al2W99dbz5z7zmc9454VDKBYNlAg0yioR/Z8sGHmqD/d/8SptS+J/nfVb9/xG+vMiCsDD+fDGXMvydvb3i16ewH/gGcAEgxEcwR9//HE/fvazn4VHH33UAQUAg2eU0BdHHnlkWH/99fvSs3o/nRMFulFg5ACDzvHoo492c//KK6/sfgR77bVXePDBB8MWW2zh28ADLqoqCintSbBgqOFk2mQKabfddgtXXnnliwAGioQpkG984xthoYUWCkcddZQ7f1533XW+ZT0+Gx//+MfDrFmzfGteaJSlp/5PAEMUn5ozdBe/cm5bUpmyvNKWMqpslAcQRhk5AND6rXLr3JayD6sc0AQggW/WXXfd5VPOgI18Qp/qWGqppcK5557b8c+aLrTK0yT9PxwKjBxg/PSnP3WTPoBgo402CnvuuaeDjEsuucSXV2L6x8SP8FRhfp5D4UwSwKBOHIxEigAG5s4vf/nLvtIEgIElA7Pntdde68t9//SnPzktUSSs0MnTUv8ngDEcISvKVW2KBQ8ZoI1kXeJ57itlf+vaVJxlPUHuqsjeVJRJ34AmbFI4Y8YM9yHgOp0mNMweKrfOen/SztCDOuJXccwxx4TTTjvN/bGw8JC4xzMMVvQsvhdYNlnBx/1Jp9Gktfk41GdkAAPioMCI20DMC+I84Oj56U9/Ohx//PHh1FNPdadGHD0XWWQRFwoUR6+E8Ew3gMFcKruyAiCyFgwABlagZMHoxTVTe18dN6ZqnHaxOOF0R4J/dUxtqcq/RnlIbeiAKIs6w9e97nVuocO8j78BnamOLNjQ8+U1nIw7aiesFuhQlvnDY/Cb7qkdoQmryvDLQkfwDDRLKVGgSQqMBGDA7Bx/+ctfwhVXXBHkJ8DSSvwwfvjDH4a7777bO8t+HT3Jd7oBDEYtTDNhGmX1DT4sOMteddVVDtigB3FFGKmgRKBRtrPQ/8mC0aRoledFe3DgnEubML310pe+1Jdky4qRbZ/ynIZ7h/l6OmyWimMtgE/akBiFEzuHMmH1ZHk2AIP/if/CkQcalLsNNB02/eAr2gmHzkMPPdT92/74xz/6Z9V+0AE9gL7VoEQ6YNjlS/lPLwqMFGCgYDHlERwKhmctNmeUBeBjnnnm6dvRE0FByKbTFAlh1vG/YIXJm970Jqfn5ptvHi644ILw+c9/Przyla8Mn/3sZ10RS5Fkla2uJYAxfOGH1uJRliB//etf9ymtD3zgA+ELX/iC8//wS1HtC/AQq5JwtIav6NirWBGr5d7/U9CNxIqIk08+2VdCfOITn3DHcK4TrZYDoIEuEdCgzOJ3nXl+EhM0op1IDNK2sqXrjzzyiNef69SfA0C74YYbuqUYOk06XSaxrceiTsaQlZN13NE6/2idfzRUHGfPnh1t+iLaksloKxui7vfKkOeM2eNjjz0WbVokGnPHeeedN1po8Lj33nvHTTbZJNr8YHzVq14VbZmlf5N3qiSVwZa+RgunHQ2kROtk4ymnnBK/+c1vxquvvjratEz8yU9+Eg3lR1utEm0KIdracC9T1e9UKQvPqK4WqyKauTLafHs0k240xR1t5OrZQItuSXlQZrNMoGWd5hZe3V8jX/NZiQYk/Nhhhx38O9YxOG1t2iQa2ChtH9X5/vvvjzaqia95zWuiTVtFU+LRRjjRnErjD37wg/jjH/84Pvnkk9GAYTSfgaHRrBstxv0etEaG4Afz9I9moo62Z0y0GCUd/uOZNhzm+xQt9kqcM2dOK8ojmiAHa665pvO6daDRLJ7x9ttvjw888EA0J0eXa3gUHQWdobfeHXf+6VV+6okuM3+taAOOON9880UDXdFAl+sCdK0BrvjmN785Grh1uvTSP72+me4nCpRRYCQWDJCXFSj86Ec/8ngNeDyvtNJKHoWSeVWQt4EMDwJjisQDbhEDwoSn5wiKfHluulgwVl99da8vjoJf/OIXfU6faRKuE3wLx1CifWIpMmXjoDc/WoFmXEsWDCfPUP+IPxlNshSbKRKWCTKlhRWKEbjaY6gFqZA51ov555/fzejvfOc7fWSMhXFUSbRjpQTxXe68805fLYXfFuXCgZnjZS97mY/QoaWsorK85Hl/VHUZxnehD9ZfLBYsW4efmN4iiBY+Puz3RIIWTEWfeeaZ4T3veY/zm+gzjHKlPKcxBYwpKyfQcVMWDKwg5n8RDVD4SARLBfmDphmhWDRKH6m/733viwY4Ovd6FVZlnEQLhimOaMtNfQSC5cicOJ0utAl0wzJjiiOayTOawvVRi5nefXQn2hbRj3ukZMEook6z16A1I0z435zx4r777hvNXB1trxi3cPE1tUezX+4/NyyIyN9DDz1UWf76/0r1N6ALvI5+WG211dzKaYH43Dp32WWXRQPU0fbt8dG7LX+PNgXlNB2WdbJ6yYf7JHThwFqD3rPYFh0dYXsY+cdtKXs0X7dovinRQJhbi7nRFl4bLoVS7qOiAOi1cpKANzFFgvkS5sd8jxKzpVXO7CgDcy6LFngrmpdzNMuGKw6+XcWUpzJOIsCwOBhx5513dnqhWDENiy6cUahMA1k8kWh7lcSPfOQj0cKMu+KhkXmmKOl6AhhF1Gn2GrTOAgxb/ZMARkUSQ7s8wABwM433ne98J1rcl2jW0AgQN8fnaTGVB03Qi+hkBhi2PN1BxAILLOBTsLbDslOX+7NtSnuxxRaL5k8TbZWJX6+iUys2T3osUWAuCoxsisQEw5dQscU45k2bh+4EgbJSelwAvJ+5J5NnFUMT75L3JE2RqN7UyxSFm0ExaWIChj7ZRP15BrpWpRvvpCmSLBWH8xs6k0yp+8EUyde+9jWPW8D+MZis2zRFQvwZ66h86pIpEso/SlM630cGiPuC8/Jtt90WPvShD4UNNtjAp0TMdyiY1cUPTZXg8MmKEsqtsk/KNAn04DDLha+8Y6k6NHnrW9/q9Nlxxx1dP/AMCb1gIMN5Dsd6NkHknujiD6U/iQINUmBkAKPBOrwoKwRmUgHGiypa8g91R4FyQAsO/V/ySueZ5INRRqFmrtMWpAQw6tFTsp0FGMsvv7zvV8QABX8DQBEgQwADwMYqiUkDGKIFoIHBFIABnzY2i2Q7AXPS7si1ABXvEBOHJaz4uuHXxjXdr9cq6a1EgXIKjBxg0CGS8p0gjM9RdM8vlvzhnUkGGKKJ6FJEBtEuT9OiZ7nG8zybAEYZhZq5rrYTwGCZKhaMY489NiQLRm8aQz9kOwswGIVDOwEMrBiADDpPLHiTCDBEB6K/EqiNKL44vi666KLBVuH58lPJdB48iAfz13tTPz2RKNA/BUYOMPovcvc3JHyTOEXSveb170oZJYBRn4ZV3pRyHzbAUHtWKVO3Z8ZhiuSDH/yg77MDmMCCIYBB7JdJBBi0LfzDqhD2JGJahP2cFl98cY9zw6o7tX8ZiMhaObu1f7qXKDAoBRLAMNMq87T4MmBGRSjLBLMOsRF2KQUUwxNPPOEBsW655ZbAHCkKAoEf5TyoFFICGHVauPo70Jk0LIAhXlMHopKJr/V/1fMgAEN15Vtl8qTyVpU5ns9bMKYTwKD+5uQaCKxHEL1DDjnEgcb73/9+jwaLNUe6pIzmVds+PZco0AQFEsBIAKMz4kkAowmRKs9Dne4wAIY6X/Jmnt1WqnhBAM+M5Ot0OHUABuXg4Huqbx486xkKSHnlqNwLaPBeHmAQAZWdgmXBkB/GpFkwqDvggn1GzjnnHN8OANqtuOKK4bDDDvN4FtBGtCznwnQnUWDqKJAARgIYnQ4hAYzhCp463KYBhjpeVqXQhjfccEOYM2eOd/LLLrts2MrCRbNzJs/1AzT6ARjkzUHdMN+zVwgOiPhAsKkWoalJeoZdZC0qrG9ySEAvQpHjmNmtfLybBRgWXTbkLRgADJw8ARjUeRJ8MKgz4AJ6sQkkew5RrzXWWCMcfPDBvmdTAhfDld2Uez0KNA4wsiOSekUa/C3KgA8GeykgeMTcJ3IdCkfztCggHMFwDhvlFMlXv/pVV7r5Ud7gVKieA4obxZ4ARnWa1XkSOpOaBBjqdJmHnzVrlnc+7PEDP3GssMIKweLNuG+C2rlq2fsBGNQJ0PDggw/6CPvyyy/3zdxwPCSipMW6cVnk2xb0yfcSOeusswKOrkSYBQQxZYgPRVlSXeXkOR0AhsCFbavgDsEADIAYjq3777+/g7cELso4Jl0fNQUaAxgWKCtYYCc3zY7KTCcFyvnmm2/ubEPcVoDBWn75YHQbuQ2bSaAXnRHL3Ai7zOgXszPmZ0AZI0I5z2VBmZb/UfZRln/Y9Gkqf+hMagpgKD/m5E8//XTnJfKmvWwfHp82sIiXPj+PTPJ8P+1UBWCQJ8czzzwTzjjjDO8EWQZJ4vq73/1uj++BEyJlo6ysnMF/gDDeAHxWQwD42extr732cjN/UTnJj850ugAM6stUF9sAEE7eguj58lKL+uptzWApgQtntfSnpRRoBGBgvkNhsOcFSmRUAEM0pgxs+b799tu31oLx/e9/P2yzzTa+WyyKpEihqj5TdbbNzHw/E0aY7I2B+TkBjOaoTzuT4E+OQZepqsOF14l7wFJFphrYOdc2DvTOm2kKgEId/uoHYMAz+9m26Rb+P7z+9a8PtpGbWzDY64IAYrZpmteZfYawLFpU2rDUUkv5SPy8884LtlFZWHrppd36YdF9C616qu90ABjUFXCBRQjdapsZuqWHdibmBVNOPDNKy2dzkpFymlQKDAwwbOdOdzyiMyLqHwqtDUzPKIrNvuggifSHibYtUyR77rlnsN1nfe7UQnq74q3TATTNlJi477vvPu+MLOSwR/pLAKM5KtMhkJoAGOTFgUWALd9tTxPnb7YvnzlzZsfnYRC+6gdg4HcBTzPaJromnSDAJwsw8BHBQdG2AfCpSs5bbLGFj87ZnMv20PDOlI0Oi0bm1HfSLRhqV8AFssgUKtE30a3oWqw8TOmSBmlbzyD9SRQYMgUGAhi2RbIzPTt24nQEsEBA2sD4KHHADhHrmCJpC8CwfVYCAIM5akYhhDFuS0J529bv7puy1lprBaIkJoDRXOsgG6QmAQZz87Ytd7C9OMKMGTO8g950003dKVC8VVceqwAMUUedP9/CEmYbawX0QxZg2P4gvvqBKRLCWR9xxBFu9cR3ZPfddw84e7IjMNMk5JcfqOgbk2rBoH4c6C1CfgPS2Gma3ZGhCxbZOlNdaqN0ThSYagoMBDBscx0HGJhDbYMtVxog77oKranKI6R4XF911VU+D902HwwABhH46MA1rTRqmkF7HAVZX8+ceLJgNMWNL+QDX5KaABjkQ36Y0HGQZOrhXe96l/tBMNXAvXwHzTv9pH4ABvnyTUDqww8/XAgw8O0h0uT5558f2Nvk5JNPDksuuaSDo+22287B7NZbb+17s5BXXiaU/yQCDNUNgI+DOpYLfKIWXnhhB19Fob/7acv0bKLASChgjF05mfJoZLv2yh+s8aDKyM6B5p0e55lnnmjOlPGUU07xnUavvvpq33WQLZ9tGiWa2TZa3ADf4dIUf+mOozWK4q9QHvJlK2VTHtFMyHH99df3ctkSs85uqHXzH/Q9ysdBslUk0TqnaA6d0RRatA7Ad6q0iIHRPPajjUyjAbdpsUvloHQtel+0NhDuPMeW4tbhDrSbKruHzj///CAX32XXfBn80/DcoMmcRfverp3vUgZbHutlMgtGNPDjRUHmbGmlX19kkUWidaSR3T5tNUk0h2Hfft2cjEuLDf3M36uzXbs5iPq27ewMak6u0fwUPE/4mJ2HzfE0smszssd7on/pB0Z0Q/ViN2R2P7b9RJwe5rcSL7300o6O4LmUEgXGiQIDWzA+97nP+aiFtdmEqcXhc9ROniaIY7FMVatITCnPNVqbarTJaBFHO0aQmN3TKpLmW8AUg2dKe3MM6uRJZlgu1ltvvcDUG9OArDRgaSjfmmoLBuVB9sqmSLBs4ICKb5QBjGCg350/L7roInd4xrdgo4028hUxeesFeVMn8p8kCwZ1Ei9cdtllvjoEvxosOwcddJBbhnkGehTRBLqklCjQWgoY81ZOIGhGAjZH6COD2WYlYCRiZrxoDl6OtLk/yqQy3njjjdGW6rXWgmHzzfHAAw90UpmCGSXJOhaM+++/P5piSxaMIbUGvMnRpAWDNrOloG4VMGDhlibJwKDVaNqCgfVunXXWKbVg8D3zNSgttuqFJcSW38Zxt2BQH6wr5psSbZl/tGWnbs2yadNoPhit0KeljZFuJApUoACjgspJAp4ARmWSuZIAQOSnSBLAqE7DSXkS+eFoAmCQD3xFZ2uWQ++0mSqxWBSdb+h7denXNMCwpazRls96WZkGuOaaa3yK8qSTTopm9YzIRLdpQ+rDAGYSAAZ1QSdAEwuYFS2+jE8R2fLwCGhUXeu2XXovUaANFEgAY0Q+GAlgtIH9p7YM6vCbAhh0tvgR2QqMaOZz76Bs2af7yHCPYxDrWNMAw5zB3WpHWS18eDzttNMifii23biDDlsFE20Za2nnqk533AEG9WCQZlM90VYARZsaitDaluf6NdVzarkzfS1RoHkKNO6DYcIxUh8MI5HP0+KJvdlmm/nvtq0iYVlh2k21tbOGQysYvEmyTr8z786SzWOPPdYDThGQiuXePFdlvp3nbBQcrrvuOo/Ayv4fLMs2B10PZoVPA5EyzbJRq079riLhI8h/mQ8Gyy9ZPUXsCwM/vnyc5aw2VejXWf1CWHH8M6hb3oeEa+Q/zj4YlJ+6ExSN2B/UFzrjR0Po73nnnbew7rUaML2UKDBiCiSAMeK9SBQqPK9Mp5Iv1KGlvUiGS3XoTGoKYJAXeRJf4qijjvLOimWO8BKdlo2K3fEaB2yuqZ15r0oaBGAQpVaBtthzZLHFFnNwMMeWqu6xxx4elAsABCBih1B2QyX6KFEryxLlH1eAQdk5ABcAsMMPP9yXhNsqNx8ImRXH24xnRqkLymifricK1KFAAhgJYHQ6ngQw6ohQ9XfoPEhNAgzyI9/HbOUP+1UQUprt2umI2Stm9dVXD/1aRsiTVAdgUBY6UFaX3Xvvvb6ihUij2uyMuhNEimBaNtXhtMDKstJKK3mnK2tLkQWHvMcRYFBuDsAFMnbAAQd4jB7CuhPDBFrIcpXAxfO8l/5OBgVaATBQGgggSYqFs373Q2opoUmeIsnTKksf3cte43c3WvIO9xPAyFOt2f/VNk0DDEpJ3gRIu/POO3367emnn3YLxjLLLBOIylqn46oLMNj4zGKn+NJZNl1jyTMjddVfUwTf/e533frCTsdslMgeJuLFIspzb9wABmXmML8bbxssluxDRFh0QBihv7Wsv5uMFtEjXUsUaDsFRg4wUBgoXM5KKEMJXb+KUUpo0gCGFBU06pcmomvZmbwTwCijTnPXoTNpWACDvJvspOoADMrQK4kO+bKKD8ve5/44AQyVF98TtpYniuk999wT2H9o11139dgfZbQoo0G6nigwThQYKcAA1bMp2SOPPBIs4p4rXvZPQLFhKmVEw34deUXUjcAS6kkCGChVDkzfbEgG3aATW6hjWlXC4Q9HP0aIWZqxLTa0LAMm0IznkwVDlBzOWZ3JMACGSpwF6rpW1u66X3YeBGBQDvEVvJXlR77HPdGD/4ue4Xo28Tz5joOTp8qKVemGG24I+FiwZxNB0NhjhIBiPFOl3lkapN+JAuNEgZEADASLgz1MTjjhhHDYYYd558c1LBeACvZVYHMfdodkjrZqIg+U0KQADOoDsHjiiSfcMY6oh5i/bZmfe52ziy3PkJjzZmTEniI40Ok6G05BZxzquFak7LmWAEZVLqv3nNpjmACjXsmK3xoEYBTnONhV6DcOAINy0sbsMMs00P62OgT5xQ+FPUaYMpIc5mVxMAqltxMF2kWBkQIMOsKjjz7al2sxEmeJFp7vOKwRRpl5SrzKN954YxfIKiMxKaFJABjUBcvOtdde6xtAsTsl10iYWW0PhvDhD3+4Y3K3wEW+RBHFhtVCymuppZYKF154YWceXNfFilJ2CWCIIsM5q+0SwKhHX+jXdoBBGWnf3/3ud+Hiiy92h05+swSX0N9sZU8d0GV5OaxHlfRWokB7KTBygGEhcj0OAM5gjL4333zzMHPmTPeIB3Sw9bRF+nPLhjrCbuSUEhp3gEE9OJhCYnWAbebk4AuTK9MkAAysEhYy2RUa87xs2Y3VB4sPO7Va6GG/x3bPO++8c3jlK1/ZGTllaSi6JoCRpUrzv6EzKQGMerSFfm0GGJSP6Ukss+edd16wqKT+/worrOBWWq2kkX9ZPSqktxIFxogCJhSVkwl3I3uRkI8pWd+Z00AFWje++c1vjhZ0yKP43XrrrdGWcHmEu3XXXTeyyyCJ93ollXHc9yIRjcwaEW176/jJT37SoxyaxcLpxf4vhFrmOUIO2wZJvp8BtGQ/EfNr8XvQuVcSXdNeJL0oNdh96MzRRCTPwUpS7e06kTyr5VzvKWhnHXgrQ4VTNqJzmvU12koR3yeF8N8bbLBBfPDBByvLYj3KpLcSBdpJAUa0lZMEHEFiG+TZNTc7I588wABQ2Ijct7G+/PLLfV8CFJwt43KFzDscvZLKOO4AQ/WETma18G2tCZHMXgWACAEM7gMwLFiRKzYbHcXlllvOlRrghLZCKXdLomsCGN2oNPg98fC4AoxefDQ4hbrnINluW6hwyoWcAerZrM0sr75h4Kc+9alojp2ut0ZNu+6UTXcTBYZDgdZMkeB/gXmfwEBEJbTdWd0pCgcp1vGbEHeWrnYzEBmZ/NlxnyLJ1lF1wnueaSQcxwxg+BQJ9DLl5Y6dmGQJPQwtP/CBDwQDGu6jsdBCC3nQpbI5X/LnXpoiyVK9+d/QmWSg0I+i7dpxzoXXy9qq+VKV58gU2wILLBDMghbe+c53epmr+EGV5zjYHejHQZjtz3/+8770EydnnCaJBEp5OfDjYjrw5S9/ua+yIuAY5VbZm6Qt5WFVFytEiJhK1FK+v/7663sIdMX20LcHo0B6O1FgvCjQGoCBEnjFK17hIII5zLe+9a3ufW1TA305RCHwKOhJAhjUhwPFuttuu3kQoyzAoMOyXRnDvvvu6xEC+R/nUBQpgZaOOOKIgCImFSlXaMb1BDCGK7zQmVQGMACHOOeqPYZbmt6524Z8DjDOPfdcB7Tw4Cg7Ssn2HAs3ToAqIoICpNdee+2RAAzKA7h46KGH3McCH6j55pvP/chYloo+45lR0qx3K6cnEgWGR4FWAAyQP45POCcygsOJ0eYvA+Dis5/9rDs0QoKizjFPGimhSQIY1ImDUVIRwEDx/+EPf3CFywocgMjVV1/tig9HWUZTp556aicccZ6O5J0ARp6Tmv8fOpPyAIOVVFibCBkNwKA9823UfGl656gInFgRAfw2DTDSckE/OmsihbK0nY79/e9/f1hnnXWmFGBQDg7AhU0rOrBnEzeWjm+33XZhl1126SwTT+CiN5+lJyaXAq0AGOwmiSkRQIHHNSM5BBawQRwMVktg7kSoeylenkFBTyeAQZ1RZJxJBNy66aabwg477BAAHHivn3322YF4GHo2y9JcSwAjS5Hh/Fb7ZAEGvE1niQUPfu/F38MpWXGuAH34irg0DABU/uKnp+4q5SA2DMHmll12Wd+JdKqmSPg2BzJ2xx13eHROLCksqWfahvDftGURrQQ2BCDV1sqT/3VNz2Tz0b38NV2fuhZIX0oUqEaB1gAMW0US2MYcAUVw2VnxgQce8Mh3J554YlhxxRULO8d8NRG+6QowqDcHNCCwD0F9WC7H/LmWtHJPik604xpKKk2RiCLDOUNnUhHAwHrH3H2bOgt2aQVY4NdQ1mkOh1Ldc4XHiS0BrQAYttJsSiwYtB/fBtywnwhTkgS3e8c73uG+UZ/+9Kc7wIJn2dlWU5UMkGhjgAnWRixVGjRhGeI5QBIgE8sIz2B95Hv81hQyssv/lIH75AHQSilRoI0UaA3AYGdBAAZzq4QO11QAI4MDDzzQtzRG2FB43ZKUwHS1YEAjDiwX7H1AgC0sF6eddpr7Y0CfBDC6cdDw7kF7UhZg2NJsD6KGsy7xTuhgaL82AI3FF188IJeUEZ8fOrU87wyPWnPnDP2gC9u7s+U7nftU+WBIrwAECHzHpmU4Xb/73e/20N+2HNXBBeWj/Z588kkPtEXwO4AAocE5GDSxuyzAaOutt3aask8JjrQbbrhhWHXVVcN9993nzqL4TRH6/9JLL3VfmK1s51Wi8c6aNSvcddddwZaj+34mDCBEm7mplq4kCoyQAsaYlZMJztDiYLBM9bjjjouG+qOZ96OFCo8mrH6+7rrrKq8jVxknZZkqjUOdrFPyZXBapmpKpRMHgyVwLGW1yKjRFFK04FweO8NWk0Qb+cQ11ljDl7GSD0c+6VpappqnTLP/i/42QvXl2LSXgcBoUxDRAsr5Uke+qPZo9uv958YycZtei7b9updp1EstoQtlmOplqnyXNjPfj3jmmWd6zB6zQETz/4jSTZSL55BT5M8AkC+1N/8Qb1vzY/Hl5LYCLNp0cLTpFG8Qm4aKBv6jBRqM5ovmz5j/lP9v+5ZEAxnRon/6O7SFRTX275pFNxrYiFtuuaW/Q2Zt4Zv+OS29MakUAPlWThJw1nwbko+zG4yDgbAgdASVsq2bo1kqXPGaD4Z3mny7igCpjJMKMMzxjmFwBGDYSMrbDlBmy3ojQbjMTBs322yzaN7s0Uab0aJ4xpNPPtmfK6OfrieAUVkUaj0InTmKAAYdByCR+3RWdFSjPgQwFCiKco+yTNCFMkwlwKA9iDMDGCQQIHoKQLjyyitHs5J6e0ETtS2MYTumOjCj8ydxnzw4BB523HFHvwfAOOOMM1xeyZ9nAC22gicuscQS0Swe0aaqPL4G3yVwF4OIn/70p/GjH/1oNOukP8P3+U5KiQJtosDIAYZNhXhHCKDgYMRtZuJo69ijhbuO5uzZEeIqhJOCnlSAwYgIRcNoBkVEsvloV35EDrR5XL/Pbwsn7sG3bM7Wn4M2RUnXE8Aook5z16AzRxnAACiS1B7NfbleTgIYtlqjLxms97Xeb0GXqbRgqK3Mnykefvjh0TZ/c/liAHT33Xc7TWS5oPQ8TydvTp/Rpk6iTWlEgt0JMAIerrrqKgcP5mvmFQZgmFO7AwwCDcIbDBzmmWeeuM0223iePGNbBbhVwxzi/T0C69lSWJdxQIto4zfTn0SBllBgpD4YFt7a5xpZQmmC5RNFOLqxVTvxG1he2e/8otHV50AnyQcDwlAvdlH91re+FUy5+Xp7ixTou84yN85yOYuA6nO/psjCjBkzfD6XzZXkoFc2r0/e3EtOns6CQ/sDnUnWCflRFGiL+Xq1x9AKUjHj6bybKm1gwMCdpdkHyKZvXY7wlWEDRvYCsk79RT5hvMOBg/UBBxwQZs+e7U7r+FpoHyC2bt9yyy3d3wLndRw2L7jgAvfpYJkyy/J5b4sttuisoMPvA18qtnnfc889fV8hfDPYo+mcc87xsvUTjLBi86fHEgUGp4AJROUklDzoFIk+SH6M2pjbZN4SMySjcfIncZ+jn6QyTpIFo2r9RS/RLP9/WT56PlkwyijUzHW1R7Jg1KMn9JsKCwZWCHQQUzFM29oqjWgraXz68Ze//GVXawFl5P0f/ehHPoXBVCWWjDlz5vj0B1OZTH/0smDgPyU/DSyQs2bN6vhpQD0LRhjx67AovRG/DtGmHmXTW4kCw6HASKZIelVFwoKg9pv07qQCDCkwztkErWSuFQ34v0pSXglgVKFW/WegM0cCGPVoKL4epg8G3wBcABDYS4RpWzp79hgxC2Kljpw8kMdHH33UQQIgY6eddnIfG02RCDxo+oNnslMkeYCBc6kcQaFeFmBceeWVlcpVj+rprUSB+hRoBcCQQCKU/Oaom3iXjnVSAUYVuvRLPz2fAEYV6tZ/BjpzJIBRj4bQbpgWDPIHXOCkadOz7hvGLs82NeG+FNyvMuhRG1PWOWa52GSTTeJiiy0WH374YV8hhwXDpkKcCAlg1OOF9NZ4UKAVAKNJUkkJTWeA0S89oRkpAYx+Kdff89BZnY/5zfiUoJapsoqkaSdPtWt/pXzh6enk5AmtaJMf/vCHcc0113TLhcXgiYceeqiDDu5XARdQj+fICyCJpcHi+PhqD4vbES1+RSRfVoPgAMq0MPdZpj9z5syOk2eyYLzAh+nX+FIgAYxnn+0oA1lQmmxOKSY8yOlAWF7G6IhRDIqFVFVxNVmubF7qiBLAyFKl+d/QmWNYFgzyZtQsftL3ONdJdQEG36MMKkv++/zPvfyhcpeVVe81OUWisrJEeLYtuyfuBNMiFlis0+HzDEeVxHOs8MDXguW95tQZbfuDaE6YPsVC2VlODsgw5/Zou6/6ihMsJdkpEqZDtJQVH4xuUyTJB6NKy6RnRkGBkawiGdw1tTwHI+JEriIpr/Hgd6BZWkUyOB175QCdSdaRNr6KRHzPiiJWHbCSiGuEn2ZlVp0InP2uIuF7Ogw8eB35LmGxs9+3Ttg3TsvTi2eIZFqWVEciaJoPw8DbtausBi585QaRby2omK9cI1Ioq7R4BtkoW4GVLavKx1YH5nMRWCVHGG+zRviqkpVWWslXjRCZkzD+fNeAhK+Y47tEAyWS8S233OLvW1C9YBYUf+6KK67w39SbDdXY8G2WRfRkB9eDDjoorLbaanOtasmWLf1OFBgFBRLA+L8dXG3U4kqwqjKp2lhSYupUWMJGSHSUiI1QXNHwTFYBV827qeekRNMy1aYoWpwPdCaJF5papkq+dOiEp2YXXRvRhscff9w7RQsIFfbbbz8HGWrn4tLNfbVfgAFwAOCY2d+XPLNfyGte8xrfwJAz3+fgvkXr7SxN58vInTk6BsqLLBYl3uUbTQAMlcV8IJxm7Csyx7aBJ6w+QGO99dbrC1xkywu4Iy+zVvj+I+Z/4SG++SbJ/Dz8njmBegh2dmE1/wynFbvWwhe8S1hwWyXi/MISdZ6fYcvPCd9uVjBvb/aLYSm/6FsFCGXLmn4nCgyTAhMFMCTAKHA2I7KIlq6QiPH/3ve+1zcGYuMmhJF16WwJry3iRw0wRqkYoBvfN8/5wIZNKEe26mafBzZTYgQMzehwsjQjvgbAiHdHWf5hCkiTeWf5Ex5tAmCQJ3mxf8/BBx/se1rQJlgNGD2zDTxxFmg3tXPVOvUDMAA4AAf26WDfmzvvvNPLRYdt5v3AviaAAzpfRuPEggCMiG+Qv1VWWSVcdtllvndHUVm51gTAoP7kRVkoL1YD9jexyJlOQ0AO3xFvV6VXt+ey9cn/5j3RoVse6V6iwLhRoHGAgaIpG4FMFXFQDgTa2nzzzccCYEiZTRV98t+RwksAI0+ZZv+HzqSmLBjkB+8wij3iiCM88BKgD7M7nSVgGmCNWZ3OUu1ctVZVAAZ5clAGi3YZCJpHZ0m5qCebgQlg8D/TAueee65b7wBAAB+Be4LCnX322T5NUlRWrpHvoBYM6k8+ADyLoumbzVlkXJ/GsP1FvD7Qa5BE/qoD9MgDiPz97LP8zv5POapeG6TM6d1EgaYp0AjAwFzH6IkRCEpklAADQUR4zRvco+LxfxstGDfffLOP4pirZSSVV0BNN3S3/KTMGAUzfUNnwfwvuzkmC0Y3yvV3DzqTmgQY8I6FjA9b2U6bTEmwdThz8vC8vleXt/oBGEzPHHbYYcGcG4Ptf+MWFaYDsxYMBh/PPvus7ybKtA3RMPF1sLDYThNzfPZdRsv0B/VBtgcFGNBDeVngrGChvb2ctsGi51/2/f5aOz2dKJAoMDDA2GGHHbxDYgSAMxNgo65Ca7I5CKVLSG1GSDhPUT46yzZMkRDuFzOxpmnUETRZ/zp50XY4jzFtZBspuXk9AYw6lCx+R+3cBMBQB2nLIIPtwulbvQMImOI6+uijBwYX1KAKwOA5yoJjKVuI49iIT4Dto9HpuLFg4IdAvSmvrC0MSAiB/YpXvIJseuoN1bkJgOEf/L8/apcqZci+l34nCiQKdKfAQADj9ttvDwAMBJ5OCeSPsLYBYDBawhyLoxSjubYADEZMAhh03uw90ZYEzXB6o1wJYDTfKurImgAYlI78fvazn4VddtnFHTuxHBx55JHuoMg3mIIYJFUFGPoG5eFgRQRAh9UUeQsGlo599tnHgQVTIuzrYUs03c8Hv5Fu1gPybsqCoTKTHwdTIoNOiyjPdE4USBR4ngIDAQwUCAAD06zt8tnZeKsNgorH9XnnnRds59HWTZEAMGzNvXcELIVri9Xnscce89Ev1p80RdK8iqCDJDUJMPCbwZnZtvX2ztziKri1gG8NKof9Agx11qyA+MxnPhMYgGQBBvVmMIJTJZt+AYCwXiy99NJu8WDDLgB32QBlGACj+VZOOSYKJAqIAgMDDNu0x0cAmGktAt5I5zClwFF0WkWCUmujD8att97qipYdEpvoDNSg/Z5FM5Q6nRUdA51AWkXSLyV7Py9aNwUw+CLTEuuuu64vWcRKd/7554dFFlmkMyrvXaryJ/oFGOSE7GHBKAIY1B/esm3Gg4Xjdt8jplRYTcLyzKOOOsrrUiYPXCd/8mgiDkZ5zdOdRIFEgSYo0BjAOPbYYx1gtGEVCYoIJ0qsAyikNgIM4mCgJDEX0+GUjdqaaOQqefB9RsFsLY0lIwGMKlTr7xn4ktQkwKCjtsiw4Re/+IWvGPnmN7/plsSyTrqfEg8DYFB3Vm9YVFuP/cCqk8svv9ytGWuvvbavOCnbsj4BjH5aLz2bKDB6CjQGMNpgwYCcUkIsUx2HOBgADEDQoObsQVgJmgEwUqCtQajY+13oTGoKYJCfhaP2FSQ4NONc+fWvfz2suOKKzlPd/Bl6l7a6k2c2r14WDPhcdOAMQNpiiy0cbCy55JK+TLXMAsPzyYKRpXb6nSjQbgokgDHiSJ4JYLRbQJosnTrWJgAGeXEQFI2lnpdccolHeGSZse3U6fcAGADHutaxYVgwKIvKDljAWoaTKstbmeIh/DVnnsmDbq4lgNEkR6a8EgWGS4EEMBLAcGWeLBjDFTRyp4MkNQUw6GxxsD755JPD/vvv79MMWC++9rWvhfnnn987aNqV4Ft10jAAhkAC4IfyE/ETR/GHHnrIQ4ozxUOgMJ7LAyO9m3ww6rRmeidRYOopkABGAhgdZZ6mSIYrgE0CDEpKfoAVgsrhO8OSVZYYr2SbauF/BEAgzPtSSy01V2ddpaZNAgwii7Jaij1SsLqwnwYOnkTSZOULMWFwDMXRsywlgFFGmXQ9UaCdFEgAIwGMBDCmSDaHATDIEysGHTWhup977jkHHUwvsESbIHNnnHFGrRg1dQAG5cEaQaAtgA+hygm0xV4kAhRsLEZQPlaPsAcITp3LLrtssO3KPbQ4eeSnR2girqcpkili1vSZRIEGKJAARgIYCWA0IEhVsqCDJDUxRZL9HvnSeTO9cPrpp3unTUdM4KpVV13VNx9jG3Sey087ZPPJ/64LMNgZlBD4WMRw2Dz++OPdr4IonmyENnPmTAcKlJHt5Nn/Y+edd3aQ0a2M3EsAI99K6f9EgfZSoHUAQ0pEJEMhFo1mdD9/1vuTuIqEunFAk24dRdXnRDvlmaZIRJHhnKEzqWmAQZ7kTb7saHr//fd7SG6mHRZeeGHv5LvxC+8XpboAg7DhrG5hOSqBtAi2xRlwwDWmcojoSXmJPkoZsbaQupWTOiaAUdRS6VqiQDsp0BqAgfKQAmEDJ+JpoGwYeTESq5qUxyQBDJQqSXUDcEEbHaKNaMj/vKP7Ouu5/Jn3eCYBjDxlmv0fOpOGBTDIPwvG1a51a1EHYGS/VeX7ognvwYPdEs8mgNGNQuleokC7KNAKgCHF8Yc//MGj9LGmnz07GNUwN4tnfNU1/cprUgAGCpURIfPsmJihERu2MYfNCJVwy1LMdFw8+9RTT/mmZTj4zTfffL7hW7bjybMgNEsAI0+V5v+HzqRhAAyVVvyv/2nXbm2v54rOdQFGvgwCxHwjf49r2fv8X5b0blpFUkahdD1RoF0UGDnAQGmgcDHtEuaY+VnABYoR6wV7nDCvzFwtz6ozLSOjlNC4AwzqwQGwuOCCC9xRDg98AQxWChAJlOBE0IRncZhjHh6HP+jJ9tdEMd1xxx1Ll/5BR94lj2TBKOOqZq5DZ9IwAUYzJX0+l7oAo8kyZPOCfsmCkaVI+p0o0G4KjBRgoDA4GJmzlp+dFVG+bLHO6Jwld5tuumnYe++9O51gL3JKCU0CwECZPvHEE+HAAw90kAHIYvqIHU8Z9a222moOvogbwOoBNncjaBE05Fm89NnlFo/+gw46yOMhFAE0aJYARi/OGvw+dCYlgFGPlpLtZMGoR7/0VqLAVFNg5ACDtfHXX3992Hbbbd20z9bNrOln5E2nh3PYAgssMO0ABoyAQgVMXHfddeGmm27yQETEEGDZ4SOPPBIWXHBBBx+ERGejMiwat912W1h00UV9eeK1114bfvCDH/jSP6xAyyyzjOeZN5kngDE1YpcAxmB0TgBjMPqltxMFppoCIwMYKAsO/AUIrsNSNiwXdJbHHXdcJ/pg0Yi7G5H+f3tnHnpF9Ybx+TcoArGsQH7SYkjSioFbZHtmodKO2a6tWGnRBmaEli1EoGU7alZYFmZZFFRkVJYlJEUEZnuUEmaU0j8/P288erre+/UuM3Ov9/scmHvmzpz1OTPnfeY97zlHndCursFQHakPBq847FCwwmeqHztS9unTJ4gZy42/8sorsSIieF1yySXZzTffnM2bNy82U8MOgyWkGSpBK1Jpz0Ie1mAI8eJ8cMZZg9Ecxnq3rcFoDj/HMgJlI9B2gsF0tsmTJ8eXN9PVtGmadmVF8DVCMtQJdQvB0AMBMeBg2GPx4sWx3wTDIGh+pk6dms2dOzeWi0YDBLm48MILIxxaDYxlWSXxnnvuieQq8TTBEMrF+vUQDOyOOsVBYNEeYhvFRmo8f5Xar7LLCjlbu3att2svG3jnZwSaQKCtBIPystofdhZsN40K//nnn982JNJMZ9atBIN6cWzYsCGbM2dONmPGjKx///5hnzJmzJggD+xBwboCrOjIdutvvPFGEA2GoRhyYmtspvySTkoy9N9Gnk28QQ1EAWdcLQ0GQ1+saokgT9ungSxyDdq3b994FzEcZsEskf5cM2kgMfADG4YJNRw4bNiweNYh28yu4mD2lGy4wJO9WOhL1J90ArYNVNtBjcAui0BbCQaCDzuBiy66KNu4cWNMR6UzY/YDnYk6hEbQVSfUbRoM6oVgWrNmTayS+P7778fyywyX0KHefvvtYeR54IEHxpDT6NGjw3aDPSn++uuvjP/z588Pw1nSSjtZ/TfBaORJazwsOONqEQyGtBCGao/Gc8g3BvuFsGkae4UMHDiw7RoMcBHBwJiZj5OhQ4dmZ5xxRhg1m2Dk2/5OzQi0ikBbCcaWLVuy5cuXh1En6zew3sXChQuzfv36mWAkLUvHysGUVb5yIRN8sWEMy7AH2h9sMhg6gWDMnj07O+2004K8TZw4MQjGqaeemi1YsMAEI8G17FPaEFdJMLBBYoty2lMEo+yyVcuP5bsR2pMmTcoYemMGU0pMq8Up+hr58zHCe7Bu61bvLDPOVHZrMIpG3ukbgcYRaCvBoKNldgRf2b/++mt8jTDVEtU/nbE1GP82KFig7XnvvffC9gIVMcNJDJUgmFgfg6msdLopwWD2CdhC5MaNGxfrY1Tbk4L06bitwWj8BWokRjWCwQZfs2bN2qbCb7cAT+sjQpEu5pbeb9c5OKLJYLgPgjF27FgTjHY1hvM1Aj0g0DaCoTKxaiczR7766quYTsl6GIyr0omYYPw7VZWxbzaQYhfKpUuXZgcddFB26623ZhMmTIivYTa6QlDNnDkzpq6yngid7rJly2INDHBkGAoDWgm5VJBxzQRDT2RxvrBPNRi0G1oohgWZXkxbqT2KK0l9KUP+sWHQXiKUO31u6kslv1DCD+KDcfjff/9tDUZ+8DolI5A7Am0jGHQWHGx8NG3atJhmydDIlVdemd14442xiqcIRiOdGmnyddMNNhiqCxtDMXWXqbwsEQ5GTDsVLthYYLty7bXXZhjmodq+4oorYvVPsER4gTHXwcbTVHN/j+pKkPbEpQQDw9wHHngghrSwwegkI0/ZYGC7c8ghh4RAr3x26qp4ToGEH+8D78BHH30UHyMYNHuIJCeQnYwRyBGBthMMxlMxImNqJV/qzIJgXYdRo0bF1xwrUWIRXq+TUN7VCYY6U63QCaFgmOSYY47Jpk+fHmuG8B8DT8bHWWALLQXLhWN/AbFg2XXsLgYPHhwaDmxcSFfETZhyzRoMoUgRsGIAABRTSURBVFGcrzatRjCwI3j66aeDWKs9iitJfSl7qfD6cHIoI2AEqiPQNoKRFueLL77IpkyZEloHvrD5Gtl///2DWGA7wJd5NcGYpqFzwnWDBkPCaPXq1bFwFltwM+bM8ul85UIuGBs/8cQTQ7PBAlwsBw6hIBzaoPXr1wduZ511VsaXMvFw0nykmJlgCI3ifLVpNYLBTAgIRidpMDptHQy9214Ho7hn1CkbgTwRaDvBoNNAWLKkNZoLxlYZY4UksLInRopY2eMIWykcK8FQJ9QNGgw0Ohh2YsWPESyaB3ABAw4IxvHHHx8rdkI8Pv3005hNgo8Q4/6QIUPCiPCII46oSdKEq408K5+mfP+DM25nBEPtkW/ujadmDUbjmDmGETAC2xFoO8GgKCIFzIZ4+eWXs08++SS2HWeK3Mknn5zxBb4zYqEqKa1uIBjUhRkj7KbKFFWNf8sQEJ8VFsGH2SEQEr7uXnzxxZi6ijEoqneGnXC1MJRAM8HQU1SMD844E4zm8NW77aXCm8PPsYxA2Qh0BMGg0voy51wdCQKUoxGnuLs6wUjrLMGUXtN5ShoULr1GOK5XXlP89L4JRopK/udqHxOM5rAFP/oJE4zm8HMsI1A2Ah1DMKg4nQedCKQCgci5/tcLjDqhbiEY1AeBVMuBkzQbhBGGwk9Y1orPdfIgvAlGTyi1fg+ccSYYzWGpd9sEozn8HMsIlI1ARxGMtPLqjBF8jTh1Qt1CMBqpe7NhwcwEo1n06o+nZ9oEo37M0pB6t00wUlR8bgQ6F4GOJRjNQqZOyASjfgRNMOrHqpWQZRAM5UE51a6NknTVsVkjT+WrdGr59YZTfMJ7iERo2DcCnY+ACcbWdTYwkGSYQcMJzXbI1ZqbTpFDX60//PBDLJK1YsWK7JprromZM3SajdqaVMur2Wvq6D1E0iyC9cUDZ5yeBaYWa6GtdJqq2qO+VLeHIp7ikgeO57rZ57lRgkHePMup0zuVXuO8MmytcGk8xbEGI0XF50agcxHoFQSDWRYsd8xUTmamML+fxbuYBstCXiYYHiIp4xVFQOKKIBikzSyiTZs2ZSwd/9tvv4WwZzfU/fbb7z92OvXWtVGCQf6bN2/eNs2cjdt4vyr3MlFZWYGWOITbbbfddghXWU4TjEpE/N8IdDYCvZZgsALmHnvs0XaCwc6odJzWYHT2i5JH6WhnXN4Eg3S1P8cTTzyRvfbaa7HIGs/UscceG5vgQawJ14g2oxGCAbH48ssvY90WfHZHHjBgQHbcccdlRx11VJB7YcgeImvWrIm9ctDosUEfu/2yHDlko1YZKb+HSISifSPQ+Qi0RDA+/vjj2BOAl56NtE455ZToPNNZDe2AgPKkNhipBoO9Oug4O41g1OpUy8BPgufzzz+PVUNZe4P9Hdh0Dq0P2h4EFJilWh+EgVTb7Sx/GRjlkQc44/IkGKTJQnUffvhhdtNNN2UrV67cphFAM8fS8izFjyZB7VxvXeohGKTJwZ5CrLj71ltvxUqyaCaoJ/uZ3HLLLdnll18eWhSI0Ntvv51NnTo1NjikryA+C8GxJ8vw4cOjeNWeJ8KZYNTbeg5nBNqPQNcRDHVCrIDJLq10SOecc04MkbAEeTWCwdLa7bLBuO2226KM7dJggBeODh2Ccdlll8XiXhAMOnswg2AwrCSCwTUwM8Fo7AUW1nkRDNLj+Prrr2MvHxapY6jh0EMPjf1otNQ3e9NUE9g7K30jBOOnn36KFXdff/312O2XPXHYAgBtxZFHHhkkh91i2TUZckE43sX+/fvHuhYQDxaFe+yxx+KZo16VZeaaCcbOWs33jUDnINAUweAl5wuFryV2NdyyZcu2Dco4b5ewTGFlp8UbbrghygLBOOyww+Irji9xaTD4OufLrmyCwcZufMVBgNj1lM61sjNN61L0uTpzvkLZEh7hgNHhiBEjorNHa5ESDGFmgtFYy4AzLg+CQVocCPCXXnoplpPnOTr88MOzOXPmZEcffXTcb+VdrIdgUB/KwRDJunXr4pzVZUUkli9fnmEHcscdd2TnnXde9uqrr2YXX3xxPO88/9dff31oXiBHxHv44YeznjblM8EAcTsjsGsg0BLBQIgjIFGp77XXXvH1ROfZTmEp2OnwMHSDUJx//vnR8fJ1J0HJVzk2GFyDYMgQjbLnWX4JAgkVzSKhQ0WFnWdeqnuzPmVF3Q42bDI3cuTIIGAiGMJMBAPMPERSP9rgi9Oz0MosEtJC2P7888/ZzJkzQzDvvffeMTMJux7ywJU5i4T89LxTt8cffzxmTO2zzz5B9idOnBgb882aNWsb6RgzZkzs+nvnnXfGrsBo9CZPnhx1qxxqVZ09iwSk7YxA5yPQEMGgOnRq6iAx5mIb8c8++ywEJVoNdTDyicN5OxzCmzFgvpzYlwPBCMFAUCI0UfWLYGiIhHLmLfSpvzBj0zLGmp955pnABTw5hJewkl8mbtSbA0E1fvz40Pqg5YGIaXikDK1PmXUuMy+1qZ6FVggG5SY9NAVXXXVVaMQGDhwYApz9e3imKgV0o3WtV4OhdCmPDmayoEmZMWNGDIOwkeFJJ50Umoynt+4aq7Ji58O7gP0G+fHBwq7ApFP5HnKNeplgCHH7RqCzEWiYYPCSq4Nkmhm7nzIGjHoWy3FUthyarsYXcSpABQfp5OXSjkhCkrTpYBnnZet3BCOkggOCIUHJ9tio+osmGGAAAQMnxqvZ8XTjxo2BE7iBJcNL3AczMAajFKf0vFXsUsxIi/9oI8ABbdSArTMAIGQQC7ASZiJlYFaU1qfVunVqfLWf3p9WCQb1XL16dXbuuecG0cD2YuHChWFvxPPWyvAIaTdKMIhDHcmbPgFbC4ZIKNejjz4a5J5rS5cuzQYNGhS7AGOfsWTJkuzSSy8Nss+wydy5c0lqB6e0TTB2gMYXjEBHItAQwVAHKWGJwOQcgcj8+/RAYEIyJCwJR3ylUQQaEpr4CEoEoHyEJYKSr3EOCUoZKxIujZ9n+dQxIljATJiAEZj98ccf4QszSIawFV7y8yyX0qLeHAgkYQYeYCZND9iBGdcY2imalKls3eSrDfMkGKtWrQrjyB9//DE0TosWLcowpuR9K5tgUD8O2YWgWeH/mWeeGaTh22+/3aZtSQkGu/9iXMy7yIyvJ598ctu7mLY/aVEvE4wUFZ8bgc5FoGmCQSeJoIRESGuBgJQWg+t8kaszVedTNBQiCRKWCEK0FBwiGQyLcC7tRWpLQPmURh5lpd44OkYRDHCphhnXetJg5FGeammovvhgAWaQCHASEQMvYSablSJJWbVy7urX9CzonchDg8HwJAJ87dq1oblYsGBBaAzIqx0EA2KMVhNNxTvvvBNk5957740p7AznMBTy5ptv/keDIYLB+8hw5rx586o2NXUywagKjS8agY5EoCmCwYuuThKSgcCU0IRYcKSCkrDqXOUXgQYCUsKSzlUkA4HIgcAU2UCAcuiLXXEVP8/yUWcOOkc6YA5hJoKm/+Ap7QXhcUVjRh7UW5hBHIQZpELYgRfnqfZCuJGGXc8IqB317rRKMEiPqaB8/bMOBnZGDz74YCxaxbNDO7biGhki0fON0SllwM4IWx4MNpmZxHOCBgMjzmeffTYIBjNGWIRr8eLFEQ5t2aRJk8JolfQq30XlYQ1GK63quEagPAQaIhgUi5ccRycpgYlQFKHAR1hKkBKGQ/HkRyI5/6QdUiosIREcIhUISASlrhNWceXnWTTVWViADZgJN/ASfhI+xCE8TvHzLJPSSuvLOVggmMBGOIlsiFxwj7DpofTs10ZA7ag2boVgkBbH999/H1PE0VxoFgkLbun5V5vWLlXtO/USDJXlzz//zF544YXQXlBHZojcf//9Wb9+/aI/2LBhQzZ79uwgINhFcT5q1KgYEqHM++67b6znwR49pEkdUsc13gkTjBQVnxuBzkWgaYKhl52OBIEpX8KT/xKohC3bSfhJYCI0EZj6+kZIco37CotflBMGwqUaZuCl+yIXRZWnVrrCIyUZOhdm/BdmpFMkbrXKuSteT58B2jkPgoH9DuRi2rRp8eyw3gvaAwwraUued7RQzbhGCAYE+Z2tQyJTpkyJ4ZqhQ4fGwlssA069Iac4tBeaMYKPzQUkhAW2Dj744Oyhhx7KTjjhhOg7eM5SRzomGCkiPjcCnY1AwwSD6qij1AsvoYivg3u6r/BlQiEBiE9Hy0GHJV/nCkfZihaUKSYpmRBmXGsnZmAAPilmlXjxX2HKwIw8usXpPVB7t0IwwIT0aCtsHhhaYJiE5/qAAw7IRo8eHTOoOEeIN/Ns10sweG6Zsn7dddfFUuEQGoY+hgwZErOjIBecsx4Ns6cuuOCC0EIMHjw4Y0otNhjfffddnGPgWWvfFL0b1mB0yxvhenQ7Ai0TDACqFIz6T4egTrVdQNKxpgIz/a9zysZ5kS7FQbiAk7CSr3tFlqWntIUDvohEpU/8FLue0vO97QjoGciLYJAyaWJYvWzZstjz45dffomhN64zJMjy2/Pnzw9NBtfUvttLVfusHoLBc4v2Qit0UhbySPOCcGCI+sgjj8TUbKasYvjJkArh0IyhvWCBsLPPPvs/cdPSEZb8TDBSVHxuBDoXgaYIBtXhZZfjvPKoDKOwZfvqUFOBqPP0XhnlSjEjv2qkojJMGeWqzEP4cD3FqNr1yrj+XxsBtW2eBIPcSJcZSCtWrAghjkYDQY9RM7uU3nfffTFEQTi1Z+1Sbr9TD8EgTQjGu+++G/YT5KuhDYgp9yEQEJ3p06cHaWUpekjGc889F+X83//+F4aqEyZMiPvbS/DfM9IywfgvJv5nBDoZgaYJBpXihU+d/suvFiYNX/R5ZWeq//LJPz0vujykn2KT/k+vp+dllCnNoxIP/ZdP2PQ8jevznhFQu+ZNMMgVwUu7IOxZE4OVNJlijOEki6U14+ohGKRLvZiiztL8Kkdlfqw9w9AHZSQ8NkgQjfXr18dquyzuxvWeni3um2BUIuv/RqBzEWiJYKhavPi7muupIyujLsasDJQ7Kw+1eREEg5pKuOf1bNdLMJpBGSxUTs71X9eqpUkYE4xqyPiaEehMBHIhGLWqRofQCa6nTqsTypeWwZilaHTXudq2KIIhtCSIee516F4jfqMEA+Ffy1UrB+XkqHavWjqql20wqqHja0ag8xAolGB0XnVdIiPQPgQQkLiiCUZeNWyUYOSVb610TDBqIePrRqAzETDB6Mx2cam6EAETjNYa1QSjNfwc2wiUjYAJRtmIO79ei4AJRmtNb4LRGn6ObQTKRsAEo2zEnV+vRcAEo7WmryQYH3zwQcaKoaeffnrslNynT5+YqcKsmT333DOuaUNDpsxy4LD5sDMCRqB4BEwwisfYORiBQGBXJxgYcWqNi3Y0aTWCMWzYsCAYTMllGiwkA9sRCAbXTDDa0VLO0wj8i4AJhp8EI1ASAjsjGE899VSsvqlwJRWrZjZ9+/bNWARr0aJFsfspa1dIC1AzUoE3wIXjm2++ya6++uoMDYYIxu677x7kwgSjwAZw0kagQQRMMBoEzMGNQLMIiDhUm0XCzqNsX86ql7hUja94zebbbLwBAwZk/fv3j91OBw4cGLNf2kkwqAdalHXr1sWOrStXrgyCwSqhqQaDIRK2frcGo9mWdzwjkA8CJhj54OhUjMBOERBRqCQYbFs+aNCgbOzYsUEwFG6nCRYc4O67746hBvYHYct1VgltJ8EAF4gXe5gsWbIkY9+V4cOHZ+PGjQsyIe2FCAZ7oHiIpOCHxMkbgR4QMMHoARzfMgJ5IiDiIILBVutsU37XXXeF6p/7CkO+6Xme5ag3LeWfalPqjVt0OMrEXisjRozIxo8fH1vSQyx0sDQ5BIMN39iyHtsR1UN+0WV0+kagtyNggtHbnwDXv1QEENoiGFu2bMlWrVoVJIO9Q3BoCf75558Ig80DQwJaIVMCv6wCU05cOw07K+sqcgAWkIiRI0eG9gcyAbnAuJPhEWwyuMZW8RAMNC/EVfzKdP3fCBiB/BEwwcgfU6doBGoigGCEMCC8IRP4mzdvzn7//fcMjcamTZvCZ/MwrkNCRDSIq6NmBl18A3IAUeCA9KCdQIsBiYBspAf2F9yDXGDXYoLRxQ+Gq9axCJhgdGzTuGDdiEBKMCAOkAgOtlvHtoDtzjn4D7kQCYGU9GZywbMgDYSIBuQBcgGRgFCgtcAXudDwCAQjjduNz5XrZAQ6EQETjE5sFZepaxEQSdAwCcMhKcmAWHBwDXIBCSGs4uH3VpeSBDQSEAdIBkQCY06GRCAbHDLuJAzaDsLjSMPOCBiBchAwwSgHZ+diBAIBEQXZVkAgIBIcIhXSXMgWI9VemGD8a0cBYagkGSIaaDVkeyFyIWIh34+jETACxSNgglE8xs7BCGxDQAQBX1oMSAYHhIJDwyK6L1JCIoq/LcFediKCgC+CAYlAk6EhE7QWOghD2DReL4PM1TUCbUPABKNt0Dvj3oqASIK0GCIS+CIbnOs+OClOb8UsrbcIg0gGBEOHhkSkuRDBIL5IRpqWz42AESgOAROM4rB1ykagKgIiC/gcGgIRqcDXPfkkxLnddmNPsBDJgEhwiFikJETnxs4IGIFyETDBKBdv52YEAoGULHAukqFzAlWGMXQ7aiFEHuSDkbQWXJNLz3XNvhEwAsUiYIJRLL5O3QjURKCSQOg/vs5rRvaNQCAlDjrH1zmB0nPDZgSMQHkImGCUh7VzMgI7IFCNSFS7tkNEX9gBgWpEotq1HSL6ghEwAoUgYIJRCKxO1Ag0hoBJRWN47Sy0icXOEPJ9I1A8AiYYxWPsHIxA0wiYePQMnYlEz/j4rhFoJwImGO1E33kbASNgBIyAEehSBEwwurRhXS0jYASMgBEwAu1EwASjneg7byNgBIyAETACXYqACUaXNqyrZQSMgBEwAkagnQj8H5Z1aIwcJA0wAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo que vimos la clase pasada es el paradigma de *split-apply-combine*:\n",
    "        \n",
    "\n",
    "![Unknown.png](attachment:ea1c01ee-2466-4c36-91d0-98fe1e58b400.png)\n",
    "\n",
    "Esto lo hacemos con el comando `<dataframe>.groupby(<forma_agrupar>).apply(<funciona_a_aplicar>)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PREGUNTA 2**: ¿Cuál es el total de minutos por `gender`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    1.189086e+06\n",
       "Male      2.979078e+06\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('gender')['duration'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pregunta**: ¿Cuál es la duración promedio de los mayores/menores de 42 años?  (nacidos <=/> 1979)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birth_date\n",
       "False    26.263470\n",
       "True     11.824613\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = (finalData['birth_date']<=1979)\n",
    "finalData.groupby(tmp)['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type  birth_date\n",
       "Casual             False         39.560557\n",
       "                   True                NaN\n",
       "Registered         False         11.620833\n",
       "                   True          11.824613\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = (finalData['birth_date']<=1979)\n",
    "finalData.groupby(['subscription_type',tmp])['duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birth_date\n",
       "False    11.620833\n",
       "True     11.824613\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = finalData[finalData['subscription_type']=='Registered']['birth_date']<=1979\n",
    "finalData[finalData['subscription_type']=='Registered'].groupby(tmp)['duration'].mean()"
   ]
  },
  {
   "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": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prop_menos_30(duration):\n",
    "    num_menos_30 = sum(duration<=30)\n",
    "    total = len(duration)\n",
    "    return num_menos_30/total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.878459639129749"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(finalData['duration']<= 30)/len(finalData['duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.878459639129749"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_menos_30(finalData['duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station\n",
       "10    0.905792\n",
       "11    0.932901\n",
       "12    0.913983\n",
       "13    0.933980\n",
       "14    0.957670\n",
       "        ...   \n",
       "94    0.875486\n",
       "95    0.930403\n",
       "96    0.919028\n",
       "97    0.743590\n",
       "98    0.838290\n",
       "Name: duration, Length: 95, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('start_station')['duration'].apply(prop_menos_30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9057921402163495"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(finalData[finalData['start_station']=='10']['duration']<= 30)/len(finalData[finalData['start_station']=='10']['duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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: 551611, dtype: category\n",
       "Categories (885, object): ['? (0x241EC230)', '? (0x67D8478F)', '? (0xA533D104)', 'B00001', ..., 'T01053', 'T01093', 'T01168', 'T01380']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['bike_nr']"
   ]
  },
  {
   "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": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bike_nr\n",
       "? (0x241EC230)    192\n",
       "? (0x67D8478F)    129\n",
       "? (0xA533D104)    142\n",
       "B00001            933\n",
       "B00002            703\n",
       "                 ... \n",
       "T01415             93\n",
       "T01053             12\n",
       "T01093             13\n",
       "T01168              0\n",
       "T01380              4\n",
       "Name: duration, Length: 885, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('bike_nr')['duration'].mean()\n",
    "finalData.groupby('bike_nr')['duration'].std()\n",
    "finalData.groupby('bike_nr')['duration'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21.616271490839257, 58.05628902563768, 551611]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statsBike(finalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bike_nr\n",
       "? (0x241EC230)     [33.170138888888886, 60.41472501744495, 192]\n",
       "? (0x67D8478F)      [25.73062015503876, 40.00103415496366, 129]\n",
       "? (0xA533D104)     [38.82852112676056, 107.23705791448033, 142]\n",
       "B00001            [18.051804215791353, 32.528162361042305, 933]\n",
       "B00002             [23.102133712660027, 60.71039928794951, 703]\n",
       "                                      ...                      \n",
       "T01415             [20.909856630824372, 31.952862912239976, 93]\n",
       "T01053              [11.477777777777776, 7.142444384418529, 12]\n",
       "T01093               [10.517948717948716, 7.71445991226163, 13]\n",
       "T01168                                            [nan, nan, 0]\n",
       "T01380                            [14.95, 5.812837007354847, 4]\n",
       "Length: 885, dtype: object"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('bike_nr').apply(statsBike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def statsBikeDF(x):\n",
    "    prom = x['duration'].mean()\n",
    "    dev = x['duration'].std()\n",
    "    num = x['duration'].count()\n",
    "    return pd.DataFrame({'mean_duration': [prom], 'stddev_duration': [dev], 'num_trips': [num]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>stddev_duration</th>\n",
       "      <th>num_trips</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bike_nr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>? (0x241EC230)</th>\n",
       "      <th>0</th>\n",
       "      <td>33.170139</td>\n",
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       "      <td>32.528162</td>\n",
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       "    <tr>\n",
       "      <th>B00002</th>\n",
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       "      <th>T01053</th>\n",
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       "      <th>T01093</th>\n",
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       "      <td>10.517949</td>\n",
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       "    <tr>\n",
       "      <th>T01168</th>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean_duration  stddev_duration  num_trips\n",
       "bike_nr                                                    \n",
       "? (0x241EC230) 0      33.170139        60.414725        192\n",
       "? (0x67D8478F) 0      25.730620        40.001034        129\n",
       "? (0xA533D104) 0      38.828521       107.237058        142\n",
       "B00001         0      18.051804        32.528162        933\n",
       "B00002         0      23.102134        60.710399        703\n",
       "...                         ...              ...        ...\n",
       "T01415         0      20.909857        31.952863         93\n",
       "T01053         0      11.477778         7.142444         12\n",
       "T01093         0      10.517949         7.714460         13\n",
       "T01168         0            NaN              NaN          0\n",
       "T01380         0      14.950000         5.812837          4\n",
       "\n",
       "[885 rows x 3 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby('bike_nr').apply(statsBikeDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>level_1</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>stddev_duration</th>\n",
       "      <th>num_trips</th>\n",
       "    </tr>\n",
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       "      <td>32.528162</td>\n",
       "      <td>933</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B00002</td>\n",
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       "      <th>880</th>\n",
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       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>T01053</td>\n",
       "      <td>0</td>\n",
       "      <td>11.477778</td>\n",
       "      <td>7.142444</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>T01093</td>\n",
       "      <td>0</td>\n",
       "      <td>10.517949</td>\n",
       "      <td>7.714460</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>T01168</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>884</th>\n",
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       "      <td>14.950000</td>\n",
       "      <td>5.812837</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>885 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bike_nr  level_1  mean_duration  stddev_duration  num_trips\n",
       "0    ? (0x241EC230)        0      33.170139        60.414725        192\n",
       "1    ? (0x67D8478F)        0      25.730620        40.001034        129\n",
       "2    ? (0xA533D104)        0      38.828521       107.237058        142\n",
       "3            B00001        0      18.051804        32.528162        933\n",
       "4            B00002        0      23.102134        60.710399        703\n",
       "..              ...      ...            ...              ...        ...\n",
       "880          T01415        0      20.909857        31.952863         93\n",
       "881          T01053        0      11.477778         7.142444         12\n",
       "882          T01093        0      10.517949         7.714460         13\n",
       "883          T01168        0            NaN              NaN          0\n",
       "884          T01380        0      14.950000         5.812837          4\n",
       "\n",
       "[885 rows x 5 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = finalData.groupby('bike_nr').apply(statsBikeDF).reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.92610974333186"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mean_duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uniendo Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Supongamos que queremos calcular la distancia de cada viaje. Podemos estimarlo a partir de la ubicación (lat/lon) de cada estación, que está en otra tabla de datos station. ¿Como podemos unir estas tablas para usar esos datos?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Colleges of the Fenway</td>\n",
       "      <td>42.340021</td>\n",
       "      <td>-71.100812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Tremont St. at Berkeley St.</td>\n",
       "      <td>42.345392</td>\n",
       "      <td>-71.069616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Northeastern U / North Parking Lot</td>\n",
       "      <td>42.341814</td>\n",
       "      <td>-71.090179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Cambridge St. at Joy St.</td>\n",
       "      <td>42.361285</td>\n",
       "      <td>-71.065140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Fan Pier</td>\n",
       "      <td>42.353412</td>\n",
       "      <td>-71.044624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>94</td>\n",
       "      <td>Charlestown - Main St at Austin St</td>\n",
       "      <td>42.375603</td>\n",
       "      <td>-71.064608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>95</td>\n",
       "      <td>Cambridge St - at Columbia St / Webster Ave</td>\n",
       "      <td>42.372969</td>\n",
       "      <td>-71.094445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>96</td>\n",
       "      <td>Cambridge Main Library at Broadway / Trowbridg...</td>\n",
       "      <td>42.373379</td>\n",
       "      <td>-71.111075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>97</td>\n",
       "      <td>Harvard  University River Houses at DeWolfe St...</td>\n",
       "      <td>42.369182</td>\n",
       "      <td>-71.117152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>98</td>\n",
       "      <td>Charlestown - Warren St at Chelsea St</td>\n",
       "      <td>42.371848</td>\n",
       "      <td>-71.060292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                                               name        lat  \\\n",
       "0    3                             Colleges of the Fenway  42.340021   \n",
       "1    4                        Tremont St. at Berkeley St.  42.345392   \n",
       "2    5                 Northeastern U / North Parking Lot  42.341814   \n",
       "3    6                           Cambridge St. at Joy St.  42.361285   \n",
       "4    7                                           Fan Pier  42.353412   \n",
       "..  ..                                                ...        ...   \n",
       "90  94                 Charlestown - Main St at Austin St  42.375603   \n",
       "91  95        Cambridge St - at Columbia St / Webster Ave  42.372969   \n",
       "92  96  Cambridge Main Library at Broadway / Trowbridg...  42.373379   \n",
       "93  97  Harvard  University River Houses at DeWolfe St...  42.369182   \n",
       "94  98              Charlestown - Warren St at Chelsea St  42.371848   \n",
       "\n",
       "          lng  \n",
       "0  -71.100812  \n",
       "1  -71.069616  \n",
       "2  -71.090179  \n",
       "3  -71.065140  \n",
       "4  -71.044624  \n",
       "..        ...  \n",
       "90 -71.064608  \n",
       "91 -71.094445  \n",
       "92 -71.111075  \n",
       "93 -71.117152  \n",
       "94 -71.060292  \n",
       "\n",
       "[95 rows x 4 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataStations = pd.read_csv(\"Data/stations.csv\", dtype={'id': 'category', 'name': 'string', 'lat': 'float64', 'lng': 'float64'})\n",
    "dataStations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>duration</th>\n",
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       "      <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>payment</th>\n",
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       "      <td>8</td>\n",
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       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</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>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 12 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   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040            23   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040            23   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040            23   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040            46   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040            25   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040            16   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040            61   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040          67  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040          26  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040          47  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040          33  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040          81  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender  payment  \n",
       "0           1976.0    Male      0.0  \n",
       "1           1966.0    Male      0.0  \n",
       "2           1943.0    Male      0.0  \n",
       "3           1981.0  Female      0.0  \n",
       "4           1983.0  Female      0.0  \n",
       "...            ...     ...      ...  \n",
       "552068         NaN     NaN      0.0  \n",
       "552069      1986.0    Male      0.0  \n",
       "552070      1965.0    Male      0.0  \n",
       "552071      1986.0    Male      0.0  \n",
       "552072      1989.0    Male      0.0  \n",
       "\n",
       "[551611 rows x 12 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para unir datos entre dos o mas DataFrames, se requiere un identificador comun entre las dos, y lo que se genera es una nueva tabla con el \"producto cruz\" de estas dos tablas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
    "                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "                    'hire_date': [2004, 2008, 2012, 2014]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee  hire_date\n",
       "0     Lisa       2004\n",
       "1      Bob       2008\n",
       "2     Jake       2012\n",
       "3      Sue       2014"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso, se definió el identificador común (columna *employee*) y se cruzaron los datos para generar una nueva tabla."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OJO**: Los datos se cruzan, es decir, si hay mas de una columna que coincide, se pondrán todos. Por ejemplo, supongamos que 'Lisa' también trabaja en 'HR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue', 'Lisa'],\n",
    "                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR', 'HR']})\n",
    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
    "                    'hire_date': [2004, 2008, 2012, 2014]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR\n",
       "4     Lisa           HR"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee  hire_date\n",
       "0     Lisa       2004\n",
       "1      Bob       2008\n",
       "2     Jake       2012\n",
       "3      Sue       2014"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>HR</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3     Lisa           HR       2004\n",
       "4      Sue           HR       2014"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También puede haber datos faltantes, en cuyo caso se entrega la intersección."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ann</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group\n",
       "0      Bob   Accounting\n",
       "1     Jake  Engineering\n",
       "2     Lisa  Engineering\n",
       "3      Sue           HR\n",
       "4      Ann           HR"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue', 'Ann'],\n",
    "                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR', 'HR']})\n",
    "df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue', 'Mike'],\n",
    "                    'hire_date': [2004, 2008, 2012, 2014, 2003]})\n",
    "df1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>employee</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jake</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mike</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee  hire_date\n",
       "0     Lisa       2004\n",
       "1      Bob       2008\n",
       "2     Jake       2012\n",
       "3      Sue       2014\n",
       "4     Mike       2003"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ann</td>\n",
       "      <td>HR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mike</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting     2008.0\n",
       "1     Jake  Engineering     2012.0\n",
       "2     Lisa  Engineering     2004.0\n",
       "3      Sue           HR     2014.0\n",
       "4      Ann           HR        NaN\n",
       "5     Mike          NaN     2003.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,how='outer')"
   ]
  },
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![merge.png](attachment:79f6a567-c5bd-4457-8f55-bd1cd0654d73.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ann</td>\n",
       "      <td>HR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mike</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting     2008.0\n",
       "1     Jake  Engineering     2012.0\n",
       "2     Lisa  Engineering     2004.0\n",
       "3      Sue           HR     2014.0\n",
       "4      Ann           HR        NaN\n",
       "5     Mike          NaN     2003.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>employee</th>\n",
       "      <th>group</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sue</td>\n",
       "      <td>HR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee        group  hire_date\n",
       "0      Bob   Accounting       2008\n",
       "1     Jake  Engineering       2012\n",
       "2     Lisa  Engineering       2004\n",
       "3      Sue           HR       2014"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a usar el DF de trips para a cada registro agregarle la lat/lng, usando el dato de station recordemos que en trips, esta información está en `start_station` pero en stations, la columna que corresponde es `id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>payment</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</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</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>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 12 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   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040            23   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040            23   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040            23   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040            46   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040            25   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040            16   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040            61   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040          67  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040          26  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040          47  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040          33  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040          81  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender  payment  \n",
       "0           1976.0    Male      0.0  \n",
       "1           1966.0    Male      0.0  \n",
       "2           1943.0    Male      0.0  \n",
       "3           1981.0  Female      0.0  \n",
       "4           1983.0  Female      0.0  \n",
       "...            ...     ...      ...  \n",
       "552068         NaN     NaN      0.0  \n",
       "552069      1986.0    Male      0.0  \n",
       "552070      1965.0    Male      0.0  \n",
       "552071      1986.0    Male      0.0  \n",
       "552072      1989.0    Male      0.0  \n",
       "\n",
       "[551611 rows x 12 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>lng</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Colleges of the Fenway</td>\n",
       "      <td>42.340021</td>\n",
       "      <td>-71.100812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Tremont St. at Berkeley St.</td>\n",
       "      <td>42.345392</td>\n",
       "      <td>-71.069616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Northeastern U / North Parking Lot</td>\n",
       "      <td>42.341814</td>\n",
       "      <td>-71.090179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Cambridge St. at Joy St.</td>\n",
       "      <td>42.361285</td>\n",
       "      <td>-71.065140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Fan Pier</td>\n",
       "      <td>42.353412</td>\n",
       "      <td>-71.044624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>94</td>\n",
       "      <td>Charlestown - Main St at Austin St</td>\n",
       "      <td>42.375603</td>\n",
       "      <td>-71.064608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>95</td>\n",
       "      <td>Cambridge St - at Columbia St / Webster Ave</td>\n",
       "      <td>42.372969</td>\n",
       "      <td>-71.094445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>96</td>\n",
       "      <td>Cambridge Main Library at Broadway / Trowbridg...</td>\n",
       "      <td>42.373379</td>\n",
       "      <td>-71.111075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>97</td>\n",
       "      <td>Harvard  University River Houses at DeWolfe St...</td>\n",
       "      <td>42.369182</td>\n",
       "      <td>-71.117152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>98</td>\n",
       "      <td>Charlestown - Warren St at Chelsea St</td>\n",
       "      <td>42.371848</td>\n",
       "      <td>-71.060292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                                               name        lat  \\\n",
       "0    3                             Colleges of the Fenway  42.340021   \n",
       "1    4                        Tremont St. at Berkeley St.  42.345392   \n",
       "2    5                 Northeastern U / North Parking Lot  42.341814   \n",
       "3    6                           Cambridge St. at Joy St.  42.361285   \n",
       "4    7                                           Fan Pier  42.353412   \n",
       "..  ..                                                ...        ...   \n",
       "90  94                 Charlestown - Main St at Austin St  42.375603   \n",
       "91  95        Cambridge St - at Columbia St / Webster Ave  42.372969   \n",
       "92  96  Cambridge Main Library at Broadway / Trowbridg...  42.373379   \n",
       "93  97  Harvard  University River Houses at DeWolfe St...  42.369182   \n",
       "94  98              Charlestown - Warren St at Chelsea St  42.371848   \n",
       "\n",
       "          lng  \n",
       "0  -71.100812  \n",
       "1  -71.069616  \n",
       "2  -71.090179  \n",
       "3  -71.065140  \n",
       "4  -71.044624  \n",
       "..        ...  \n",
       "90 -71.064608  \n",
       "91 -71.094445  \n",
       "92 -71.111075  \n",
       "93 -71.117152  \n",
       "94 -71.060292  \n",
       "\n",
       "[95 rows x 4 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataStations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>551606</th>\n",
       "      <td>621802</td>\n",
       "      <td>5.616667</td>\n",
       "      <td>2012-10-01 14:01:00.040</td>\n",
       "      <td>95</td>\n",
       "      <td>2012-10-01 14:06:00.040</td>\n",
       "      <td>80</td>\n",
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      "text/plain": [
       "          id_x  duration              start_date start_station  \\\n",
       "551606  621802  5.616667 2012-10-01 14:01:00.040            95   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "551606 2012-10-01 14:06:00.040          80  B00634        Registered    02139   \n",
       "\n",
       "        birth_date gender  payment id_y  \\\n",
       "551606      1982.0   Male      0.0   95   \n",
       "\n",
       "                                               name        lat        lng  \n",
       "551606  Cambridge St - at Columbia St / Webster Ave  42.372969 -71.094445  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = pd.merge(finalData,dataStations, left_on='start_station', right_on='id')\n",
    "tmp[tmp['id_x']==621802]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
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       "      <td>B00634</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02139</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80</td>\n",
       "      <td>MIT Stata Center at Vassar St / Main St</td>\n",
       "      <td>42.362429</td>\n",
       "      <td>-71.090188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id_x  duration              start_date start_station  \\\n",
       "542241  621802  5.616667 2012-10-01 14:01:00.040            95   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "542241 2012-10-01 14:06:00.040          80  B00634        Registered    02139   \n",
       "\n",
       "        birth_date gender  payment id_y  \\\n",
       "542241      1982.0   Male      0.0   80   \n",
       "\n",
       "                                           name        lat        lng  \n",
       "542241  MIT Stata Center at Vassar St / Main St  42.362429 -71.090188  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = pd.merge(finalData,dataStations, left_on='end_station', right_on='id')\n",
    "tmp[tmp['id_x']==621802]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lo que hizo fue \"pegar\" las columnas correspondientes a cada station como columnas adicionales de trip, por lo que podemos usar las columnas lat y lng de la estación de inicio.  Hagamoslo nuevamente con la estación de termino."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = pd.merge(finalData,dataStations, left_on='start_station', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalDataCoords = pd.merge(tmp, dataStations, left_on='end_station', right_on='id' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_x                                                     149096\n",
       "duration                                               6.766667\n",
       "start_date                           2011-11-17 13:46:00.050000\n",
       "start_station                                                40\n",
       "end_date                             2011-11-17 13:52:00.050000\n",
       "end_station                                                  23\n",
       "bike_nr                                                  B00295\n",
       "subscription_type                                    Registered\n",
       "zip_code                                                  02124\n",
       "birth_date                                               1951.0\n",
       "gender                                                     Male\n",
       "payment                                                     0.0\n",
       "id_y                                                         40\n",
       "name_x                              Lewis Wharf - Atlantic Ave.\n",
       "lat_x                                                 42.363871\n",
       "lng_x                                                -71.050877\n",
       "id                                                           23\n",
       "name_y               Mayor Thomas M. Menino - Government Center\n",
       "lat_y                                                 42.359677\n",
       "lng_y                                                -71.059364\n",
       "Name: 1059, dtype: object"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDataCoords.iloc[1059]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculemos la distancia entre dos puntos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sin, cos, sqrt, atan2, radians\n",
    "\n",
    "def computeDistance(lat_x, lon_x, lat_y, lon_y):\n",
    "    # approximate radius of earth in km\n",
    "    R = 6373.0\n",
    "\n",
    "    lat1 = radians(lat_x)\n",
    "    lon1 = radians(lon_x)\n",
    "    lat2 = radians(lat_y)\n",
    "    lon2 = radians(lon_y)\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "\n",
    "    distance = R * c\n",
    "    return distance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278.54558935106695"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeDistance(52.2296756,21.0122287,52.406374, 16.9251681)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sin, cos, sqrt, atan2, radians\n",
    "\n",
    "def computeDistanceDF(x):\n",
    "    # approximate radius of earth in km\n",
    "    R = 6373.0\n",
    "    lat_x = x['lat_x']\n",
    "    lon_x = x['lng_x']\n",
    "    lat_y = x['lat_y']\n",
    "    lon_y = x['lng_y']\n",
    "\n",
    "    lat1 = radians(lat_x)\n",
    "    lon1 = radians(lon_x)\n",
    "    lat2 = radians(lat_y)\n",
    "    lon2 = radians(lon_y)\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "\n",
    "    distance = R * c\n",
    "    return distance\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalDataCoords['distance'] = finalDataCoords.apply(computeDistanceDF, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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_x</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>...</th>\n",
       "      <th>payment</th>\n",
       "      <th>id_y</th>\n",
       "      <th>name_x</th>\n",
       "      <th>lat_x</th>\n",
       "      <th>lng_x</th>\n",
       "      <th>id</th>\n",
       "      <th>name_y</th>\n",
       "      <th>lat_y</th>\n",
       "      <th>lng_y</th>\n",
       "      <th>distance</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</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>0.000000</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</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>0.000000</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</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>0.000000</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</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>0.000000</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</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>23</td>\n",
       "      <td>Mayor Thomas M. Menino - Government Center</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>551606</th>\n",
       "      <td>585381</td>\n",
       "      <td>7.883333</td>\n",
       "      <td>2012-09-19 11:08:00.040</td>\n",
       "      <td>96</td>\n",
       "      <td>2012-09-19 11:16:00.040</td>\n",
       "      <td>89</td>\n",
       "      <td>B00335</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96</td>\n",
       "      <td>Cambridge Main Library at Broadway / Trowbridg...</td>\n",
       "      <td>42.373379</td>\n",
       "      <td>-71.111075</td>\n",
       "      <td>89</td>\n",
       "      <td>Harvard Law School at Mass Ave / Jarvis St</td>\n",
       "      <td>42.379011</td>\n",
       "      <td>-71.119945</td>\n",
       "      <td>0.961065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551607</th>\n",
       "      <td>593276</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2012-09-21 15:17:00.040</td>\n",
       "      <td>96</td>\n",
       "      <td>2012-09-21 15:24:00.040</td>\n",
       "      <td>89</td>\n",
       "      <td>T01078</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96</td>\n",
       "      <td>Cambridge Main Library at Broadway / Trowbridg...</td>\n",
       "      <td>42.373379</td>\n",
       "      <td>-71.111075</td>\n",
       "      <td>89</td>\n",
       "      <td>Harvard Law School at Mass Ave / Jarvis St</td>\n",
       "      <td>42.379011</td>\n",
       "      <td>-71.119945</td>\n",
       "      <td>0.961065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551608</th>\n",
       "      <td>602506</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>2012-09-24 10:42:00.040</td>\n",
       "      <td>96</td>\n",
       "      <td>2012-09-24 10:48:00.040</td>\n",
       "      <td>89</td>\n",
       "      <td>B00402</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96</td>\n",
       "      <td>Cambridge Main Library at Broadway / Trowbridg...</td>\n",
       "      <td>42.373379</td>\n",
       "      <td>-71.111075</td>\n",
       "      <td>89</td>\n",
       "      <td>Harvard Law School at Mass Ave / Jarvis St</td>\n",
       "      <td>42.379011</td>\n",
       "      <td>-71.119945</td>\n",
       "      <td>0.961065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551609</th>\n",
       "      <td>570885</td>\n",
       "      <td>11.183333</td>\n",
       "      <td>2012-09-14 20:30:00.040</td>\n",
       "      <td>95</td>\n",
       "      <td>2012-09-14 20:41:00.040</td>\n",
       "      <td>89</td>\n",
       "      <td>B00640</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02143</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95</td>\n",
       "      <td>Cambridge St - at Columbia St / Webster Ave</td>\n",
       "      <td>42.372969</td>\n",
       "      <td>-71.094445</td>\n",
       "      <td>89</td>\n",
       "      <td>Harvard Law School at Mass Ave / Jarvis St</td>\n",
       "      <td>42.379011</td>\n",
       "      <td>-71.119945</td>\n",
       "      <td>2.200465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551610</th>\n",
       "      <td>600959</td>\n",
       "      <td>46.466667</td>\n",
       "      <td>2012-09-23 18:23:00.040</td>\n",
       "      <td>95</td>\n",
       "      <td>2012-09-23 19:09:00.040</td>\n",
       "      <td>89</td>\n",
       "      <td>T01098</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>95</td>\n",
       "      <td>Cambridge St - at Columbia St / Webster Ave</td>\n",
       "      <td>42.372969</td>\n",
       "      <td>-71.094445</td>\n",
       "      <td>89</td>\n",
       "      <td>Harvard Law School at Mass Ave / Jarvis St</td>\n",
       "      <td>42.379011</td>\n",
       "      <td>-71.119945</td>\n",
       "      <td>2.200465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id_x   duration              start_date start_station  \\\n",
       "0            8   0.150000 2011-07-28 10:12:00.040            23   \n",
       "1            9   3.666667 2011-07-28 10:21:00.040            23   \n",
       "2           10   0.933333 2011-07-28 10:33:00.040            23   \n",
       "3           11   1.066667 2011-07-28 10:35:00.040            23   \n",
       "4           12   0.200000 2011-07-28 10:37:00.040            23   \n",
       "...        ...        ...                     ...           ...   \n",
       "551606  585381   7.883333 2012-09-19 11:08:00.040            96   \n",
       "551607  593276   6.500000 2012-09-21 15:17:00.040            96   \n",
       "551608  602506   5.916667 2012-09-24 10:42:00.040            96   \n",
       "551609  570885  11.183333 2012-09-14 20:30:00.040            95   \n",
       "551610  600959  46.466667 2012-09-23 18:23:00.040            95   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "551606 2012-09-19 11:16:00.040          89  B00335            Casual      NaN   \n",
       "551607 2012-09-21 15:24:00.040          89  T01078            Casual      NaN   \n",
       "551608 2012-09-24 10:48:00.040          89  B00402            Casual      NaN   \n",
       "551609 2012-09-14 20:41:00.040          89  B00640        Registered    02143   \n",
       "551610 2012-09-23 19:09:00.040          89  T01098            Casual      NaN   \n",
       "\n",
       "        birth_date  ... payment  id_y  \\\n",
       "0           1976.0  ...     0.0    23   \n",
       "1           1966.0  ...     0.0    23   \n",
       "2           1943.0  ...     0.0    23   \n",
       "3           1981.0  ...     0.0    23   \n",
       "4           1983.0  ...     0.0    23   \n",
       "...            ...  ...     ...   ...   \n",
       "551606         NaN  ...     0.0    96   \n",
       "551607         NaN  ...     0.0    96   \n",
       "551608         NaN  ...     0.0    96   \n",
       "551609      1985.0  ...     0.0    95   \n",
       "551610         NaN  ...     2.0    95   \n",
       "\n",
       "                                                   name_x      lat_x  \\\n",
       "0              Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "1              Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "2              Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "3              Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "4              Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "...                                                   ...        ...   \n",
       "551606  Cambridge Main Library at Broadway / Trowbridg...  42.373379   \n",
       "551607  Cambridge Main Library at Broadway / Trowbridg...  42.373379   \n",
       "551608  Cambridge Main Library at Broadway / Trowbridg...  42.373379   \n",
       "551609        Cambridge St - at Columbia St / Webster Ave  42.372969   \n",
       "551610        Cambridge St - at Columbia St / Webster Ave  42.372969   \n",
       "\n",
       "            lng_x  id                                      name_y      lat_y  \\\n",
       "0      -71.059364  23  Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "1      -71.059364  23  Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "2      -71.059364  23  Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "3      -71.059364  23  Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "4      -71.059364  23  Mayor Thomas M. Menino - Government Center  42.359677   \n",
       "...           ...  ..                                         ...        ...   \n",
       "551606 -71.111075  89  Harvard Law School at Mass Ave / Jarvis St  42.379011   \n",
       "551607 -71.111075  89  Harvard Law School at Mass Ave / Jarvis St  42.379011   \n",
       "551608 -71.111075  89  Harvard Law School at Mass Ave / Jarvis St  42.379011   \n",
       "551609 -71.094445  89  Harvard Law School at Mass Ave / Jarvis St  42.379011   \n",
       "551610 -71.094445  89  Harvard Law School at Mass Ave / Jarvis St  42.379011   \n",
       "\n",
       "            lng_y  distance  \n",
       "0      -71.059364  0.000000  \n",
       "1      -71.059364  0.000000  \n",
       "2      -71.059364  0.000000  \n",
       "3      -71.059364  0.000000  \n",
       "4      -71.059364  0.000000  \n",
       "...           ...       ...  \n",
       "551606 -71.119945  0.961065  \n",
       "551607 -71.119945  0.961065  \n",
       "551608 -71.119945  0.961065  \n",
       "551609 -71.119945  2.200465  \n",
       "551610 -71.119945  2.200465  \n",
       "\n",
       "[551611 rows x 21 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDataCoords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1209c6970>]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(finalDataCoords['distance'],finalDataCoords['duration'],'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra forma de usar la función original (sin dataframes) es usar una `lambda` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.000000\n",
       "1         0.000000\n",
       "2         0.000000\n",
       "3         0.000000\n",
       "4         0.000000\n",
       "            ...   \n",
       "551606    0.961065\n",
       "551607    0.961065\n",
       "551608    0.961065\n",
       "551609    2.200465\n",
       "551610    2.200465\n",
       "Length: 551611, dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDataCoords.apply(lambda x : computeDistance(x['lat_x'],x['lng_x'],x['lat_y'],x['lng_y']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y calculemos las distancias recorridas por cada bicicleta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bike_nr\n",
       "T01168       0.000000\n",
       "T01380       6.660330\n",
       "T01016      14.321797\n",
       "T01093      23.244400\n",
       "T01064      23.621586\n",
       "             ...     \n",
       "B00444    1710.222498\n",
       "B00114    1733.186912\n",
       "B00401    1742.830206\n",
       "B00490    1747.224324\n",
       "B00563    1767.688107\n",
       "Name: distance, Length: 885, dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDataCoords.groupby('bike_nr')['distance'].sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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: 551611, dtype: category\n",
       "Categories (885, object): ['? (0x241EC230)', '? (0x67D8478F)', '? (0xA533D104)', 'B00001', ..., 'T01053', 'T01093', 'T01168', 'T01380']"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData['bike_nr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bike_nr         start_station\n",
       "? (0x241EC230)  10               0.964519\n",
       "                11               2.124605\n",
       "                12               2.961844\n",
       "                13               2.004517\n",
       "                14               1.855257\n",
       "                                   ...   \n",
       "T01380          94                    NaN\n",
       "                95                    NaN\n",
       "                96                    NaN\n",
       "                97                    NaN\n",
       "                98                    NaN\n",
       "Name: distance, Length: 84075, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDataCoords.groupby(['bike_nr','start_station'])['distance'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tablas Pivotes\n",
    "Una herramienta muy común en algunos softwares de datos (incluyendo Excel) son las tablas pivotes.  Pandas incluye un propio comando para esto."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agreguemos un par de columnas, como el viaje de cada mes o el dia de la semana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-188-7770d18f8ed3>: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['month']=finalData['start_date'].dt.month\n",
      "<ipython-input-188-7770d18f8ed3>:2: 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['day']=finalData['start_date'].dt.day_name()\n"
     ]
    }
   ],
   "source": [
    "finalData['month']=finalData['start_date'].dt.month\n",
    "finalData['day']=finalData['start_date'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>payment</th>\n",
       "      <th>month</th>\n",
       "      <th>day</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</td>\n",
       "      <td>2011-07-28 10:12:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2011-07-28 10:21:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>2011-07-28 10:25:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>2011-07-28 10:33:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>2011-07-28 10:34:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>2011-07-28 10:35:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>2011-07-28 10:36:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>2011-07-28 10:37:00.040</td>\n",
       "      <td>23</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552068</th>\n",
       "      <td>623507</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>46</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>67</td>\n",
       "      <td>B00486</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552069</th>\n",
       "      <td>623508</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>2012-10-01 20:24:00.040</td>\n",
       "      <td>25</td>\n",
       "      <td>2012-10-01 20:28:00.040</td>\n",
       "      <td>26</td>\n",
       "      <td>B00170</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02118</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552070</th>\n",
       "      <td>623509</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>2012-10-01 20:25:00.040</td>\n",
       "      <td>16</td>\n",
       "      <td>2012-10-01 20:34:00.040</td>\n",
       "      <td>47</td>\n",
       "      <td>T01067</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02113</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552071</th>\n",
       "      <td>623512</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>2012-10-01 20:29:00.040</td>\n",
       "      <td>61</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>33</td>\n",
       "      <td>B00621</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02135</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552072</th>\n",
       "      <td>623517</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>2012-10-01 20:32:00.040</td>\n",
       "      <td>22</td>\n",
       "      <td>2012-10-01 20:36:00.040</td>\n",
       "      <td>81</td>\n",
       "      <td>B00012</td>\n",
       "      <td>Registered</td>\n",
       "      <td>02116</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551611 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  duration              start_date start_station  \\\n",
       "0            8  0.150000 2011-07-28 10:12:00.040            23   \n",
       "1            9  3.666667 2011-07-28 10:21:00.040            23   \n",
       "2           10  0.933333 2011-07-28 10:33:00.040            23   \n",
       "3           11  1.066667 2011-07-28 10:35:00.040            23   \n",
       "4           12  0.200000 2011-07-28 10:37:00.040            23   \n",
       "...        ...       ...                     ...           ...   \n",
       "552068  623507  8.783333 2012-10-01 20:24:00.040            46   \n",
       "552069  623508  3.766667 2012-10-01 20:24:00.040            25   \n",
       "552070  623509  8.916667 2012-10-01 20:25:00.040            16   \n",
       "552071  623512  7.150000 2012-10-01 20:29:00.040            61   \n",
       "552072  623517  3.316667 2012-10-01 20:32:00.040            22   \n",
       "\n",
       "                      end_date end_station bike_nr subscription_type zip_code  \\\n",
       "0      2011-07-28 10:12:00.040          23  B00468        Registered    97217   \n",
       "1      2011-07-28 10:25:00.040          23  B00554        Registered    02215   \n",
       "2      2011-07-28 10:34:00.040          23  B00456        Registered    02108   \n",
       "3      2011-07-28 10:36:00.040          23  B00554        Registered    02116   \n",
       "4      2011-07-28 10:37:00.040          23  B00554        Registered    97214   \n",
       "...                        ...         ...     ...               ...      ...   \n",
       "552068 2012-10-01 20:32:00.040          67  B00486            Casual      NaN   \n",
       "552069 2012-10-01 20:28:00.040          26  B00170        Registered    02118   \n",
       "552070 2012-10-01 20:34:00.040          47  T01067        Registered    02113   \n",
       "552071 2012-10-01 20:36:00.040          33  B00621        Registered    02135   \n",
       "552072 2012-10-01 20:36:00.040          81  B00012        Registered    02116   \n",
       "\n",
       "        birth_date  gender  payment  month       day  \n",
       "0           1976.0    Male      0.0      7  Thursday  \n",
       "1           1966.0    Male      0.0      7  Thursday  \n",
       "2           1943.0    Male      0.0      7  Thursday  \n",
       "3           1981.0  Female      0.0      7  Thursday  \n",
       "4           1983.0  Female      0.0      7  Thursday  \n",
       "...            ...     ...      ...    ...       ...  \n",
       "552068         NaN     NaN      0.0     10    Monday  \n",
       "552069      1986.0    Male      0.0     10    Monday  \n",
       "552070      1965.0    Male      0.0     10    Monday  \n",
       "552071      1986.0    Male      0.0     10    Monday  \n",
       "552072      1989.0    Male      0.0     10    Monday  \n",
       "\n",
       "[551611 rows x 14 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos agrupar por mas de una columna, por ejemplo, la duración promedio de los viajes por cada dia de la semana los distintos meses.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month  day      \n",
       "3      Friday       19.545181\n",
       "       Monday       17.236796\n",
       "       Saturday     27.604671\n",
       "       Sunday       35.256211\n",
       "       Thursday     16.415541\n",
       "                      ...    \n",
       "11     Saturday     31.242051\n",
       "       Sunday       42.704608\n",
       "       Thursday     15.774854\n",
       "       Tuesday      14.522431\n",
       "       Wednesday    16.158131\n",
       "Name: duration, Length: 63, dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(['month','day'])['duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un forma mas efectiva de visualizar esto es en una tabla, esto se llama una tabla pivote.  Donde decido que son las fila `index`, las columnas `columns` y los valores a desplegar `values` así como el *agregador* a aplicar a cada dato `aggfunc`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>19.545181</td>\n",
       "      <td>16.820292</td>\n",
       "      <td>15.464950</td>\n",
       "      <td>15.666985</td>\n",
       "      <td>17.240976</td>\n",
       "      <td>22.937421</td>\n",
       "      <td>20.721785</td>\n",
       "      <td>23.349817</td>\n",
       "      <td>18.109054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>17.236796</td>\n",
       "      <td>18.137857</td>\n",
       "      <td>18.692415</td>\n",
       "      <td>14.263635</td>\n",
       "      <td>16.399548</td>\n",
       "      <td>21.783316</td>\n",
       "      <td>21.351371</td>\n",
       "      <td>22.025409</td>\n",
       "      <td>16.299021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>27.604671</td>\n",
       "      <td>26.355502</td>\n",
       "      <td>27.477282</td>\n",
       "      <td>24.149847</td>\n",
       "      <td>29.738141</td>\n",
       "      <td>35.570739</td>\n",
       "      <td>29.044474</td>\n",
       "      <td>43.287357</td>\n",
       "      <td>31.242051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>35.256211</td>\n",
       "      <td>25.138158</td>\n",
       "      <td>28.333665</td>\n",
       "      <td>26.782958</td>\n",
       "      <td>28.903865</td>\n",
       "      <td>31.176206</td>\n",
       "      <td>30.437344</td>\n",
       "      <td>47.896037</td>\n",
       "      <td>42.704608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>16.415541</td>\n",
       "      <td>13.436867</td>\n",
       "      <td>14.504003</td>\n",
       "      <td>14.660568</td>\n",
       "      <td>16.536273</td>\n",
       "      <td>20.718764</td>\n",
       "      <td>15.386106</td>\n",
       "      <td>18.068377</td>\n",
       "      <td>15.774854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>15.587873</td>\n",
       "      <td>15.635472</td>\n",
       "      <td>12.129703</td>\n",
       "      <td>13.642497</td>\n",
       "      <td>16.119691</td>\n",
       "      <td>21.361825</td>\n",
       "      <td>17.831251</td>\n",
       "      <td>19.350771</td>\n",
       "      <td>14.522431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>16.231925</td>\n",
       "      <td>13.055404</td>\n",
       "      <td>13.101969</td>\n",
       "      <td>13.338051</td>\n",
       "      <td>20.309822</td>\n",
       "      <td>21.869728</td>\n",
       "      <td>16.645311</td>\n",
       "      <td>17.930382</td>\n",
       "      <td>16.158131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month             3          4          5          6          7          8   \\\n",
       "day                                                                           \n",
       "Friday     19.545181  16.820292  15.464950  15.666985  17.240976  22.937421   \n",
       "Monday     17.236796  18.137857  18.692415  14.263635  16.399548  21.783316   \n",
       "Saturday   27.604671  26.355502  27.477282  24.149847  29.738141  35.570739   \n",
       "Sunday     35.256211  25.138158  28.333665  26.782958  28.903865  31.176206   \n",
       "Thursday   16.415541  13.436867  14.504003  14.660568  16.536273  20.718764   \n",
       "Tuesday    15.587873  15.635472  12.129703  13.642497  16.119691  21.361825   \n",
       "Wednesday  16.231925  13.055404  13.101969  13.338051  20.309822  21.869728   \n",
       "\n",
       "month             9          10         11  \n",
       "day                                         \n",
       "Friday     20.721785  23.349817  18.109054  \n",
       "Monday     21.351371  22.025409  16.299021  \n",
       "Saturday   29.044474  43.287357  31.242051  \n",
       "Sunday     30.437344  47.896037  42.704608  \n",
       "Thursday   15.386106  18.068377  15.774854  \n",
       "Tuesday    17.831251  19.350771  14.522431  \n",
       "Wednesday  16.645311  17.930382  16.158131  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.pivot_table(values='duration', columns='month', index='day', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"4\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>birth_date</th>\n",
       "      <th colspan=\"2\" halign=\"left\">duration</th>\n",
       "      <th colspan=\"2\" halign=\"left\">birth_date</th>\n",
       "      <th colspan=\"2\" halign=\"left\">duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subscription_type</th>\n",
       "      <th>Registered</th>\n",
       "      <th>Casual</th>\n",
       "      <th>Registered</th>\n",
       "      <th>Casual</th>\n",
       "      <th>Registered</th>\n",
       "      <th>Casual</th>\n",
       "      <th>Registered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976.303260</td>\n",
       "      <td>45.063842</td>\n",
       "      <td>11.206018</td>\n",
       "      <td>0</td>\n",
       "      <td>10275</td>\n",
       "      <td>4066</td>\n",
       "      <td>10366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1976.244984</td>\n",
       "      <td>36.188459</td>\n",
       "      <td>11.149880</td>\n",
       "      <td>0</td>\n",
       "      <td>31247</td>\n",
       "      <td>12958</td>\n",
       "      <td>31502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1976.230049</td>\n",
       "      <td>34.640274</td>\n",
       "      <td>11.034746</td>\n",
       "      <td>0</td>\n",
       "      <td>36075</td>\n",
       "      <td>17376</td>\n",
       "      <td>36405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1976.204013</td>\n",
       "      <td>32.213688</td>\n",
       "      <td>11.140130</td>\n",
       "      <td>0</td>\n",
       "      <td>39272</td>\n",
       "      <td>16353</td>\n",
       "      <td>39611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1976.124669</td>\n",
       "      <td>35.532269</td>\n",
       "      <td>11.686938</td>\n",
       "      <td>0</td>\n",
       "      <td>46138</td>\n",
       "      <td>26739</td>\n",
       "      <td>46454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1976.211597</td>\n",
       "      <td>41.005865</td>\n",
       "      <td>12.453465</td>\n",
       "      <td>0</td>\n",
       "      <td>69793</td>\n",
       "      <td>51704</td>\n",
       "      <td>70175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1977.003398</td>\n",
       "      <td>38.658436</td>\n",
       "      <td>11.822884</td>\n",
       "      <td>0</td>\n",
       "      <td>76812</td>\n",
       "      <td>46200</td>\n",
       "      <td>77271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1975.803002</td>\n",
       "      <td>57.553389</td>\n",
       "      <td>12.737517</td>\n",
       "      <td>0</td>\n",
       "      <td>25381</td>\n",
       "      <td>13656</td>\n",
       "      <td>25682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1975.118448</td>\n",
       "      <td>47.556765</td>\n",
       "      <td>11.062853</td>\n",
       "      <td>0</td>\n",
       "      <td>17856</td>\n",
       "      <td>6992</td>\n",
       "      <td>18101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean                            count             \\\n",
       "                    birth_date   duration            birth_date              \n",
       "subscription_type   Registered     Casual Registered     Casual Registered   \n",
       "month                                                                        \n",
       "3                  1976.303260  45.063842  11.206018          0      10275   \n",
       "4                  1976.244984  36.188459  11.149880          0      31247   \n",
       "5                  1976.230049  34.640274  11.034746          0      36075   \n",
       "6                  1976.204013  32.213688  11.140130          0      39272   \n",
       "7                  1976.124669  35.532269  11.686938          0      46138   \n",
       "8                  1976.211597  41.005865  12.453465          0      69793   \n",
       "9                  1977.003398  38.658436  11.822884          0      76812   \n",
       "10                 1975.803002  57.553389  12.737517          0      25381   \n",
       "11                 1975.118448  47.556765  11.062853          0      17856   \n",
       "\n",
       "                                       \n",
       "                  duration             \n",
       "subscription_type   Casual Registered  \n",
       "month                                  \n",
       "3                     4066      10366  \n",
       "4                    12958      31502  \n",
       "5                    17376      36405  \n",
       "6                    16353      39611  \n",
       "7                    26739      46454  \n",
       "8                    51704      70175  \n",
       "9                    46200      77271  \n",
       "10                   13656      25682  \n",
       "11                    6992      18101  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.pivot_table(values=['duration','birth_date'], columns='subscription_type', index='month', aggfunc=['mean', 'count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">duration</th>\n",
       "      <th colspan=\"2\" halign=\"left\">birth_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Casual</th>\n",
       "      <td>45.063842</td>\n",
       "      <td>4066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>11.206018</td>\n",
       "      <td>10366</td>\n",
       "      <td>1976.303260</td>\n",
       "      <td>10275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>Casual</th>\n",
       "      <td>36.188459</td>\n",
       "      <td>12958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>11.149880</td>\n",
       "      <td>31502</td>\n",
       "      <td>1976.244984</td>\n",
       "      <td>31247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>Casual</th>\n",
       "      <td>34.640274</td>\n",
       "      <td>17376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>11.034746</td>\n",
       "      <td>36405</td>\n",
       "      <td>1976.230049</td>\n",
       "      <td>36075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>Casual</th>\n",
       "      <td>32.213688</td>\n",
       "      <td>16353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>11.140130</td>\n",
       "      <td>39611</td>\n",
       "      <td>1976.204013</td>\n",
       "      <td>39272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>Casual</th>\n",
       "      <td>35.532269</td>\n",
       "      <td>26739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>11.686938</td>\n",
       "      <td>46454</td>\n",
       "      <td>1976.124669</td>\n",
       "      <td>46138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>Casual</th>\n",
       "      <td>41.005865</td>\n",
       "      <td>51704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>12.453465</td>\n",
       "      <td>70175</td>\n",
       "      <td>1976.211597</td>\n",
       "      <td>69793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9</th>\n",
       "      <th>Casual</th>\n",
       "      <td>38.658436</td>\n",
       "      <td>46200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>11.822884</td>\n",
       "      <td>77271</td>\n",
       "      <td>1977.003398</td>\n",
       "      <td>76812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>Casual</th>\n",
       "      <td>57.553389</td>\n",
       "      <td>13656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>12.737517</td>\n",
       "      <td>25682</td>\n",
       "      <td>1975.803002</td>\n",
       "      <td>25381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">11</th>\n",
       "      <th>Casual</th>\n",
       "      <td>47.556765</td>\n",
       "      <td>6992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registered</th>\n",
       "      <td>11.062853</td>\n",
       "      <td>18101</td>\n",
       "      <td>1975.118448</td>\n",
       "      <td>17856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          duration          birth_date       \n",
       "                              mean  count         mean  count\n",
       "month subscription_type                                      \n",
       "3     Casual             45.063842   4066          NaN      0\n",
       "      Registered         11.206018  10366  1976.303260  10275\n",
       "4     Casual             36.188459  12958          NaN      0\n",
       "      Registered         11.149880  31502  1976.244984  31247\n",
       "5     Casual             34.640274  17376          NaN      0\n",
       "      Registered         11.034746  36405  1976.230049  36075\n",
       "6     Casual             32.213688  16353          NaN      0\n",
       "      Registered         11.140130  39611  1976.204013  39272\n",
       "7     Casual             35.532269  26739          NaN      0\n",
       "      Registered         11.686938  46454  1976.124669  46138\n",
       "8     Casual             41.005865  51704          NaN      0\n",
       "      Registered         12.453465  70175  1976.211597  69793\n",
       "9     Casual             38.658436  46200          NaN      0\n",
       "      Registered         11.822884  77271  1977.003398  76812\n",
       "10    Casual             57.553389  13656          NaN      0\n",
       "      Registered         12.737517  25682  1975.803002  25381\n",
       "11    Casual             47.556765   6992          NaN      0\n",
       "      Registered         11.062853  18101  1975.118448  17856"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalData.groupby(['month','subscription_type'])[['duration','birth_date']].agg(['mean', 'count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instalar \n",
    "- plotnine\n",
    "- geopandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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