Chapter1-Stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import random\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy import stats\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('examples/trip.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146167\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>usertype</th>\n",
       "      <th>...</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>starttime_mod</th>\n",
       "      <th>starttime_date</th>\n",
       "      <th>starttime_year</th>\n",
       "      <th>starttime_month</th>\n",
       "      <th>starttime_day</th>\n",
       "      <th>starttime_hour</th>\n",
       "      <th>tripduration_mean</th>\n",
       "      <th>male_tripduration_mean</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>212893</th>\n",
       "      <td>231080</td>\n",
       "      <td>7/8/2016 16:22</td>\n",
       "      <td>7/8/2016 16:53</td>\n",
       "      <td>SEA00423</td>\n",
       "      <td>1817.524</td>\n",
       "      <td>E Pine St &amp; 16th Ave</td>\n",
       "      <td>E Pine St &amp; 16th Ave</td>\n",
       "      <td>CH-07</td>\n",
       "      <td>CH-07</td>\n",
       "      <td>Member</td>\n",
       "      <td>...</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>2016-07-08 16:22:00</td>\n",
       "      <td>2016-07-08</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>1817.524</td>\n",
       "      <td>482.552</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133697</th>\n",
       "      <td>147070</td>\n",
       "      <td>9/20/2015 16:06</td>\n",
       "      <td>9/20/2015 16:15</td>\n",
       "      <td>SEA00067</td>\n",
       "      <td>547.429</td>\n",
       "      <td>Terry Ave &amp; Stewart St</td>\n",
       "      <td>2nd Ave &amp; Pine St</td>\n",
       "      <td>SLU-20</td>\n",
       "      <td>CBD-13</td>\n",
       "      <td>Member</td>\n",
       "      <td>...</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>2015-09-20 16:06:00</td>\n",
       "      <td>2015-09-20</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>547.429</td>\n",
       "      <td>547.429</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179564</th>\n",
       "      <td>195602</td>\n",
       "      <td>4/3/2016 19:02</td>\n",
       "      <td>4/3/2016 19:03</td>\n",
       "      <td>SEA00308</td>\n",
       "      <td>61.203</td>\n",
       "      <td>Terry Ave &amp; Stewart St</td>\n",
       "      <td>Terry Ave &amp; Stewart St</td>\n",
       "      <td>SLU-20</td>\n",
       "      <td>SLU-20</td>\n",
       "      <td>Member</td>\n",
       "      <td>...</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>2016-04-03 19:02:00</td>\n",
       "      <td>2016-04-03</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>61.203</td>\n",
       "      <td>61.203</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116828</th>\n",
       "      <td>129239</td>\n",
       "      <td>8/15/2015 17:27</td>\n",
       "      <td>8/15/2015 17:34</td>\n",
       "      <td>SEA00051</td>\n",
       "      <td>406.541</td>\n",
       "      <td>Terry Ave &amp; Stewart St</td>\n",
       "      <td>2nd Ave &amp; Pine St</td>\n",
       "      <td>SLU-20</td>\n",
       "      <td>CBD-13</td>\n",
       "      <td>Member</td>\n",
       "      <td>...</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>2015-08-15 17:27:00</td>\n",
       "      <td>2015-08-15</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>406.541</td>\n",
       "      <td>406.541</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133730</th>\n",
       "      <td>147105</td>\n",
       "      <td>9/20/2015 17:04</td>\n",
       "      <td>9/20/2015 17:12</td>\n",
       "      <td>SEA00460</td>\n",
       "      <td>475.856</td>\n",
       "      <td>2nd Ave &amp; Pine St</td>\n",
       "      <td>Terry Ave &amp; Stewart St</td>\n",
       "      <td>CBD-13</td>\n",
       "      <td>SLU-20</td>\n",
       "      <td>Member</td>\n",
       "      <td>...</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>2015-09-20 17:04:00</td>\n",
       "      <td>2015-09-20</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>475.856</td>\n",
       "      <td>475.856</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        trip_id        starttime         stoptime    bikeid  tripduration       from_station_name  \\\n",
       "212893   231080   7/8/2016 16:22   7/8/2016 16:53  SEA00423      1817.524    E Pine St & 16th Ave   \n",
       "133697   147070  9/20/2015 16:06  9/20/2015 16:15  SEA00067       547.429  Terry Ave & Stewart St   \n",
       "179564   195602   4/3/2016 19:02   4/3/2016 19:03  SEA00308        61.203  Terry Ave & Stewart St   \n",
       "116828   129239  8/15/2015 17:27  8/15/2015 17:34  SEA00051       406.541  Terry Ave & Stewart St   \n",
       "133730   147105  9/20/2015 17:04  9/20/2015 17:12  SEA00460       475.856       2nd Ave & Pine St   \n",
       "\n",
       "               to_station_name from_station_id to_station_id usertype  ...  birthyear  \\\n",
       "212893    E Pine St & 16th Ave           CH-07         CH-07   Member  ...     1931.0   \n",
       "133697       2nd Ave & Pine St          SLU-20        CBD-13   Member  ...     1936.0   \n",
       "179564  Terry Ave & Stewart St          SLU-20        SLU-20   Member  ...     1936.0   \n",
       "116828       2nd Ave & Pine St          SLU-20        CBD-13   Member  ...     1936.0   \n",
       "133730  Terry Ave & Stewart St          CBD-13        SLU-20   Member  ...     1936.0   \n",
       "\n",
       "             starttime_mod starttime_date starttime_year  starttime_month  starttime_day  \\\n",
       "212893 2016-07-08 16:22:00     2016-07-08           2016                7              8   \n",
       "133697 2015-09-20 16:06:00     2015-09-20           2015                9             20   \n",
       "179564 2016-04-03 19:02:00     2016-04-03           2016                4              3   \n",
       "116828 2015-08-15 17:27:00     2015-08-15           2015                8             15   \n",
       "133730 2015-09-20 17:04:00     2015-09-20           2015                9             20   \n",
       "\n",
       "        starttime_hour  tripduration_mean  male_tripduration_mean   age  \n",
       "212893              16           1817.524                 482.552  85.0  \n",
       "133697              16            547.429                 547.429  79.0  \n",
       "179564              19             61.203                  61.203  80.0  \n",
       "116828              17            406.541                 406.541  79.0  \n",
       "133730              17            475.856                 475.856  79.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(data))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date range of dataset: 10/13/2014 10:32 - 9/1/2016 0:20\n"
     ]
    }
   ],
   "source": [
    "data = data.sort_values(by='starttime')\n",
    "data.reset_index()\n",
    "print('Date range of dataset: %s - %s'%(data.loc[1, 'starttime'], data.loc[len(data)-1, 'stoptime']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10518c50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105185f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_user = data.groupby('usertype').size()\n",
    "groupby_user.plot.bar(title = 'Distribution of user types')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10518630>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9a40f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_gender = data.groupby('gender').size()\n",
    "groupby_gender.plot.bar(title = 'Distribution of genders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc09bd30>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc09b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.sort_values(by='birthyear')\n",
    "groupby_birthyear = data.groupby('birthyear').size()\n",
    "groupby_birthyear.plot.bar(title = 'Distribution of birth years', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc1ce518>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1db128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_mil = data[(data['birthyear'] >= 1977) & (data['birthyear']<=1994)]\n",
    "groupby_mil = data_mil.groupby('usertype').size()\n",
    "groupby_mil.plot.bar(title = 'Distribution of user types')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc1ce5f8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b3/52Jk6cyOqrr84FF1zAWWedxbbbbstWW23F5Zc3Hz/ybW97G5tuuilbb701//Zv/8Zuu+0GwPLLL8+ll17Kpz/9abbddlu22247fv/737ejapVFve7PbjBx4sScPn16p9OQJElSl/OWDZ13zz33sMUWW7RseS+88AKrrLIKL774IrvuuitnnHEGO+ywQ8uW3wn1tlFE3JKZEweK9Zo+SZIkST3lmGOOYebMmcyfP58pU6Z0fYNvuGz0SZIkSeopF154YadTGFG8pk+SJEmSepiNPkmSJEnqYTb6JEmSJKmH2eiTJEmSpB7mQC6SJEmSRpQJx/+spcubder+A84TEbz73e/m/PPPB2DhwoWst956TJ48mZ/+9KcN46ZOncrXvva1pvN0mj19kiRJkka9lVdembvuuouXXnoJgGuvvZYNNtigw1m1ho0+SZIkSQL23Xdffvazopfxoosu4ogjjlg87eabb+aNb3wj22+/PW984xu57777loifN28e73//+5k0aRLbb789l19+edtyb8ZGnyRJkiQBhx9+OBdffDHz58/nzjvvZPLkyYunbb755vzmN7/htttu4+STT+azn/3sEvGnnHIKe+yxB9OmTeP666/nk5/8JPPmzWtnFerymj5JkiRJArbZZhtmzZrFRRddxH777feqaXPnzmXKlCk88MADRAQLFixYIv6aa67hiiuu4Gtf+xoA8+fP55FHHmGLLbZoS/6N2OiTJEmSpNKBBx7IJz7xCaZOncpTTz21uPzzn/88b3rTm/jxj3/MrFmz2H333ZeIzUwuu+wyNttsszZmPDBP75QkSZKk0vvf/36+8IUvsPXWW7+qfO7cuYsHdjnnnHPqxu69996cfvrpZCYAt91221LNdbDs6ZMkSZI0ogzmFgtLy/jx4znuuOOWKP/Upz7FlClT+PrXv84ee+xRN/bzn/88H/nIR9hmm23ITCZMmDAibuUQfa3QbjNx4sScPn16p9OQJElSl9v63K0bTpsxZUYbMxm97rnnno5f9zbS1dtGEXFLZk4cKNbTOyVJkiSph9nokyRJkqQeNuA1fRFxNvBW4MnMfENZ9lXgAOBvwB+AIzPz2XLaZ4CjgEXAhzPz6rJ8H+CbwBjgzMw8tSzfBLgYWAu4FXhPZv6tlZWUJEmSGpnx8COdTkFaqgbT03cOsE+/smuBN2TmNsD9wGcAImJL4HBgqzLmOxExJiLGAN8G9gW2BI4o5wX4D+Abmbkp8AxFg1GSJEmS1AIDNvoy8zfA0/3KrsnMheXLG4Hx5fODgIsz86+Z+TDwILBT+XgwMx8qe/EuBg6KiAD2AC4t488FDh5mnSRJkiRJpVZc0/d+4Ofl8w2AR2umzS7LGpWvDTxb04DsK68rIo6JiOkRMX3OnDktSF2SJEmSetuw7tMXEScAC4EL+orqzJbUb1xmk/nryswzgDOguGXDkJKVJEmS1B1OWr3Fy5s74Cxjxox51Q3Zf/KTnzBhwoTW5lE655xzmD59Ot/61reWyvL7q9zoi4gpFAO87Jmv3OxvNrBhzWzjgcfK5/XK/wKsERHLlr19tfNLkiRJUlusuOKK3H777Z1OY6modHpnORLnp4EDM/PFmklXAIdHxNhyVM5NgZuBacCmEbFJRCxPMdjLFWVj8XrgkDJ+CnB5tapIkiRJUussWrSIT37yk0yaNIltttmG//7v/wZg6tSp7Lbbbrzzne/k9a9/PccffzwXXHABO+20E1tvvTV/+MMfALjyyiuZPHky22+/PW9+85t54oknlljHnDlzeMc73sGkSZOYNGkSv/vd71pejwEbfRFxEXADsFlEzI6Io4BvAasC10bE7RHxPYDMvBv4ITAT+AVwbGYuKnvxPghcDdwD/LCcF4rG48ci4kGKa/zOamkNJUmSJGkAL730Ettttx3bbbcdb3vb2wA466yzWH311Zk2bRrTpk3j+9//Pg8//DAAd9xxB9/85jeZMWMG559/Pvfffz8333wzRx99NKeffjoAu+yyCzfeeCO33XYbhx9+OP/5n/+5xHqPO+44PvrRjzJt2jQuu+wyjj766JbXbcDTOzPziDrFDRtmmXkKcEqd8quAq+qUP0QxuqckSZIkdUS90zuvueYa7rzzTi69tLjZwNy5c3nggQdYfvnlmTRpEuuttx4Ar3vd69hrr70A2Hrrrbn++usBmD17NocddhiPP/44f/vb39hkk02WWO8vf/lLZs6cufj1c889x/PPP8+qq67asroNayAXSZIkSepVmcnpp5/O3nvv/aryqVOnMnbs2MWvl1lmmcWvl1lmGRYuLG5O8KEPfYiPfexjHHjggUydOpWTTjppiXW8/PLL3HDDDay44opLrR6tuGWDJEmSJPWcvffem+9+97ssWLAAgPvvv5958+YNOn7u3LlssEFxR7pzzz237jx77bXXq0bxXBqDydjTJ0mSJGlkGcQtFtrh6KOPZtasWeywww5kJuPGjeMnP/nJoONPOukkDj30UDbYYAN23nnnxdcD1jrttNM49thj2WabbVi4cCG77ror3/ve91pZDeKVuy10l4kTJ+b06dM7nYYkSZK6XbN7wo2Qxkevu+eee9hiiy06ncaIVm8bRcQtmTlxoFhP75QkSZKkHmajT5IkSZJ6mI0+SZIkSephNvokSZIkqYfZ6JMkSZKkHmajT5IkSZJ6mPfpkyRJkjSibH3u1i1d3owpMwY13+zZszn22GOZOXMmL7/8Mm9961v56le/ysyZM3nsscfYb7/9gOL+e6ussgqf+MQnWprn0mJPnyRJkqRRLzN5+9vfzsEHH8wDDzzA/fffzwsvvMAJJ5zA7bffzlVXXdWydS1atKhlyxoMG32SJEmSRr3rrruOFVZYgSOPPBKAMWPG8I1vfIMzzzyTT33qU1xyySVst912XHLJJQDMnDmT3Xffnde+9rWcdtppi5fzgx/8gJ122ontttuOf/mXf1ncwFtllVX4whe+wOTJk7nhhhvaWjcbfZIkSZJGvbvvvpsdd9zxVWWrrbYaEyZM4HOf+xyHHXYYt99+O4cddhgA9957L1dffTU333wzX/ziF1mwYAH33HMPl1xyCb/73e+4/fbbGTNmDBdccAEA8+bN4w1veAM33XQTu+yyS1vr5jV9kiRJkka9zCQiBl2+//77M3bsWMaOHctrXvMannjiCX71q19xyy23MGnSJABeeuklXvOa1wBFz+E73vGOpVuJBmz0SZIkSRr1ttpqKy677LJXlT333HM8+uijjBkzZon5x44du/j5mDFjWLhwIZnJlClT+Pd///cl5l9hhRXqLqcdPL1TkiRJ0qi355578uKLL3LeeecBxWArH//4x3nf+97Huuuuy/PPPz+oZVx66aU8+eSTADz99NP88Y9/XKp5D4Y9fZIkSZJGlMHeYqGVIoIf//jHfOADH+BLX/oSL7/8Mvvttx9f+cpXmDdvHqeeeirbbbcdn/nMZxouY8stt+TLX/4ye+21Fy+//DLLLbcc3/72t9l4443bWJMl2eiTJEmSJGDDDTfkyiuvXKJ87NixTJs2rWHcXXfdtfj5YYcdtniwl1ovvPBCa5KswNM7JUmSJKmH2eiTJEmSpB42YKMvIs6OiCcj4q6asrUi4tqIeKD8u2ZZHhFxWkQ8GBF3RsQONTFTyvkfiIgpNeU7RsSMMua0qDceqiRJkqSelpmdTmHEGu62GUxP3znAPv3Kjgd+lZmbAr8qXwPsC2xaPo4BvgtFIxE4EZgM7ASc2NdQLOc5piau/7okSZIk9bAVVliBp556yoZfHZnJU089xQorrFB5GQMO5JKZv4mICf2KDwJ2L5+fC0wFPl2Wn5fFu3VjRKwREeuV816bmU8DRMS1wD4RMRVYLTNvKMvPAw4Gfl65RpIkSZK6yvjx45k9ezZz5szpdCoj0gorrMD48eMrx1cdvXPdzHwcIDMfj4jXlOUbAI/WzDe7LGtWPrtOeV0RcQxFryAbbbRRxdQlSZIkjSTLLbccm2yySafT6FmtHsil3vV4WaG8rsw8IzMnZubEcePGVUxRkiRJkkaPqj19T0TEemUv33rAk2X5bGDDmvnGA4+V5bv3K59alo+vM78kSZLUFhPmX9hw2qz2pSEtNVV7+q4A+kbgnAJcXlP+3nIUz52BueVpoFcDe0XEmuUALnsBV5fTno+InctRO99bsyxJkiRJ0jAN2NMXERdR9NKtExGzKUbhPBX4YUQcBTwCHFrOfhWwH/Ag8CJwJEBmPh0RXwL6bmN/ct+gLsC/UYwQuiLFAC4O4iJJkiRJLTKY0TuPaDBpzzrzJnBsg+WcDZxdp3w68IaB8pAkSZIkDV2rB3KRJEmSJI0gNvokSZIkqYfZ6JMkSZKkHmajT5IkSZJ6mI0+SZIkSephNvokSZIkqYfZ6JMkSZKkHmajT5IkSZJ6mI0+SZIkSephNvokSZIkqYfZ6JMkSZKkHmajT5IkSZJ62LKdTkCSJEl6lZNWbzJtbvvykHqEPX2SJEmS1MNs9EmSJElSD7PRJ0mSJEk9zEafJEmSJPUwG32SJEmS1MNs9EmSJElSD7PRJ0mSJEk9bFiNvoj4aETcHRF3RcRFEbFCRGwSETdFxAMRcUlELF/OO7Z8/WA5fULNcj5Tlt8XEXsPr0qSJEmSpD6VG30RsQHwYWBiZr4BGAMcDvwH8I3M3BR4BjiqDDkKeCYz/x74RjkfEbFlGbcVsA/wnYgYUzUvSZIkSdIrhnt657LAihGxLLAS8DiwB3BpOf1c4ODy+UHla8rpe0ZElOUXZ+ZfM/Nh4EFgp2HmJUmSJEliGI2+zPwT8DXgEYrG3lzgFuDZzFxYzjYb2KB8vgHwaBm7sJx/7dryOjGSJEmSpGEYzumda1L00m0CrA+sDOxbZ9bsC2kwrVF5vXUeExHTI2L6nDlzhp60JEmSJI0ywzm9883Aw5k5JzMXAP8DvBFYozzdE2A88Fj5fDawIUA5fXXg6dryOjGvkplnZObEzJw4bty4YaQuSZIkSaPDcBp9jwA7R8RK5bV5ewIzgeuBQ8p5pgCXl8+vKF9TTr8uM7MsP7wc3XMTYFPg5mHkJUmSJEkqLTvwLPVl5k0RcSlwK7AQuA04A/gZcHFEfLksO6sMOQs4PyIepOjhO7xczt0R8UOKBuNC4NjMXFQ1L0mSpFHhpNWbTJvbvjwkjXiVG30AmXkicGK/4oeoM/pmZs4HDm2wnFOAU4aTiyRJknrDhPkXNpw2q31pSD1juLdskCRJkiSNYMPq6ZMkSVKX8bRQadSxp0+SJEmSepiNPkmSJEnqYZ7eKUmSpKWn0emknkoqtY09fZIkSZLUw2z0SZIkSVIPs9EnSZIkST3MRp8kSZIk9TAHcpEkSZKGyvsdqovY6JMkSepCE+Zf2HDarPalIakLeHqnJEmSJPUwG32SJEmS1MNs9EmSJElSD7PRJ0mSJEk9zEafJEmSJPUwG32SJEmS1MO8ZYMkSZJ6g/fOk+qyp0+SJEmSepg9fZIkSRqYvWhS17KnT5IkSZJ62LAafRGxRkRcGhH3RsQ9EfEPEbFWRFwbEQ+Uf9cs542IOC0iHoyIOyNih5rlTCnnfyAipgy3UpIkSZKkwnBP7/wm8IvMPCQilgdWAj4L/CozT42I44HjgU8D+wKblo/JwHeByRGxFnAiMBFI4JaIuCIznxlmbpIkSepnwvwLG06b1b40NFSeXqthqNzTFxGrAbsCZwFk5t8y81ngIODccrZzgYPL5wcB52XhRmCNiFgP2Bu4NjOfLht61wL7VM1LkiRJkvSK4Zze+VpgDvD/IuK2iDgzIlYG1s3MxwHKv68p598AeLQmfnZZ1qh8CRFxTERMj4jpc+bMGUbqkiRJkjQ6DKfRtyywA/DdzNwemEdxKmcjUacsm5QvWZh5RmZOzMyJ48aNG2q+kiRJkjTqDKfRNxuYnZk3la8vpWgEPlGetkn598ma+TesiR8PPNakXJIkSZI0TJUbfZn5Z+DRiNisLNoTmAlcAfSNwDkFuLx8fgXw3nIUz52BueXpn1cDe0XEmuVIn3uVZZIkSZKkYRru6J0fAi4oR+58CDiSoiH5w4g4CngEOLSc9ypgP+BB4MUN5qgjAAAgAElEQVRyXjLz6Yj4EjCtnO/kzHx6mHlJkiRJkhhmoy8zb6e41UJ/e9aZN4FjGyznbODs4eQiSZIkSVrScHv6JEmSJA2W99tTBwxnIBdJkiRJ0ghnT58kSZI0RBPmX9hw2qz2pSENij19kiRJktTD7OmTJEnSgKr2bDWKaxYjqbXs6ZMkSZKkHmZPnyRJkjTCeQ2hhsOePkmSJEnqYTb6JEmSJKmH2eiTJEmSpB5mo0+SJEmSepiNPkmSJEnqYTb6JEmSJKmH2eiTJEmSpB5mo0+SJEmSepiNPkmSJEnqYct2OgFJkiSpFSbMv7DhtFntS0MacezpkyRJkqQeZk+fJEmS1KtOWr1B+dz25qGOsqdPkiRJknqYPX2SJEmd1qg3BuyRkTRsw+7pi4gxEXFbRPy0fL1JRNwUEQ9ExCURsXxZPrZ8/WA5fULNMj5Tlt8XEXsPNydJkiRJUqEVp3ceB9xT8/o/gG9k5qbAM8BRZflRwDOZ+ffAN8r5iIgtgcOBrYB9gO9ExJgW5CVJkiRJo96wGn0RMR7YHzizfB3AHsCl5SznAgeXzw8qX1NO37Oc/yDg4sz8a2Y+DDwI7DScvCRJkiRJheH29P0X8Cng5fL12sCzmbmwfD0b2KB8vgHwKEA5fW45/+LyOjGvEhHHRMT0iJg+Z86cYaYuSZIkSb2v8kAuEfFW4MnMvCUidu8rrjNrDjCtWcyrCzPPAM4AmDhxYt15JEmSpJHKG8irE4Yzeuf/AQ6MiP2AFYDVKHr+1oiIZcvevPHAY+X8s4ENgdkRsSywOvB0TXmf2hhJkiRJ0jBUbvRl5meAzwCUPX2fyMx3RcSPgEOAi4EpwOVlyBXl6xvK6ddlZkbEFcCFEfF1YH1gU+DmqnlJkiRJqm7rc7duOG3GlBltzEStsjTu0/dp4OKI+DJwG3BWWX4WcH5EPEjRw3c4QGbeHRE/BGYCC4FjM3PRUshLkiRJkkadljT6MnMqMLV8/hB1Rt/MzPnAoQ3iTwFOaUUukiRJkqqb8fAjnU5BLdaK+/RJkiRJkkaopXF6pyRJUldr9zVNjugoaWmy0SdJknpaowacA1JIGi1s9EmSpJ7m9UmSRjsbfZIkSf3YUJTUS2z0SZIkST2q0fWis9qbhjrM0TslSZIkqYfZ0ydJkiRpMUeT7T02+iRJUnudtHqTaXPbl4ckjRKe3ilJkiRJPcyePkmSpFaxF1PSCGSjT5IktZXXC0lSe9nokyRJ6seGqaRe4jV9kiRJktTD7OmTJKmXeE2ZJKkfG32S1E08oB89fK+X5DaRpEps9EmSpGq6pBHW6Pq8We1NQ5I6xmv6JEmSJKmH2eiTJEmSpB7m6Z2SJEmShq9LTvkejWz0SZK0NHkQNKp4fz9JI1Hl0zsjYsOIuD4i7omIuyPiuLJ8rYi4NiIeKP+uWZZHRJwWEQ9GxJ0RsUPNsqaU8z8QEVOGXy1JkiRJEgyvp28h8PHMvDUiVgVuiYhrgfcBv8rMUyPieOB44NPAvsCm5WMy8F1gckSsBZwITASyXM4VmfnMMHKTJA2XPVQaYexFk6RqKjf6MvNx4PHy+fMRcQ+wAXAQsHs527nAVIpG30HAeZmZwI0RsUZErFfOe21mPg1QNhz3AS6qmpsk9aquOOi1sdhRXbGPSJLaqiXX9EXEBGB74CZg3bJBSGY+HhGvKWfbAHi0Jmx2WdaovN56jgGOAdhoo41akbokaTSq2jC1QfsqNjAl1fIzYeQa9i0bImIV4DLgI5n5XLNZ65Rlk/IlCzPPyMyJmTlx3LhxQ09WkiRJkkaZYfX0RcRyFA2+CzLzf8riJyJivbKXbz3gybJ8NrBhTfh44LGyfPd+5VOHk5ckaXTY+tytG06bMWVGw2lVf432V2xJUjeq3OiLiADOAu7JzK/XTLoCmAKcWv69vKb8gxFxMcVALnPLhuHVwFf6RvkE9gI+UzUvSdLoMePhRzqdwoBsKEqSOm04PX3/B3gPMCMibi/LPkvR2PthRBwFPAIcWk67CtgPeBB4ETgSIDOfjogvAdPK+U7uG9RFkjR6NOq1a9Zj18tsLEqSWmU4o3f+lvrX4wHsWWf+BI5tsKyzgbOr5iJJGjna2VixYSRJ0sCGPZCLJEmSJGnkasktGyRJUpfzdhSS1LNs9ElSJ3iALUmS2sRGnyT1ui5pYHbDSJySJHUjr+mTJEmSpB5mT58kSXIkVEnqYTb6JKlPo9Mgl8IpkB5gS5KkdvH0TkmSJEnqYfb0SZLqsjdSkqTeYE+fJEmSJPUwe/qkZrpkqHtJkqRRp43X4nc7G32SVGp0OuOs9qYxarn9JUlaOmz0SVKP89o8SdKI5plVS52NPnWO/+Cjh++1JElSx9jok7TU9XRPkw1aSZI6wssCBs9Gn9TtbHRIkiSpCRt9kobGRqYkSVJXsdEnNVH5tMQ2NozafepkT5+qWYHbQ5IkjXQ2+iSNWFufu3XDaTOmzGhjJpIkSd3LRp80kniTUUmSNMq09ayZUXqZio0+jQ5t/gf3lL8OG6Uf6JIkqbnReow2Yhp9EbEP8E1gDHBmZp7a4ZSk3tbDDaPR+oEuSZJUz4ho9EXEGODbwFuA2cC0iLgiM2d2NrPRZcLxP2s4bdap+zcO7ILGQ7c0Atp5v5lu2CYzHn6k0ylIkiRVP04eIUZEow/YCXgwMx8CiIiLgYOAEdnoa/Smd8MbvjS0ffRIt/+o0Q0NU0mSpIZGSOdIZGbbVtYwiYhDgH0y8+jy9XuAyZn5wX7zHQMcU77cDLivwSLXAf4yxDSqxLQ7rhtyrBpnjp2N64Ycq8aZY2fjzLGzcd2QY9U4c+xsnDl2Nq4bcqwaZ45Di9s4M8cNuITM7PgDOJTiOr6+1+8BTh/G8qa3I6bdcd2QYy/XrRtytG7mONLizNG6jea6dUOOvVy3bsjRupnj0oyrfSzDyDAb2LDm9XjgsQ7lIkmSJEk9Y6Q0+qYBm0bEJhGxPHA4cEWHc5IkSZKkrjciBnLJzIUR8UHgaopbNpydmXcPY5FntCmm3XHdkGPVOHPsbFw35Fg1zhw7G2eOnY3rhhyrxpljZ+PMsbNx3ZBj1ThzbF3cYiNiIBdJkiRJ0tIxUk7vlCRJkiQtBTb6JEmSJKmH2eiTJEmSpB5mo0+SJEmSepiNvi4REWtFxJpLO6Zb9HLdJEmjUzd8t1XNsRvqVlUv1029o2dG74yIdYENgAQey8wnlkZMO+MiYiPgP4E9gWeBAFYDrgOOz8xZrYjpRN2qxHRL3SIigJ1qY4Cbc4B/tqpxVXKsGtfuug0jrsr+2NbtX2V97iOti2tn3aqsq+r6umEfqZpnN+wjVdbXDd9tVXPshrpVjelE3dr53daB/5sRX7duOSZpuMxub/RFxHbA94DVgT+VxeMp/gE/kJm3tiKmQ3E3AP8FXJqZi8qyMcChwEcyc+dWxLS7bu3cHh2o217Ad4AH+sX8fRlzTYN1VY3r5boNOW4Y26Pd279K3dxHWhfXzrq5j3S2bt3wvnXDd1vVHLuhbiP+mKQD323t/GztlrqN+GOSAWVmVz+A24HJdcp3Bu5oVUyH4h4Y6rQqMe2uWzu3Rwfqdg8woU75JsA9TdZVNa6X6zbkuGFsj3Zv/yp1cx/p4DZxH+naunXD+9YN321Vc+yGuo34Y5J27o/DjOvluo34Y5KBHsvS/VbOzJv6F2bmjRGxcgtjOhF3S0R8BzgXeLQs2xCYAtzWwpjh5NjO7d8NdVsWmF2n/E/Ack3WVTWul+tWJa7q9mj39q+yPveR1sW1s27uI63Jsxv2karr64bvtqo5dkPduuGYpN3fbe38v+mWunXDMUlTvdDo+3lE/Aw4j1f/070X+EULYzoR917gKOCLFOf0Rhl/JXBWC2PaXbd2bo/hrK9K3NnAtIi4uF/M4QPkWDWul+tWJa7q9mj39q+yPveR1sW1s27uI63Jsxv2karr64bvtqo5dkPduuGYpN3fbe38v+mWunXDMUlTXX9NH0BE7AscxCv/dLOBKzLzqlbGdCKundpZt3ZvjzbXbYsGMTMHWFfVuF6u25DjhrE92r39q9TNfaR1ce2sm/tIZ+s24t+3qjwmaU1cNxyTdOC7rZ2frd1StxF/TNJ0mb3Q6BuNIuKtmfnTpR3TLXq5bpKk0akbvtuq5tgNdauql+um7tXT9+mLiGPaEdOJOGBSm2LaWrc2b4921+2kiuuqGtfLdRty3DC2x5DX1e71uY+0NK6ddXMfWTLupHbEDDOunZ8lI/67jYo5Vo3zmGT4MWXcSW2O6+W6DTmu3XWDHm/0UXSHtiOm7XGZeWI7YkrtrFvbtsdw1lcx7paK66oa18t1qxJXdXu0e/tXWZ/7SOvi2lk395EltbNuI/5964bvtqo5dkPdqq6rzXVr93dbO/9vuqVu3XBM4umdI11E7ARkZk6LiC2BfYB7h3KeeUScl5nvXWpJtklELE9xEetjmfnLiPgn4I0UQ9uekZkLOpqgJEkVRMTmvHL9TlLciPmKzLyno4nVaMXxSLkcj0mkDujpRl9EHJmZ/69O+WSK+1w8FxErAscDOwAzga9k5twmy3wd8DaKUXQWUtw48aJmMWXc5hQf5jdl5gs15ftkZt0RfCLiRGBfilFWrwUmA1OBNwNXZ+YpdWKu6F8EvAm4DiAzD2yWZ81ydgF2Au7KJjeBrLItI+LDwI8z89H+0wbI6QKKbbESxY0tVwH+B9iTYl+e0iS26vu2N3Awr/4ivrzRezbAsr6QmScPsK7xwK8yc1ZN+fsz8+wGMUFxI9gELgX2oDhwuBf4Xma+PIT8rsvMPZpMXycz/1Lz+t2U+wjw/WzwYRIRbwN+nZlPR8Q44P8C21PsIx/PzHrDEhMRXwcuy8zfDaEOawEfpHifzgI+C/wDxZfwVzLzmSaxbwLewav3kTMz88EB1tmyfaRcXsP9xH1kibgh7yNlXKX9xH1kULk13UfKeYa8n3TLPlLGDmk/iYhPA0cAF/PKMO3jKRoUF2fmqUPJvVxm3eOfmulDOiapcjxSxnlMUj92yMck7fwcqVlfRz5LltbnSDlfTx+TNM2nxxt9j2TmRnXK7wa2zcyFEXEG8CLFzrlnWf72Bsv7MHAA8GtgP4qbLj5D8Y/7gcyc2iTuWIo3eTvguMy8vJx2a2bu0CBuRjn/WODPwPiaD7KbMnObOjG3Uuy4Z1L80wVwEcWXB5n56wbrujkzdyqf/3OZ74+BvYArG33pVNmWETEXmAf8ocztR5k5p97y+8XdmZnbRMSyFPcqWT8zF5UfMnfU2x5lXNX37b+A11MMtVv7RfxeihuvHjdQzv2WV3d/LKd9BdgFuLXM9b8y8/RyWrN95DvAa4Dlgeco9pUry3o+0SjHiLizfxFFXe8DaLRv9eUREZ8D/hG4EHgrMDszP9pgXTMzc8vy+SXAjcCPKA4W3pWZb2kQNwf4IzAOuITiC7HZvY+IiKuAGcBqwBbl8x8Cb6HYHw9qEHcqsC7wK4ov1YeB+4EPUHww/6hBXEv3kXKZjT633EeWjBvyPlLGDXk/cR+pGzfkfaR/LoPdT7phHynjhryfRMT9wFbZr2coip6kuzNz04HyrbPMZt83Qz4mqXI80rc8PCbpHzfkY5J2fo6U09r2WdLOz5Fy3p49JhlQVryr+0h5AHc2eMwA/tog5p6a57f2m3Z7k3XNAMaUz1cCppbPNwJuGyBulfL5BGA6xYcsA8TdVu95szwprtP8KMUvcduVZQ8NYjvWrmsaMK58vjIwo0nckLclxU1Ll6H48D4LmENxv5IpwKpN1nUXxQfJmsDzwFpl+Qq1ebTwfbu/QXlQfMjWm/Zcg8fzwMIBcly2fL4GcBXwjUHsIzPKv8sBTwHLl6+XHeB9uwL4AbA5sHG5Xz5aPt94EPvIrRQ3He1bd7N13Vfz/JbB7CO16wM2BT4P3E3xa+GJwOsbxNxe8x79aQjrmlHzfFngd+XzNSl+WW7ZPlJ1P3Efac0+UnU/cR9pzT5SdT/phn2k6n5S5rPE9iq3431N1jXk45+a/WRIxyRUOB4pp3lMUn/7D+mYhDZ+jtTk2JbPEtr4OVJO79ljkoEevTCQy7oUv3QcUOfxVIOYuyLiyPL5HRExESAiXg8MdA523w3txwKrAmTmIxQ7WSNjsjx9Iosu8t2Bfcuu4mYXcv4tIlYqn+/YVxgRqwN1u8gz8+XM/AZwJHBCRHyrJudmlomINSNibYoe4Dnl8uZRdCs3UmVbZpnnNZl5FLA+8B2K6wMearKusyj+wW4HTgB+FBHfp/hCuHiA+lV53+ZHcQ1Df5OA+Q1ingU2zczV+j1WBR5vll9mLizzepZi/10tIn5E8aXSSF/MAmBaZv6tfL0QWNQoKItTai4DzqD4tWkWsCAz/5iZf2wQtmJEbB8RO1Ls0/Nq1t1wXcDUiDi5/EV4akQcDItPXWh2em2Wy38gM7+UmVsB76T4Qm10DckyEbEmxekQq0TEhHJda9N8O75cnoYBxf44plz3MzT/H62yj0C1/cR9pE6a5fKHso9Atf3EfaSfivsIVNtPumEfgWr7yUeAX0XEzyPijPLxC4pf+Zv14FQ5/oFqxyRDPh4pl+8xSX1DPSZp5+cItPGzpM2fI9DbxyTNVW0tjpQHxT/dLg2mXdigfHXgHIqu/JsoPggeouhq37bJuo6j+BXtDIp/9CPL8nHAb5rEXUf5C1dN2bIU3fSLmsSNbVC+DrD1ILfP/hRdwQPNN6vcBg+Xf/+uLF+F5r9GDHlb0vxXohUHyHN9ilMooPj16RBgpwFiqr5vO5R1mglcUz7uKct2bBDz5Ub5AP/RZF0/BXZrsLyXm8T9nPIX237lfwfcPIj3fWXg6xS/tM0eYN7r+z3WK8vXBqY3iVsOOAl4pHy8TPEr44XARk3iGu4nTWKOAJ4oH+8AfknxC/OfgGOaxB1GcdrGNWWO+9fsI3U/R6ruI1X3E/eR1uwjTfaTXzbbT4axj+zoPrLE/FOHup+MkH1kaX6WLAPsXK7rkPL5mAFyHPLxTzltyMcktOB4pJzfY5IKxyS08bumnNb2zxKG/jky4r9vaPPnyECPnr6mbyARsSrwWooPu9mZ+cQgYraiOC/3rsy8d5DrGU/Rjf7nOtP+Tw7totAPZOZ3Bjt/1Zia2JWAdTPz4QHmG/S2jIjXZ+b9VfKps6xB1a3K+1YT+3cUF04HRd2WeB+Hq/zFicx8qc60DTLzT0Nc3soUpzo8Ocj5twX+ITO/N5T1lLFjKA4IXhzEvKtT/ILY7FfovnlXyZoBBoaYT2RxTceyFNeh/Ckzm/2q2XfB9WuBB7P4ZXMo63QfaR67DLDCSNlHytgh7yfuIwPOX3kfKeMH9VkykveRMm7I+0lEbAQ8l5nPlr0BEylOEby7Su4DrKvSMUmDHO/NzLsGWF+luAbLGlHHJMPYJpWOSdrxOVKup2OfJe36HCnn7cljkobL7IVGX3lAQWa+HMWFz28AZmXm062MGU5cneUM2FiJiI/1LwI+A3ylzOHrg4iBYrSghjHDybFVcUtre7Q4x1UoLi5+aAhf5EOO6ZY4c6RvoIUFWX6QlqeH7EAx+ELDEdWaxM3MzJ+3Kqbdcd2QYwfqtk1m9h+oYEBV4tq5rnbHdUOOVeMi4njgX4C/Al8DPgH8jqK376xm323taohVzbEb6jaMmE7UbSI1ozkOtsHYDXHdkGPVuHbn2FBW7CIcKQ+KUW2eoDg3+SCKru7rKEY3OqBVMcOM+1i/x8eBv/S9bhL3PMUIQV+guFD0RIoRnk4ETmxVzDBz7B/3sYHi2rk9hrm+79Q834Wii/16iguM92tVTLfEmWPduDuANcvnnwR+D3yO4vSNf68Yd2ob19XSuCr1GgV1WwQ8CHwJ2LLRfK2Ia+e6rFtL63Y3sCLFaWnP8+pBS5oNEnQ8xemP9wJHl3/PKpfX7LttyHHDyLEb6lZ1XW2rG7AbxYA7v6Q47vkpRQNzKrBhk3WN+LhuyLFb6jbgZ03VwJHyoBh16e+ATShGJNqsLN+YxtcGDDlmmHFVGysbUQw1/B/ASmVZ01GvqsQMM8d2NkzbXbdba55fD+xQPn9tk31ryDHdEmeOdePuqnk+nfL6D4pTiu5sZVw719XLOXagbrdRnBFyCkVj4A6Kg74JjWKqxrVzXdatpXW7s/w7BngSWKbeflcnrm0NsWHk2A11q7quttWt3K/65tuE4r6CUAz9f80A++OIjuuGHLulbgM9emH0TjLzz1mc3/1IZvbd1+OP0Lh+VWKGEbcVxYfCysBXM/OLwDOZ+cXyeaN1PZKZh1D8onxtRBzSLLeqMcPJsWJc27bHMOtWa7XMvLXM46FyeUsjplvizLHwXES8oXz+F4pRvKBoCDT7TKgS18519XKOVeOqrisz867MPCEz/x74Z4r7WP1vRPy+xXHtXJd1a13crRFxIcWNvX8FnBsR74qIsygG7mhkURbXXD0LvEQ5YmeWoxi2OK5qjt1Qt6rramfdxuQr9w58hKKzgcy8luIav26O64Ycq8a1O8emBjNs7ogXEctk5svA+2vKxtBkONQqMVXjshiG95CIOIiisfKNgWv1qvjLI+KXFKMNzR5g9koxVXOsEtfu7TGM9W0exU1DA5gQEWtm5jNRXNfZaFjlKjHdEmeOS/pX4IKIuIPil97pEfFrYBvKa01bGNfOdfVyju2u26uG187Mm4GbI+LjwK4tjmvnutod1w05Vo07GjiUYkj4S4HJFKP+3Qd8u8m6+hodK/NKo+MXwB4MriE2lLiqOXZD3aquq511m142Jn9FcXnRVFg8sE2zHya7Ia4bcuyWujXV9QO5RMQkipsYzu9XPoFiKOMftCJmOHH95l2ZorEyOTObfXF0TNUcq8S1e3sMZX0RsXG/oscz828RsQ6wa2b+TytiuiXOHOuL4kefvSgGflmW4oeIq3OAgWOqxLVzXb2cYzvrFhH/lJkXNsulVXHtXFe747ohx+HEVRHFSID1Gh2PAN9u1HNUNa6d2lm3dm+PijkuR9FrvCXFKcNnZ+aiKEbZfE02uJ9dN8R1Q47dUreBdH2jr5dFMYLgpyju7TEe+BvFvWe+l5nntCqmW/Ry3SRJo1OT77bvZua5ncytT9Ucu6FuVfVy3dSbuv6avohYJSJOjoi7I2JuRMyJiBsj4n2tjOlEHHABxU1F9wa+CJwGvAd4U0Q0Op2oSkxb69bm7dEVdeuGOHO0br2QYy/XrRtytG51Nfpu2yOqfbdNqZhjs7hKOXZD3aquq511q4m5q+L+OGLjuiHHbqnbQLq+py8iLgd+TDGs6TspzpG+mGIo7T9l5mdbEdOhuDsyc9ua19Myc1IU1xnNzMzNWxHT7rq1c3t0Ud1GfJw5WrdeyLGX69YNOVq3unXrhu+2qjl2Q91G/DFJN+zH1q3zdRtQVhz2c6Q8gDv6vZ5W/l2G4kaXLYnpUNzvKa4VBDiA4tqRvmn3tSqm3XVr5/boorqN+DhztG69kGMv160bcrRudWO64butao7dULcRf0zSDfuxdet8jgM9uv70TmBeROwCEBEHAE8DZDHCZrQwphNx/wp8PSKeBT4NfKhcxjgajwxVJabddWvn9uiWunVDnDlat17IsZfr1g05WrcldcN3W9Ucu6Fu3XBM0g37sXXrfI7NVW0tjpQHxXDZN1Pc7+S3wOvL8nHAh1sV04m4Aep9ZCtj2lm3dm+PbqhbN8SZo3XrhRx7uW7dkKN1qx/XZHkj4rutao7dULdWb4+lUbdu2Y+t28j8H1283KqB3fBo9k/XypgOxT3Sjph2162d26OL6jbi48zRuo2kdVm37szRutWN6Ybvtqo5dkPdRvwxSTfsx9at8zlmZvcP5NJMRDySmRst7ZilFRfFzaLrTqJo9Y9tRcxwcmx1XKu3x9LIsWrcSNq3Wh1njp2NM8fOxpljZ+O6Icdmcd3w3VY1x26oW9WYkVK3kbIfL424bsixaly7c4Ti5rJdbYB/unVbFdOJuHLa3sAzdeJ+38KYttatzdujK+rWDXHm2Nk4c+xsnDl2Nq4bchxG3Ij/bquaY9W4bti3aGPdumQ/tm4dznEgXd/oo40Now7E/RRYJTNv7z8hIqa2MGY4ObZz+/dy3bohzhw7G2eOnY0zx87GdUOOVeO64butao7dULduOCbphv24alw35Fg1rt05NtULjb52NozaGpeZRzWZ9k+tihlOjhXj2rY9hrO+inFdsW9VjDPHzsaZY2fjzLGzcd2QY6W4bvhuq5pjN9St6rraXLcRvx8PI64bcqwa1+4cm+rpa/okSZIkabTrhfv0SZIkSZIasNEnSZIkST3MRp8kqadFxISIuKtO+ZkRsWWDmI9ExEo1r19YmjlKkrQ02eiTJI1KmXl0Zs7sXx4RY4CPACstGbX0RUQvDLImSRpBbPRJkkaDZSPi3Ii4MyIujYiVImJqREyEoicvIk6OiJuAE4D1gesj4vq+BUTEKRFxR0TcGBHrRsSqEfFwRCxXTl8tImZFxHIR8bqI+EVE3BIR/xsRm5fzHBARN0XEbRHxy4hYtyw/KSLOiIhrgPPavXEkSb3NRp8kaTTYDDgjM7cBngM+0G/6ysBdmTk5M08GHgPelJlvqpl+Y2ZuC/wG+OfMfB6YCuxfznM4cFlmLgDOAD6UmTsCnwC+U87zW2DnzNweuBj4VE0OOwIHDTDcuyRJQ+YpJJKk0eDRzPxd+fwHwIf7TV8EXNYk/m8U904CuAV4S/n8TIqG20+AI4F/johVgDcCP4qIvvix5d/xwCURsR6wPPBwzTquyMyXhlIpSZIGw0afJGk06H9T2v6v52fmoibxC/KVG9suovz+zMzflQPF7AaMycy7ImI14NnM3K7Ock4Hvp6ZV0TE7sBJNdPmDbIukiQNiad3SpJGg40i4h/K50dQnGbZzPPAqsiCZxkAAADJSURBVINc9nnARcD/A8jM54CHI+JQgChsW867OvCn8vmUQS5fkqRhsdEnSRoN7gGmRMSdwFrAdweY/wzg57UDuTRxAbAmRcOvz7uAoyLiDuBu4KCy/CSK0z7/F/jL4NOXJOn/t3OvNgACQRBAZ8sDjaUqGj6BwIDgzCWb9yoYO9nPvHq2VQCAv6rqyP2A5VydBQDeuOkDgElVdSXZkuyrswDAF5M+AACAxtz0AQAANKb0AQAANKb0AQAANKb0AQAANKb0AQAANDYAqrS0WtL16Q4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc17dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_birthyear_gender = data.groupby(['birthyear', 'gender'])['birthyear'].count().unstack('gender').fillna(0)\n",
    "groupby_birthyear_gender[['Male','Female','Other']].plot.bar(title = 'Distribution of birth years by Gender', stacked=True, figsize = (15,4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbb3ec18>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbba5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_birthyear_user = data.groupby(['birthyear', 'usertype'])['birthyear'].count().unstack('usertype').fillna(0)\n",
    "groupby_birthyear_user['Member'].plot.bar(title = 'Distribution of birth years by Usertype', stacked=True, figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['usertype']=='Short-Term Pass Holder']['birthyear'].isnull().values.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['usertype']=='Short-Term Pass Holder']['gender'].isnull().values.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "List_ = list(data['starttime'])\n",
    "\n",
    "List_ = [datetime.datetime.strptime(x, \"%m/%d/%Y %H:%M\") for x in List_]\n",
    "data['starttime_mod'] = pd.Series(List_,index=data.index)\n",
    "data['starttime_date'] = pd.Series([x.date() for x in List_],index=data.index)\n",
    "data['starttime_year'] = pd.Series([x.year for x in List_],index=data.index)\n",
    "data['starttime_month'] = pd.Series([x.month for x in List_],index=data.index)\n",
    "data['starttime_day'] = pd.Series([x.day for x in List_],index=data.index)\n",
    "data['starttime_hour'] = pd.Series([x.hour for x in List_],index=data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc05ae10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5a5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('starttime_date')['tripduration'].mean().plot.bar(title = 'Distribution of Trip duration by date', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x147b5e48>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14706a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 1.  Determine the distribution of number of trips by year.  Do you see a specific pattern?\n",
    "data.groupby('starttime_year')['trip_id'].sum().plot.bar(title = 'Distribution of number of trips by year', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14678b70>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x145952b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 2.  Determine the distribution of number of trips by month.  Do you see a specific pattern?\n",
    "data.groupby('starttime_month')['trip_id'].sum().plot.bar(title = 'Distribution of number of trips by month', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14520f98>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc584dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 3.  Determine the distribution of number of trips by day.  Do you see a specific pattern?\n",
    "data.groupby('starttime_day')['trip_id'].sum().plot.bar(title = 'Distribution of number of trips by day', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152524e0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1525f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 4.  Determine the distribution of number of trips by day.  Do you see a specific pattern?\n",
    "# I think they meant hour here?\n",
    "data.groupby('starttime_hour')['trip_id'].sum().plot.bar(title = 'Distribution of number of trips by hour', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15495240>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154a1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 5.  Plot a frequency distribution of trips on a daily basis\n",
    "data.groupby('starttime_date')['trip_id'].sum().plot.bar(title = 'Distribution of number of trips by day', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1926c9b0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkiRJ0nLS5SyajwYOA76T5MJ223HAIUn2Bgq4Enh+hzFIkiRJ0rLR5SyaXwYyza6Pd1WnJEmSJC1nI5lFU5IkSZLUvYESvCR7dh2IJEmSJGl+Bu3Be1eSryV5YZLtOo1IkiRJkrRJBkrwqup3gGcDOwNrk7wvyRM7jUySJEmSNJSBr8GrqsuA44FjgMcCf5vke0n+oKvgJEmSJEmDG2gWzSS/ARwBPAk4D3hKVX0zyf2BC4APdxfi0rX62HPHHYIkSZKkZWTQ2yT8HXAycFxV3bZxY1Vdk+T4TiKTJEmSJA1l0ATvQOC2qroTIMk9gC2r6mdVdUZn0UmSJEmSBjboNXifAe41aX2rdpskSZIkaZEYNMHbsqpu3bjSLm/VTUiSJEmSpE0xaIL30yQP37iS5DeB22Y5XpIkSZI0YoNeg/dy4Kwk17TrOwLP7CYkSZIkSdKmGCjBq6qvJ3ko8BAgwPeq6hedRiZJkiRJGsqgPXgAjwBWt+fsk4SqOr2TqCRJkiRJQxv0RudnALsBFwJ3tpsLMMGTJEmSpEVi0B68NcAeVVVdBiNJkiRJ2nSDzqJ5MfBrXQYiSZIkSZqfQXvwVgLfTfI14PaNG6vqqZ1EJUmSJEka2qAJ3gldBiFJkiRJmr+BhmhW1ReBK4HN2+WvA9+c7ZwkOyf5fJJLk1yS5GXt9h2SnJfksvbn9vN8DpIkSZIkBkzwkjwPOBt4d7tpJ+Ajc5y2ATi6qh4GPAp4UZI9gGOBz1bV7sBn23VJkiRJ0jwNOsnKi4BHAzcDVNVlwH1nO6Gqrq2qb7bLtwCX0iSGBwGntYedBjxt+LAlSZIkSVMNmuDdXlV3bFxJshnNffAGkmQ1sA/wVeB+VXUtNEkgMySKSY5KsjbJ2vXr1w9alSRJkiQtW4MmeF9MchxwryRPBM4C/nmQE5NsDXwIeHlV3TxoYFV1UlWtqao1ExMTg54mSZIkScvWoAnescB64DvA84GPA8fPdVKSzWmSu/dW1Yfbzdcl2bHdvyOwbtigJUmSJEl3N9BtEqrql8DJ7WMgSQK8B7i0qt46adc5wOHAG9qfHx04WkmSJEnSjAZK8JL8gGmuuauqB85y2qOBw4DvJLmw3XYcTWJ3ZpIjgauAPxwqYkmSJEnStAa90fmaSctb0iRlO8x2QlV9GcgMu/cdsF5JkiRJ0oAGvdH5jyc9/qOq3gY8oePYJEmSJElDGHSI5sMnrd6Dpkdvm04ikiRJkiRtkkGHaL5l0vIG4ErgGQsejSRJkiRpkw06i+bjuw5EkiRJkjQ/gw7RfMVs+6fcBkGSJEmSNAbDzKL5CJp72AE8BTgf+FEXQUmSJEmShjdogrcSeHhV3QKQ5ATgrKp6bleBSZIkSZKGM9BtEoBVwB2T1u8AVi94NJIkSZKkTTZoD94ZwNeS/BNQwMHA6Z1FJUmSJEka2qCzaL4+ySeAx7Sbjqiqb3UXliRJkiRpWIMO0QTYCri5qv4fcHWSXTuKSZIkSZK0CQZK8JK8GjgGeGW7aXPgH7sKSpIkSZI0vEF78A4Gngr8FKCqrgG26SooSZIkSdLwBk3w7qiqoplghST37i4kSZIkSdKmGDTBOzPJu4HtkjwP+AxwcndhSZIkSZKGNegsmm9O8kTgZuAhwKuq6rxOI5MkSZIkDWXOBC/JCuBTVbUfYFI3IquPPXfGfVe+4UkjjESSJEnSUjHnEM2quhP4WZJtRxCPJEmSJGkTDTREE/g58J0k59HOpAlQVS/tJCpJkiRJ0tAGTfDObR8DS3IK8GRgXVXt2W47AXgesL497Liq+vgw5UqSJEmSpjdrgpdkVVVdVVWnbULZpwJ/B5w+ZfuJVfXmTShPkiRJkjSLua7B+8jGhSQfGqbgqjofuGFTgpIkSZIkDW+uIZqZtPzABarzxUn+GFgLHF1VN05bcXIUcBTAqlWrFqhqzZeze2o5mu19D773JUnS4jFXD17NsLyp3gnsBuwNXAu8ZcaKq06qqjVVtWZiYmIBqpYkSZKkfpurB2+vJDfT9OTdq12mXa+qus8wlVXVdRuXk5wMfGyY8yVJkiRJM5s1wauqFQtZWZIdq+radvVg4OKFLF+SJEmSlrNBb5MwtCTvBx4HrExyNfBq4HFJ9qYZ7nkl8Pyu6pckSZKk5aazBK+qDplm83u6qm8c5pp4YVycEEKSJElanuaaZEWSJEmStESY4EmSJElST5jgSZIkSVJPmOBJkiRJUk+Y4EmSJElST3Q2i6aWpsU6M6jUpeX4vp/vbLuzne9MvZIkjY89eJIkSZLUEyZ4kiRJktQTJniSJEmS1BMmeJIkSZLUEyZ4kiRJktQTzqK5DI1rxsD5ztonLVbzmVFysf5eLMeZRSVJ6gN78CRJkiSpJ0zwJEmSJKknTPAkSZIkqSdM8CRJkiSpJ5xkRcvCfCbB6KNxTewx34k7lmNbLTdLddKZLuPy80uSNAx78CRJkiSpJ0zwJEmSJKknOkvwkpySZF2Siydt2yHJeUkua39u31X9kiRJkrTcdNmDdyqw/5RtxwKfrardgc+265IkSZKkBdBZgldV5wM3TNl8EHBau3wa8LSu6pckSZKk5WbUs2jer6quBaiqa5Pcd6YDkxwFHAWwatWqEYW3NMx3JsKlaJzPeTnOYLdYn/NijUuay7jeu4t1VlJJUncW7SQrVXVSVa2pqjUTExPjDkeSJEmSFr1RJ3jXJdkRoP25bsT1S5IkSVJvjTrBOwc4vF0+HPjoiOuXJEmSpN7q8jYJ7wcuAB6S5OokRwJvAJ6Y5DLgie26JEmSJGkBdDbJSlUdMsOufbuqU1pOFuuEI8txEiBpXOb7+7ZYP0ckSZtu0U6yIkmSJEkajgmeJEmSJPWECZ4kSZIk9YQJniRJkiT1hAmeJEmSJPVEZ7NoSsNajrMvLsXnvFhjniuuPs4IuFSf82J9D0mS1Af24EmSJElST5jgSZIkSVJPmOBJkiRJUk+Y4EmSJElST5jgSZIkSVJPOIumFsxSnRlvqcYtzaWP7+3FOnNoH1/ruSzWtpCk5c4ePEmSJEnqCRM8SZIkSeoJEzxJkiRJ6gkTPEmSJEnqCSdZkXpoOU74MJdxvSbLsS2W43PW3c32PnAClrvr6vVyMhxp+bEHT5IkSZJ6wgRPkiRJknpiLEM0k1wJ3ALcCWyoqjXjiEOSJEmS+mSc1+A9vqquH2P9kiRJktQrDtGUJEmSpJ4YVw9eAZ9OUsC7q+qkqQckOQo4CmDVqlUjDk+SFgdnpFw4i3U2wcXaxuOMa7HOwLlY49JdLdbfdWlUxtWD9+iqejhwAPCiJL879YCqOqmq1lTVmomJidFHKEmSJElLzFgSvKq6pv25Dvgn4JHjiEOSJEmS+mTkCV6SeyfZZuMy8HvAxaOOQ5IkSZL6ZhzX4N0P+KckG+t/X1V9cgxxSJIkSVKvjDzBq6orgL1GXa8kqf/mMzHIYp3spI+6fK3nM8GG7x+Bk+lo6fM2CZIkSZLUEyZ4kiRJktQTJniSJEmS1BMmeJIkSZLUEyZ4kiRJktQT47hNgrRsOKuaJKlri/VvzXzicrbK0ZnPzLNznW87joc9eJIkSZLUEyZ4kiRJktQTJniSJEmS1BMmeJIkSZLUEyZ4kiRJktQTzqIpzcNinblMkvpqvp+7fm7f1WJ9PeY7s+N8ypaWOnvwJEmSJKknTPAkSZIkqSdM8CRJkiSpJ0zwJEmSJKknnGRFkiRpEXNSkLsb12vSZb1zTRwzn7rnMynNYjWfiXi6nMRnMbAHT5IkSZJ6wgRPkiRJknpiLAlekv2TfD/J5UmOHUcMkiRJktQ3I0/wkqwA3g4cAOwBHJJkj1HHIUmSJEl9M44evEcCl1fVFVV1B/AB4KAxxCFJkiRJvZKqGm2FydOB/avque36YcBvVdWLpxx3FHBUu/oQ4PsjDXR2K4Hrxx2E5sU2XPpsw6XPNlz6bMOlzzZc+mzDpW/QNtylqibmOmgct0nINNvulmVW1UnASd2HM7wka6tqzbjj0KazDZc+23Dpsw2XPttw6bMNlz7bcOlb6DYcxxDNq4GdJ60/ALhmDHFIkiRJUq+MI8H7OrB7kl2TbAE8CzhnDHFIkiRJUq+MfIhmVW1I8mLgU8AK4JSqumTUcczTohw6qqHYhkufbbj02YZLn2249NmGS59tuPQtaBuOfJIVSZIkSVI3xnKjc0mSJEnSwjPBkyRJkqSeMMGbRZL9k3w/yeVJjp1m/z2TfLDd/9Ukq0cfpWYzQBu+Isl3k1yU5LNJdhlHnJrZXG046binJ6kkThW9yAzShkme0f4uXpLkfaOOUbMb4LN0VZLPJ/lW+3l64Dji1PSSnJJkXZKLZ9ifJH/btu9FSR4+6hg1uwHa8Nlt212U5F+T7DXqGDW7udpw0nGPSHJne+/wTWKCN4MkK4C3AwcAewCHJNljymFHAjdW1YOAE4E3jjZKzWbANvwWsKaqfgM4G/ib0Uap2QzYhiTZBngp8NXRRqi5DNKGSXYHXgk8uqp+HXj5yAPVjAb8PTweOLOq9qGZHfsdo41SczgV2H+W/QcAu7ePo4B3jiAmDedUZm/DHwCPbb/P/BVOvLIYncrsbbjx8/aNNJNRbjITvJk9Eri8qq6oqjuADwAHTTnmIOC0dvlsYN8k093IXeMxZxtW1eer6mft6ldo7suoxWOQ30No/pj9DfDzUQangQzShs8D3l5VNwJU1boRx6jZDdKGBdynXd4W72+7qFTV+cANsxxyEHB6Nb4CbJdkx9FEp0HM1YZV9a8bP0Px+8yiNMDvIcBLgA8B8/o7aII3s52AH01av7rdNu0xVbUB+AnwP0YSnQYxSBtOdiTwiU4j0rDmbMMk+wA7V9XHRhmYBjbI7+GDgQcn+ZckX0ky6384NXKDtOEJwKFJrgY+TvMlRUvHsH8vtbj5fWYJSrITcDDwrvmWNfL74C0h0/XETb2nxCDHaHwGbp8khwJrgMd2GpGGNWsbJrkHzfDo54wqIA1tkN/DzWiGhj2O5r/OX0qyZ1Xd1HFsGswgbXgIcGpVvSXJbwNntG34y+7D0wLw+0xPJHk8TYL3O+OORUN7G3BMVd053wGBJngzuxrYedL6A7j7kJONx1ydZDOaYSlzdb1qdAZpQ5LsB/wFzdj120cUmwYzVxtuA+wJfKH9MPw14JwkT62qtSOLUrMZ9LP0K1X1C+AHSb5Pk/B9fTQhag6DtOGRtNeWVNUFSbYEVjLPYUYamYH+XmpxS/IbwN8DB1TVj8cdj4a2BvhA+31mJXBgkg1V9ZFhC3KI5sy+DuyeZNckW9BcNH7OlGPOAQ5vl58OfK68c/xiMmcbtsP73g081et+FqVZ27CqflJVK6tqdVWtprnuwORucRnks/QjwOMBkqykGbJ5xUij1GwGacOrgH0BkjwM2BJYP9IoNR/nAH/czqb5KOAnVXXtuIPS4JKsAj4MHFZV/zbueDS8qtp10veZs4EXbkpyB/bgzaiqNiR5Mc0sNiuAU6rqkiSvBdZW1TnAe2iGoVxO03P3rPFFrKkGbMM3AVsDZ7X/Mbmqqp46tqB1FwO2oRaxAdvwU8DvJfkucCfwZ/73efEYsA2PBk5O8qc0Q/ue4z88F48k76cZAr2yvU7y1cDmAFX1LprrJg8ELgd+Bhwxnkg1kwHa8FU080C8o/0+s6GqvG3QIjJAGy5cXX7+SpIkSVI/OERTkiRJknrCBE+SJEmSesIET5IkSZJ6wgRPkiRJknrCBE+SJEmSOpLklCTrklw8wLGrknw+ybeSXJTkwGHrM8GTJEmSpO6cCuw/4LHHA2dW1T40t2B7x7CVmeBJkiRJUkeq6nyae2b/SpLdknwyyTeSfCnJQzceDtynXd4WuGbY+rzRuSRJkiSN1knAC6rqsiS/RdNT9wTgBODTSV4C3BvYb9iCTfAkSZIkaUSSbA38L+CsJBs337P9eQhwalW9JclvA2ck2bOqfjlo+SZ4kiRJkjQ69wBuqqq9p9l3JO31elV1QZItgZXAumEKlyRJkiSNQFXdDPwgyR8CpLFXu/sqYN92+8OALYH1w5SfqlrAcCVJkiRJGyV5P/A4mp6464BXA58D3gnsCGwOfKCqXptkD+BkYGtNbo7DAAAAUElEQVSaCVf+vKo+PVR9JniSJEmS1A8O0ZQkSZKknjDBkyRJkqSeMMGTJEmSpJ4wwZMkSZKknjDBkyRJkqSeMMGTJEmSpJ4wwZMkSZKknvgvXG2HK51HMq8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cd59e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 5.  Plot a frequency distribution of trips on a daily basis\n",
    "data.groupby('starttime_date')['trip_id'].sum().plot.hist(bins=100, title = 'Distribution of number of trips by day', figsize = (15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of trip duration: 1202.612210\n",
      "Median of trip duration: 633.235000\n",
      "Mode of station originating from: Pier 69 / Alaskan Way & Clay St\n"
     ]
    }
   ],
   "source": [
    "trip_duration = list(data['tripduration'])\n",
    "station_from = list(data['from_station_name'])\n",
    "print('Mean of trip duration: %f'%statistics.mean(trip_duration))\n",
    "print('Median of trip duration: %f'%statistics.median(trip_duration))\n",
    "print('Mode of station originating from: %s'%statistics.mode(station_from))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nHjMzq5QTj5mZVcqJx8zMKuXEY2ZmlXLiMTOzSrU08Uh6WNKdkm6T1J6x8ZLaJK3O53EZl6RFkjok3SFpaqmeeVl+taR5pfjhWX9HbqtW9sfMzPqvijOeN0XEYRExLV8vAK6PiCnA9fkaYBYwJR/zgfOhSFTAQuBI4AhgYS1ZZZn5pe1mtr47ZmbWH4Mx1DYHWJrLS4ETSvFlUbgJGCtpH+BYoC0iNkbEJqANmJnrxkTEjRERwLJSXWZmNkS1OvEE8ANJt0qan7G9I2IdQD7vlfGJwJrStp0Z6y7eWSf+IpLmS2qX1L5hw4Z+dsnMzPpjdIvrPyoi1kraC2iTdF83Zetdn4k+xF8cjFgMLAaYNm1a3TJmZlaNlp7xRMTafF4PXEVxjebRHCYjn9dn8U5gv9Lmk4C1PcQn1YmbmdkQ1rLEI2kXSbvVloEZwF3ACqA2M20ecHUurwBOztlt04EtORS3EpghaVxOKpgBrMx1T0qanrPZTi7VZWZmQ1Qrh9r2Bq7KGc6jgW9FxPclrQKWSzoVeAQ4MctfC8wGOoBngFMAImKjpLOBVVnucxGxMZdPAy4Cdgauy4eZmQ1hLUs8EfEgcGid+OPAMXXiAZzeoK4lwJI68Xbg1f1urJmZVcZ3LjAzs0o58ZiZWaWceMzMrFJOPGZmVqlW/wfSEWXygmsGuwlmZsOez3jMzKxSTSUeSZ6ybGZmA6LZM55vSLpF0ockjW1pi8zMbERrKvFExBuA91DcM61d0rckvbWlLTMzsxGp6Ws8EbEaOBP4Z+CvgEWS7pP0jlY1zszMRp5mr/G8RtK5wL3Am4HjI+LgXD63he0zM7MRptnp1OcB3wQ+FRG/qwXzt3bObEnLzMxsRGo28cwGfhcRfwSQtB2wU0Q8ExEXt6x1ZmY24jR7jeeHFD89UPOSjJmZmfVKs4lnp4h4qvYil1/SmiaZmdlI1mzieVrS1NoLSYcDv+umvJmZWV3NXuM5A/iOpLX5eh/gXa1pkpmZjWRNJZ6IWCXplcArAAH3RcQfWtoyMzMbkXpzd+rXAZNzm9dKIiKWtaRVZmY2YjWVeCRdDLwMuA34Y4YDcOIxM7NeafaMZxpwSEREb3cgaRTQDvwmIv5a0gHAZcB44BfA+yLiOUk7UiSyw4HHgXdFxMNZxyeBUymS3kciYmXGZwJfA0YBF0TEOb1tn5mZVavZWW13AX/Rx318lOJWOzVfBM6NiCnAJoqEQj5vioiDKG7D80UASYcAc4FXATOBf5c0KhPa14FZwCHASVnWzMyGsGYTz57APZJWSlpRe/S0kaRJwHHABflaFPd3uyKLLAVOyOU5+Zpcf0yWnwNcFhHPRsRDQAdwRD46IuLBiHiO4ixqTpP9MTOzQdLsUNtZfaz/q8A/Abvl6z2AzRGxNV93AhNzeSKwBiAitkrakuUnAjeV6ixvs6ZL/Mh6jZA0H5gPsP/++/exK2ZmNhCanU79E0kvBaZExA8lvYTiukpDkv4aWB8Rt0p6Yy1cr/oe1jWK1ztbq3sNKiIWA4sBpk2b1uvrVD2ZvOCa55cfPue4ga7ezGxEaXZW299RnDGMp5jdNhH4BnBMN5sdBbxN0mxgJ2AMxRnQWEmj86xnElD7T6mdFD801ylpNLA7sLEUrylv0yhuZmZDVLPXeE6nSCRPwPM/CrdXdxtExCcjYlJETKaYHPCjiHgP8GPgnVlsHnB1Lq/I1+T6H+UsuhXAXEk75oy4KcAtwCpgiqQDJO2Q++jxupOZmQ2uZq/xPJtTngHIM5K+Dln9M3CZpM8DvwQuzPiFwMWSOijOdOYCRMTdkpYD9wBbgdNLP8/wYWAlxbDfkoi4u49tMjOzijSbeH4i6VPAzpLeCnwI+F6zO4mIG4AbcvlBihlpXcv8HjixwfZfAL5QJ34tcG2z7TAzs8HX7FDbAmADcCfwQYove//yqJmZ9Vqzs9r+RPHT199sbXPMzGyka3ZW20PUuaYTEQcOeIvMzGxE68292mp2orgWM37gm2NmZiNdU9d4IuLx0uM3EfFVilvfmJmZ9UqzQ21TSy+3ozgD2q1BcTMzs4aaHWr7cml5K/Aw8DcD3hozMxvxmp3V9qZWN8TMzLYNzQ61/WN36yPiKwPTHDMzG+l6M6vtdbxwL7TjgZ/y5z9LYGZm1qNmE8+ewNSIeBJA0lnAdyLiA61qmJmZjUzN3jJnf+C50uvngMkD3hozMxvxmj3juRi4RdJVFHcweDuwrGWtMjOzEavZWW1fkHQd8JcZOiUiftm6ZpmZ2UjV7FAbwEuAJyLiaxS/EnpAi9pkZmYjWFOJR9JCih9w+2SGtgcuaVWjzMxs5Gr2jOftwNuApwEiYi2+ZY6ZmfVBs4nnuYgI8qcRJO3SuiaZmdlI1mziWS7pP4Cxkv4O+CH+UTgzM+uDZme1fUnSW4EngFcAn4mItpa2zMzMRqQez3gkjZL0w4hoi4hPRMTHm0k6knaSdIuk2yXdLemzGT9A0s2SVku6XNIOGd8xX3fk+smluj6Z8fslHVuKz8xYh6QFfXkDzMysWj0mnoj4I/CMpN17WfezwJsj4lDgMGCmpOnAF4FzI2IKsAk4NcufCmyKiIOAc7Mckg4B5gKvAmYC/57JcBTwdWAWcAhwUpY1M7MhrNk7F/weuFNSGzmzDSAiPtJog5yM8FS+3D4fQfHLpe/O+FLgLOB8YE4uA1wBnCdJGb8sIp4FHpLUARyR5Toi4kEASZdl2Xua7JOZmQ2CZhPPNfnolTwruRU4iOLs5AFgc0RszSKdwMRcnkje7ToitkraAuyR8ZtK1Za3WdMlfmSDdswH5gPsv//+ve2GmZkNoG4Tj6T9I+KRiFjal8pzmO4wSWOBq4CD6xWr7a7BukbxesOEUSdGRCwGFgNMmzatbhkzM6tGT9d4vltbkHRlX3cSEZuBG4DpFFOyawlvErA2lzuB/XJfo4HdgY3leJdtGsXNzGwI6ynxlM82DuxNxZIm5JkOknYG3gLcC/wYeGcWmwdcncsr8jW5/kd5nWgFMDdnvR0ATAFuAVYBU3KW3A4UExBqP1RnZmZDVE/XeKLBcjP2AZbmdZ7tgOUR8V+S7gEuk/R54JfAhVn+QuDinDywkSKREBF3S1pOMWlgK3B6DuEh6cPASmAUsCQi7u5lG83MrGI9JZ5DJT1Bceazcy6TryMixjTaMCLuAF5bJ/4gL8xKK8d/D5zYoK4vAF+oE78WuLaHPpiZ2RDSbeKJiFFVNcTMzLYNvfk9HjMzs35z4jEzs0o58ZiZWaWceMzMrFJOPGZmViknHjMzq5QTj5mZVcqJx8zMKuXEY2ZmlXLiMTOzSjnxmJlZpZx4zMysUk48ZmZWqZ5+FsF6afKCa55ffvic4waxJWZmQ5PPeMzMrFJOPGZmViknHjMzq5QTj5mZVcqJx8zMKtWyxCNpP0k/lnSvpLslfTTj4yW1SVqdz+MyLkmLJHVIukPS1FJd87L8aknzSvHDJd2Z2yySpFb1x8zMBkYrz3i2Ah+LiIOB6cDpkg4BFgDXR8QU4Pp8DTALmJKP+cD5UCQqYCFwJHAEsLCWrLLM/NJ2M1vYHzMzGwAtSzwRsS4ifpHLTwL3AhOBOcDSLLYUOCGX5wDLonATMFbSPsCxQFtEbIyITUAbMDPXjYmIGyMigGWluszMbIiq5BqPpMnAa4Gbgb0jYh0UyQnYK4tNBNaUNuvMWHfxzjrxevufL6ldUvuGDRv62x0zM+uHliceSbsCVwJnRMQT3RWtE4s+xF8cjFgcEdMiYtqECRN6arKZmbVQSxOPpO0pks6lEfGfGX40h8nI5/UZ7wT2K20+CVjbQ3xSnbiZmQ1hrZzVJuBC4N6I+Epp1QqgNjNtHnB1KX5yzm6bDmzJobiVwAxJ43JSwQxgZa57UtL03NfJpbrMzGyIauVNQo8C3gfcKem2jH0KOAdYLulU4BHgxFx3LTAb6ACeAU4BiIiNks4GVmW5z0XExlw+DbgI2Bm4Lh9mZjaEtSzxRMTPqH8dBuCYOuUDOL1BXUuAJXXi7cCr+9FMMzOrmO9cYGZmlXLiMTOzSjnxmJlZpZx4zMysUk48ZmZWKSceMzOrlBOPmZlVyonHzMwq5cRjZmaVauUtc7Z5kxdc8/zyw+ccN4gtMTMbOnzGY2ZmlXLiMTOzSjnxmJlZpZx4zMysUk48ZmZWKSceMzOrlBOPmZlVyonHzMwq5cRjZmaVcuIxM7NKtSzxSFoiab2ku0qx8ZLaJK3O53EZl6RFkjok3SFpammbeVl+taR5pfjhku7MbRZJUqv6YmZmA6eVZzwXATO7xBYA10fEFOD6fA0wC5iSj/nA+VAkKmAhcCRwBLCwlqyyzPzSdl33ZWZmQ1DLEk9E/BTY2CU8B1iay0uBE0rxZVG4CRgraR/gWKAtIjZGxCagDZiZ68ZExI0REcCyUl1mZjaEVX2NZ++IWAeQz3tlfCKwplSuM2PdxTvrxOuSNF9Su6T2DRs29LsTZmbWd0PlZxHqXZ+JPsTriojFwGKAadOmNSzXSv6JBDOzQtVnPI/mMBn5vD7jncB+pXKTgLU9xCfViZuZ2RBXdeJZAdRmps0Dri7FT87ZbdOBLTkUtxKYIWlcTiqYAazMdU9Kmp6z2U4u1WVmZkNYy4baJH0beCOwp6ROitlp5wDLJZ0KPAKcmMWvBWYDHcAzwCkAEbFR0tnAqiz3uYioTVg4jWLm3M7AdfkwM7MhrmWJJyJOarDqmDplAzi9QT1LgCV14u3Aq/vTRjMzq57vXGBmZpVy4jEzs0o58ZiZWaWceMzMrFJOPGZmViknHjMzq5QTj5mZVWqo3Kttm+L7tpnZtsxnPGZmViknHjMzq5QTj5mZVcqJx8zMKuXEY2ZmlfKstkHmGW5mtq3xGY+ZmVXKicfMzCrlobYhxMNuZrYtcOIZopyEzGykcuIZBpyEzGwkceIZZspJqMwJycyGi2GfeCTNBL4GjAIuiIhzBrlJg6JRQipzcjKzoWBYJx5Jo4CvA28FOoFVklZExD2D27KhqZnk1CwnMTPrq2GdeIAjgI6IeBBA0mXAHMCJp8UGMokNZU6wZgNvuCeeicCa0utO4MiuhSTNB+bny6ck3d+Hfe0JPNaH7YYy96kH+uJA1dQvPk7Dw0ju00sHstLhnnhUJxYvCkQsBhb3a0dSe0RM608dQ437NDy4T8OD+9S84X7ngk5gv9LrScDaQWqLmZk1YbgnnlXAFEkHSNoBmAusGOQ2mZlZN4b1UFtEbJX0YWAlxXTqJRFxd4t216+huiHKfRoe3KfhwX1qkiJedEnEzMysZYb7UJuZmQ0zTjxmZlYpJ54mSJop6X5JHZIWDHZ7uiPpYUl3SrpNUnvGxktqk7Q6n8dlXJIWZb/ukDS1VM+8LL9a0rxB6McSSesl3VWKDVg/JB2e71NHbltvan6r+3OWpN/ksbpN0uzSuk9m2+6XdGwpXvezmBNsbs5+Xp6TbVpK0n6SfizpXkl3S/poxofzcWrUp2F7rCTtJOkWSbdnnz7bXTsk7ZivO3L95L72taGI8KObB8WkhQeAA4EdgNuBQwa7Xd2092Fgzy6xfwMW5PIC4Iu5PBu4juL/Q00Hbs74eODBfB6Xy+Mq7sfRwFTgrlb0A7gFeH1ucx0waxD6cxbw8TplD8nP2Y7AAfn5G9XdZxFYDszN5W8Ap1VwjPYBpubybsCvsu3D+Tg16tOwPVb53u2ay9sDN+f7X7cdwIeAb+TyXODyvva10cNnPD17/rY8EfEcULstz3AyB1iay0uBE0rxZVG4CRgraR/gWKAtIjZGxCagDZhZZYMj4qfAxi7hAelHrhsTETdG8Re1rFRXlf1pZA5wWUQ8GxEPAR0Un8O6n8U8C3gzcEVuX35vWiYi1kXEL3L5SeBeiruJDOfj1KhPjQz5Y5Xv91P5cvt8RDftKB+/K4Bjst296mt3bXLi6Vm92/J090EcbAH8QNKtKm4VBLB3RKyD4g8L2Cvjjfo2VPs8UP2YmMtd44PhwznstKQ2JEXv+7MHsDkitnaJVyaHY15L8a/pEXGcuvQJhvGxkjRK0m3AeorE/kA37Xi+7bl+S7Z7wL4vnHh61tRteYaQoyJiKjALOF1kyqqlAAACIElEQVTS0d2UbdS34dbn3vZjqPTvfOBlwGHAOuDLGR9W/ZG0K3AlcEZEPNFd0TqxIdmvOn0a1scqIv4YEYdR3N3lCODgbtrR8j458fRsWN2WJyLW5vN64CqKD9mjOWxBPq/P4o36NlT7PFD96MzlrvFKRcSj+YXwJ+CbFMcKet+fxyiGrUZ3ibecpO0pvqAvjYj/zPCwPk71+jQSjhVARGwGbqC4xtOoHc+3PdfvTjFMPGDfF048PRs2t+WRtIuk3WrLwAzgLor21mYKzQOuzuUVwMk522g6sCWHRlYCMySNyyGFGRkbbAPSj1z3pKTpOXZ9cqmuytS+nNPbKY4VFP2Zm7OLDgCmUFxkr/tZzOsfPwbemduX35tWtl/AhcC9EfGV0qphe5wa9Wk4HytJEySNzeWdgbdQXLtq1I7y8Xsn8KNsd6/62m2jWjmbYqQ8KGbj/IpiXPTTg92ebtp5IMWMktuBu2ttpRifvR5Ync/jMy6KH9J7ALgTmFaq628pLh52AKcMQl++TTGk8QeKf1GdOpD9AKZRfHk8AJxH3sWj4v5cnO29I/9Q9ymV/3S27X5KM7kafRbz2N+S/fwOsGMFx+gNFEMqdwC35WP2MD9Ojfo0bI8V8Brgl9n2u4DPdNcOYKd83ZHrD+xrXxs9fMscMzOrlIfazMysUk48ZmZWKSceMzOrlBOPmZlVyonHzMwq5cRjZmaVcuIxM7NK/X+m1uGoJTLlewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19437278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['tripduration'].plot.hist(bins=100, title='Frequency distribution of Trip duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notes:\n",
    "# [Q1 – 1.5 (IQR) ,  Q3 + 1.5 (IQR) ] (i.e. IQR = Q3 - Q1)\n",
    "# Number of outliers values = Length of all values - Length of all non outliers values\n",
    "# Ratio of outliers = ( Number of outliers values / Length of all values ) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15665470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = data.boxplot(column=['tripduration'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of values as outlier: 9.548218 percent\n"
     ]
    }
   ],
   "source": [
    "q75, q25 = np.percentile(trip_duration, [75 ,25])\n",
    "iqr = q75 - q25\n",
    "print('Proportion of values as outlier: %f percent'%(\n",
    "    (len(data) - len([x for x in trip_duration if q75+(1.5*iqr) >=x>= q25-(1.5*iqr)]))*100/float(len(data))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of trip duration: 711.726573\n"
     ]
    }
   ],
   "source": [
    "mean_trip_duration = np.mean([x for x in trip_duration if q75+(1.5*iqr) >=x>= q25-(1.5*iqr)])\n",
    "upper_whisker = q75+(1.5*iqr)\n",
    "print('Mean of trip duration: %f'%mean_trip_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FA4yZmZXCAcbMzErhAGNmZqVwgDEzs1I4wJiZWSkcYMzMrBQOMGZmVgoHGDMzK4UDjJmZlcIBxszMSuEAY2ZmpXCAMTOzUjjAmJlZKRxgzMysFA4wZmZWCgcYMzMrhQOMmZmVwgHGzMxK4QBjZmalcIAxM7NSOMCYmVkpHGDMzKwUDjBmZlYKBxgzMyuFA4yZmZXCAcbMzEpRWoCRNFrSzyQtlbRE0ik5fSdJCyU9kv8Oy+mSdKGkZZLukzSuUNbUnP8RSVML6W+XdH9e5kJJKmt7zMysNWXewbwKfD4i9gEOAU6StC8wHbg5IsYCN+f3AJOAsfk1DbgYUkACzgIOBg4CzqoEpZxnWmG5iSVuj5mZtaC0ABMRqyLi3jy9HlgKjASmALNzttnAUXl6CjAnkjuAoZJGAEcACyNiXUQ8AywEJuZ5O0bELyMigDmFsszMrMN65RmMpDHA24A7gd0iYhWkIATsmrONBJYXFluR0xqlr6iRXmv90yQtkrRo7dq1m7s5ZmbWhNIDjKTtgWuBUyPi+UZZa6TFJqRvnBhxaUSMj4jxw4cP76nKZmbWBqUGGElbkILLlRHxo5y8Ondvkf+uyekrgNGFxUcBK3tIH1Uj3czM+oAyR5EJ+B6wNCIuKMyaB1RGgk0FbiikH5dHkx0CPJe70BYAEyQNyw/3JwAL8rz1kg7J6zquUJaZmXXYkBLLPhT4GHC/pMU57QzgPOAaSScCTwDH5HnzgcnAMuAl4HiAiFgn6Wzg7pzvKxGxLk9/BpgFbAPcmF9mZtYHlBZgIuJ2aj8nAXhvjfwBnFSnrJnAzBrpi4C3bkY1zcysJP4mv5mZlcIBxszMSuEAY2ZmpXCAMTOzUjjAmJlZKRxgzMysFA4wZmZWCgcYMzMrhQOMmZmVwgHGzMxK4QBjZmalcIAxM7NSOMCYmVkpHGDMzKwUDjBmZlYKBxgzMyuFA4yZmZXCAcbMzErhAGNmZqVwgDEzs1I4wJiZWSmaCjCS3lp2RczMrLs0ewdziaS7JH1W0tBSa2RmZl2hqQATEe8EPgKMBhZJ+r6kw0utmZmZ9WtNP4OJiEeAM4HTgL8BLpT0kKQPlFU5MzPrv5p9BrO/pG8AS4H3AEdGxD55+hsl1s/MzPqpIU3m+zZwGXBGRLxcSYyIlZLOLKVmZmbWrzUbYCYDL0fEawCSBgFbR8RLEXF5abUzM7N+q9lnMD8Ftim83zanmZmZ1dRsgNk6Il6ovMnT25ZTJTMz6wbNBpgXJY2rvJH0duDlBvnNzGyAazbAnAr8QNLPJf0cuBo4udECkmZKWiPpgULaDElPSlqcX5ML806XtEzSw5KOKKRPzGnLJE0vpO8l6U5Jj0i6WtKWzW60mZmVr9kvWt4N/CXwGeCzwD4RcU8Pi80CJtZI/0ZEHJhf8wEk7QscC+yXl/l3SYMlDQYuAiYB+wIfynkBvpbLGgs8A5zYzLaYmVnvaOXHLt8B7A+8jXShP65R5oi4DVjXZNlTgLkR8YeI+C2wDDgov5ZFxGMR8QowF5giSaTv4PwwLz8bOKqFbTEzs5I1+0XLy4GvA+8kBZp3AOM3cZ0nS7ovd6ENy2kjgeWFPCtyWr30nYFnI+LVqvR69Z8maZGkRWvXrt3EapuZWSua/R7MeGDfiIjNXN/FwNlA5L/nAycAqpE3qB0Ao0H+miLiUuBSgPHjx2/uNpiZWROa7SJ7AHjj5q4sIlZHxGsR8SfSLwMclGetIP2QZsUoYGWD9KeAoZKGVKWbmVkf0WyA2QV4UNICSfMqr1ZXJmlE4e3RpMAFMA84VtJWkvYCxgJ3AXcDY/OIsS1JAwHm5TupnwEfzMtPBW5otT5mZlaeZrvIZrRasKSrgMOAXSStAM4CDpN0IKk763HgUwARsUTSNcCDwKvASYWfpTkZWAAMBmZGxJK8itOAuZLOAX4FfK/VOpqZWXmaCjARcaukPYGxEfFTSduSLviNlvlQjeS6QSAizgXOrZE+H5hfI/0x/tzFZmZmfUyzo8g+SRoS/J2cNBK4vqxKmZlZ/9fsM5iTgEOB5+H1fz62a1mVMjOz/q/ZAPOH/EVHAPLoLQ/3NTOzupoNMLdKOgPYRtLhwA+AH5dXLTMz6++aDTDTgbXA/aSRX/MB/ydLMzOrq9lRZJUvRl5WbnXMzKxbNBVgJP2WGs9cImLvttfIzMy6Qiu/RVaxNXAMsFP7q2NmZt2i2f8H83Th9WREfJP0c/lmZmY1NdtFNq7wdhDpjmaHUmpkZmZdodkusvML06+SfkfsH9peGzMz6xrNjiJ7d9kVMTOz7tJsF9nnGs2PiAvaUx0zM+sWrYwiewfp/7YAHAncxob/ztjMzOx1zQaYXYBxEbEeQNIM4AcR8YmyKmZmZv1bsz8VswfwSuH9K8CYttfGzMy6RrN3MJcDd0m6jvSN/qOBOaXVyszM+r1mR5GdK+lG4K9z0vER8avyqmVmZv1ds11kANsCz0fEt4AVkvYqqU5mZtYFmv2XyWcBpwGn56QtgCvKqpSZmfV/zd7BHA38HfAiQESsxD8VY2ZmDTQbYF6JiCD/ZL+k7cqrkpmZdYNmA8w1kr4DDJX0SeCn+J+PmZlZA82OIvu6pMOB54G3AP8SEQtLrZmZmfVrPQYYSYOBBRHxPsBBxczMmtJjF1lEvAa8JOkNvVAfMzPrEs1+k//3wP2SFpJHkgFExD+VUiszM+v3mg0wP8kvMzOzpjQMMJL2iIgnImJ2b1XIzMy6Q0/PYK6vTEi6tuS6mJlZF+kpwKgwvXeZFTEzs+7SU4CJOtM9kjRT0hpJDxTSdpK0UNIj+e+wnC5JF0paJuk+SeMKy0zN+R+RNLWQ/nZJ9+dlLpQkzMysz+gpwBwg6XlJ64H98/TzktZLer6HZWcBE6vSpgM3R8RY4Ob8HmASMDa/pgEXQwpIwFnAwcBBwFmVoJTzTCssV70uMzProIYBJiIGR8SOEbFDRAzJ05X3O/aw7G3AuqrkKUBlwMBs4KhC+pxI7iD9JM0I4AhgYUSsi4hnSF/0nJjn7RgRv8y/kTanUJaZmfUBrfw/mHbYLSJWAeS/u+b0kcDyQr4VOa1R+ooa6TVJmiZpkaRFa9eu3eyNMDOznvV2gKmn1vOT2IT0miLi0ogYHxHjhw8fvolVNDOzVvR2gFmdu7fIf9fk9BXA6EK+UcDKHtJH1Ug3M7M+orcDzDygMhJsKnBDIf24PJrsEOC53IW2AJggaVh+uD+B9MObq4D1kg7Jo8eOK5RlZmZ9QLM/FdMySVcBhwG7SFpBGg12Hul/y5wIPAEck7PPByYDy4CXgOMBImKdpLOBu3O+r0REZeDAZ0gj1bYBbswvMzPrI0oLMBHxoTqz3lsjbwAn1SlnJjCzRvoi4K2bU0czMytPX3nIb2ZmXcYBxszMSuEAY2ZmpXCAMTOzUjjAmJlZKRxgzMysFA4wZmZWCgcYMzMrhQOMmZmVwgHGzMxK4QBjZmalcIAxM7NSOMCYmVkpHGDMzKwUDjBmZlYKBxgzMyuFA4yZmZXCAcbMzErhAGNmZqVwgDEzs1I4wJiZWSkcYMzMrBQOMGZmVgoHGDMzK8WQTlfAeteY6T95ffrx897fwZqYWbdzgOnHisGiEQcSM+sEB5gBoNlAZGbWTg4w/Uw7g0W9snzHY2bt4If8ZmZWCt/B9AO93cXlgQBm1g4OMNaQg42ZbaqOBBhJjwPrgdeAVyNivKSdgKuBMcDjwD9ExDOSBHwLmAy8BHw8Iu7N5UwFzszFnhMRs3tzO8rkB/Nm1t918g7m3RHxVOH9dODmiDhP0vT8/jRgEjA2vw4GLgYOzgHpLGA8EMA9kuZFxDO9uREDie9mzKwVfamLbApwWJ6eDdxCCjBTgDkREcAdkoZKGpHzLoyIdQCSFgITgat6t9oDk4ONmfWkUwEmgJskBfCdiLgU2C0iVgFExCpJu+a8I4HlhWVX5LR66RuRNA2YBrDHHnu0czvayt1iZtZNOhVgDo2IlTmILJT0UIO8qpEWDdI3TkwB7FKA8ePH18xjm853M2ZWS0cCTESszH/XSLoOOAhYLWlEvnsZAazJ2VcAowuLjwJW5vTDqtJvKbnq1gMHGzOr6PUAI2k7YFBErM/TE4CvAPOAqcB5+e8NeZF5wMmS5pIe8j+Xg9AC4KuShuV8E4DTe3FT2sLdYmbWrTpxB7MbcF0afcwQ4PsR8Z+S7gaukXQi8ARwTM4/nzREeRlpmPLxABGxTtLZwN0531cqD/zNzKzzej3ARMRjwAE10p8G3lsjPYCT6pQ1E5jZ7jpae7i7zGxg60vDlK2LOdiYDTz+sUszMyuF72A6wA/2zWwgcICxXufuMrOBwQHGOsp3c2bdy89gzMysFL6D6SX+pG5mA43vYMzMrBS+g7E+qd4dnwcFmPUfvoMxM7NS+A7G+hUPcTbrPxxgrN9ysDHr2xxgrCs42Jj1PQ4w1nUcbMz6BgeYEvm7L51XvQ8ccMx6jwOMDSge/mzWexxgzHC3mlkZHGDMqvgux6w9HGDMmuS7HLPWOMC0mR/sDwy+yzHrmQOMWRs18wHDQcgGCgcYsw5yt5t1MwcYs15W7y7H3W7WbRxg2sDPXaxMvsux/soBxqwfafUZj4OTdZIDjFmXabULrhEHJdscDjBmVlerQckByYocYMysbTxM24ocYDaRH+ybbZp2nTsOVH2fA0wLHFTM+g4PeOj7HGDMrGtt7oAHB6LN0+8DjKSJwLeAwcB3I+K8DlfJzLpEb/ZadOPdVr8OMJIGAxcBhwMrgLslzYuIBztbMzOz1rRzeHktnQhUg3p9je11ELAsIh6LiFeAucCUDtfJzMzo53cwwEhgeeH9CuDg6kySpgHT8tsXJD1cp7xdgKfaWsP+ze2xMbfJxtwmG+tzbaKvbXYRe7a6QH8PMKqRFhslRFwKXNpjYdKiiBjfjop1A7fHxtwmG3ObbMxtkvT3LrIVwOjC+1HAyg7VxczMCvp7gLkbGCtpL0lbAscC8zpcJzMzo593kUXEq5JOBhaQhinPjIglm1Fkj91oA4zbY2Nuk425TTbmNgEUsdEjCzMzs83W37vIzMysj3KAMTOzUjjAkH5uRtLDkpZJmt7p+vQmSY9Lul/SYkmLctpOkhZKeiT/HZbTJenC3E73SRrX2dq3h6SZktZIeqCQ1nIbSJqa8z8iaWontqUd6rTHDElP5uNksaTJhXmn5/Z4WNIRhfSuOa8kjZb0M0lLJS2RdEpOH7DHSVMiYkC/SIMDHgX2BrYEfg3s2+l69eL2Pw7sUpX2b8D0PD0d+FqengzcSPr+0SHAnZ2uf5va4F3AOOCBTW0DYCfgsfx3WJ4e1ulta2N7zAC+UCPvvvmc2QrYK59Lg7vtvAJGAOPy9A7Ab/K2D9jjpJmX72D8czO1TAFm5+nZwFGF9DmR3AEMlTSiExVsp4i4DVhXldxqGxwBLIyIdRHxDLAQmFh+7duvTnvUMwWYGxF/iIjfAstI51RXnVcRsSoi7s3T64GlpF8SGbDHSTMcYGr/3MzIDtWlEwK4SdI9+Sd1AHaLiFWQTixg15w+kNqq1TYYCG1zcu7umVnpCmIAtoekMcDbgDvxcdKQA0yTPzfTxQ6NiHHAJOAkSe9qkHegtxXUb4Nub5uLgTcBBwKrgPNz+oBqD0nbA9cCp0bE842y1kjr2napxwFmgP/cTESszH/XANeRujZWV7q+8t81OftAaqtW26Cr2yYiVkfEaxHxJ+Ay0nECA6g9JG1BCi5XRsSPcrKPkwYcYAbwz81I2k7SDpVpYALwAGn7K6NbpgI35Ol5wHF5hMwhwHOV7oEu1GobLAAmSBqWu48m5LSuUPWs7WjScQKpPY6VtJWkvYCxwF102XklScD3gKURcUFhlo+TRjo9yqAvvEgjPn5DGvXypU7Xpxe3e2/S6J5fA0sq2w7sDNwMPJL/7pTTRfoHb48C9wPjO70NbWqHq0jdPn8kfcI8cVPaADiB9JB7GXB8p7erze1xed7e+0gXzxGF/F/K7fEwMKmQ3jXnFfBOUlfWfcDi/Jo8kI+TZl7+qRgzMyuFu8jMzKwUDjBmZlYKBxgzMyuFA4yZmZXCAcbMzErhAGNmZqVwgDEzs1LBDw86AAAABklEQVT8f51BmE2mo1QNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4b6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def transform_tripduration(x):\n",
    "    \n",
    "    if x > upper_whisker:\n",
    "        return mean_trip_duration\n",
    "    return x\n",
    "\n",
    "data['tripduration_mean'] = data['tripduration'].apply(lambda x: transform_tripduration(x))\n",
    "\n",
    "data['tripduration_mean'].plot.hist(bins=100, title='Frequency distribution of mean transformed Trip duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of trip duration: 711.726573\n",
      "Standard deviation of trip duration: 435.517297\n",
      "Median of trip duration: 633.235000\n"
     ]
    }
   ],
   "source": [
    "print('Mean of trip duration: %f'%data['tripduration_mean'].mean())\n",
    "print('Standard deviation of trip duration: %f'%data['tripduration_mean'].std())\n",
    "print('Median of trip duration: %f'%data['tripduration_mean'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male Mean of trip duration: 563.402797\n",
      "Male Median of trip duration: 458.451500\n",
      "Male Mode of trip duration: 466.17400000000004\n"
     ]
    }
   ],
   "source": [
    "# Excercise 1.  Find the mean, median, and mode of the trip duration of gender type male.\n",
    "male_trip_duration = list(data[data['gender'] == 'Male']['tripduration'])\n",
    "\n",
    "print('Male Mean of trip duration: %f'%statistics.mean(male_trip_duration))\n",
    "print('Male Median of trip duration: %f'%statistics.median(male_trip_duration))\n",
    "print('Male Mode of trip duration: %s'%statistics.mode(male_trip_duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excercise 2. By looking at the numbers obtained earlier, in your opinion is the distribution symmetric or skewed? \n",
    "# If skewed, then is it positively skewed or negatively skewed?\n",
    "# Slightly skewed, positively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19523358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 3. Plot a frequency distribution of trip duration for trips availed by gender type male. \n",
    "# Does it validate your inference as you did so in the previous question?\n",
    "data[data['gender'] == 'Male']['tripduration'].plot.hist(bins=100, title='Frequency distribution of Male Trip duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19b7b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Excercise 4. Plot a box plot of the trip duration of trips taken by males. Do you think any outliers exist?\n",
    "box = data[data['gender'] == 'Male'].boxplot(column=['tripduration'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of values as outlier: 5.030104 percent\n"
     ]
    }
   ],
   "source": [
    "# Exercise 5. Apply the formula in Listing 1-6 to determine the percentage of observations for which outliers exists.\n",
    "q75, q25 = np.percentile(male_trip_duration, [75 ,25])\n",
    "iqr = q75 - q25\n",
    "print('Proportion of values as outlier: %f percent'%(\n",
    "    (len(male_trip_duration) - len([x for x in male_trip_duration if q75+(1.5*iqr) >=x>= q25-(1.5*iqr)]))*100/float(len(male_trip_duration))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d3a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercise 6. Perform the treatment of outliers by incorporating one of the methods we discussed earlier for the treatment of outliers.\n",
    "male_mean_trip_duration = np.mean([x for x in male_trip_duration if q75+(1.5*iqr) >=x>= q25-(1.5*iqr)])\n",
    "male_upper_whisker = q75+(1.5*iqr)\n",
    "\n",
    "def male_transform_tripduration(x):\n",
    "    \n",
    "    if x > male_upper_whisker:\n",
    "        return male_mean_trip_duration\n",
    "    return x\n",
    "\n",
    "data['male_tripduration_mean'] = data['tripduration'].apply(lambda x: male_transform_tripduration(x))\n",
    "\n",
    "data[data['gender'] == 'Male']['male_tripduration_mean'].plot.hist(bins=100, title='Frequency distribution of male mean transformed Trip duration')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ec1b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data['age'] = data['starttime_year'] - data['birthyear']\n",
    "seaborn.pairplot(data, vars=['age', 'tripduration'], kind='reg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              tripduration    age\n",
      "tripduration         1.000  0.058\n",
      "age                  0.058  1.000\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.width', 100)\n",
    "pd.set_option('precision', 3)\n",
    "\n",
    "correlations = data[['tripduration','age']].corr(method='pearson')\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category: gender\n",
      "\n",
      "Female Male\n",
      "Statistic: -38.572176, P value: 0.000000\n",
      "\n",
      "\n",
      "Female Other\n",
      "Statistic: 48.248666, P value: 0.000000\n",
      "\n",
      "\n",
      "Male Other\n",
      "Statistic: 53.180282, P value: 0.000000\n",
      "\n",
      "\n",
      "Category: usertype\n",
      "\n",
      "Member Short-Term Pass Holder\n",
      "Statistic: 14.393456, P value: 0.000000\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('examples/trip.csv')\n",
    "\n",
    "List_ = list(data['starttime'])\n",
    "\n",
    "List_ = [datetime.datetime.strptime(x, \"%m/%d/%Y %H:%M\") for x in List_]\n",
    "data['starttime_mod'] = pd.Series(List_,index=data.index)\n",
    "data['starttime_date'] = pd.Series([x.date() for x in List_],index=data.index)\n",
    "data['starttime_year'] = pd.Series([x.year for x in List_],index=data.index)\n",
    "data['starttime_month'] = pd.Series([x.month for x in List_],index=data.index)\n",
    "data['starttime_day'] = pd.Series([x.day for x in List_],index=data.index)\n",
    "data['starttime_hour'] = pd.Series([x.hour for x in List_],index=data.index)\n",
    "\n",
    "for cat in ['gender','usertype']:\n",
    "\n",
    "    print('Category: %s\\n'%cat)\n",
    "    groupby_category = data.groupby(['starttime_date', cat])['starttime_date'].count().unstack(cat)\n",
    "    groupby_category = groupby_category.dropna()\n",
    "    category_names = list(groupby_category.columns)\n",
    "\n",
    "    for comb in [(category_names[i],category_names[j]) for i in range(len(category_names)) for j in range(i+1, len(category_names))]:\n",
    "\n",
    "        print('%s %s'%(comb[0], comb[1]))\n",
    "        t_statistics = stats.ttest_ind(list(groupby_category[comb[0]]), list(groupby_category[comb[1]]))\n",
    "        print('Statistic: %f, P value: %f'%(t_statistics.statistic, t_statistics.pvalue))\n",
    "        print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x194d5f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_tickets = list(data.groupby('starttime_date').size())\n",
    "sample_tickets = []\n",
    "checkpoints = [1, 10, 100, 300, 500, 1000]\n",
    "plot_count = 1\n",
    "\n",
    "random.shuffle(daily_tickets)\n",
    "\n",
    "plt.figure(figsize=(15,7))\n",
    "binrange=np.array(np.linspace(0,700,101))\n",
    "\n",
    "for i in range(1000):\n",
    "    if daily_tickets:\n",
    "        sample_tickets.append(daily_tickets.pop())\n",
    "\n",
    "    if i+1 in checkpoints or not daily_tickets:\n",
    "        plt.subplot(2,3,plot_count)\n",
    "        plt.hist(sample_tickets, binrange)\n",
    "        plt.title('n=%d' % (i+1),fontsize=15)        \n",
    "        plot_count+=1\n",
    "\n",
    "    if not daily_tickets:\n",
    "        break\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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