homework6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"frontmatter text-center\">\n",
    "<h2> MATH5027 Scientific Python, Fall 2017</h2>\n",
    "<h2> Homework 6, Due: Tuesday October 31st, 6:00pm </h2>\n",
    "</div>\n",
    "\n",
    "Instructions: complete the selection of exercises from the week's class notebooks. Use markdown cells between code examples to explain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions \n",
    "\n",
    "## Class 9\n",
    "Recall we have two csvs: passengers that we know survived or perished, a new data on passengers without survival information. Predict the chances of survival of these new data points using their age, sex, and pclass. Specifically:\n",
    "\n",
    "First using the old data:\n",
    "\n",
    "1) bin the age variable into 3 groups (young, middle-aged, old),\n",
    "\n",
    "2) calculate the average rate of survival for people in each age/sex/pclass group, \n",
    "\n",
    "Then on the new data:\n",
    "\n",
    "3) infer the chance of survival of each of the new passengers using these averages.\n",
    "\n",
    "4) plot a histogram of the estimated survivial chances for the new passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) bin the age variable into 3 groups (young, middle-aged, old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('titanic.csv',header=0,sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bins = [0, 25, 55, 100]\n",
    "label_names = [\"young\", \"middle-aged\", \"old\"]\n",
    "df[\"age_group\"] = pd.cut(df[\"Age\"], bins, labels = label_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 ) calculate the average rate of survival for people in each age/sex/pclass group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>middle-aged</th>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>middle-aged</th>\n",
       "      <td>0.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>middle-aged</th>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>0.965517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>middle-aged</th>\n",
       "      <td>0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>middle-aged</th>\n",
       "      <td>0.361111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>0.507692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>middle-aged</th>\n",
       "      <td>0.152542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>0.155039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Survived\n",
       "Pclass Sex    age_group            \n",
       "1      female middle-aged  0.980000\n",
       "              old          1.000000\n",
       "              young        0.928571\n",
       "       male   middle-aged  0.441176\n",
       "              old          0.157895\n",
       "              young        0.500000\n",
       "2      female middle-aged  0.909091\n",
       "              old          0.000000\n",
       "              young        0.965517\n",
       "       male   middle-aged  0.070175\n",
       "              old          0.166667\n",
       "              young        0.277778\n",
       "3      female middle-aged  0.361111\n",
       "              old          1.000000\n",
       "              young        0.507692\n",
       "       male   middle-aged  0.152542\n",
       "              old          0.000000\n",
       "              young        0.155039"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surv = pd.DataFrame(df.groupby([\"Pclass\",\"Sex\",\"age_group\"])[\"Survived\"].mean())\n",
    "surv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) infer the chance of survival of each of the new passengers using these averages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surv.reset_index(level=surv.index.names, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2= pd.read_csv('titanic2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2[\"age_group\"] = pd.cut(df[\"Age\"], bins, labels = label_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2full = pd.merge(df2, surv, how = \"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>age_group</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>young</td>\n",
       "      <td>0.155039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "      <td>0.155039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>902</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilieff, Mr. Ylio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349220</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "      <td>0.155039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>911</td>\n",
       "      <td>3</td>\n",
       "      <td>Assaf Khalil, Mrs. Mariana (Miriam\")\"</td>\n",
       "      <td>female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2696</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.155039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>919</td>\n",
       "      <td>3</td>\n",
       "      <td>Daher, Mr. Shedid</td>\n",
       "      <td>male</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2698</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>young</td>\n",
       "      <td>0.155039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                   Name     Sex   Age  \\\n",
       "0          892       3                       Kelly, Mr. James    male  34.5   \n",
       "1          901       3                Davies, Mr. John Samuel    male  21.0   \n",
       "2          902       3                       Ilieff, Mr. Ylio    male   NaN   \n",
       "3          911       3  Assaf Khalil, Mrs. Mariana (Miriam\")\"  female  45.0   \n",
       "4          919       3                      Daher, Mr. Shedid    male  22.5   \n",
       "\n",
       "   SibSp  Parch     Ticket     Fare Cabin Embarked age_group  Survived  \n",
       "0      0      0     330911   7.8292   NaN        Q     young  0.155039  \n",
       "1      2      0  A/4 48871  24.1500   NaN        S     young  0.155039  \n",
       "2      0      0     349220   7.8958   NaN        S     young  0.155039  \n",
       "3      0      0       2696   7.2250   NaN        C       NaN  0.155039  \n",
       "4      0      0       2698   7.2250   NaN        C     young  0.155039  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2full.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) plot a histogram of the estimated survivial chances for the new passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tix = [x / 10 for x in range(0,11)]\n",
    "tix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XAJemk5oB99YyZieSR4f/UdIrkm6TtDWwY0R8mC7zEZX0cUg6V9J0SdMXL15c\nyyqYmVl18r2C+DZwHLASICIWAtvWMmZToBdwc0T0TMtcpzkpIoJK3lgXEWMjondE9G7btm0tq2Bm\nZtXJN0F8mXvQTs/4a2sBsCAiXkzHHyJJGP+S1C4tvx2waANimJnZBso3QYyXdAvQStI5wCRq+fKg\niPgIeF9S13TSEcAbJF/CG5pOGwo8XpvyzcysbuT7LKZR6buoPwO6AldExNMbEPfHwH2Svga8TXKH\nVBOSRHQW8C5+GKCZWYOqNkFI2gKYlD6wb0OSQrmImMG6LyEqc0RdlG9mZhuu2iamiFgNrElvTzUz\ns81Evt+kXgG8Julp0juZACLiJ/VSKzMza3D5JohH0h8zM9tMVJkgJHWIiPciotbPXTIzs41TdX0Q\nj5UNSHq4nutiZmaNSHUJQjnDneuzImZm1rhUlyCikmEzM9vEVddJvY+kz0iuJLZMh0nHIyK2q9fa\nmZlZg6kyQUTEFoWqiJmZNS41eR+EmZltRpwgzMwskxOEmZllcoIwM7NMThBmZpbJCcLMzDI5QZiZ\nWSYnCDMzy+QEYWZmmZwgzMwskxOEmZllcoIwM7NMThBmZpapwRKEpC0kvSLpiXS8taSnJc1Nf2/f\nUHUzM7OGvYK4AJidMz4CmBwRXYDJ6biZmTWQBkkQknYBBgK35UweDNyVDt8FHF/oepmZ2VoNdQVx\nPXAxsCZn2o4R8WE6/BGwY8FrZWZm5QqeICQNAhZFxEuVLRMRQSXvwJZ0rqTpkqYvXry4vqppZrbZ\na4griIOA4ySVAg8Ah0u6F/iXpHYA6e9FWStHxNiI6B0Rvdu2bVuoOpuZbXYKniAi4tKI2CUiOgKn\nAM9ExHeBCcDQdLGhwOOFrpuZma3VmL4HcQ1wlKS5wJHpuJmZNZCmDRk8IqYAU9LhJcARDVkfMzNb\nqzFdQZiZWSPiBGFmZpkatImpoXUc8WRDV8HMrNHyFYSZmWVygjAzs0xOEGZmlskJwszMMm3WndRW\nOA11Q0DpNQMbJK7ZpsBXEGZmlskJwszMMjlBmJlZJicIMzPL5E7qzYi/OW5mNeErCDMzy+QEYWZm\nmZwgzMwskxOEmZllcoIwM7NMThBmZpbJCcLMzDI5QZiZWSYnCDMzy+QEYWZmmQr+qA1JuwJ3AzsC\nAYyNiN9Lag2MAzoCpcCQiFha6PrZpsWPFzGrvYa4glgF/DwiugEHAOdL6gaMACZHRBdgcjpuZmYN\npOAJIiJxnMCKAAAKnUlEQVQ+jIiX0+HlwGygPTAYuCtd7C7g+ELXzczM1mrQPghJHYGewIvAjhHx\nYTrrI5ImqKx1zpU0XdL0xYsXF6SeZmabowZLEJK2AR4GhkfEZ7nzIiJI+ifWExFjI6J3RPRu27Zt\nAWpqZrZ5apAEIakZSXK4LyIeSSf/S1K7dH47YFFD1M3MzBIFTxCSBNwOzI6Ia3NmTQCGpsNDgccL\nXTczM1urId4odxDwPeA1STPSaZcB1wDjJZ0FvAsMaYC6mZlZquAJIiKeA1TJ7CMKWRczM6ucv0lt\nZmaZnCDMzCyTE4SZmWVygjAzs0xOEGZmlskJwszMMjlBmJlZJicIMzPL5ARhZmaZnCDMzCyTE4SZ\nmWVygjAzs0xOEGZmlskJwszMMjlBmJlZJicIMzPL5ARhZmaZnCDMzCyTE4SZmWVygjAzs0xOEGZm\nlskJwszMMjlBmJlZpkaXICQdI+lNSfMkjWjo+piZba4aVYKQtAVwI/BNoBtwqqRuDVsrM7PNU6NK\nEEAxMC8i3o6IL4EHgMENXCczs81S04auQAXtgfdzxhcA++cuIOlc4Nx0dIWkNzcgXhvg4w1Y33Eb\nf+zNbpv1681vmzfDuBv6d94tn4UaW4KoVkSMBcbWRVmSpkdE77ooy3EbZ2xv8+YRe3OLW6jYja2J\n6QNg15zxXdJpZmZWYI0tQUwDukjqJOlrwCnAhAauk5nZZqlRNTFFxCpJPwKeArYA7oiI1+sxZJ00\nVTluo47tbd48Ym9ucQsSWxFR3zHMzGwj1NiamMzMrJFwgjAzs0ybfIKo7tEdSvwhnT9TUq8Cxt5T\n0j8lfSHpwgLGPS3d1tck/UPSPgWKOziNO0PSdEkH10XcfGLnLNdH0ipJJxYirqR+kpal2zxD0hV1\nETef2DnxZ0h6XdKzhYgr6aKc7Z0labWk1gWK3VLS/0l6Nd3mMwoUd3tJj6af7xJJ3eso7h2SFkma\nVcn8ejt+ARARm+wPSUf3fKAz8DXgVaBbhWWOBf4CCDgAeLGAsb8O9AGuBi4sYNwDge3T4W/WxTbn\nGXcb1vZ7FQFzCrXNOcs9A/wZOLFA29wPeKKBPtutgDeADmWft0Lt65zlvwU8U8Btvgz4dTrcFvgE\n+FoB4v4WuDId3hOYXEfbfCjQC5hVyfx6OX6V/WzqVxD5PLpjMHB3JF4AWklqV4jYEbEoIqYBX9VB\nvJrE/UdELE1HXyD5vkkh4q6I9FMNbA3U1R0S+T6i5cfAw8CiAsetD/nE/g7wSES8B8nnrUBxc50K\n3F8HcfONHcC2kkRyQvIJsKoAcbuRnHwQEXOAjpJ23MC4RMRUkm2oTH0dv4BNv4kp69Ed7WuxTH3F\nrg81jXsWyRlIQeJK+rakOcCTwJl1EDev2JLaA98Gbq6jmHnFTR2YXv7/RdLeBYy9B7C9pCmSXpJ0\neoHiAiBpK+AYkqRcF/KJfQOwF7AQeA24ICLWFCDuq8AJAJKKSR5lURcnXnVRt1rb1BOEVUFSf5IE\ncUmhYkbEoxGxJ3A88N+FigtcD1xSBweLmnqZpImnCBgNPFbA2E2B/YCBwADgckl7FDD+t4DnI6Kq\nM+C6NgCYAewM7AvcIGm7AsS9huTsfQbJleorwOoCxK1XjeqLcvUgn0d31NfjPRrqsSF5xZVUBNwG\nfDMilhQqbpmImCqps6Q2EbGhDzvLJ3Zv4IGk5YE2wLGSVkXEhhywq40bEZ/lDP9Z0k0F3OYFwJKI\nWAmslDQV2Ad4q57jljmFumteyjf2GcA1aVPmPEnvkPQJlNRn3PTvfAYkHcfAO8DbGxCzzuq2Qeqy\nQ6Ox/ZAkwLeBTqztXNq7wjIDWbeTp6RQsXOWHUnddVLns80dgHnAgQXe199gbSd1r/SDrELu63T5\nO6mbTup8tnmnnG0uBt4r1DaTNLVMTpfdCpgFdC/EvgZakrSdb13gz9jNwMh0eMf0M9amAHFbkXaG\nA+eQ9AvU1XZ3pPJO6no5fpWXX5eFNcYfkl7+t0juQvhFOu084Lx0WCQvKZpP0mbZu4CxdyI5y/sM\n+DQd3q4AcW8DlpJcis8Aphdoey8BXk9j/hM4uFD7usKyd1IHCSLPbf5Rus2vktwQUJdJudptBi4i\nuZNpFjC8gHGHAQ/U1bbWYH/vDExM/5dnAd8tUNy+6fw3gUdI7xKsg7j3Ax+S3MiygKRJuCDHr4jw\nozbMzCybO6nNzCyTE4SZmWVygjAzs0xOEGZmlskJwszMMjlBWKMm6RfpUznLngK7fx2Ve1xVT32t\nZt2Rdfn03Zxy+0l6oobrTJHUOx3+s6RW6c8Pc5bZWdJDdV1f2/Rt6t+kto2YpL7AIKBXRHwhqQ3J\nF5XyXb9pRGQ+qC0iJtCI3ncuaYP/FyPi2LSsjsAPgZvS6QuBOnm8uW1efAVhjVk74OOI+AIgIj5O\nD3ZIKk0TBpJ6S5qSDo+UdI+k54F7JL2Q+4C8sjNuScMk3ZC+P+BdSU3S+VtLel9SM0nnSJqWvlvg\n4fThc5WSdKekMUredfGWpEHp9BaS/qjk/RuvpM/AIq3DBEnPkHzjGWA7SU+m7x4Yk1Ovm9NyX5f0\ny0ril+2Ta4Dd0yuu30rqWPY+AUlbpNOmpVdl30+nt5M0VWvf4XBIjf9atslxgrDGbCKwa3qwvUnS\nYXmu1w04MiJOBcYBQyA5CALtImJ62YIRsYzkm91lZQ8CnoqIr0geld0nIvYBZpN8i7U6HUkeqTEQ\nGCOpBXB+Eip6kDz++q50OiSPHDkxIsriF5M87K0bsDvpE0JJvr3bm+Q9Goelz9KqzAhgfkTsGxEX\nVZh3FrAsIvqQvIvkHEmdSB4N/lRE7EvyvKYZeWyrbeKcIKzRiogVJE8jPRdYDIyTNCyPVSdExH/S\n4fGsbV4ZAmS1xY8DTk6HT0nHAbpL+ruk14DTgHwe1T0+ItZExFyS5/fsCRwM3Jtu0xzgXZJHcQM8\nHes+7bQkkvcOrCZ5zELZW/eGSHqZ5Cmhe5MkkNo4Gjg9feroi8AOQBdgGnCGpJFAj4hYXsvybRPi\nPghr1NID5RRgSnqgHkryLKVVrD3BaVFhtZU5638gaUl6xn0yyXNsKpoA/I+S12LuR/rilzTO8RHx\napqY+uVT5WrGK1pZYXy99dMz/AuBPhGxVNKdrL/N+RLw44h4ar0Z0qEkVz53Sro2Iu6uZQzbRPgK\nwhotSV0ldcmZtC/J2TdAKcnBHOD/VVPUOOBioGVEzKw4M71SmQb8nuT1oGXP8d8W+FBSM5IriHyc\nJKmJpN1JXlH5JvD3svWVvI+hQzo9S7GkTmnfw8nAc8B2JIlkmZK3lH2zmjosT+ue5SngB+k2IWmP\ntN9lN+BfEXErycMc6/bdxrZR8hWENWbbAKMltSK5YphH0twE8Evgdkn/TXKFUZWHSA7+Vb2gaBzw\nIOteJVxO0gyzOP1d2UE313sk7x7YjuSJm59Lugm4Ob0CWgUMS+/Kylp/Gslb0b4B/A14NCLWSHoF\nmEPy9rDnq6pARCyR9HzaMf0Xkqd9lrmNpJ/kZSUVWEzy8qZ+wEWSvgJWAHXx9jnbyPlprmZ1JG36\neSIi/J0D2yS4icnMzDL5CsLMzDL5CsLMzDI5QZiZWSYnCDMzy+QEYWZmmZwgzMws0/8HDUgZ8o54\nQlQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1ee78d4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=df2full.plot(kind='hist',y='Survived', label = \"Number of people per survival chances\")\n",
    "ax.set_title('Survival chances of new passengers');\n",
    "ax.set_xlabel(\"Survival porbalities\")\n",
    "ax.set_xticks(tix)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class 10\n",
    "Using the Divvy Bike data...\n",
    "\n",
    "1) Write a function that rounds each start time to the nearest half hour and add such columns to our dataframe as starthalfhour and endhalfhour.\n",
    "\n",
    "2) Use the datetime library's .dayofweek method to add the day of the week (i.e. Monday, Tuesday,...) to the dataframe for each trip.\n",
    "\n",
    "3) Groupby day of the week and hours and count the number of trips. Use the unstack method on the grouped frame and plot the result.\n",
    "\n",
    "4) Investigate the relationship between station capacity and traffic. Which two stations would you expand if you had the budget? \n",
    "\n",
    "5) Which stations are sources and which stations are sinks? i.e. which stations have much more departures than arrivals, and vice versa. Replot the geographic locations of the stations and using color or size visualize which stations are sources and sinks. Repeat the exercise for only weekday and only weekend trips. Do you see a difference?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class 10\n",
    "1) Write a function that rounds each start time to the nearest half hour and add such columns to our dataframe as starthalfhour and endhalfhour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\peter\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "trips=pd.read_csv('divvy_trips_august_2013.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trips.starttime= pd.to_datetime(trips.starttime,format=\"%Y-%m-%d %H:%M\")\n",
    "trips.stoptime= pd.to_datetime(trips.stoptime,format=\"%m/%d/%Y %H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "def round_halfhour(dt, mins=30):\n",
    "    \"\"\"\n",
    "    Rounds a datetime object to its nearest hour (by default).\n",
    "    (e.g. 2014-02-01 10:45:13 becomes 2014-02-01 11:00:00)\n",
    "    \"\"\"\n",
    "    dt += timedelta(minutes=mins/2.)\n",
    "    dt -= timedelta(minutes=dt.minute % mins, seconds=dt.second)\n",
    "    return dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trips['starthalfhour'] = trips.starttime.apply(round_halfhour)\n",
    "trips['stophalfhour'] = trips.stoptime.apply(round_halfhour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starttime</th>\n",
       "      <th>starthalfhour</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>stophalfhour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-01 00:13:00</td>\n",
       "      <td>2013-08-01 00:00:00</td>\n",
       "      <td>2013-08-01 00:28:00</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-01 00:28:00</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "      <td>2013-08-01 00:41:00</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-01 00:57:00</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 03:42:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-01 00:57:00</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 03:43:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-01 00:58:00</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 02:32:00</td>\n",
       "      <td>2013-08-01 02:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-08-01 00:58:00</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 02:32:00</td>\n",
       "      <td>2013-08-01 02:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-08-01 01:22:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-08-01 01:25:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "      <td>2013-08-01 01:44:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-08-01 01:25:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "      <td>2013-08-01 01:44:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-08-01 01:28:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "      <td>2013-08-01 01:44:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013-08-01 01:43:00</td>\n",
       "      <td>2013-08-01 01:30:00</td>\n",
       "      <td>2013-08-01 01:56:00</td>\n",
       "      <td>2013-08-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013-08-01 02:07:00</td>\n",
       "      <td>2013-08-01 02:00:00</td>\n",
       "      <td>2013-08-01 02:24:00</td>\n",
       "      <td>2013-08-01 02:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-08-01 02:13:00</td>\n",
       "      <td>2013-08-01 02:00:00</td>\n",
       "      <td>2013-08-01 02:34:00</td>\n",
       "      <td>2013-08-01 02:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-08-01 02:41:00</td>\n",
       "      <td>2013-08-01 02:30:00</td>\n",
       "      <td>2013-08-01 02:49:00</td>\n",
       "      <td>2013-08-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013-08-01 03:35:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>2013-08-01 04:14:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-08-01 03:36:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>2013-08-01 04:14:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-08-01 03:43:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>2013-08-01 04:12:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013-08-01 03:42:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>2013-08-01 04:12:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013-08-01 03:45:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "      <td>2013-08-01 04:12:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-08-01 03:49:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "      <td>2013-08-01 04:02:00</td>\n",
       "      <td>2013-08-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             starttime       starthalfhour            stoptime  \\\n",
       "0  2013-08-01 00:13:00 2013-08-01 00:00:00 2013-08-01 00:28:00   \n",
       "1  2013-08-01 00:28:00 2013-08-01 00:30:00 2013-08-01 00:41:00   \n",
       "2  2013-08-01 00:57:00 2013-08-01 01:00:00 2013-08-01 03:42:00   \n",
       "3  2013-08-01 00:57:00 2013-08-01 01:00:00 2013-08-01 03:43:00   \n",
       "4  2013-08-01 00:58:00 2013-08-01 01:00:00 2013-08-01 02:32:00   \n",
       "5  2013-08-01 00:58:00 2013-08-01 01:00:00 2013-08-01 02:32:00   \n",
       "6  2013-08-01 01:22:00 2013-08-01 01:30:00 2013-08-01 01:30:00   \n",
       "7  2013-08-01 01:25:00 2013-08-01 01:30:00 2013-08-01 01:44:00   \n",
       "8  2013-08-01 01:25:00 2013-08-01 01:30:00 2013-08-01 01:44:00   \n",
       "9  2013-08-01 01:28:00 2013-08-01 01:30:00 2013-08-01 01:44:00   \n",
       "10 2013-08-01 01:43:00 2013-08-01 01:30:00 2013-08-01 01:56:00   \n",
       "11 2013-08-01 02:07:00 2013-08-01 02:00:00 2013-08-01 02:24:00   \n",
       "12 2013-08-01 02:13:00 2013-08-01 02:00:00 2013-08-01 02:34:00   \n",
       "13 2013-08-01 02:41:00 2013-08-01 02:30:00 2013-08-01 02:49:00   \n",
       "14 2013-08-01 03:35:00 2013-08-01 03:30:00 2013-08-01 04:14:00   \n",
       "15 2013-08-01 03:36:00 2013-08-01 03:30:00 2013-08-01 04:14:00   \n",
       "16 2013-08-01 03:43:00 2013-08-01 03:30:00 2013-08-01 04:12:00   \n",
       "17 2013-08-01 03:42:00 2013-08-01 03:30:00 2013-08-01 04:12:00   \n",
       "18 2013-08-01 03:45:00 2013-08-01 04:00:00 2013-08-01 04:12:00   \n",
       "19 2013-08-01 03:49:00 2013-08-01 04:00:00 2013-08-01 04:02:00   \n",
       "\n",
       "          stophalfhour  \n",
       "0  2013-08-01 00:30:00  \n",
       "1  2013-08-01 00:30:00  \n",
       "2  2013-08-01 03:30:00  \n",
       "3  2013-08-01 03:30:00  \n",
       "4  2013-08-01 02:30:00  \n",
       "5  2013-08-01 02:30:00  \n",
       "6  2013-08-01 01:30:00  \n",
       "7  2013-08-01 01:30:00  \n",
       "8  2013-08-01 01:30:00  \n",
       "9  2013-08-01 01:30:00  \n",
       "10 2013-08-01 02:00:00  \n",
       "11 2013-08-01 02:30:00  \n",
       "12 2013-08-01 02:30:00  \n",
       "13 2013-08-01 03:00:00  \n",
       "14 2013-08-01 04:00:00  \n",
       "15 2013-08-01 04:00:00  \n",
       "16 2013-08-01 04:00:00  \n",
       "17 2013-08-01 04:00:00  \n",
       "18 2013-08-01 04:00:00  \n",
       "19 2013-08-01 04:00:00  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips[[\"starttime\", \"starthalfhour\", \"stoptime\", \"stophalfhour\"]].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Use the datetime library's .dayofweek method to add the day of the week (i.e. Monday, Tuesday,...) to the dataframe for each trip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>starthalfhour</th>\n",
       "      <th>stophalfhour</th>\n",
       "      <th>day_of_the_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134677</td>\n",
       "      <td>2013-08-01 00:13:00</td>\n",
       "      <td>2013-08-01 00:28:00</td>\n",
       "      <td>135</td>\n",
       "      <td>864</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Michigan Ave &amp; Oak St</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Ogden Ave &amp; Chicago Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 00:00:00</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134679</td>\n",
       "      <td>2013-08-01 00:28:00</td>\n",
       "      <td>2013-08-01 00:41:00</td>\n",
       "      <td>135</td>\n",
       "      <td>767</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Ogden Ave &amp; Chicago Ave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Congress Pkwy &amp; Ogden Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>134682</td>\n",
       "      <td>2013-08-01 00:57:00</td>\n",
       "      <td>2013-08-01 03:42:00</td>\n",
       "      <td>183</td>\n",
       "      <td>9920</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Lake Shore Dr &amp; Ohio St</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Michigan Ave &amp; Lake St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>134683</td>\n",
       "      <td>2013-08-01 00:57:00</td>\n",
       "      <td>2013-08-01 03:43:00</td>\n",
       "      <td>512</td>\n",
       "      <td>9931</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Lake Shore Dr &amp; Ohio St</td>\n",
       "      <td>309.0</td>\n",
       "      <td>Indiana Ave &amp; Roosevelt Rd</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134684</td>\n",
       "      <td>2013-08-01 00:58:00</td>\n",
       "      <td>2013-08-01 02:32:00</td>\n",
       "      <td>656</td>\n",
       "      <td>5678</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 02:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trip_id           starttime            stoptime  bikeid  tripduration  \\\n",
       "0   134677 2013-08-01 00:13:00 2013-08-01 00:28:00     135           864   \n",
       "1   134679 2013-08-01 00:28:00 2013-08-01 00:41:00     135           767   \n",
       "2   134682 2013-08-01 00:57:00 2013-08-01 03:42:00     183          9920   \n",
       "3   134683 2013-08-01 00:57:00 2013-08-01 03:43:00     512          9931   \n",
       "4   134684 2013-08-01 00:58:00 2013-08-01 02:32:00     656          5678   \n",
       "\n",
       "   from_station_id            from_station_name  to_station_id  \\\n",
       "0             42.0        Michigan Ave & Oak St           44.0   \n",
       "1             44.0      Ogden Ave & Chicago Ave            NaN   \n",
       "2            337.0      Lake Shore Dr & Ohio St           43.0   \n",
       "3            337.0      Lake Shore Dr & Ohio St          309.0   \n",
       "4             68.0  Western Ave & Winnebago Ave           68.0   \n",
       "\n",
       "               to_station_name  usertype gender  birthyear  \\\n",
       "0      Ogden Ave & Chicago Ave  Customer    NaN        NaN   \n",
       "1    Congress Pkwy & Ogden Ave  Customer    NaN        NaN   \n",
       "2       Michigan Ave & Lake St  Customer    NaN        NaN   \n",
       "3   Indiana Ave & Roosevelt Rd  Customer    NaN        NaN   \n",
       "4  Western Ave & Winnebago Ave  Customer    NaN        NaN   \n",
       "\n",
       "        starthalfhour        stophalfhour  day_of_the_week  \n",
       "0 2013-08-01 00:00:00 2013-08-01 00:30:00                3  \n",
       "1 2013-08-01 00:30:00 2013-08-01 00:30:00                3  \n",
       "2 2013-08-01 01:00:00 2013-08-01 03:30:00                3  \n",
       "3 2013-08-01 01:00:00 2013-08-01 03:30:00                3  \n",
       "4 2013-08-01 01:00:00 2013-08-01 02:30:00                3  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips[\"day_of_the_week\"] = trips.starttime.dt.dayofweek\n",
    "trips.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Groupby day of the week and hours and count the number of trips. Use the unstack method on the grouped frame and plot the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# As I see it, this is one of the examples for which the solution has already been provided.\n",
    "\n",
    "weekdays = trips['starttime'].apply(lambda d: d.dayofweek)\n",
    "hours = trips['starttime'].apply(lambda d: d.hour)\n",
    "by_weekday_hour = trips.groupby([weekdays, hours])['trip_id'].count()\n",
    "by_weekday_hour.index.names = ['weekday', 'hour'] # rename MultiIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dmF1ERERc7Oi+PUx9/WVCYytz3ePP4u3j6+5IIhelevNWDB39KlNeHc3E5x6n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tjfi07qc092r+Us4wkmWZ9JWrSP7xR0wqVsBr/nxUbm7P/zwPPFecEpNTFIpjcv/5ueJ7i25Z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\nFH3/QwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1ed5cb390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_weekday_hour.unstack(0).plot(figsize=(16,8))\n",
    "plt.title('Trips by weekday hour')\n",
    "plt.ylabel('# of trips started')\n",
    "plt.xlabel('Hour of day')\n",
    "plt.xticks(range(24))\n",
    "plt.xlim(0, 23);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Investigate the relationship between station capacity and traffic. Which two stations would you expand if you had the budget?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stations = pd.read_csv('Divvy_Stations_2013.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>dpcapacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State St &amp; Harrison St</td>\n",
       "      <td>41.873958</td>\n",
       "      <td>-87.627739</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wilton Ave &amp; Diversey Pkwy</td>\n",
       "      <td>41.932500</td>\n",
       "      <td>-87.652681</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morgan St &amp; 18th St</td>\n",
       "      <td>41.858086</td>\n",
       "      <td>-87.651073</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Racine Ave &amp; 18th St</td>\n",
       "      <td>41.858181</td>\n",
       "      <td>-87.656487</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wood St &amp; North Ave</td>\n",
       "      <td>41.910329</td>\n",
       "      <td>-87.672516</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name   latitude  longitude  dpcapacity\n",
       "0      State St & Harrison St  41.873958 -87.627739          19\n",
       "1  Wilton Ave & Diversey Pkwy  41.932500 -87.652681          19\n",
       "2         Morgan St & 18th St  41.858086 -87.651073          15\n",
       "3        Racine Ave & 18th St  41.858181 -87.656487          15\n",
       "4         Wood St & North Ave  41.910329 -87.672516          15"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>starthalfhour</th>\n",
       "      <th>stophalfhour</th>\n",
       "      <th>day_of_the_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134677</td>\n",
       "      <td>2013-08-01 00:13:00</td>\n",
       "      <td>2013-08-01 00:28:00</td>\n",
       "      <td>135</td>\n",
       "      <td>864</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Michigan Ave &amp; Oak St</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Ogden Ave &amp; Chicago Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 00:00:00</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134679</td>\n",
       "      <td>2013-08-01 00:28:00</td>\n",
       "      <td>2013-08-01 00:41:00</td>\n",
       "      <td>135</td>\n",
       "      <td>767</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Ogden Ave &amp; Chicago Ave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Congress Pkwy &amp; Ogden Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "      <td>2013-08-01 00:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>134682</td>\n",
       "      <td>2013-08-01 00:57:00</td>\n",
       "      <td>2013-08-01 03:42:00</td>\n",
       "      <td>183</td>\n",
       "      <td>9920</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Lake Shore Dr &amp; Ohio St</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Michigan Ave &amp; Lake St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>134683</td>\n",
       "      <td>2013-08-01 00:57:00</td>\n",
       "      <td>2013-08-01 03:43:00</td>\n",
       "      <td>512</td>\n",
       "      <td>9931</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Lake Shore Dr &amp; Ohio St</td>\n",
       "      <td>309.0</td>\n",
       "      <td>Indiana Ave &amp; Roosevelt Rd</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 03:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134684</td>\n",
       "      <td>2013-08-01 00:58:00</td>\n",
       "      <td>2013-08-01 02:32:00</td>\n",
       "      <td>656</td>\n",
       "      <td>5678</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Western Ave &amp; Winnebago Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-01 01:00:00</td>\n",
       "      <td>2013-08-01 02:30:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trip_id           starttime            stoptime  bikeid  tripduration  \\\n",
       "0   134677 2013-08-01 00:13:00 2013-08-01 00:28:00     135           864   \n",
       "1   134679 2013-08-01 00:28:00 2013-08-01 00:41:00     135           767   \n",
       "2   134682 2013-08-01 00:57:00 2013-08-01 03:42:00     183          9920   \n",
       "3   134683 2013-08-01 00:57:00 2013-08-01 03:43:00     512          9931   \n",
       "4   134684 2013-08-01 00:58:00 2013-08-01 02:32:00     656          5678   \n",
       "\n",
       "   from_station_id            from_station_name  to_station_id  \\\n",
       "0             42.0        Michigan Ave & Oak St           44.0   \n",
       "1             44.0      Ogden Ave & Chicago Ave            NaN   \n",
       "2            337.0      Lake Shore Dr & Ohio St           43.0   \n",
       "3            337.0      Lake Shore Dr & Ohio St          309.0   \n",
       "4             68.0  Western Ave & Winnebago Ave           68.0   \n",
       "\n",
       "               to_station_name  usertype gender  birthyear  \\\n",
       "0      Ogden Ave & Chicago Ave  Customer    NaN        NaN   \n",
       "1    Congress Pkwy & Ogden Ave  Customer    NaN        NaN   \n",
       "2       Michigan Ave & Lake St  Customer    NaN        NaN   \n",
       "3   Indiana Ave & Roosevelt Rd  Customer    NaN        NaN   \n",
       "4  Western Ave & Winnebago Ave  Customer    NaN        NaN   \n",
       "\n",
       "        starthalfhour        stophalfhour  day_of_the_week  \n",
       "0 2013-08-01 00:00:00 2013-08-01 00:30:00                3  \n",
       "1 2013-08-01 00:30:00 2013-08-01 00:30:00                3  \n",
       "2 2013-08-01 01:00:00 2013-08-01 03:30:00                3  \n",
       "3 2013-08-01 01:00:00 2013-08-01 03:30:00                3  \n",
       "4 2013-08-01 01:00:00 2013-08-01 02:30:00                3  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['trip_id', 'starttime', 'stoptime', 'bikeid', 'tripduration',\n",
      "       'from_station_id', 'from_station_name', 'to_station_id',\n",
      "       'to_station_name', 'usertype', 'gender', 'birthyear', 'starthalfhour',\n",
      "       'stophalfhour', 'day_of_the_week'],\n",
      "      dtype='object')\n",
      "Index(['name', 'latitude', 'longitude', 'dpcapacity'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(trips.columns)\n",
    "print(stations.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "trips = pd.merge(trips, stations, how='left', left_on='from_station_name', right_on='name')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trip_id', 'starttime', 'stoptime', 'bikeid', 'tripduration',\n",
       "       'from_station_id', 'from_station_name', 'to_station_id',\n",
       "       'to_station_name', 'usertype', 'gender', 'birthyear', 'starthalfhour',\n",
       "       'stophalfhour', 'day_of_the_week', 'name', 'latitude', 'longitude',\n",
       "       'dpcapacity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trips_started</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>620054072</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>397856862</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>396592868</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410948581</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>394753377</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trips_started  capacity\n",
       "0      620054072        43\n",
       "1      397856862        27\n",
       "2      396592868        27\n",
       "3      410948581        15\n",
       "4      394753377        31"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caputil = trips.groupby([\"from_station_id\"]).agg({\"trip_id\": np.sum, \"dpcapacity\": np.mean})\n",
    "caputil = pd.DataFrame(caputil)\n",
    "caputil.reset_index(level=caputil.index.names, inplace=True)\n",
    "caputil = caputil.rename(columns={'trip_id': 'trips_started', 'dpcapacity': 'capacity'})\n",
    "caputil.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Drk3bmDttohOCWRWlqtbt7LyyLlW1+m9m7GhaUpA0H7gReG9EPJz3+02f3OFfbLNhuKrV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\nqyHSjPwGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23bb468a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=caputil.plot.scatter(x = \"capacity\",y='trips_started')\n",
    "ax.set_title('Station capacities and trips started');\n",
    "ax.set_xlabel(\"Station capacity\")\n",
    "ax.set_ylabel(\"Number of trips\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "caputil[\"tripspercap\"] = caputil[\"trips_started\"] / caputil[\"capacity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trips_started</th>\n",
       "      <th>capacity</th>\n",
       "      <th>tripspercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1211580292</td>\n",
       "      <td>11</td>\n",
       "      <td>1.101437e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1023355649</td>\n",
       "      <td>15</td>\n",
       "      <td>6.822371e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>775969822</td>\n",
       "      <td>15</td>\n",
       "      <td>5.173132e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>970199731</td>\n",
       "      <td>19</td>\n",
       "      <td>5.106314e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>711718719</td>\n",
       "      <td>15</td>\n",
       "      <td>4.744791e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1576603099</td>\n",
       "      <td>35</td>\n",
       "      <td>4.504580e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>517217731</td>\n",
       "      <td>15</td>\n",
       "      <td>3.448118e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1184471371</td>\n",
       "      <td>35</td>\n",
       "      <td>3.384204e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>478095841</td>\n",
       "      <td>15</td>\n",
       "      <td>3.187306e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>469102415</td>\n",
       "      <td>15</td>\n",
       "      <td>3.127349e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>707973619</td>\n",
       "      <td>23</td>\n",
       "      <td>3.078146e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>579942878</td>\n",
       "      <td>19</td>\n",
       "      <td>3.052331e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>457803241</td>\n",
       "      <td>15</td>\n",
       "      <td>3.052022e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>330896854</td>\n",
       "      <td>11</td>\n",
       "      <td>3.008153e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>546207731</td>\n",
       "      <td>19</td>\n",
       "      <td>2.874778e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>653004167</td>\n",
       "      <td>23</td>\n",
       "      <td>2.839149e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>423249661</td>\n",
       "      <td>15</td>\n",
       "      <td>2.821664e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410948581</td>\n",
       "      <td>15</td>\n",
       "      <td>2.739657e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>506795472</td>\n",
       "      <td>19</td>\n",
       "      <td>2.667345e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>385152647</td>\n",
       "      <td>15</td>\n",
       "      <td>2.567684e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>370107451</td>\n",
       "      <td>15</td>\n",
       "      <td>2.467383e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>270385194</td>\n",
       "      <td>11</td>\n",
       "      <td>2.458047e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>547933876</td>\n",
       "      <td>23</td>\n",
       "      <td>2.382321e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>632278759</td>\n",
       "      <td>27</td>\n",
       "      <td>2.341773e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>706331521</td>\n",
       "      <td>31</td>\n",
       "      <td>2.278489e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>404313862</td>\n",
       "      <td>19</td>\n",
       "      <td>2.127968e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>383166864</td>\n",
       "      <td>19</td>\n",
       "      <td>2.016668e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>460253341</td>\n",
       "      <td>23</td>\n",
       "      <td>2.001101e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>369593500</td>\n",
       "      <td>19</td>\n",
       "      <td>1.945229e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>369196282</td>\n",
       "      <td>19</td>\n",
       "      <td>1.943138e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>18200773</td>\n",
       "      <td>11</td>\n",
       "      <td>1.654616e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>22915421</td>\n",
       "      <td>15</td>\n",
       "      <td>1.527695e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>21202401</td>\n",
       "      <td>15</td>\n",
       "      <td>1.413493e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>19883293</td>\n",
       "      <td>15</td>\n",
       "      <td>1.325553e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>19031865</td>\n",
       "      <td>15</td>\n",
       "      <td>1.268791e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>13085678</td>\n",
       "      <td>11</td>\n",
       "      <td>1.189607e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>20864968</td>\n",
       "      <td>19</td>\n",
       "      <td>1.098156e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>16202206</td>\n",
       "      <td>15</td>\n",
       "      <td>1.080147e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>11874641</td>\n",
       "      <td>11</td>\n",
       "      <td>1.079513e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>16037691</td>\n",
       "      <td>15</td>\n",
       "      <td>1.069179e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>11273846</td>\n",
       "      <td>11</td>\n",
       "      <td>1.024895e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>15215790</td>\n",
       "      <td>15</td>\n",
       "      <td>1.014386e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>15050872</td>\n",
       "      <td>15</td>\n",
       "      <td>1.003391e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>14757126</td>\n",
       "      <td>15</td>\n",
       "      <td>9.838084e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>9844078</td>\n",
       "      <td>11</td>\n",
       "      <td>8.949162e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>11445167</td>\n",
       "      <td>15</td>\n",
       "      <td>7.630111e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>11315122</td>\n",
       "      <td>15</td>\n",
       "      <td>7.543415e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>9813128</td>\n",
       "      <td>15</td>\n",
       "      <td>6.542085e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>10632340</td>\n",
       "      <td>19</td>\n",
       "      <td>5.595968e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>8195227</td>\n",
       "      <td>15</td>\n",
       "      <td>5.463485e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>7587221</td>\n",
       "      <td>15</td>\n",
       "      <td>5.058147e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>9116318</td>\n",
       "      <td>19</td>\n",
       "      <td>4.798062e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>5927949</td>\n",
       "      <td>15</td>\n",
       "      <td>3.951966e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7494143</td>\n",
       "      <td>19</td>\n",
       "      <td>3.944286e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>3664653</td>\n",
       "      <td>15</td>\n",
       "      <td>2.443102e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>3406057</td>\n",
       "      <td>15</td>\n",
       "      <td>2.270705e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2434389</td>\n",
       "      <td>15</td>\n",
       "      <td>1.622926e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1421065</td>\n",
       "      <td>19</td>\n",
       "      <td>7.479289e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>348703</td>\n",
       "      <td>15</td>\n",
       "      <td>2.324687e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>266785</td>\n",
       "      <td>15</td>\n",
       "      <td>1.778567e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trips_started  capacity   tripspercap\n",
       "180     1211580292        11  1.101437e+08\n",
       "33      1023355649        15  6.822371e+07\n",
       "83       775969822        15  5.173132e+07\n",
       "23       970199731        19  5.106314e+07\n",
       "199      711718719        15  4.744791e+07\n",
       "18      1576603099        35  4.504580e+07\n",
       "32       517217731        15  3.448118e+07\n",
       "6       1184471371        35  3.384204e+07\n",
       "39       478095841        15  3.187306e+07\n",
       "81       469102415        15  3.127349e+07\n",
       "34       707973619        23  3.078146e+07\n",
       "25       579942878        19  3.052331e+07\n",
       "194      457803241        15  3.052022e+07\n",
       "138      330896854        11  3.008153e+07\n",
       "193      546207731        19  2.874778e+07\n",
       "40       653004167        23  2.839149e+07\n",
       "146      423249661        15  2.821664e+07\n",
       "3        410948581        15  2.739657e+07\n",
       "16       506795472        19  2.667345e+07\n",
       "161      385152647        15  2.567684e+07\n",
       "46       370107451        15  2.467383e+07\n",
       "195      270385194        11  2.458047e+07\n",
       "27       547933876        23  2.382321e+07\n",
       "15       632278759        27  2.341773e+07\n",
       "8        706331521        31  2.278489e+07\n",
       "96       404313862        19  2.127968e+07\n",
       "198      383166864        19  2.016668e+07\n",
       "181      460253341        23  2.001101e+07\n",
       "216      369593500        19  1.945229e+07\n",
       "116      369196282        19  1.943138e+07\n",
       "..             ...       ...           ...\n",
       "184       18200773        11  1.654616e+06\n",
       "74        22915421        15  1.527695e+06\n",
       "148       21202401        15  1.413493e+06\n",
       "87        19883293        15  1.325553e+06\n",
       "176       19031865        15  1.268791e+06\n",
       "136       13085678        11  1.189607e+06\n",
       "150       20864968        19  1.098156e+06\n",
       "71        16202206        15  1.080147e+06\n",
       "157       11874641        11  1.079513e+06\n",
       "77        16037691        15  1.069179e+06\n",
       "191       11273846        11  1.024895e+06\n",
       "207       15215790        15  1.014386e+06\n",
       "218       15050872        15  1.003391e+06\n",
       "90        14757126        15  9.838084e+05\n",
       "182        9844078        11  8.949162e+05\n",
       "132       11445167        15  7.630111e+05\n",
       "192       11315122        15  7.543415e+05\n",
       "109        9813128        15  6.542085e+05\n",
       "217       10632340        19  5.595968e+05\n",
       "205        8195227        15  5.463485e+05\n",
       "183        7587221        15  5.058147e+05\n",
       "62         9116318        19  4.798062e+05\n",
       "65         5927949        15  3.951966e+05\n",
       "97         7494143        19  3.944286e+05\n",
       "149        3664653        15  2.443102e+05\n",
       "134        3406057        15  2.270705e+05\n",
       "173        2434389        15  1.622926e+05\n",
       "43         1421065        19  7.479289e+04\n",
       "158         348703        15  2.324687e+04\n",
       "131         266785        15  1.778567e+04\n",
       "\n",
       "[219 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caputil.sort_values(\"tripspercap\", ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: station 180 and 33 should see some capacity additions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Which stations are sources and which stations are sinks? i.e. which stations have much more departures than arrivals, and vice versa. Replot the geographic locations of the stations and using color or size visualize which stations are sources and sinks. Repeat the exercise for only weekday and only weekend trips. Do you see a difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trip_id', 'starttime', 'stoptime', 'bikeid', 'tripduration',\n",
       "       'from_station_id', 'from_station_name', 'to_station_id',\n",
       "       'to_station_name', 'usertype', 'gender', 'birthyear', 'starthalfhour',\n",
       "       'stophalfhour', 'day_of_the_week', 'name', 'latitude', 'longitude',\n",
       "       'dpcapacity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime, time\n",
    "days = trips[\"starttime\"].max() - trips[\"starttime\"].min()\n",
    "days = days.days+1\n",
    "days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trips.to_station_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>net_trips</th>\n",
       "      <th>station_name</th>\n",
       "      <th>source_station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.774194</td>\n",
       "      <td>900 W Harrison</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.161290</td>\n",
       "      <td>Aberdeen St &amp; Jackson Blvd</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.870968</td>\n",
       "      <td>Aberdeen St &amp; Madison St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.129032</td>\n",
       "      <td>Adler Planetarium</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.129032</td>\n",
       "      <td>Ashland Ave &amp; 21st St</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   net_trips                station_name  source_station\n",
       "0  -0.774194              900 W Harrison           False\n",
       "1  -1.161290  Aberdeen St & Jackson Blvd           False\n",
       "2   2.870968    Aberdeen St & Madison St            True\n",
       "3   0.129032           Adler Planetarium            True\n",
       "4  -0.129032       Ashland Ave & 21st St           False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nettrips = trips.groupby([\"to_station_name\"])['trip_id'].count() / days-trips.groupby([\"from_station_name\"])['trip_id'].count() / days\n",
    "#nettrips.reset_index()\n",
    "nettrips = pd.DataFrame(nettrips)\n",
    "nettrips[\"station_name\"] = nettrips.index\n",
    "nettrips.reset_index(level=nettrips.index.names, inplace=True)\n",
    "nettrips.columns = ['net_trips', 'station_name']\n",
    "nettrips[\"source_station\"] = nettrips[\"net_trips\"] >= 0\n",
    "\n",
    "nettrips.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'latitude', 'longitude', 'dpcapacity'], dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dailysinksandsources = pd.merge(stations, nettrips, how='inner', left_on='name', right_on='station_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>dpcapacity</th>\n",
       "      <th>net_trips</th>\n",
       "      <th>station_name</th>\n",
       "      <th>source_station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State St &amp; Harrison St</td>\n",
       "      <td>41.873958</td>\n",
       "      <td>-87.627739</td>\n",
       "      <td>19</td>\n",
       "      <td>2.935484</td>\n",
       "      <td>State St &amp; Harrison St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wilton Ave &amp; Diversey Pkwy</td>\n",
       "      <td>41.932500</td>\n",
       "      <td>-87.652681</td>\n",
       "      <td>19</td>\n",
       "      <td>6.064516</td>\n",
       "      <td>Wilton Ave &amp; Diversey Pkwy</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morgan St &amp; 18th St</td>\n",
       "      <td>41.858086</td>\n",
       "      <td>-87.651073</td>\n",
       "      <td>15</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>Morgan St &amp; 18th St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Racine Ave &amp; 18th St</td>\n",
       "      <td>41.858181</td>\n",
       "      <td>-87.656487</td>\n",
       "      <td>15</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>Racine Ave &amp; 18th St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wood St &amp; North Ave</td>\n",
       "      <td>41.910329</td>\n",
       "      <td>-87.672516</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.645161</td>\n",
       "      <td>Wood St &amp; North Ave</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name   latitude  longitude  dpcapacity  net_trips  \\\n",
       "0      State St & Harrison St  41.873958 -87.627739          19   2.935484   \n",
       "1  Wilton Ave & Diversey Pkwy  41.932500 -87.652681          19   6.064516   \n",
       "2         Morgan St & 18th St  41.858086 -87.651073          15   0.580645   \n",
       "3        Racine Ave & 18th St  41.858181 -87.656487          15   0.387097   \n",
       "4         Wood St & North Ave  41.910329 -87.672516          15  -0.645161   \n",
       "\n",
       "                 station_name  source_station  \n",
       "0      State St & Harrison St            True  \n",
       "1  Wilton Ave & Diversey Pkwy            True  \n",
       "2         Morgan St & 18th St            True  \n",
       "3        Racine Ave & 18th St            True  \n",
       "4         Wood St & North Ave           False  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dailysinksandsources.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "dailysources = dailysinksandsources[dailysinksandsources[\"source_station\"] == True]\n",
    "dailysinks = dailysinksandsources[dailysinksandsources[\"source_station\"] == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>dpcapacity</th>\n",
       "      <th>net_trips</th>\n",
       "      <th>station_name</th>\n",
       "      <th>source_station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State St &amp; Harrison St</td>\n",
       "      <td>41.873958</td>\n",
       "      <td>-87.627739</td>\n",
       "      <td>19</td>\n",
       "      <td>2.935484</td>\n",
       "      <td>State St &amp; Harrison St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wilton Ave &amp; Diversey Pkwy</td>\n",
       "      <td>41.932500</td>\n",
       "      <td>-87.652681</td>\n",
       "      <td>19</td>\n",
       "      <td>6.064516</td>\n",
       "      <td>Wilton Ave &amp; Diversey Pkwy</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morgan St &amp; 18th St</td>\n",
       "      <td>41.858086</td>\n",
       "      <td>-87.651073</td>\n",
       "      <td>15</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>Morgan St &amp; 18th St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Racine Ave &amp; 18th St</td>\n",
       "      <td>41.858181</td>\n",
       "      <td>-87.656487</td>\n",
       "      <td>15</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>Racine Ave &amp; 18th St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wood St &amp; Division St</td>\n",
       "      <td>41.903320</td>\n",
       "      <td>-87.672730</td>\n",
       "      <td>15</td>\n",
       "      <td>2.483871</td>\n",
       "      <td>Wood St &amp; Division St</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name   latitude  longitude  dpcapacity  net_trips  \\\n",
       "0      State St & Harrison St  41.873958 -87.627739          19   2.935484   \n",
       "1  Wilton Ave & Diversey Pkwy  41.932500 -87.652681          19   6.064516   \n",
       "2         Morgan St & 18th St  41.858086 -87.651073          15   0.580645   \n",
       "3        Racine Ave & 18th St  41.858181 -87.656487          15   0.387097   \n",
       "5       Wood St & Division St  41.903320 -87.672730          15   2.483871   \n",
       "\n",
       "                 station_name  source_station  \n",
       "0      State St & Harrison St            True  \n",
       "1  Wilton Ave & Diversey Pkwy            True  \n",
       "2         Morgan St & 18th St            True  \n",
       "3        Racine Ave & 18th St            True  \n",
       "5       Wood St & Division St            True  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dailysources.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nmzezbdu2YpEhGFJTU5k1axZLlizJl6aOi5GqVatSr149qlevzscff1wCUlrK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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23bb4cce240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(dailysources['longitude'], dailysources['latitude'], s = dailysources['net_trips']*5, c = \"k\", label = \"sinks\")\n",
    "ax.scatter(dailysinks['longitude'], dailysinks['latitude'], s = dailysinks['net_trips']*(-5), c = \"r\", label = \"sources\")\n",
    "ax.grid(True)\n",
    "plt.legend(loc='lower left')\n",
    "plt.title(\"Source and sink stations in the Chicago Divvy network\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106897"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips_weekdays = trips[trips['day_of_the_week'] < 5]\n",
    "len(trips_weekdays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nettrips_weekdays = trips_weekdays.groupby([\"to_station_name\"])['trip_id'].count() / days-trips_weekdays.groupby([\"from_station_name\"])['trip_id'].count() / days\n",
    "#nettrips_weekdays.reset_index()\n",
    "nettrips_weekdays = pd.DataFrame(nettrips_weekdays)\n",
    "nettrips_weekdays[\"station_name\"] = nettrips_weekdays.index\n",
    "nettrips_weekdays.reset_index(level=nettrips_weekdays.index.names, inplace=True)\n",
    "nettrips_weekdays.columns = ['net_trips_weekdays', 'station_name']\n",
    "nettrips_weekdays[\"source_station\"] = nettrips_weekdays[\"net_trips_weekdays\"] >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekdaysinksandsources = pd.merge(stations, nettrips_weekdays, how='inner', left_on='name', right_on='station_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekdaysources = weekdaysinksandsources[weekdaysinksandsources[\"source_station\"] == True]\n",
    "weekdaysinks = weekdaysinksandsources[weekdaysinksandsources[\"source_station\"] == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n6NmzJ61bt2bdunU+lVm2bBnTp0/n1KlTZGRkkJSUxJo1a/L1evbs2cOhQ4dY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\nbUpMTAxhYWH07du3wPzt2rX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uqgTM0FFYRgbfA3NDQzn1888+yXPnnXeyatUqrrnmGlq1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\nCmOMMT5ZoTDGGOPT/wd7BUlB6XZEPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23bb53d6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(weekdaysources['longitude'], weekdaysources['latitude'], s = weekdaysources['net_trips_weekdays']*5, c = \"k\", label = \"sinks\")\n",
    "ax.scatter(weekdaysinks['longitude'], weekdaysinks['latitude'], s = weekdaysinks['net_trips_weekdays']*(-5), c = \"r\", label = \"sources\")\n",
    "ax.grid(True)\n",
    "plt.legend(loc='lower left')\n",
    "plt.title(\"Source and sink stations in the Chicago Divvy network\\n- weekdays only -\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63611"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips_weekends = trips[trips['day_of_the_week'] > 4]\n",
    "len(trips_weekends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nettrips_weekends = trips_weekends.groupby([\"to_station_name\"])['trip_id'].count() / days-trips_weekends.groupby([\"from_station_name\"])['trip_id'].count() / days\n",
    "#nettrips_weekends.reset_index()\n",
    "nettrips_weekends = pd.DataFrame(nettrips_weekends)\n",
    "nettrips_weekends[\"station_name\"] = nettrips_weekends.index\n",
    "nettrips_weekends.reset_index(level=nettrips_weekends.index.names, inplace=True)\n",
    "nettrips_weekends.columns = ['net_trips_weekends', 'station_name']\n",
    "nettrips_weekends[\"source_station\"] = nettrips_weekends[\"net_trips_weekends\"] >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekendsinksandsources = pd.merge(stations, nettrips_weekends, how='inner', left_on='name', right_on='station_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekendsources = weekendsinksandsources[weekendsinksandsources[\"source_station\"] == True]\n",
    "weekendsinks = weekendsinksandsources[weekendsinksandsources[\"source_station\"] == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MsENPllz85z//Ye/evVx33XWu05x33nmcd955+S982DCzBDUz0/QsguCNN94g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23bb54d4668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(weekendsources['longitude'], weekendsources['latitude'], s = weekendsources['net_trips_weekends']*5, c = \"k\", label = \"sinks\")\n",
    "ax.scatter(weekendsinks['longitude'], weekendsinks['latitude'], s = weekendsinks['net_trips_weekends']*(-5), c = \"r\", label = \"sources\")\n",
    "ax.grid(True)\n",
    "plt.legend(loc='lower left')\n",
    "plt.title(\"Source and sink stations in the Chicago Divvy network\\n- weekends only -\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference mainly comes in the long(-87.64 - -87.64) lat (41.875 - 41.890) area where you have where the source stations see less negative traffic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: I defined sinks and sources the other way around in the code and I just realized it at the end. I decided to switch the labels and keep the code unchanged. This way the variable names are somehow misleading but it does not affect the visual representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}