{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "  var password,\n",
       "      teacher_mode,\n",
       "      isHtml;\n",
       "      \n",
       "  var class_output,\n",
       "      class_input,\n",
       "      class_answer;\n",
       "      \n",
       "  function code_toggle(e) {\n",
       "    var orig_e = e;\n",
       "    while (!e.closest(class_output).previousElementSibling.classList.contains(class_input)) {\n",
       "      e = e.closest(class_output).previousElementSibling;\n",
       "    }\n",
       "    var target = e.closest(class_output).previousElementSibling;\n",
       "    if (target.getAttribute(\"style\") == \"\" || target.getAttribute(\"style\") == null) {\n",
       "      target.style.display = \"none\";\n",
       "      orig_e.innerHTML = 'show code';\n",
       "    }\n",
       "    else {\n",
       "      target.style.removeProperty(\"display\");\n",
       "      orig_e.innerHTML = 'hide code';\n",
       "    }\n",
       "  }\n",
       "  \n",
       "  function hide_answer(e) {\n",
       "    var target = e.closest(class_answer).nextElementSibling;\n",
       "    //e.closest(class_output).previousElementSibling.style.display = \"none\";\n",
       "    if (target.getAttribute(\"style\") == \"\" || target.getAttribute(\"style\") == null) {\n",
       "      target.style.display = \"none\";\n",
       "      e.innerHTML = 'show answer';\n",
       "    }\n",
       "    else if (teacher_mode) {\n",
       "        e.innerHTML = 'hide answer';\n",
       "        target.style.removeProperty(\"display\");\n",
       "    }\n",
       "  }\n",
       "  \n",
       "  function done() { \n",
       "    document.getElementById(\"popup\").style.display = \"none\";\n",
       "    var input = document.getElementById(\"password\").value;\n",
       "    if (input==password) { teacher_mode=1; alert(\"Unlocked!\");}\n",
       "    else { teacher_mode=0; alert(\"Wrong password!\");}       \n",
       "  };\n",
       "\n",
       "  function unlock() {\n",
       "    document.getElementById(\"popup\").style.display = \"block\";\n",
       "  }\n",
       "  \n",
       "  $(document).ready(function() {\n",
       "    $.ajax({\n",
       "      type: \"GET\",  \n",
       "      url: \"https://raw.githubusercontent.com/ming-zhao/ming-zhao.github.io/master/data/course.csv\",\n",
       "      dataType: \"text\",       \n",
       "      success: function(data)  \n",
       "      {\n",
       "        var items = data.split(',');\n",
       "        var url = window.location.pathname;\n",
       "        var filename = url.substring(url.lastIndexOf('/')+1);\n",
       "        password='123';\n",
       "        for (var i = 0, len = items.length; i < len; ++i) {\n",
       "            if (filename.includes(items[i].trim()) && i%2==0 && i<items.length) {\n",
       "                password=items[i+1].trim();\n",
       "                break;\n",
       "            }\n",
       "        }\n",
       "        var code_blocks = document.getElementsByClassName('nbinput docutils container');\n",
       "        if (code_blocks[0]==null) { \n",
       "            isHtml=0;\n",
       "            code_blocks = document.getElementsByClassName('input');\n",
       "            class_output=\".output_wrapper\";\n",
       "            class_input=\"input\";\n",
       "            class_answer='.cell';\n",
       "        }\n",
       "        else { \n",
       "            isHtml=1;\n",
       "            class_output=\".nboutput\";\n",
       "            class_input=\"nbinput\";\n",
       "            class_answer=\".nboutput\";\n",
       "        }\n",
       "        \n",
       "        for (var i = 0, len = code_blocks.length; i < len; ++i) {\n",
       "          if (\n",
       "              code_blocks[i].innerHTML.indexOf(\"toggle\") !== -1 \n",
       "              || code_blocks[i].innerHTML.indexOf(\"button onclick\") !== -1\n",
       "             ) {\n",
       "            code_blocks[i].style.display = \"none\";\n",
       "          }\n",
       "        }\n",
       "        for (var i = 0, len = code_blocks.length; i < len; ++i) {\n",
       "          if (code_blocks[i].innerHTML.indexOf(\"hide_answer\") !== -1) {\n",
       "            code_blocks[i].style.display = \"none\";\n",
       "            if (isHtml) {\n",
       "              code_blocks[i].nextElementSibling.nextElementSibling.style.display = \"none\";\n",
       "            }\n",
       "            else{\n",
       "              code_blocks[i].closest(class_answer).nextElementSibling.style.display = \"none\";\n",
       "            }            \n",
       "          }\n",
       "        }\n",
       "        \n",
       "        \n",
       "      }\n",
       "    });\n",
       "  });\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"popup\" style=\"padding-bottom:5px; display:none;\">\n",
       "    <div>Enter Password:</div>\n",
       "    <input id=\"password\" type=\"password\"/>\n",
       "    <button onclick=\"done()\" style=\"border-radius: 12px;\">Submit</button>    \n",
       "</div>\n",
       "<button onclick=\"unlock()\" style=\"border-radius: 12px;\">Unclock</button>\n",
       "<a href=\"#\" onclick=\"code_toggle(this); return false;\">show code</a>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%run ../initscript.py\n",
    "HTML(\"\"\"\n",
    "<div id=\"popup\" style=\"padding-bottom:5px; display:none;\">\n",
    "    <div>Enter Password:</div>\n",
    "    <input id=\"password\" type=\"password\"/>\n",
    "    <button onclick=\"done()\" style=\"border-radius: 12px;\">Submit</button>    \n",
    "</div>\n",
    "<button onclick=\"unlock()\" style=\"border-radius: 12px;\">Unclock</button>\n",
    "<a href=\"#\" onclick=\"code_toggle(this); return false;\">show code</a>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vhizvgsQ9JI7"
   },
   "source": [
    "# Exploring Data\n",
    "\n",
    "Data and information are so prevalent in our lives today, that it is known as the \"Information Age\". Being literate today means not just being able to read, but being able to understand the massive amount of information thrown at us every day – much of it on the computer. Statistics is the science of making effective use of numerical data. It deals with all aspects of data, including the collection, analysis and interpretation of data. However, it can be easily misinterpreted and manipulated if we don't do it carefully. As Mark Twain said\n",
    "<center>\n",
    "\"There are lies, damned lies, and statistics.\"\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "39llJy5VKejU"
   },
   "source": [
    "## Percentage\n",
    "\n",
    "Percentage is the most frequently used concept in business analytics because of many uncertainties in today's complex business environment. But many times people use it without really understanding its meaning.\n",
    "\n",
    "![pct](https://github.com/ming-zhao/Business-Analytics/raw/master/images/pct.png)\n",
    "\n",
    "\n",
    "Percentage is a relative terminology. It is always important to ask \"percentage of what\" as shown in this simple example. \n",
    "\n",
    "... of what?\n",
    "\n",
    "* Pay: &dollar;10,000 per month.\n",
    "\n",
    "* \"Sorry guys. You have to have a 10% pay cut.\"\n",
    "\n",
    "* Pay: &dollar;9,000 per month.\n",
    "\n",
    "* \"Now I can give you a 10% pay rise.\"\n",
    "\n",
    "* Pay: &dollar;9,900 per month.\n",
    "\n",
    "What does \"60% sure or confidence\" mean? This is about probability. If we flip a coin 100 times and see the head 60 times, then we could say that we are 60% sure that next toss will show a head. If someone says \"I have 60% confidence that this campaign will increase sales\", the statistical meaning is that if a decision maker can try 100 times under current business environment, s/he may see a sales increase for 60 times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:**\n",
    "\n",
    "There are 3 sequences (each of them has 10 symbols with 6 Xs and 4 Os):\n",
    "\n",
    "1) OXXOXOXOXX\n",
    "\n",
    "2) XXOXOOXXXO\n",
    "\n",
    "3) XOXXOXOXOX\n",
    "\n",
    "Predict the next symbol for those 3 sequences\n",
    "\n",
    "* A) 1-O, 2-X, 3-O\n",
    "* B) 1-X, 2-X, 3-X\n",
    "* C) 1-O, 2-O, 3-O\n",
    "* D) 1-X, 2-O, 3-X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <a href=\"#\" onclick=\"hide_answer(this); return false;\">show answer</a>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hide_answer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- B is correct. \n",
    "\n",
    "- An psychology experiment gives subjects a random series of Xs and Os and asks them to predict what the next one will be. For instance, they may see: <center>OXXOXOXOXOXXOOXXOXOXXXOXX</center>\n",
    "\n",
    "- Most people realize that there are slightly more Xs than Os — if you count, you'll see it's 60 percent Xs, 40 percent Os — so they guess X most of the time, but throw in some Os to reflect that balance. \n",
    "\n",
    "- However, if you want to maximize your chances of a correct prediction, you would always choose X. Then you would be right 60 percent of the time. \n",
    "\n",
    "- If you randomize 60/40, as most participants do, your prediction ends up being correct 52 percent of the time [Sixty percent of the time you choose X and are correct 60 percent of the time, while 40 percent of the time you choose O and are correct only 40 percent of the time. On average, this is $0.6^2 + 0.4^2 = 0.52$], only slightly better than if you had not  bothered to assess relative frequencies of Xs and Os and instead just guessed one or the other (50/50)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2:**\n",
    "\n",
    "The chance a baby will be a boy (or girl) is 50%. There are two hospitals:\n",
    "\n",
    "- A - 45 births per day\n",
    "- B - 15 births per day\n",
    "\n",
    "Which hospital would have more days when 60% or more of the babies born are boys?\n",
    "\n",
    "* A) Hospital A\n",
    "* B) Hospital B\n",
    "* C) Equal chance\n",
    "* D) Uncertain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <a href=\"#\" onclick=\"hide_answer(this); return false;\">show answer</a>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hide_answer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- B is correct. \n",
    "\n",
    "- The smaller hospital is correct because the larger the number of events (in this case, births), the likelier each daily outcome will be close to the average (in this case, 50 percent). \n",
    "\n",
    "- To see how this works, imagine you are flipping coins. You are more likely to get heads every time if you flip five coins than if you flip fifty coins. \n",
    "\n",
    "- Thus, the smaller hospital — precisely because it has fewer births — is more likely to have more extreme outcomes away from the average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GRWhHR58NU8X"
   },
   "source": [
    "## Average\n",
    "\n",
    "If you were a real-estate agent and trying to convince people to move into a particular neighborhood. You could, with perfect honesty and \"truthfulness\" tell different people that the average income in the neighborhood is: a), b) or c). \n",
    "\n",
    "![house](https://github.com/ming-zhao/Business-Analytics/raw/master/images/house.png)\n",
    "\n",
    "because we have mean, median and mode to characterize the central tendency.\n",
    "\n",
    "![avg](https://github.com/ming-zhao/Business-Analytics/raw/master/images/avg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U8-f3te5OW7I"
   },
   "source": [
    "## Data Visualization\n",
    "\n",
    "If your goal is to lie, cheat, manipulate, or mislead, **Graphical Displays** are your friend...\n",
    "\n",
    "![pie](https://github.com/ming-zhao/Business-Analytics/raw/master/images/pie.png)\n",
    "\n",
    "**Example 1:**\n",
    "\n",
    "![chart1](https://github.com/ming-zhao/Business-Analytics/raw/master/images/chart1.png)\n",
    "\n",
    "**Example 2:**\n",
    "\n",
    "![chart2](https://github.com/ming-zhao/Business-Analytics/raw/master/images/chart2.png)\n",
    "\n",
    "**Example 3:**\n",
    "\n",
    "![chart3](https://github.com/ming-zhao/Business-Analytics/raw/master/images/chart3.png)\n",
    "\n",
    "**Example 4:**\n",
    "\n",
    "![chart4](https://github.com/ming-zhao/Business-Analytics/raw/master/images/chart4.png)\n",
    "\n",
    "As \"Statistics is the art of never having to say you’re wrong\", I would like to recommend a book\n",
    "\n",
    "![book_lie](https://github.com/ming-zhao/Business-Analytics/raw/master/images/book_lie.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fx1gIpY_KiNx"
   },
   "source": [
    "## [Case](https://github.com/ming-zhao/Business-Analytics/blob/master/notebooks/parking_violation.ipynb): NYC Parking Violation\n",
    "\n",
    "We consider packing violation data in NYC from August 2013 to June 2014. The [dataset](https://data.cityofnewyork.us/City-Government/Parking-Violations-Issued-Fiscal-Year-2014-August-/jt7v-77mi\n",
    ") is available in [NYC Open Data](https://opendata.cityofnewyork.us/). The website NYC Open Data is a collection of 750 New York City public datasets made available by city agencies and organizations.\n",
    "\n",
    "The original dataset has 9.1M rows and 43 columns with size more than 1G. The dataset used in this note is already filtered with only hydrant paking violations. The excel file can be downloaded from this [link](https://github.com/ming-zhao/Business-Analytics/raw/master/data/regression/Parking_Violations.xlsx)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "udhbE-qZ8YDA"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <a href=\"#\" onclick=\"code_toggle(this); return false;\">show code</a>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load python packages and data\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import IFrame\n",
    "\n",
    "dataurl = 'https://raw.githubusercontent.com/ming-zhao/Business-Analytics/master/data/regression/'\n",
    "df_park = pd.read_csv(dataurl+'Parking_Violations.csv', parse_dates=['Time'])\n",
    "\n",
    "# run pivot table\n",
    "df_pivot = df_park[(df_park['Street Code1']!=0) &\\\n",
    "                   (df_park['Street Code2']!=0) &\\\n",
    "                   (df_park['Street Code2']!=0)].pivot_table(values='Summons Number',\n",
    "                                                  index='Address',\n",
    "                                                  margins=False,\n",
    "                                                  aggfunc='count').sort_values(by='Summons Number',\n",
    "                                                                               ascending=False).head(10)\n",
    "df_pivot['ticket'] = df_pivot['Summons Number']\n",
    "df_pivot['fine'] = df_pivot['ticket']*115\n",
    "df_pivot = df_pivot.drop(['Summons Number'], axis=1)\n",
    "\n",
    "toggle()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oA1fbzPX_cCY"
   },
   "source": [
    "Show the first 5 rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "H9QlTRvr8lEC",
    "outputId": "831f20c0-8268-4f4a-d7af-3c1192485bbe"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Summons Number</th>\n",
       "      <th>Registration State</th>\n",
       "      <th>Issue Date</th>\n",
       "      <th>Vehicle Body Type</th>\n",
       "      <th>Street Code1</th>\n",
       "      <th>Street Code2</th>\n",
       "      <th>Street Code3</th>\n",
       "      <th>Vehicle Make</th>\n",
       "      <th>Violation Time</th>\n",
       "      <th>Violation County</th>\n",
       "      <th>Vehicle Color</th>\n",
       "      <th>Vehicle Year</th>\n",
       "      <th>Time</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1356906515</td>\n",
       "      <td>NY</td>\n",
       "      <td>9/18/1971</td>\n",
       "      <td>SDN</td>\n",
       "      <td>13610</td>\n",
       "      <td>37270</td>\n",
       "      <td>37290</td>\n",
       "      <td>MAZDA</td>\n",
       "      <td>0914P</td>\n",
       "      <td>NY</td>\n",
       "      <td>BLK</td>\n",
       "      <td>2010</td>\n",
       "      <td>9:14 PM</td>\n",
       "      <td>4165 BROADWAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1365454538</td>\n",
       "      <td>NY</td>\n",
       "      <td>2/12/1976</td>\n",
       "      <td>VAN</td>\n",
       "      <td>37290</td>\n",
       "      <td>10740</td>\n",
       "      <td>10940</td>\n",
       "      <td>TOYOT</td>\n",
       "      <td>0458A</td>\n",
       "      <td>Q</td>\n",
       "      <td>BLK</td>\n",
       "      <td>2007</td>\n",
       "      <td>4:58 AM</td>\n",
       "      <td>49-11 BROADWAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1355329360</td>\n",
       "      <td>NY</td>\n",
       "      <td>12/9/1990</td>\n",
       "      <td>SUBN</td>\n",
       "      <td>35290</td>\n",
       "      <td>31240</td>\n",
       "      <td>31290</td>\n",
       "      <td>FORD</td>\n",
       "      <td>0902A</td>\n",
       "      <td>Q</td>\n",
       "      <td>BK</td>\n",
       "      <td>2003</td>\n",
       "      <td>9:02 AM</td>\n",
       "      <td>4402 BEACH CHANNEL DR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1364794688</td>\n",
       "      <td>NY</td>\n",
       "      <td>1/12/1991</td>\n",
       "      <td>SUBN</td>\n",
       "      <td>27106</td>\n",
       "      <td>9340</td>\n",
       "      <td>9540</td>\n",
       "      <td>ME/BE</td>\n",
       "      <td>0223P</td>\n",
       "      <td>Q</td>\n",
       "      <td>SILVE</td>\n",
       "      <td>2005</td>\n",
       "      <td>2:23 PM</td>\n",
       "      <td>40-30 235 ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1357592103</td>\n",
       "      <td>NY</td>\n",
       "      <td>1/4/2000</td>\n",
       "      <td>SDN</td>\n",
       "      <td>0</td>\n",
       "      <td>40404</td>\n",
       "      <td>40404</td>\n",
       "      <td>NISSA</td>\n",
       "      <td>1045P</td>\n",
       "      <td>R</td>\n",
       "      <td>SILVE</td>\n",
       "      <td>2008</td>\n",
       "      <td>10:45 PM</td>\n",
       "      <td>140 LUDWIGE LANE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Summons Number Registration State Issue Date Vehicle Body Type  \\\n",
       "0      1356906515                 NY  9/18/1971               SDN   \n",
       "1      1365454538                 NY  2/12/1976               VAN   \n",
       "2      1355329360                 NY  12/9/1990              SUBN   \n",
       "3      1364794688                 NY  1/12/1991              SUBN   \n",
       "4      1357592103                 NY   1/4/2000               SDN   \n",
       "\n",
       "   Street Code1  Street Code2  Street Code3 Vehicle Make Violation Time  \\\n",
       "0         13610         37270         37290        MAZDA          0914P   \n",
       "1         37290         10740         10940        TOYOT          0458A   \n",
       "2         35290         31240         31290         FORD          0902A   \n",
       "3         27106          9340          9540        ME/BE          0223P   \n",
       "4             0         40404         40404        NISSA          1045P   \n",
       "\n",
       "  Violation County Vehicle Color  Vehicle Year      Time  \\\n",
       "0               NY           BLK          2010   9:14 PM   \n",
       "1                Q           BLK          2007   4:58 AM   \n",
       "2                Q            BK          2003   9:02 AM   \n",
       "3                Q         SILVE          2005   2:23 PM   \n",
       "4                R         SILVE          2008  10:45 PM   \n",
       "\n",
       "                 Address  \n",
       "0          4165 BROADWAY  \n",
       "1         49-11 BROADWAY  \n",
       "2  4402 BEACH CHANNEL DR  \n",
       "3           40-30 235 ST  \n",
       "4       140 LUDWIGE LANE  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_park.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gF0xfIMxARu3"
   },
   "source": [
    "The top 10 hydrants that collect most of the tickets. Note that the fine for hydrant parking violation is <span>&dollar;</span>115. So the column \"fine\" is the revenue generated by each hydrant and the total fine of the top 10 hydrants is <span>&dollar;</span>144,440."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "id": "w7tF_VAP8nAz",
    "outputId": "560494a0-b904-410e-f507-3262e60f488b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total annual revenue of top 10 hydrants\n",
      " 144440\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>ticket</th>\n",
       "      <th>fine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152 Forsyth St</th>\n",
       "      <td>179</td>\n",
       "      <td>20585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104 Forsyth St</th>\n",
       "      <td>137</td>\n",
       "      <td>15755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100 Overlook Ter</th>\n",
       "      <td>135</td>\n",
       "      <td>15525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720 Lenox Ave</th>\n",
       "      <td>127</td>\n",
       "      <td>14605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122 Montague St</th>\n",
       "      <td>126</td>\n",
       "      <td>14490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21 W 58th St</th>\n",
       "      <td>125</td>\n",
       "      <td>14375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960 Fredrick Douglas Blv</th>\n",
       "      <td>119</td>\n",
       "      <td>13685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44 Court St</th>\n",
       "      <td>114</td>\n",
       "      <td>13110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498 3rd Ave</th>\n",
       "      <td>99</td>\n",
       "      <td>11385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41-28 Main St</th>\n",
       "      <td>95</td>\n",
       "      <td>10925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ticket   fine\n",
       "Address                                 \n",
       "152 Forsyth St                179  20585\n",
       "104 Forsyth St                137  15755\n",
       "100 Overlook Ter              135  15525\n",
       "720 Lenox Ave                 127  14605\n",
       "122 Montague St               126  14490\n",
       "21 W 58th St                  125  14375\n",
       "2960 Fredrick Douglas Blv     119  13685\n",
       "44 Court St                   114  13110\n",
       "1498 3rd Ave                   99  11385\n",
       "41-28 Main St                  95  10925"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Total annual revenue of top 10 hydrants', df_pivot.fine.sum())\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "colab_type": "code",
    "id": "Xyuimiem8u8C",
    "outputId": "94d2328f-bd90-4388-a66f-e934b92535cd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = df_pivot.plot.pie(y='ticket', autopct='%1.1f%%', figsize=(5, 5))\n",
    "axes.legend(loc='best', bbox_to_anchor=(2,.8))\n",
    "axes.set_ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KqRL7poIBbk5"
   },
   "source": [
    "The most \"valuable\" hydrant is shown in [google street view](https://www.google.com/maps/@40.7206121,-73.9917312,3a,75y,288.92h,55.77t/data=!3m6!1e1!3m4!1s_SBRnIVor2FDGiszffialA!2e0!7i13312!8i6656\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "ILrVOV5Z83F7",
    "outputId": "cc937596-2460-4e69-e4b4-f6a2f682a2ed"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.google.com/maps/embed?pb=!4v1557893815788!6m8!1m7!1s_SBRnIVor2FDGiszffialA!2m2!1d40.72061441911959!2d-73.99172978854598!3f288.92!4f0!5f0.7820865974627469\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1c9c1a95ef0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame('https://www.google.com/maps/embed?pb=!4v1557893815788!6m8!1m7!1s_SBRnIVor2FDGiszffialA!2m2!1d40.72061441911959!2d-73.99172978854598!3f288.92!4f0!5f0.7820865974627469', width=700, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cmj83gIa8_6M"
   },
   "source": [
    "However, according to NYC department of transportation (DOT), this may not be considered as a parking violation. ![hydrant](https://github.com/ming-zhao/Business-Analytics/raw/master/images/hydrant.png)\n",
    "\n",
    "The issue is first spotted by Ben Wellington who is the author of blog [I Quant NY](https://iquantny.tumblr.com/). It certainly has impacts on NYC DOT. Today, the google street map shows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "3G1UD5BSCquJ",
    "outputId": "4758c5a6-297c-40b0-8635-9d3c7bb6953b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.google.com/maps/embed?pb=!4v1557932957501!6m8!1m7!1s04LptdatMEwvnW3J_tjGvw!2m2!1d40.72061130331954!2d-73.99171284164994!3f264.0115330665066!4f-27.9676492146982!5f0.7820865974627469\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
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       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1c9c1adb3c8>"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame('https://www.google.com/maps/embed?pb=!4v1557932957501!6m8!1m7!1s04LptdatMEwvnW3J_tjGvw!2m2!1d40.72061130331954!2d-73.99171284164994!3f264.0115330665066!4f-27.9676492146982!5f0.7820865974627469', width=700, height=400)"
   ]
  }
 ],
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   "version": "0.3.2"
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