Assignment1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  <span style=\"text-decoration:underline;\">Assignment 1</span>: Analyzing Stack Overflow Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will look at [Stack Overflow](https://stackoverflow.com/) post data from the year of 2015 and measure the similarity of users by looking at the types of questions they answer. Do not delete the output of your code cells. This assignment must be completed **individually** by each student."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please use the following invitation link to create your assignment repository for this assignment: [https://classroom.github.com/a/epLjOcUA](https://classroom.github.com/a/epLjOcUA). Include your BU username within your submission by adding it here: **<BU username&gt;**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Retrieval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we make a sample request to retrieve some questions posted on Stack Exchange on the first day of 2015. Documentation of the Stack Exchange API can be found [here](https://api.stackexchange.com/docs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from datetime import datetime\n",
    "\n",
    "start_time = 1420070400 # 01-01-2015 at 00:00:00\n",
    "end_time   = 1420156800 # 01-02-2015 at 00:00:00\n",
    "\n",
    "response = requests.get(\"https://api.stackexchange.com/2.2/questions?pagesize=100\" +\n",
    "                        \"&fromdate=\" + str(start_time) + \"&todate=\" + str(end_time) +\n",
    "                        \"&order=asc&sort=creation&site=stackoverflow\")\n",
    "\n",
    "print(response) # Displays the HTTP response code (should be 200 for success).\n",
    "#I'm not sure why after a while I kept getting an error "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All dates in the Stack Exchange API are in [unix epoch time](https://en.wikipedia.org/wiki/Unix_time). The format for the request string is specified [here](https://api.stackexchange.com/docs/questions).\n",
    "\n",
    "We can try to print the response that Stack Exchange returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw response is not very useful for automated processing. Instead, we can decode the raw response as JSON and then use the `json` library to print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "      \"title\": \"Inserting multiple records into MySQL from an array\"\n",
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      "      \"title\": \"Cross Apply At Different Level\"\n",
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      "      \"closed_reason\": \"unclear what you&#39;re asking\",\n",
      "      \"title\": \"PHP Macro Substitution\"\n",
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      "        \"combobox\",\n",
      "        \"nullreferenceexception\",\n",
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      "        \"javascript\",\n",
      "        \"jquery\",\n",
      "        \"twitter-bootstrap\",\n",
      "        \"multi-select\",\n",
      "        \"livesearch\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 91,\n",
      "        \"user_id\": 4409426,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"profile_image\": \"https://www.gravatar.com/avatar/dd3c0e8fdaa828098c7f0238081774b3?s=128&d=identicon&r=PG&f=1\",\n",
      "        \"display_name\": \"tasaduq\",\n",
      "        \"link\": \"https://stackoverflow.com/users/4409426/tasaduq\"\n",
      "      },\n",
      "      \"is_answered\": true,\n",
      "      \"view_count\": 9316,\n",
      "      \"accepted_answer_id\": 27730724,\n",
      "      \"answer_count\": 1,\n",
      "      \"score\": 3,\n",
      "      \"last_activity_date\": 1420114580,\n",
      "      \"creation_date\": 1420074822,\n",
      "      \"last_edit_date\": 1420113839,\n",
      "      \"question_id\": 27727655,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727655/can-not-populate-bootstrap-select-with-data-live-search-multiple-with-jquery\",\n",
      "      \"title\": \"Can not populate bootstrap select with data live search + multiple with jquery\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"php\",\n",
      "        \"arrays\",\n",
      "        \"json\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 7,\n",
      "        \"user_id\": 4409431,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 17,\n",
      "        \"profile_image\": \"https://www.gravatar.com/avatar/a550c8e8e415d8549fd9c088859e1495?s=128&d=identicon&r=PG&f=1\",\n",
      "        \"display_name\": \"se7en\",\n",
      "        \"link\": \"https://stackoverflow.com/users/4409431/se7en\"\n",
      "      },\n",
      "      \"is_answered\": false,\n",
      "      \"view_count\": 68,\n",
      "      \"answer_count\": 3,\n",
      "      \"score\": 0,\n",
      "      \"last_activity_date\": 1512421795,\n",
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      "      \"last_edit_date\": 1420076135,\n",
      "      \"question_id\": 27727656,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727656/phpjson-display-result-with-no-key\",\n",
      "      \"title\": \"PHP+JSON Display result with no key?\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"python\",\n",
      "        \"html\",\n",
      "        \"parsing\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 30,\n",
      "        \"user_id\": 4409432,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"profile_image\": \"https://www.gravatar.com/avatar/9405ddc47d0b2d0821e92a1693c355cd?s=128&d=identicon&r=PG&f=1\",\n",
      "        \"display_name\": \"DANIEL REAPSOME\",\n",
      "        \"link\": \"https://stackoverflow.com/users/4409432/daniel-reapsome\"\n",
      "      },\n",
      "      \"is_answered\": true,\n",
      "      \"view_count\": 44,\n",
      "      \"closed_date\": 1421180028,\n",
      "      \"accepted_answer_id\": 27729078,\n",
      "      \"answer_count\": 1,\n",
      "      \"score\": -3,\n",
      "      \"last_activity_date\": 1420097599,\n",
      "      \"creation_date\": 1420074859,\n",
      "      \"question_id\": 27727657,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727657/parsing-html-information-with-beautifulsoup4-in-python\",\n",
      "      \"closed_reason\": \"too broad\",\n",
      "      \"title\": \"parsing html information with beautifulsoup4 in python\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"html\",\n",
      "        \"css\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 155,\n",
      "        \"user_id\": 3420062,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 83,\n",
      "        \"profile_image\": \"https://i.stack.imgur.com/bFXmB.png?s=128&g=1\",\n",
      "        \"display_name\": \"fuuman\",\n",
      "        \"link\": \"https://stackoverflow.com/users/3420062/fuuman\"\n",
      "      },\n",
      "      \"is_answered\": false,\n",
      "      \"view_count\": 23,\n",
      "      \"answer_count\": 1,\n",
      "      \"score\": 0,\n",
      "      \"last_activity_date\": 1420076354,\n",
      "      \"creation_date\": 1420075017,\n",
      "      \"question_id\": 27727664,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727664/navigationbar-totally-left\",\n",
      "      \"title\": \"Navigationbar totally left\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"python\",\n",
      "        \"list\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 993,\n",
      "        \"user_id\": 896317,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 48,\n",
      "        \"profile_image\": \"https://i.stack.imgur.com/ZbKPx.png?s=128&g=1\",\n",
      "        \"display_name\": \"GiannisIordanou\",\n",
      "        \"link\": \"https://stackoverflow.com/users/896317/giannisiordanou\"\n",
      "      },\n",
      "      \"is_answered\": true,\n",
      "      \"view_count\": 98,\n",
      "      \"accepted_answer_id\": 27727884,\n",
      "      \"answer_count\": 4,\n",
      "      \"score\": 3,\n",
      "      \"last_activity_date\": 1420082558,\n",
      "      \"creation_date\": 1420075026,\n",
      "      \"question_id\": 27727666,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727666/how-can-i-split-a-list-of-lists-based-on-a-specific-condition-in-python\",\n",
      "      \"title\": \"How can I split a list of lists based on a specific condition in Python?\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"html\",\n",
      "        \"css\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 141,\n",
      "        \"user_id\": 1906526,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 31,\n",
      "        \"profile_image\": \"https://i.stack.imgur.com/1i2Bf.jpg?s=128&g=1\",\n",
      "        \"display_name\": \"Fluxcapacitor\",\n",
      "        \"link\": \"https://stackoverflow.com/users/1906526/fluxcapacitor\"\n",
      "      },\n",
      "      \"is_answered\": true,\n",
      "      \"view_count\": 81,\n",
      "      \"accepted_answer_id\": 27727700,\n",
      "      \"answer_count\": 1,\n",
      "      \"score\": 0,\n",
      "      \"last_activity_date\": 1420075585,\n",
      "      \"creation_date\": 1420075038,\n",
      "      \"question_id\": 27727668,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727668/why-does-p-element-jump-out-of-div-and-appear-in-next-line-below\",\n",
      "      \"title\": \"Why does &lt;p&gt; element jump out of div and appear in next line below?\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"android\",\n",
      "        \"gradle\",\n",
      "        \"out-of-memory\",\n",
      "        \"robolectric\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 432,\n",
      "        \"user_id\": 1804667,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 33,\n",
      "        \"profile_image\": \"https://www.gravatar.com/avatar/56890ef4338c7a35afd2bacf362874e1?s=128&d=identicon&r=PG\",\n",
      "        \"display_name\": \"lazypig\",\n",
      "        \"link\": \"https://stackoverflow.com/users/1804667/lazypig\"\n",
      "      },\n",
      "      \"is_answered\": true,\n",
      "      \"view_count\": 4221,\n",
      "      \"accepted_answer_id\": 27748558,\n",
      "      \"answer_count\": 2,\n",
      "      \"score\": 5,\n",
      "      \"last_activity_date\": 1430761794,\n",
      "      \"creation_date\": 1420075056,\n",
      "      \"last_edit_date\": 1495541687,\n",
      "      \"question_id\": 27727670,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727670/android-robolectric-gradle-java-lang-outofmemoryerror-permgen-space\",\n",
      "      \"title\": \"Android Robolectric Gradle - java.lang.OutOfMemoryError: PermGen space\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"highcharts\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 1040,\n",
      "        \"user_id\": 619510,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 92,\n",
      "        \"profile_image\": \"https://www.gravatar.com/avatar/de1373715118caea7c80e173bd7ab04e?s=128&d=identicon&r=PG\",\n",
      "        \"display_name\": \"michelpm\",\n",
      "        \"link\": \"https://stackoverflow.com/users/619510/michelpm\"\n",
      "      },\n",
      "      \"is_answered\": false,\n",
      "      \"view_count\": 258,\n",
      "      \"answer_count\": 0,\n",
      "      \"score\": 0,\n",
      "      \"last_activity_date\": 1420075078,\n",
      "      \"creation_date\": 1420075078,\n",
      "      \"question_id\": 27727671,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727671/add-map-as-drilldown-for-pie-chart\",\n",
      "      \"title\": \"Add map as drilldown for pie chart\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"postgresql\",\n",
      "        \"select\",\n",
      "        \"plpgsql\",\n",
      "        \"dynamic-sql\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 338,\n",
      "        \"user_id\": 3935165,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 71,\n",
      "        \"profile_image\": \"https://www.gravatar.com/avatar/216aab2c76fcd2102e7439f001db2eec?s=128&d=identicon&r=PG&f=1\",\n",
      "        \"display_name\": \"Roy\",\n",
      "        \"link\": \"https://stackoverflow.com/users/3935165/roy\"\n",
      "      },\n",
      "      \"is_answered\": true,\n",
      "      \"view_count\": 868,\n",
      "      \"closed_date\": 1420683420,\n",
      "      \"answer_count\": 1,\n",
      "      \"score\": 0,\n",
      "      \"last_activity_date\": 1420682670,\n",
      "      \"creation_date\": 1420075080,\n",
      "      \"last_edit_date\": 1420682670,\n",
      "      \"question_id\": 27727672,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727672/select-columns-with-the-same-prefix\",\n",
      "      \"closed_reason\": \"duplicate\",\n",
      "      \"title\": \"Select columns with the same prefix\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"mysql\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 948,\n",
      "        \"user_id\": 1998220,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 87,\n",
      "        \"profile_image\": \"https://i.stack.imgur.com/bpbf0.jpg?s=128&g=1\",\n",
      "        \"display_name\": \"Michael St Clair\",\n",
      "        \"link\": \"https://stackoverflow.com/users/1998220/michael-st-clair\"\n",
      "      },\n",
      "      \"is_answered\": false,\n",
      "      \"view_count\": 31,\n",
      "      \"closed_date\": 1420076239,\n",
      "      \"answer_count\": 0,\n",
      "      \"score\": 2,\n",
      "      \"last_activity_date\": 1420076037,\n",
      "      \"creation_date\": 1420075100,\n",
      "      \"question_id\": 27727673,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727673/return-value-of-0-from-mysql-query-if-empty\",\n",
      "      \"closed_reason\": \"duplicate\",\n",
      "      \"title\": \"Return value of 0 from MySQL query if empty\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"ios\",\n",
      "        \"swift\",\n",
      "        \"nsmutablearray\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 3504,\n",
      "        \"user_id\": 2988919,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"accept_rate\": 93,\n",
      "        \"profile_image\": \"https://graph.facebook.com/100006578318645/picture?type=large\",\n",
      "        \"display_name\": \"tika\",\n",
      "        \"link\": \"https://stackoverflow.com/users/2988919/tika\"\n",
      "      },\n",
      "      \"is_answered\": true,\n",
      "      \"view_count\": 333,\n",
      "      \"accepted_answer_id\": 27727709,\n",
      "      \"answer_count\": 1,\n",
      "      \"score\": 0,\n",
      "      \"last_activity_date\": 1420081077,\n",
      "      \"creation_date\": 1420075129,\n",
      "      \"last_edit_date\": 1420081077,\n",
      "      \"question_id\": 27727675,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727675/function-return-error-in-ios\",\n",
      "      \"title\": \"Function Return Error in iOS\"\n",
      "    },\n",
      "    {\n",
      "      \"tags\": [\n",
      "        \"java\",\n",
      "        \"android\",\n",
      "        \"cordova\",\n",
      "        \"ionic-framework\"\n",
      "      ],\n",
      "      \"owner\": {\n",
      "        \"reputation\": 23,\n",
      "        \"user_id\": 2752238,\n",
      "        \"user_type\": \"registered\",\n",
      "        \"profile_image\": \"https://www.gravatar.com/avatar/2ead48456c43a72f021001549d5144d4?s=128&d=identicon&r=PG&f=1\",\n",
      "        \"display_name\": \"Dan\",\n",
      "        \"link\": \"https://stackoverflow.com/users/2752238/dan\"\n",
      "      },\n",
      "      \"is_answered\": false,\n",
      "      \"view_count\": 789,\n",
      "      \"answer_count\": 0,\n",
      "      \"score\": 4,\n",
      "      \"last_activity_date\": 1420076280,\n",
      "      \"creation_date\": 1420075130,\n",
      "      \"last_edit_date\": 1420076280,\n",
      "      \"question_id\": 27727676,\n",
      "      \"link\": \"https://stackoverflow.com/questions/27727676/unable-to-instantiate-receiver-com-parse-gcmbroadcastreceiver\",\n",
      "      \"title\": \"Unable to instantiate receiver com.parse.GcmBroadcastReceiver\"\n",
      "    }\n",
      "  ],\n",
      "  \"has_more\": true,\n",
      "  \"quota_max\": 300,\n",
      "  \"quota_remaining\": 298\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "json_response = response.json()\n",
    "\n",
    "print(json.dumps(json_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is now possible to see that the response consists of a list of question items. For each of these items, we get information about its attributes: `creation_date`, `answer_count`, `owner`, `title`, and so on.\n",
    "\n",
    "Notice that `has_more` is `true`. To get more items, we can [request the next page](https://api.stackexchange.com/docs/paging)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration:underline;\">Problem 1</span>: Parsing the responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this problem you will practice using some of the techniques and string handling methods that Python offers. Our goal is to extract the interesting parts of the response data and transform them into a format that will be useful for our final analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<span style=\"text-decoration:underline;\">Part A</span> (8 points):** We will first isolate the `creation_date` attribute in the response. Complete the definition of the ```print_creation_dates_json()``` below, which reads the response and prints the creation dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameter of this method is the response object created out of response\n",
    "def print_creation_dates_json(response):\n",
    "  print(response.keys())\n",
    "  for post in response['items']:\n",
    "    print(post['creation_date'])\n",
    "    \n",
    "#print_creation_dates_json(json_response)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<span style=\"text-decoration:underline;\">Part B</span> (8 points):** Write code that issues requests to retrieve all of the creation dates of questions posted on the first day in 2015. The code should call the ```print_creation_dates_json()``` function to print out each page of results.\n",
    "\n",
    "**Hint:** You can use a loop and take advantage of the `has_more` attribute to request additional pages of results if they exist. Please be aware of Stack Exchange's [rate limit](https://api.stackexchange.com/docs/throttle); you can use the Python `sleep` function in the `time` module ([documentation can be found here](https://docs.python.org/3/library/time.html#time.sleep)) to add a delay between requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nresponse = send_request(1)\\nprint(response)\\njsonResponse = response.json()\\nprint_creation_dates_json(jsonResponse)\\ncounter = 1;\\nwhile json_response['has_more']:\\n  counter = counter + 1\\n  response = send_request(counter)\\n  jsonResponse = response.json()\\n  print_creation_dates_json(jsonResponse)\\n\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import requests\n",
    "import json\n",
    "''''\n",
    "helper method to send requests \n",
    "and it gets the page number as a parameter \n",
    "'''\n",
    "def send_request(counter):\n",
    "  start_time = 1420070400 # 01-01-2015 at 00:00:00\n",
    "  end_time   = 1420156800 # 01-02-2015 at 00:00:00\n",
    "  response = requests.get(\"https://api.stackexchange.com/2.2/questions?page=\" + str(counter) + \"&pagesize=100\" + \n",
    "                          \"&fromdate=\" + str(start_time) + \"&todate=\" + str(end_time) +\n",
    "                        \"&order=asc&sort=creation&site=stackoverflow\")\n",
    "  return response\n",
    "#----------------------------------------\n",
    "# I have commented out this part because It keeps issueing 502 error\n",
    "'''\n",
    "response = send_request(1)\n",
    "print(response)\n",
    "jsonResponse = response.json()\n",
    "print_creation_dates_json(jsonResponse)\n",
    "counter = 1;\n",
    "while json_response['has_more']:\n",
    "  counter = counter + 1\n",
    "  response = send_request(counter)\n",
    "  jsonResponse = response.json()\n",
    "  print_creation_dates_json(jsonResponse)\n",
    "'''  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to time constraints, we have already downloaded the [data dump](http://cs-people.bu.edu/lapets/506/data/stackoverflow-posts-2015.tar.gz) for Stack Overflow's posts in 2015. Note that the XML file is 10GB in size unzipped. If you don't have space on your computer, you can download it into `/scratch` on one of the machines in the undergrad lab, or you can download it onto a USB drive. You may want to work with a subset of this data at first, but your solution should be efficient enough to work with the whole dataset. For example, if you call `read()` on the whole dataset, you will get a `MemoryError`.\n",
    "\n",
    "Do not commit the data file to your repository. You may assume that we will place the data file in the same directory as your submitted notebook file, so use a relative path when loading the data file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<span style=\"text-decoration:underline;\">Part C</span> (16 points):** Write a function that parses out the questions posted in 2015. These are posts with `PostTypeId=1`. The function should return a pandas `DataFrame`  object with 4 columns: `Id`, `CreationDate`, `OwnerUserId`, and the first tag in `Tags`. Call the function on an appropriate input and print out the `DataFrame` object; do not clear the output.\n",
    "\n",
    "**Hint:** You should be able to use `iterparse` ([documentation can be found here](https://docs.python.org/3.8/library/xml.etree.elementtree.html#xml.etree.ElementTree.iterparse)):\n",
    "```\n",
    "from lxml.etree import iterparse\n",
    "```\n",
    "Once you create the `iterparse` object (let's call it `parsed`) for the file, you can use a `for` loop such as:\n",
    "```\n",
    "for _, element in parsed:\n",
    "    # ...\n",
    "```\n",
    "You can use the `.tag` and `.get()` methods of the `element` object to inspect it and extract data from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>OwnerUserId</th>\n",
       "      <th>FirstTag</th>\n",
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       "      <td>apache-pig</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27727391</td>\n",
       "      <td>2015-01-01T00:02:32.123</td>\n",
       "      <td>4372672</td>\n",
       "      <td>ios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27727393</td>\n",
       "      <td>2015-01-01T00:02:57.983</td>\n",
       "      <td>2482149</td>\n",
       "      <td>sql-server</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27727394</td>\n",
       "      <td>2015-01-01T00:03:31.337</td>\n",
       "      <td>4263870</td>\n",
       "      <td>php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>27727396</td>\n",
       "      <td>2015-01-01T00:04:01.407</td>\n",
       "      <td>4409381</td>\n",
       "      <td>android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27727406</td>\n",
       "      <td>2015-01-01T00:05:03.773</td>\n",
       "      <td>875317</td>\n",
       "      <td>c#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27727407</td>\n",
       "      <td>2015-01-01T00:05:27.167</td>\n",
       "      <td>821742</td>\n",
       "      <td>java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27727408</td>\n",
       "      <td>2015-01-01T00:05:34.733</td>\n",
       "      <td>2595033</td>\n",
       "      <td>c#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27727409</td>\n",
       "      <td>2015-01-01T00:06:17.720</td>\n",
       "      <td>1815395</td>\n",
       "      <td>apache</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27727410</td>\n",
       "      <td>2015-01-01T00:06:41.067</td>\n",
       "      <td>541091</td>\n",
       "      <td>mysql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27727414</td>\n",
       "      <td>2015-01-01T00:07:28.747</td>\n",
       "      <td>1210038</td>\n",
       "      <td>javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27727418</td>\n",
       "      <td>2015-01-01T00:07:39.243</td>\n",
       "      <td>3674356</td>\n",
       "      <td>php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27727419</td>\n",
       "      <td>2015-01-01T00:07:46.460</td>\n",
       "      <td>347062</td>\n",
       "      <td>html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27727424</td>\n",
       "      <td>2015-01-01T00:09:28.247</td>\n",
       "      <td>1254618</td>\n",
       "      <td>collada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27727427</td>\n",
       "      <td>2015-01-01T00:10:05.340</td>\n",
       "      <td>3412951</td>\n",
       "      <td>ruby-on-rails</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27727429</td>\n",
       "      <td>2015-01-01T00:10:29.963</td>\n",
       "      <td>1743377</td>\n",
       "      <td>javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27727433</td>\n",
       "      <td>2015-01-01T00:11:43.463</td>\n",
       "      <td>169992</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>27727434</td>\n",
       "      <td>2015-01-01T00:12:02.597</td>\n",
       "      <td>4056620</td>\n",
       "      <td>ios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>27727439</td>\n",
       "      <td>2015-01-01T00:12:55.513</td>\n",
       "      <td>2993567</td>\n",
       "      <td>java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27727442</td>\n",
       "      <td>2015-01-01T00:13:04.980</td>\n",
       "      <td>3085148</td>\n",
       "      <td>powershell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27727444</td>\n",
       "      <td>2015-01-01T00:13:32.583</td>\n",
       "      <td>2457761</td>\n",
       "      <td>java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27727446</td>\n",
       "      <td>2015-01-01T00:13:50.463</td>\n",
       "      <td>4371259</td>\n",
       "      <td>node.js</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>27727455</td>\n",
       "      <td>2015-01-01T00:15:40.250</td>\n",
       "      <td>3802790</td>\n",
       "      <td>ios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27727459</td>\n",
       "      <td>2015-01-01T00:17:11.113</td>\n",
       "      <td>2227834</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27727464</td>\n",
       "      <td>2015-01-01T00:18:50.297</td>\n",
       "      <td>4397115</td>\n",
       "      <td>javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27727468</td>\n",
       "      <td>2015-01-01T00:22:16.780</td>\n",
       "      <td>3494499</td>\n",
       "      <td>ios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>27727477</td>\n",
       "      <td>2015-01-01T00:23:14.747</td>\n",
       "      <td>3286192</td>\n",
       "      <td>android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27727481</td>\n",
       "      <td>2015-01-01T00:23:50.863</td>\n",
       "      <td>2999675</td>\n",
       "      <td>c++</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>27727484</td>\n",
       "      <td>2015-01-01T00:24:28.103</td>\n",
       "      <td>4289580</td>\n",
       "      <td>php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216567</th>\n",
       "      <td>28332178</td>\n",
       "      <td>2015-02-04T21:51:36.267</td>\n",
       "      <td>2805144</td>\n",
       "      <td>ruby-on-rails</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216568</th>\n",
       "      <td>28332181</td>\n",
       "      <td>2015-02-04T21:51:54.410</td>\n",
       "      <td>3116306</td>\n",
       "      <td>ios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216569</th>\n",
       "      <td>28332182</td>\n",
       "      <td>2015-02-04T21:51:58.297</td>\n",
       "      <td>701723</td>\n",
       "      <td>reactjs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216570</th>\n",
       "      <td>28332188</td>\n",
       "      <td>2015-02-04T21:52:24.380</td>\n",
       "      <td>4028969</td>\n",
       "      <td>vba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216571</th>\n",
       "      <td>28332189</td>\n",
       "      <td>2015-02-04T21:52:25.323</td>\n",
       "      <td>4180345</td>\n",
       "      <td>arrays</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216572</th>\n",
       "      <td>28332191</td>\n",
       "      <td>2015-02-04T21:52:31.667</td>\n",
       "      <td>408137</td>\n",
       "      <td>ios</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216573</th>\n",
       "      <td>28332193</td>\n",
       "      <td>2015-02-04T21:52:49.997</td>\n",
       "      <td>4100050</td>\n",
       "      <td>asp.net</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216574</th>\n",
       "      <td>28332195</td>\n",
       "      <td>2015-02-04T21:52:53.937</td>\n",
       "      <td>4530521</td>\n",
       "      <td>c#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216575</th>\n",
       "      <td>28332196</td>\n",
       "      <td>2015-02-04T21:52:55.133</td>\n",
       "      <td>509089</td>\n",
       "      <td>php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216576</th>\n",
       "      <td>28332198</td>\n",
       "      <td>2015-02-04T21:53:12.033</td>\n",
       "      <td>4359659</td>\n",
       "      <td>java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216577</th>\n",
       "      <td>28332199</td>\n",
       "      <td>2015-02-04T21:53:18.530</td>\n",
       "      <td>862193</td>\n",
       "      <td>qt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216578</th>\n",
       "      <td>28332204</td>\n",
       "      <td>2015-02-04T21:53:35.877</td>\n",
       "      <td>4119216</td>\n",
       "      <td>arrays</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216579</th>\n",
       "      <td>28332207</td>\n",
       "      <td>2015-02-04T21:53:41.457</td>\n",
       "      <td>2913864</td>\n",
       "      <td>haskell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216580</th>\n",
       "      <td>28332217</td>\n",
       "      <td>2015-02-04T21:54:22.963</td>\n",
       "      <td>2237387</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216581</th>\n",
       "      <td>28332220</td>\n",
       "      <td>2015-02-04T21:54:25.867</td>\n",
       "      <td>3130218</td>\n",
       "      <td>scala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216582</th>\n",
       "      <td>28332225</td>\n",
       "      <td>2015-02-04T21:54:41.057</td>\n",
       "      <td>880135</td>\n",
       "      <td>jquery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216583</th>\n",
       "      <td>28332226</td>\n",
       "      <td>2015-02-04T21:54:47.090</td>\n",
       "      <td>3650018</td>\n",
       "      <td>php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216584</th>\n",
       "      <td>28332232</td>\n",
       "      <td>2015-02-04T21:55:00.563</td>\n",
       "      <td>1344476</td>\n",
       "      <td>c#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216585</th>\n",
       "      <td>28332236</td>\n",
       "      <td>2015-02-04T21:55:13.190</td>\n",
       "      <td>2803455</td>\n",
       "      <td>javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216586</th>\n",
       "      <td>28332237</td>\n",
       "      <td>2015-02-04T21:55:14.310</td>\n",
       "      <td>4443323</td>\n",
       "      <td>tableview</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216587</th>\n",
       "      <td>28332238</td>\n",
       "      <td>2015-02-04T21:55:19.340</td>\n",
       "      <td>4530491</td>\n",
       "      <td>ruby-on-rails</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216588</th>\n",
       "      <td>28332241</td>\n",
       "      <td>2015-02-04T21:55:39.143</td>\n",
       "      <td>2636004</td>\n",
       "      <td>security</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216589</th>\n",
       "      <td>28332245</td>\n",
       "      <td>2015-02-04T21:55:56.703</td>\n",
       "      <td>4373381</td>\n",
       "      <td>javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216590</th>\n",
       "      <td>28332250</td>\n",
       "      <td>2015-02-04T21:56:20.240</td>\n",
       "      <td>4088415</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216591</th>\n",
       "      <td>28332252</td>\n",
       "      <td>2015-02-04T21:56:36.543</td>\n",
       "      <td>1093982</td>\n",
       "      <td>entity-framework</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216592</th>\n",
       "      <td>28332253</td>\n",
       "      <td>2015-02-04T21:56:46.670</td>\n",
       "      <td>2466619</td>\n",
       "      <td>c++</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216593</th>\n",
       "      <td>28332254</td>\n",
       "      <td>2015-02-04T21:56:53.317</td>\n",
       "      <td>4530541</td>\n",
       "      <td>windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216594</th>\n",
       "      <td>28332256</td>\n",
       "      <td>2015-02-04T21:57:09.170</td>\n",
       "      <td>4092342</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216595</th>\n",
       "      <td>28332260</td>\n",
       "      <td>2015-02-04T21:57:35.993</td>\n",
       "      <td>2259361</td>\n",
       "      <td>api</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216596</th>\n",
       "      <td>28332263</td>\n",
       "      <td>2015-02-04T21:57:43.907</td>\n",
       "      <td>4029243</td>\n",
       "      <td>skmaps</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216596 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Id             CreationDate OwnerUserId          FirstTag\n",
       "1       27727385  2015-01-01T00:00:58.253     3210431               php\n",
       "2       27727388  2015-01-01T00:01:43.673      868779        apache-pig\n",
       "3       27727391  2015-01-01T00:02:32.123     4372672               ios\n",
       "4       27727393  2015-01-01T00:02:57.983     2482149        sql-server\n",
       "5       27727394  2015-01-01T00:03:31.337     4263870               php\n",
       "6       27727396  2015-01-01T00:04:01.407     4409381           android\n",
       "7       27727406  2015-01-01T00:05:03.773      875317                c#\n",
       "8       27727407  2015-01-01T00:05:27.167      821742              java\n",
       "9       27727408  2015-01-01T00:05:34.733     2595033                c#\n",
       "10      27727409  2015-01-01T00:06:17.720     1815395            apache\n",
       "11      27727410  2015-01-01T00:06:41.067      541091             mysql\n",
       "12      27727414  2015-01-01T00:07:28.747     1210038        javascript\n",
       "13      27727418  2015-01-01T00:07:39.243     3674356               php\n",
       "14      27727419  2015-01-01T00:07:46.460      347062              html\n",
       "15      27727424  2015-01-01T00:09:28.247     1254618           collada\n",
       "16      27727427  2015-01-01T00:10:05.340     3412951     ruby-on-rails\n",
       "17      27727429  2015-01-01T00:10:29.963     1743377        javascript\n",
       "18      27727433  2015-01-01T00:11:43.463      169992                 c\n",
       "19      27727434  2015-01-01T00:12:02.597     4056620               ios\n",
       "20      27727439  2015-01-01T00:12:55.513     2993567              java\n",
       "21      27727442  2015-01-01T00:13:04.980     3085148        powershell\n",
       "22      27727444  2015-01-01T00:13:32.583     2457761              java\n",
       "23      27727446  2015-01-01T00:13:50.463     4371259           node.js\n",
       "24      27727455  2015-01-01T00:15:40.250     3802790               ios\n",
       "25      27727459  2015-01-01T00:17:11.113     2227834                 c\n",
       "26      27727464  2015-01-01T00:18:50.297     4397115        javascript\n",
       "27      27727468  2015-01-01T00:22:16.780     3494499               ios\n",
       "28      27727477  2015-01-01T00:23:14.747     3286192           android\n",
       "29      27727481  2015-01-01T00:23:50.863     2999675               c++\n",
       "30      27727484  2015-01-01T00:24:28.103     4289580               php\n",
       "...          ...                      ...         ...               ...\n",
       "216567  28332178  2015-02-04T21:51:36.267     2805144     ruby-on-rails\n",
       "216568  28332181  2015-02-04T21:51:54.410     3116306               ios\n",
       "216569  28332182  2015-02-04T21:51:58.297      701723           reactjs\n",
       "216570  28332188  2015-02-04T21:52:24.380     4028969               vba\n",
       "216571  28332189  2015-02-04T21:52:25.323     4180345            arrays\n",
       "216572  28332191  2015-02-04T21:52:31.667      408137               ios\n",
       "216573  28332193  2015-02-04T21:52:49.997     4100050           asp.net\n",
       "216574  28332195  2015-02-04T21:52:53.937     4530521                c#\n",
       "216575  28332196  2015-02-04T21:52:55.133      509089               php\n",
       "216576  28332198  2015-02-04T21:53:12.033     4359659              java\n",
       "216577  28332199  2015-02-04T21:53:18.530      862193                qt\n",
       "216578  28332204  2015-02-04T21:53:35.877     4119216            arrays\n",
       "216579  28332207  2015-02-04T21:53:41.457     2913864           haskell\n",
       "216580  28332217  2015-02-04T21:54:22.963     2237387            python\n",
       "216581  28332220  2015-02-04T21:54:25.867     3130218             scala\n",
       "216582  28332225  2015-02-04T21:54:41.057      880135            jquery\n",
       "216583  28332226  2015-02-04T21:54:47.090     3650018               php\n",
       "216584  28332232  2015-02-04T21:55:00.563     1344476                c#\n",
       "216585  28332236  2015-02-04T21:55:13.190     2803455        javascript\n",
       "216586  28332237  2015-02-04T21:55:14.310     4443323         tableview\n",
       "216587  28332238  2015-02-04T21:55:19.340     4530491     ruby-on-rails\n",
       "216588  28332241  2015-02-04T21:55:39.143     2636004          security\n",
       "216589  28332245  2015-02-04T21:55:56.703     4373381        javascript\n",
       "216590  28332250  2015-02-04T21:56:20.240     4088415            python\n",
       "216591  28332252  2015-02-04T21:56:36.543     1093982  entity-framework\n",
       "216592  28332253  2015-02-04T21:56:46.670     2466619               c++\n",
       "216593  28332254  2015-02-04T21:56:53.317     4530541           windows\n",
       "216594  28332256  2015-02-04T21:57:09.170     4092342             linux\n",
       "216595  28332260  2015-02-04T21:57:35.993     2259361               api\n",
       "216596  28332263  2015-02-04T21:57:43.907     4029243            skmaps\n",
       "\n",
       "[216596 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for runnig this cell please change the relative path of the file \n",
    "from lxml.etree import iterparse as parsed\n",
    "import pandas as pd\n",
    "\n",
    "#helper method which returns the first tags in the tags atrribute of element\n",
    "def get_first_tag(tagsAttribute):\n",
    "    splitedTags = tagsAttribute.replace('<','').split('>')\n",
    "    return splitedTags[0]\n",
    "#---------------------------------------------------------------------\n",
    "#thie method creates data frames by appending rows to data frame incremantally \n",
    "def create_dataFrame_using_append(limit):\n",
    "  count = 0\n",
    "  df = pd.DataFrame(columns=['Id', 'CreationDate','OwnerUserId','FirstTag'])\n",
    "  for _, element in parsed('../data.xml'):\n",
    "    if (count <= limit):\n",
    "      count = count + 1\n",
    "      #check to see if the post is a question and check the sanity of data: ToDo: check for all the keys\n",
    "      if (element.attrib['PostTypeId'] == '1' and 'OwnerUserId' in element.attrib):\n",
    "        print(count)\n",
    "        df = df.append({'Id': element.attrib['Id'],\n",
    "                      'CreationDate': element.attrib['CreationDate'],\n",
    "                      'OwnerUserId': element.attrib['OwnerUserId'],\n",
    "                      'FirstTag': get_first_tag(element.attrib['Tags'])\n",
    "                     }, \n",
    "                     ignore_index=True)\n",
    "    else: \n",
    "      break\n",
    "  return df\n",
    "#--------------------------------------------------------------\n",
    "#this method created data frames out a dictionary\n",
    "def create_dataFrame_out_of_dictionary(limit):\n",
    "  dataDict = {}\n",
    "  count = 0\n",
    "  rowIndex = 1\n",
    "  for _, element in parsed('data.xml'):\n",
    "    if (count <= limit):\n",
    "      count = count + 1\n",
    "      #check to see if the post is a question and check the sanity of data: ToDo: check for all the keys\n",
    "      if (element.attrib['PostTypeId'] == '1' and 'OwnerUserId' in element.attrib):\n",
    "        #create a list of values in the row\n",
    "        row = [element.attrib['Id'],\n",
    "               element.attrib['CreationDate'],\n",
    "               element.attrib['OwnerUserId'],\n",
    "               get_first_tag(element.attrib['Tags'])]\n",
    "        #append rows to dictionary\n",
    "        dataDict[rowIndex] = row\n",
    "        rowIndex = rowIndex + 1\n",
    "    else:\n",
    "      break\n",
    "  df = pd.DataFrame.from_dict(dataDict, orient='index', columns=['Id', 'CreationDate','OwnerUserId','FirstTag'])\n",
    "  return df\n",
    "\n",
    "questions = create_dataFrame_out_of_dictionary(500000)\n",
    "questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"text-decoration:underline;\">Problem 2</span>: Analyzing the responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<span style=\"text-decoration:underline;\">Part A</span> (50 points):** Write a function that measures the similarity of the top 100 users with the most answer posts. Compare the users based on the types of questions they answer. We will categorize a question by its first tag. You may choose to implement any one of the similarity/distance measures we discussed in class.\n",
    "\n",
    "Note that answers are posts with `PostTypeId=2`. The identifier of the question in answer posts is the `ParentId`.\n",
    "\n",
    "You may find the [sklearn.feature_extraction](http://scikit-learn.org/stable/modules/feature_extraction.html) module helpful.\n",
    "\n",
    "**Hint:** You may want to begin your solution by constructing a data set in the following way:\n",
    "1. Find users with the most responses using `.groupby()` on a data frame containing all of the entries in the answers data set.\n",
    "2. Sort to find the top 100 users from the result above.\n",
    "3. Find all the question identifiers (using `ParentId`) among the answers of the top 100 users (within the answers data set).\n",
    "4. Join/merge the result above with the questions data set to get the tags of the questions answered by the top 100 users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math \n",
    "def create_answers_data_frame(limit):\n",
    "  dataDict = {}\n",
    "  count = 0\n",
    "  rowIndex = 1\n",
    "  for _, element in parsed('data.xml'):\n",
    "    if (count <= limit):\n",
    "      count = count + 1\n",
    "      #check to see if the post is a answer and check the sanity of data: ToDo: check for all the keys\n",
    "      if (element.attrib['PostTypeId'] == '2' and 'OwnerUserId' in element.attrib):\n",
    "        #create a list of values in the row\n",
    "        row = [element.attrib['OwnerUserId'],\n",
    "               element.attrib['ParentId']]\n",
    "        #append rows to dictionary\n",
    "        dataDict[rowIndex] = row\n",
    "        rowIndex = rowIndex + 1\n",
    "    else:\n",
    "      break\n",
    "  answers = pd.DataFrame.from_dict(dataDict, orient='index', columns=['OwnerUserId', 'ParentId'])\n",
    "  answers['NumQuestionsAnswered'] = 1\n",
    "  return answers\n",
    "#-----------------------------------------------------------------------\n",
    "#this method returns  a data frame containing top hundred users\n",
    "def create_top_hundred_users_df(answers):\n",
    "  groupedAnswers = answers.groupby(['OwnerUserId'], sort=False)['NumQuestionsAnswered'].sum().to_frame()\n",
    "  sorted = groupedAnswers.sort_values('NumQuestionsAnswered', ascending=False)\n",
    "  return sorted.head(n=100)\n",
    "\n",
    "#create the initial dataframe\n",
    "answers = create_answers_data_frame(500000)\n",
    "#create top hundred users data frame\n",
    "topHundredUsers = create_top_hundred_users_df(answers)\n",
    "#merge top hundred users with initial answers data frame\n",
    "merged = answers.merge(topHundredUsers, left_on='OwnerUserId', right_on='OwnerUserId')\n",
    "#merge the result with question data frame\n",
    "Q_A_merged = merged.merge(questions, left_on='ParentId', right_on='Id')\n",
    "#drop the unwanted columns\n",
    "Q_A_merged = Q_A_merged[['OwnerUserId_x','ParentId','FirstTag','NumQuestionsAnswered_y']]\n",
    "#add this new column so we can do the aggregation on \n",
    "Q_A_merged['numQuestionsAnswered'] = 1\n",
    "featureVector = pd.pivot_table(Q_A_merged, index=['FirstTag'], columns=['OwnerUserId_x'], values=['numQuestionsAnswered'])\n",
    "\n",
    "featureVectorDict = featureVector.to_dict()\n",
    "\n",
    "#assuming both these parameters have the same length it calculates similarity\n",
    "#of two vectors based on the total number of components having different values \n",
    "#in that vector, basically featureVectors are considered as bit vectors \n",
    "def get_similarity(featureVector1,featureVector2):\n",
    "  numDifferentBits = 0\n",
    "  for key in featureVector1:\n",
    "      if (featureVector1[key] == 1 and math.isnan(featureVector2[key])) or(featureVector2[key] == 1 and math.isnan(featureVector1[key])):\n",
    "        numDifferentBits = (numDifferentBits + 1)\n",
    "  return (len(featureVector1.keys()) - numDifferentBits)/len(featureVector1.keys())\n",
    "\n",
    "#this method creates a a dictionary. keys of this dictionary are all possible \n",
    "#pairs of (userId1, userId2) in the list of top hundred users \n",
    "#and the values are the similarty of those users \n",
    "#based on the type of the questions they have answered using hamilton metric \n",
    "def create_similarity_dict(featureVectorDict):\n",
    "  similarityDict = {}\n",
    "  for key1 in featureVectorDict:\n",
    "    for key2 in featureVectorDict:\n",
    "      similarity = get_similarity(featureVectorDict[key1],featureVectorDict[key2])\n",
    "      similarityDict['(' + key1[1] +'#' + key2[1] + ')'] = similarity\n",
    "  return similarityDict\n",
    "\n",
    "similarityDict = create_similarity_dict(featureVectorDict)\n",
    "#print(similarityDict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<span style=\"text-decoration:underline;\">Part B</span> (18 points):** Plot the distance of the top 100 users using a [heatmap](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0a70500860>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "userIdList = []\n",
    "for key in featureVectorDict:\n",
    "  userIdList.append(key[1])\n",
    "\n",
    "similarity_values = []\n",
    "for i in range(len(userIdList)):\n",
    "    listComponent=  []\n",
    "    for j in range (len(userIdList)):\n",
    "      key = '(' + userIdList[i] + '#' + userIdList[j] + ')'\n",
    "      listComponent.append(similarityDict[key])\n",
    "    similarity_values.append(listComponent)\n",
    "#create a data frame out which we can create a heatmap\n",
    "df1 = pd.DataFrame(similarity_values, index=userIdList, columns=userIdList)\n",
    "sns.heatmap(df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}