yanhan/reprod-what-pl-weekends.ipynb Secret

## reprod-what-pl-weekends.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Reproducing \"What Programming Languages Are Used Most on Weekends?\" \n",
    "\n",
    "I have a confession to make. I don't feel that I am a very naturally creative person. I don't mean that my mind is totally empty when it comes to thinking of new ideas, it's just that I find myself entering a state of helplessness whenever I try too hard to come up with totally novel ideas that will make for good publishing material.\n",
    "\n",
    "I am looking to transition to a more machine learning oriented role for my next job. I need practice, lots of it. I lack ideas. Why not replicate what other people have done and get some hands-on practice in the process?\n",
    "\n",
    "A few weeks ago, I read https://stackoverflow.blog/2017/02/07/what-programming-languages-weekends/ . It was pretty interesting. Let's try to replicate their results here.\n",
    "\n",
    "## Source code\n",
    "\n",
    "- IPython notebook at: https://gist.github.com/yanhan/fb630af813b00a179eaad3af7560d1bc\n",
    "- Dataset at: https://www.kaggle.com/stackoverflow/stacklite/downloads/stacklite.zip\n",
    "\n",
    "**NOTE:** The dataset may be updated from time to time. Hence you may not get the same results as Julia Silge and myself. In particular, there's one part below where we use the `statsmodels` `glm` function and there was a `PerfectSeparationError`. You may or may not get that.\n",
    "\n",
    "## A little note\n",
    "\n",
    "As much as possible, I am trying to avoid loading all the data into memory because my machine is not very powerful. So there are certain operations that can probably be done easily using libraries but I have avoided doing that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## About the data\n",
    "\n",
    "The `stacklite.zip` file on Kaggle is 446MB. After decompressing, there are 2 files, `questions.csv` and `question_tags.csv`. `questions.csv` is 862MB and `questions_tags.csv` is 844MB. While it is possible to load the entire dataset into memory, there's no need to. I thought of loading the data into MySQL but then thought again and believed there was no need to.\n",
    "\n",
    "It is pretty obvious that `questions.csv` stores questions data. The first 6 lines (including the header) look like this:\n",
    "\n",
    "```\n",
    "Id,CreationDate,ClosedDate,DeletionDate,Score,OwnerUserId,AnswerCount\n",
    "1,2008-07-31T21:26:37Z,NA,2011-03-28T00:53:47Z,1,NA,0\n",
    "4,2008-07-31T21:42:52Z,NA,NA,458,8,13\n",
    "6,2008-07-31T22:08:08Z,NA,NA,207,9,5\n",
    "8,2008-07-31T23:33:19Z,2013-06-03T04:00:25Z,2015-02-11T08:26:40Z,42,NA,8\n",
    "9,2008-07-31T23:40:59Z,NA,NA,1410,1,58\n",
    "```\n",
    "Pretty self explanatory.\n",
    "\n",
    "The contents of `question_tags.csv` however, stumped me a bit:\n",
    "\n",
    "```\n",
    "Id,Tag\n",
    "1,data\n",
    "4,c#\n",
    "4,winforms\n",
    "4,type-conversion\n",
    "4,decimal\n",
    "4,opacity\n",
    "6,html\n",
    "6,css\n",
    "6,css3\n",
    "6,internet-explorer-7\n",
    "```\n",
    "\n",
    "Initially, I thought it was a list of unique tags. But after checking out [Julia Silge's kernel on kaggle](https://www.kaggle.com/juliasilge/d/stackoverflow/stacklite/weekends-and-weekdays) and upon closer inspection, I realized the `Id` column represents the question id and the `Tag` column is a tag for that question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Library imports, globals and helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter, defaultdict\n",
    "\n",
    "import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "_ASKED_ON_WEEKDAY = 0\n",
    "_ASKED_ON_WEEKEND = 1\n",
    "\n",
    "def _is_weekday(date_string):\n",
    "    return datetime.datetime.strptime(\n",
    "        date_string,\n",
    "        \"%Y-%m-%dT%H:%M:%SZ\"\n",
    "    ).weekday() < 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Check whether data is clean\n",
    "\n",
    "This is actually something we came back to do after some steps in.\n",
    "\n",
    "For `questions.csv`, we check that there are no duplicate question ids.\n",
    "\n",
    "For `question_tags.csv`, we check that there are no duplicate tags for each question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "questions.csv clean? True\n"
     ]
    }
   ],
   "source": [
    "question_ids = set()\n",
    "questions_csv_clean = True\n",
    "with open(\"questions.csv\", \"r\") as f:\n",
    "    # skip header\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        str_question_id, _, _, _, _, _, _ = line.strip().split(\",\")\n",
    "        question_id = int(str_question_id)\n",
    "        if question_id in question_ids:\n",
    "            print(\"Duplicate question id: {}\".format(question_id))\n",
    "            questions_csv_clean = False\n",
    "        else:\n",
    "            question_ids.add(question_id)\n",
    "print(\"questions.csv clean? {}\".format(questions_csv_clean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "del question_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate tag `security` for question id 9800\n",
      "Duplicate tag `localization` for question id 14158\n",
      "Duplicate tag `xslt` for question id 20863\n",
      "Duplicate tag `winapi` for question id 54086\n",
      "Duplicate tag `xslt` for question id 56567\n",
      "Duplicate tag `fun` for question id 56582\n",
      "Duplicate tag `web-applications` for question id 56959\n",
      "Duplicate tag `shell` for question id 72428\n",
      "Duplicate tag `interview-questions` for question id 85753\n",
      "Duplicate tag `security` for question id 87393\n",
      "question_tags.csv clean? False\n"
     ]
    }
   ],
   "source": [
    "question_tags = {}\n",
    "question_tags_csv_clean = True\n",
    "nr_errors = 0\n",
    "with open(\"question_tags.csv\", \"r\") as f:\n",
    "    # skip header\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        str_question_id, tag = line.strip().split(\",\")\n",
    "        question_id = int(str_question_id)\n",
    "        if question_id not in question_tags:\n",
    "            question_tags[question_id] = set([tag])\n",
    "        elif tag in question_tags[question_id]:\n",
    "            print(\"Duplicate tag `{}` for question id {}\".format(\n",
    "                tag, question_id\n",
    "            ))\n",
    "            question_tags_csv_clean = False\n",
    "            nr_errors += 1\n",
    "            if nr_errors >= 10:\n",
    "                break\n",
    "        else:\n",
    "            question_tags[question_id].add(tag)\n",
    "print(\"question_tags.csv clean? {}\".format(question_tags_csv_clean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Uh oh. The `question_tags.csv` file isn't clean. There are a lot more duplicate tags than what I've shown here but going through the entire data set causes my system to run out of memory and to save space I'm not gonna show you here.\n",
    "\n",
    "Before we clean up the data, let us see if the question ids in `question_tags.csv` come in non-decreasing order. It will simplify the cleaning up work if that's the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are question ids in question_tags.csv non-decreasing? True\n"
     ]
    }
   ],
   "source": [
    "previous_question_id = -10**9\n",
    "question_ids_non_decreasing = True\n",
    "with open(\"question_tags.csv\", \"r\") as f:\n",
    "    # skip header\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        str_question_id, _ = line.strip().split(\",\")\n",
    "        question_id = int(str_question_id)\n",
    "        if question_id < previous_question_id:\n",
    "            print(\"question ids do not come in non-decreasing order. In particular, {} comes after {}\".format(\n",
    "                question_id, previous_question_id\n",
    "            ))\n",
    "            question_ids_non_decreasing = False\n",
    "            break\n",
    "        else:\n",
    "            previous_question_id = question_id\n",
    "print(\"Are question ids in question_tags.csv non-decreasing? {}\".format(\n",
    "    question_ids_non_decreasing\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Nice. This vastly simplifies the clean-up work in that we only need to store all tags for the current question and upon encountering a larger question id, we write the tags for the current question to the new csv file. Rinse and repeat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Cleaning up `question_tags.csv`\n",
    "\n",
    "**NOTE: ** The code in the following cell creates a `question_tags_clean.csv` file on your system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with open(\"question_tags_clean.csv\", \"w\") as out_f:\n",
    "    out_f.write(\"Id,Tag\\n\")\n",
    "    with open(\"question_tags.csv\", \"r\") as in_f:\n",
    "        # skip header\n",
    "        in_f.readline()\n",
    "        current_question_id = None\n",
    "        current_question_tags = set()\n",
    "        for line in in_f:\n",
    "            str_question_id, tag = line.strip().split(\",\")\n",
    "            question_id = int(str_question_id)\n",
    "            if question_id != current_question_id:\n",
    "                # flush previous question's tags to file\n",
    "                for t in current_question_tags:\n",
    "                    out_f.write(\"{},{}\\n\".format(current_question_id, t))\n",
    "                current_question_id = question_id\n",
    "                current_question_tags = set([tag,])\n",
    "            else:\n",
    "                current_question_tags.add(tag)\n",
    "    if current_question_id is not None:\n",
    "        for tag in current_question_tags:\n",
    "            out_f.write(\"{},{}\\n\".format(current_question_id, tag))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Diving in\n",
    "\n",
    "Let us first determine how many unique tags there are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58256"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_tags = set()\n",
    "with open(\"question_tags_clean.csv\", \"r\") as f:\n",
    "    # Skip header line\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        _, tag = line.split(\",\")\n",
    "        unique_tags.add(tag)\n",
    "len(unique_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, let us extract all the tags, along with their associated questions, then only keep those tags with over 20,000 questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# tags with >= 20,000 questions: 319\n"
     ]
    }
   ],
   "source": [
    "tags_to_questions = defaultdict(set)\n",
    "with open(\"question_tags_clean.csv\", \"r\") as f:\n",
    "    # Skip header line\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        question_id, tag = line.strip().split(\",\")\n",
    "        tags_to_questions[tag.strip()].add(int(question_id))\n",
    "over20k_tags_to_questions = {}\n",
    "for tag, questions_set in tags_to_questions.items():\n",
    "    if len(questions_set) >= 20000:\n",
    "        over20k_tags_to_questions[tag] = questions_set\n",
    "del tags_to_questions\n",
    "print(\"# tags with >= 20,000 questions: {}\".format(len(over20k_tags_to_questions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's dump this subset of data to a file. We will shutdown this Jupyter notebook and load that smaller data set back in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with open(\"over20k_tags_to_questions.txt\", \"w\") as f:\n",
    "    for tag, questions_set in over20k_tags_to_questions.items():\n",
    "        f.write(\"{}:{}\\n\".format(tag, \",\".join(map(str, questions_set))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's load the subset of data back in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "over20k_tags_to_questions = {}\n",
    "with open(\"over20k_tags_to_questions.txt\", \"r\") as f:\n",
    "    for line in f:\n",
    "        tag, questions = line.split(\":\")\n",
    "        over20k_tags_to_questions[tag] = set(map(int, questions.split(\",\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## How many questions on weekdays? How many questions on weekends?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nr_questions_on_weekdays = 0\n",
    "nr_questions_on_weekends = 0\n",
    "with open(\"questions.csv\", \"r\") as f:\n",
    "    # Skip header line\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        _, creation_date, _, _, _, _, _ = line.strip().split(\",\")\n",
    "        if creation_date != \"NA\":\n",
    "            if _is_weekday(creation_date):\n",
    "                nr_questions_on_weekdays += 1\n",
    "            else:\n",
    "                nr_questions_on_weekends += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# questions on weekdays: 14227389\n",
      "# questions on weekends: 2976435\n"
     ]
    }
   ],
   "source": [
    "print(\"# questions on weekdays: {}\".format(nr_questions_on_weekdays))\n",
    "print(\"# questions on weekends: {}\".format(nr_questions_on_weekends))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Those are quite different numbers from those in Julia Silge's post:\n",
    "\n",
    "> Overall, this includes 10,451,274 questions on weekdays and 2,132,073 questions on weekends.\n",
    "\n",
    "Probably the data set was updated some time after the post was written.\n",
    "\n",
    "Something struck me for the next step. What does the author mean by relative frequency? Relative to what? Turns out she included the definition:\n",
    "\n",
    "> Instead, let’s explore which tags made up a larger share of weekend questions than they did of weekday questions, and vice versa.\n",
    "\n",
    "So we're gonna go through all the tags with at least 20,000 questions, find out how many of the questions for each tag were posted on weekdays and weekends, then do a comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Get all question ids for tags with at least 20,000 questions\n",
    "qns_with_some_tag_over_20k_qns = set()\n",
    "i = 0\n",
    "for questions_set in over20k_tags_to_questions.values():\n",
    "    qns_with_some_tag_over_20k_qns.update(questions_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# For questions we are interested in, get whether they are asked during\n",
    "# weekday or weekend\n",
    "qoi_to_wday_wend = dict.fromkeys(\n",
    "    qns_with_some_tag_over_20k_qns,\n",
    "    -1\n",
    ")\n",
    "with open(\"questions.csv\", \"r\") as f:\n",
    "    # skip header\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        str_question_id, creation_date, _, _, _, _, _ = line.strip().split(\",\")\n",
    "        question_id = int(str_question_id)\n",
    "        if question_id in qoi_to_wday_wend and creation_date != \"NA\":\n",
    "            if datetime.datetime.strptime(\n",
    "                creation_date,\n",
    "                \"%Y-%m-%dT%H:%M:%SZ\"\n",
    "            ).weekday() < 5:\n",
    "                qoi_to_wday_wend[question_id] = _ASKED_ON_WEEKDAY\n",
    "            else:\n",
    "                qoi_to_wday_wend[question_id] = _ASKED_ON_WEEKEND\n",
    "\n",
    "\n",
    "# Now go through all the tags with over 20,000 questions and count\n",
    "# the # of questions asked during weekdays vs. weekends\n",
    "tag_to_nr_qns_on_wday = Counter()\n",
    "tag_to_nr_qns_on_wend = Counter()\n",
    "for tag, questions_set in over20k_tags_to_questions.items():\n",
    "    tag_to_nr_qns_on_wday[tag] = tag_to_nr_qns_on_wend[tag] = 0\n",
    "    for question_id in questions_set:\n",
    "        if qoi_to_wday_wend[question_id] == _ASKED_ON_WEEKDAY:\n",
    "            tag_to_nr_qns_on_wday[tag] += 1\n",
    "        elif qoi_to_wday_wend[question_id] == _ASKED_ON_WEEKEND:\n",
    "            tag_to_nr_qns_on_wend[tag] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('selenium', 1.6212588979148064), ('wcf', 1.6553375679897844), ('excel', 1.7083207272949648), ('internet-explorer', 1.7140582549884504), ('oracle', 1.7265489637766018), ('excel-vba', 1.7414659008716018), ('svn', 1.7510314654119836), ('sql-server-2008', 1.7575099770444054), ('selenium-webdriver', 1.7661248332256643), ('iis', 1.774721760837079), ('vba', 1.8236177815794705), ('xslt', 1.8283513564212965), ('soap', 1.8400984372730709), ('extjs', 1.8717772104551975), ('powershell', 2.0238479773037628), ('tsql', 2.074704473127169), ('sql-server-2005', 2.1723285999710455), ('jenkins', 2.4758333202566383), ('sharepoint', 2.8651044383099826), ('reporting-services', 3.6477980122694946)]\n",
      "[('parse.com', 1.3503814514822798), ('math', 1.3551432364197442), ('class', 1.3594760064146303), ('arraylist', 1.3600894357955087), ('firebase', 1.362822521786504), ('recursion', 1.3714568879068183), ('graphics', 1.377539748907886), ('random', 1.3820943836369635), ('methods', 1.3824713337624932), ('data-structures', 1.3913036081744454), ('python-3.x', 1.3983445990946155), ('swing', 1.400912744220479), ('google-chrome-extension', 1.4026948786028015), ('c', 1.4271826837540982), ('mysqli', 1.4483936702618714), ('pointers', 1.5043601718360466), ('algorithm', 1.5074228566575227), ('opengl', 1.604622444469804), ('assembly', 1.663642477327844), ('haskell', 1.683808410440941)]\n"
     ]
    }
   ],
   "source": [
    "# Get top 20 tags with higher relative frequency of questions asked during weekdays\n",
    "\n",
    "_INF = 10**9\n",
    "wday_rel_freq_list = []\n",
    "wend_rel_freq_list = []\n",
    "total_nr_wday_qns = sum(tag_to_nr_qns_on_wday.values())\n",
    "total_nr_wend_qns = sum(tag_to_nr_qns_on_wend.values())\n",
    "for tag in over20k_tags_to_questions:\n",
    "    wday_nr_qns = tag_to_nr_qns_on_wday[tag]\n",
    "    wend_nr_qns = tag_to_nr_qns_on_wend[tag]\n",
    "    wday_freq = wday_nr_qns / total_nr_wday_qns\n",
    "    wend_freq = wend_nr_qns / total_nr_wend_qns\n",
    "    if wend_freq == 0:\n",
    "        wday_rel_freq_list.append((tag, _INF,))\n",
    "    else:\n",
    "        wday_rel_freq_list.append((tag, wday_freq / wend_freq,))\n",
    "    if wday_freq == 0:\n",
    "        wend_rel_freq_list.append((tag, _INF,))\n",
    "    else:\n",
    "        wend_rel_freq_list.append((tag, wend_freq / wday_freq,))\n",
    "\n",
    "wday_rel_freq_list.sort(key=lambda t: t[1])\n",
    "print(wday_rel_freq_list[-20:])\n",
    "\n",
    "wend_rel_freq_list.sort(key=lambda t: t[1])\n",
    "print(wend_rel_freq_list[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fNr4jUvnapgPJV0V9khyh9Cbg4Oj8rlebypSBxbYlCzb2iQjXvJuZTaMc5JqZ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uo/C5IkNUJmPgkcAvwoIn5KNf344frpXwE/AK4ApmXmY8BlEXEzcAzwBaqbWv0WOC0irgQ+S3UT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\nKmAPfPTYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc883f71438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wday_df = pd.DataFrame(\n",
    "    list(\n",
    "        map(\n",
    "            lambda t: [t[0], float(t[1]) * 100],\n",
    "            wday_rel_freq_list[-20:][::-1]\n",
    "        )\n",
    "    ),\n",
    "    columns=[\"tag\", \"relative_frequency\"],\n",
    ")\n",
    "wend_df = pd.DataFrame(\n",
    "    list(\n",
    "        map(\n",
    "            lambda t: [t[0], float(t[1]) * 100],\n",
    "            wend_rel_freq_list[-20:][::-1]\n",
    "        )\n",
    "    ),\n",
    "    columns=[\"tag\", \"relative_frequency\"],\n",
    ")\n",
    "fig, (ax1, ax2,) = plt.subplots(ncols=2, figsize=(15,15,))\n",
    "sns.barplot(\n",
    "    x=\"relative_frequency\",\n",
    "    y=\"tag\", data=wday_df,\n",
    "    color=sns.xkcd_rgb[\"coral\"],\n",
    "    ax=ax1,\n",
    ")\n",
    "sns.barplot(\n",
    "    x=\"relative_frequency\",\n",
    "    y=\"tag\",\n",
    "    data=wend_df,\n",
    "    color=sns.xkcd_rgb[\"teal\"],\n",
    "    ax=ax2,\n",
    ")\n",
    "plt.subplots_adjust(wspace=0.2)\n",
    "plt.xticks([0, 50, 100, 150, 200, 250, 300, 350, 400,])\n",
    "plt.subplots_adjust(top=0.9)\n",
    "fig.suptitle(\n",
    "    \"Which tags have the biggest weekday/weekend differences?\\nFor tags with more than 20,000 questions\",\n",
    "    size=20,\n",
    ")\n",
    "ax1.set_title(\"Weekdays\")\n",
    "ax1.set_xlabel(\"\")\n",
    "ax1.set_ylabel(\"\")\n",
    "ax2.set_title(\"Weekends\")\n",
    "ax2.set_xlabel(\"\")\n",
    "ax2.set_ylabel(\"\")\n",
    "fig.text(0.5, 0.09, \"Relative frequency\", ha=\"center\", size=18,);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "The next part deals with tags with biggest decrease in weekend activity. Here, decrease means the change between years 2016 and 2008 (ignoring trends in between those years)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "qns_with_some_tag_over_20k_qns__to__tags = defaultdict(set)\n",
    "for tag, questions_set in over20k_tags_to_questions.items():\n",
    "    for question_id in questions_set:\n",
    "        qns_with_some_tag_over_20k_qns__to__tags[question_id].add(tag)\n",
    "\n",
    "def _add_to_relevant_count(\n",
    "        tags_activity_dict,\n",
    "        question_id,\n",
    "        creation_date\n",
    "    ):\n",
    "    for tag in qns_with_some_tag_over_20k_qns__to__tags[question_id]:\n",
    "        if tag not in tags_activity_dict:\n",
    "            tags_activity_dict[tag] = (0, 0,)\n",
    "        wday_cnt, wend_cnt = tags_activity_dict[tag]\n",
    "        if _is_weekday(creation_date):\n",
    "            tags_activity_dict[tag] = (wday_cnt + 1, wend_cnt,)\n",
    "        else:\n",
    "            tags_activity_dict[tag] = (wday_cnt, wend_cnt + 1,)\n",
    "\n",
    "tags_with_over20k_qns_2008_activity = {}\n",
    "tags_with_over20k_qns_2016_activity = {}\n",
    "with open(\"questions.csv\", \"r\") as f:\n",
    "    # skip header\n",
    "    f.readline()\n",
    "    for line in f:\n",
    "        str_question_id, creation_date, _, _, _, _, _ = line.strip().split(\",\")\n",
    "        question_id = int(str_question_id)\n",
    "        if question_id in qns_with_some_tag_over_20k_qns__to__tags:\n",
    "            str_year_of_creation = creation_date[:4]\n",
    "            if str_year_of_creation == \"2008\":\n",
    "                _add_to_relevant_count(\n",
    "                    tags_with_over20k_qns_2008_activity,\n",
    "                    question_id,\n",
    "                    creation_date\n",
    "                )\n",
    "            elif str_year_of_creation == \"2016\":\n",
    "                _add_to_relevant_count(\n",
    "                    tags_with_over20k_qns_2016_activity,\n",
    "                    question_id,\n",
    "                    creation_date\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "And... in the above step, my computer ran out of memory. Lol. Time to spin up an EC2 instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('canvas', 0.4444444444444444, 0.25225225225225223), ('grails', 0.3442622950819672, 0.14285714285714285), ('numpy', 0.5, 0.24088171798659244), ('magento', 0.5, 0.13784037558685447), ('twitter', 0.625, 0.24564134495641346), ('html5', 0.6666666666666666, 0.22732272069464543), ('multidimensional-array', 0.75, 0.27277716794731066), ('web', 1.0, 0.26509023024268824), ('python-2.7', 1.0, 0.2331143105614069), ('razor', 1.0, 0.15828285488543586)]\n",
      "[('file-io', 0.12857142857142856, 0.28909090909090907), ('inheritance', 0.09836065573770492, 0.26060452120904243), ('nginx', 0.058823529411764705, 0.22553614543388723), ('opengl', 0.18032786885245902, 0.3578835416112736), ('login', 0.04838709677419355, 0.24481910783280647), ('uitableview', 0.04, 0.25097198015819816), ('swing', 0.12690355329949238, 0.34034754276405105), ('methods', 0.1, 0.31420137750081994), ('javafx', 0.07142857142857142, 0.3006620377845955), ('arraylist', 0.043478260869565216, 0.30917874396135264)]\n",
      "(21, 6)\n",
      "(15005, 2988)\n",
      "[('random', 0.16326530612244897, 0.31997571341833636), ('c', 0.21157495256166983, 0.3249356802608452), ('data-structures', 0.20625, 0.3303324099722992), ('algorithm', 0.18421052631578946, 0.34018177652180553), ('swing', 0.12690355329949238, 0.34034754276405105), ('pointers', 0.25, 0.34140249759846303), ('graphics', 0.20765027322404372, 0.3479816044966786), ('opengl', 0.18032786885245902, 0.3578835416112736), ('haskell', 0.22077922077922077, 0.36882393876130826), ('assembly', 0.32051282051282054, 0.3778652465848576)]\n"
     ]
    }
   ],
   "source": [
    "tags_wend_over_wday_ratio = []\n",
    "tags_with_zero_count = 0\n",
    "for tag in tags_with_over20k_qns_2008_activity:\n",
    "    if tag in tags_with_over20k_qns_2016_activity:\n",
    "        wday_2008, wend_2008 = tags_with_over20k_qns_2008_activity[tag]\n",
    "        wday_2016, wend_2016 = tags_with_over20k_qns_2016_activity[tag]\n",
    "        # Let's ignore tags that have 0 counts, because one of the ratios will be\n",
    "        # infinity\n",
    "        if wday_2008 != 0 and wend_2008 != 0 and wday_2016 != 0 and wend_2016 != 0:\n",
    "            tags_wend_over_wday_ratio.append(\n",
    "                (tag, wend_2008 / wday_2008, wend_2016 / wday_2016,)\n",
    "            )\n",
    "tags_wend_over_wday_ratio.sort(\n",
    "    key=lambda t: t[1] - t[2]\n",
    ")\n",
    "print(tags_wend_over_wday_ratio[-10:])\n",
    "print(sorted(tags_wend_over_wday_ratio, key=lambda t: t[2] - t[1])[-10:])\n",
    "#print(tags_with_over20k_qns_2008_activity)\n",
    "print(tags_with_over20k_qns_2008_activity[\"scala\"])\n",
    "print(tags_with_over20k_qns_2016_activity[\"scala\"])\n",
    "print(sorted(tags_wend_over_wday_ratio, key=lambda t: t[2])[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Looks very different from what's in Julia Silge's post. In fact, upon reading the R code in her Kaggle kernel, I found that our approach is completely different. Let's redo everything."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Redo\n",
    "\n",
    "Turns out that in Julia Silge's kernel, she first filters out all questions whose deletion date is NA. Let's do that and drop unnecessary columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2008-07-31T21:42:52Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>2008-07-31T22:08:08Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>2008-07-31T23:40:59Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>2008-07-31T23:55:37Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>2008-08-01T00:42:38Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id          CreationDate\n",
       "1   4  2008-07-31T21:42:52Z\n",
       "2   6  2008-07-31T22:08:08Z\n",
       "4   9  2008-07-31T23:40:59Z\n",
       "5  11  2008-07-31T23:55:37Z\n",
       "6  13  2008-08-01T00:42:38Z"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions = pd.read_csv(\"questions.csv\")\n",
    "questions = questions.loc[questions[\"DeletionDate\"].isnull(), :]\n",
    "questions.pop(\"DeletionDate\")\n",
    "questions.pop(\"ClosedDate\")\n",
    "questions.pop(\"Score\")\n",
    "questions.pop(\"OwnerUserId\")\n",
    "questions.pop(\"AnswerCount\")\n",
    "questions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we load all the tags for the questions, but only for those questions which have not been deleted. We do this by using the `merge` method of `pandas.DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "question_tags_clean = pd.read_csv(\"question_tags_clean.csv\")\n",
    "question_tags_clean = question_tags_clean.merge(\n",
    "    questions,\n",
    "    on=\"Id\",\n",
    "    how=\"inner\",\n",
    ")\n",
    "question_tags_clean.pop(\"CreationDate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We are only interested in tags which have over 20,000 questions. Time to do some filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tags_with_counts = question_tags_clean.groupby(\"Tag\").count()\n",
    "tags_with_counts = tags_with_counts.reset_index()\n",
    "tags_with_counts.columns = [\"Tag\", \"Count\"]\n",
    "tags_with_counts = tags_with_counts[tags_with_counts.Count > 20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "question_tags_clean = question_tags_clean.merge(\n",
    "    tags_with_counts,\n",
    "    on=\"Tag\",\n",
    "    how=\"inner\",\n",
    ")\n",
    "question_tags_clean.pop(\"Count\")\n",
    "\n",
    "del tags_with_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's obtain the set of questions with some tag that has over 20,000 questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "questions_with_tag_over_20k = questions.merge(\n",
    "    pd.DataFrame({\"Id\": question_tags_clean[\"Id\"].unique(),}),\n",
    "    on=\"Id\",\n",
    "    how=\"inner\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "questions_with_tag_over_20k[\"Weekday\"] = \\\n",
    "    questions_with_tag_over_20k[\"CreationDate\"].apply(_is_weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_wday_counts = question_tags_clean.merge(\n",
    "    questions_with_tag_over_20k,\n",
    "    on=\"Id\",\n",
    "    how=\"inner\"\n",
    ").groupby([\"Tag\", \"Weekday\"]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">.htaccess</th>\n",
       "      <th>False</th>\n",
       "      <td>10609</td>\n",
       "      <td>10609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>44636</td>\n",
       "      <td>44636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">.net</th>\n",
       "      <th>False</th>\n",
       "      <td>32613</td>\n",
       "      <td>32613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>214170</td>\n",
       "      <td>214170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actionscript-3</th>\n",
       "      <th>False</th>\n",
       "      <td>7179</td>\n",
       "      <td>7179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Id  CreationDate\n",
       "Tag            Weekday                      \n",
       ".htaccess      False     10609         10609\n",
       "               True      44636         44636\n",
       ".net           False     32613         32613\n",
       "               True     214170        214170\n",
       "actionscript-3 False      7179          7179"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_wday_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's get rid of the hierarchical index formed by `groupby`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.htaccess</td>\n",
       "      <td>False</td>\n",
       "      <td>10609</td>\n",
       "      <td>10609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.htaccess</td>\n",
       "      <td>True</td>\n",
       "      <td>44636</td>\n",
       "      <td>44636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.net</td>\n",
       "      <td>False</td>\n",
       "      <td>32613</td>\n",
       "      <td>32613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.net</td>\n",
       "      <td>True</td>\n",
       "      <td>214170</td>\n",
       "      <td>214170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>False</td>\n",
       "      <td>7179</td>\n",
       "      <td>7179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Tag Weekday      Id  CreationDate\n",
       "0       .htaccess   False   10609         10609\n",
       "1       .htaccess    True   44636         44636\n",
       "2            .net   False   32613         32613\n",
       "3            .net    True  214170        214170\n",
       "4  actionscript-3   False    7179          7179"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_wday_counts = tag_wday_counts.reset_index()\n",
    "tag_wday_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The `CreationDate` column is redundant. Let's remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.htaccess</td>\n",
       "      <td>False</td>\n",
       "      <td>10609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.htaccess</td>\n",
       "      <td>True</td>\n",
       "      <td>44636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.net</td>\n",
       "      <td>False</td>\n",
       "      <td>32613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.net</td>\n",
       "      <td>True</td>\n",
       "      <td>214170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>False</td>\n",
       "      <td>7179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Tag Weekday   Count\n",
       "0       .htaccess   False   10609\n",
       "1       .htaccess    True   44636\n",
       "2            .net   False   32613\n",
       "3            .net    True  214170\n",
       "4  actionscript-3   False    7179"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_wday_counts.pop(\"CreationDate\")\n",
    "tag_wday_counts.columns = [\"Tag\", \"Weekday\", \"Count\"]\n",
    "tag_wday_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We want something like `spread` in tidyr. I saw on https://chrisalbon.com/python/pandas_long_to_wide.html that we can use `pivot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_wday_counts = tag_wday_counts.pivot(\n",
    "    index=\"Tag\",\n",
    "    columns=\"Weekday\",\n",
    "    values=\"Count\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Weekday</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>.htaccess</th>\n",
       "      <td>10609</td>\n",
       "      <td>44636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>.net</th>\n",
       "      <td>32613</td>\n",
       "      <td>214170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actionscript-3</th>\n",
       "      <td>7179</td>\n",
       "      <td>33219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activerecord</th>\n",
       "      <td>4093</td>\n",
       "      <td>18690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ajax</th>\n",
       "      <td>28791</td>\n",
       "      <td>133425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Weekday         False   True \n",
       "Tag                          \n",
       ".htaccess       10609   44636\n",
       ".net            32613  214170\n",
       "actionscript-3   7179   33219\n",
       "activerecord     4093   18690\n",
       "ajax            28791  133425"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_wday_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's remove the hierarchical index using `reset_index`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Weekday</th>\n",
       "      <th>Tag</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.htaccess</td>\n",
       "      <td>10609</td>\n",
       "      <td>44636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.net</td>\n",
       "      <td>32613</td>\n",
       "      <td>214170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>7179</td>\n",
       "      <td>33219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>activerecord</td>\n",
       "      <td>4093</td>\n",
       "      <td>18690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ajax</td>\n",
       "      <td>28791</td>\n",
       "      <td>133425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Weekday             Tag  False    True\n",
       "0             .htaccess  10609   44636\n",
       "1                  .net  32613  214170\n",
       "2        actionscript-3   7179   33219\n",
       "3          activerecord   4093   18690\n",
       "4                  ajax  28791  133425"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_wday_counts = tag_wday_counts.reset_index()\n",
    "tag_wday_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's rename the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.htaccess</td>\n",
       "      <td>10609</td>\n",
       "      <td>44636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.net</td>\n",
       "      <td>32613</td>\n",
       "      <td>214170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>7179</td>\n",
       "      <td>33219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>activerecord</td>\n",
       "      <td>4093</td>\n",
       "      <td>18690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ajax</td>\n",
       "      <td>28791</td>\n",
       "      <td>133425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Tag  Weekend  Weekday\n",
       "0       .htaccess    10609    44636\n",
       "1            .net    32613   214170\n",
       "2  actionscript-3     7179    33219\n",
       "3    activerecord     4093    18690\n",
       "4            ajax    28791   133425"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_wday_counts.columns=[\"Tag\", \"Weekend\", \"Weekday\"]\n",
    "tag_wday_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To obtain the rate of questions asked during weekends and weekdays for each tag, we have to calculate the total number of questions asked during weekdays and weekends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>2250067</td>\n",
       "      <td>2250067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10990496</td>\n",
       "      <td>10990496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  CreationDate\n",
       "Weekday                        \n",
       "False     2250067       2250067\n",
       "True     10990496      10990496"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions[\"Weekday\"] = questions[\"CreationDate\"].apply(_is_weekday)\n",
    "x = questions.groupby(\"Weekday\").count()\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nr_wday_qns = x.loc[True, \"Id\"]\n",
    "nr_wend_qns = x.loc[False, \"Id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = tag_wday_counts.copy()\n",
    "x[\"Weekday\"] /= nr_wday_qns\n",
    "x[\"Weekend\"] /= nr_wend_qns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[x[\"Weekday\"] == 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x[\"WeekendOverWeekday\"] = x[\"Weekend\"] / x[\"Weekday\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>WeekendOverWeekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>haskell</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.002094</td>\n",
       "      <td>1.751218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>assembly</td>\n",
       "      <td>0.002727</td>\n",
       "      <td>0.001587</td>\n",
       "      <td>1.718643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>opengl</td>\n",
       "      <td>0.003114</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>1.644761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>pointers</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>0.002361</td>\n",
       "      <td>1.529601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>algorithm</td>\n",
       "      <td>0.007548</td>\n",
       "      <td>0.005048</td>\n",
       "      <td>1.495337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>c</td>\n",
       "      <td>0.024511</td>\n",
       "      <td>0.016914</td>\n",
       "      <td>1.449149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>python-3.x</td>\n",
       "      <td>0.005104</td>\n",
       "      <td>0.003557</td>\n",
       "      <td>1.434771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>swing</td>\n",
       "      <td>0.006523</td>\n",
       "      <td>0.004567</td>\n",
       "      <td>1.428346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>random</td>\n",
       "      <td>0.002008</td>\n",
       "      <td>0.001410</td>\n",
       "      <td>1.424809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>recursion</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>1.404361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>arraylist</td>\n",
       "      <td>0.002059</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>1.385408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>class</td>\n",
       "      <td>0.004584</td>\n",
       "      <td>0.003317</td>\n",
       "      <td>1.381949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>vector</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>1.376647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>math</td>\n",
       "      <td>0.002527</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>1.350148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>heroku</td>\n",
       "      <td>0.002220</td>\n",
       "      <td>0.001647</td>\n",
       "      <td>1.348040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>c++11</td>\n",
       "      <td>0.003423</td>\n",
       "      <td>0.002540</td>\n",
       "      <td>1.348003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Tag   Weekend   Weekday  WeekendOverWeekday\n",
       "98      haskell  0.003667  0.002094            1.751218\n",
       "26     assembly  0.002727  0.001587            1.718643\n",
       "161      opengl  0.003114  0.001893            1.644761\n",
       "171    pointers  0.003611  0.002361            1.529601\n",
       "5     algorithm  0.007548  0.005048            1.495337\n",
       "35            c  0.024511  0.016914            1.449149\n",
       "177  python-3.x  0.005104  0.003557            1.434771\n",
       "217       swing  0.006523  0.004567            1.428346\n",
       "180      random  0.002008  0.001410            1.424809\n",
       "183   recursion  0.002406  0.001713            1.404361\n",
       "19    arraylist  0.002059  0.001486            1.385408\n",
       "44        class  0.004584  0.003317            1.381949\n",
       "237      vector  0.001995  0.001449            1.376647\n",
       "137        math  0.002527  0.001871            1.350148\n",
       "99       heroku  0.002220  0.001647            1.348040\n",
       "39        c++11  0.003423  0.002540            1.348003"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.sort_values(by=\"WeekendOverWeekday\", ascending=False,)\n",
    "x[:16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "These are the exact same tags as that in the original post and in the same order. Now let's try getting at the highest weekday / weekend activity tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>WeekendOverWeekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>sharepoint</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>0.001825</td>\n",
       "      <td>0.359364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>jenkins</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.001690</td>\n",
       "      <td>0.412106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>tsql</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.003560</td>\n",
       "      <td>0.494494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>powershell</td>\n",
       "      <td>0.001753</td>\n",
       "      <td>0.003463</td>\n",
       "      <td>0.506278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>soap</td>\n",
       "      <td>0.000938</td>\n",
       "      <td>0.001722</td>\n",
       "      <td>0.544416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>vba</td>\n",
       "      <td>0.003418</td>\n",
       "      <td>0.006181</td>\n",
       "      <td>0.552990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>extjs</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>0.001748</td>\n",
       "      <td>0.553204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>sql-server-2008</td>\n",
       "      <td>0.002265</td>\n",
       "      <td>0.003982</td>\n",
       "      <td>0.568767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>xslt</td>\n",
       "      <td>0.001255</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>0.571494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>iis</td>\n",
       "      <td>0.001230</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>0.573637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>oracle</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.006498</td>\n",
       "      <td>0.574247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>excel-vba</td>\n",
       "      <td>0.002684</td>\n",
       "      <td>0.004599</td>\n",
       "      <td>0.583745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>excel</td>\n",
       "      <td>0.005340</td>\n",
       "      <td>0.009138</td>\n",
       "      <td>0.584368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>svn</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.595177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>internet-explorer</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>0.603327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>sql-server</td>\n",
       "      <td>0.009528</td>\n",
       "      <td>0.015332</td>\n",
       "      <td>0.621409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Tag   Weekend   Weekday  WeekendOverWeekday\n",
       "199         sharepoint  0.000656  0.001825            0.359364\n",
       "119            jenkins  0.000696  0.001690            0.412106\n",
       "223               tsql  0.001760  0.003560            0.494494\n",
       "174         powershell  0.001753  0.003463            0.506278\n",
       "202               soap  0.000938  0.001722            0.544416\n",
       "236                vba  0.003418  0.006181            0.552990\n",
       "76               extjs  0.000967  0.001748            0.553204\n",
       "209    sql-server-2008  0.002265  0.003982            0.568767\n",
       "257               xslt  0.001255  0.002195            0.571494\n",
       "106                iis  0.001230  0.002144            0.573637\n",
       "163             oracle  0.003731  0.006498            0.574247\n",
       "73           excel-vba  0.002684  0.004599            0.583745\n",
       "72               excel  0.005340  0.009138            0.584368\n",
       "215                svn  0.001171  0.001968            0.595177\n",
       "111  internet-explorer  0.001700  0.002817            0.603327\n",
       "208         sql-server  0.009528  0.015332            0.621409"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[-16:][::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Barring the incorrect numbers in the `WeekendOverWeekday` column which can be corrected by taking reciprocals, the tags are the same as those in the original post and in the same order.\n",
    "\n",
    "Now let's plot the bar charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "highest_wend_plot_df = x[:16].loc[:, [\"Tag\", \"WeekendOverWeekday\"]]\n",
    "highest_wend_plot_df[\"WeekendOverWeekday\"] *= 100\n",
    "\n",
    "highest_wday_plot_df = x[-16:].loc[:, [\"Tag\", \"WeekendOverWeekday\"]][::-1]\n",
    "highest_wday_plot_df[\"WeekendOverWeekday\"] = 1 / highest_wday_plot_df[\"WeekendOverWeekday\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3yqWrZempsSukZwRk79UBL7h77+rlrDwzW5eYi35VYg72fxNPFD6nOEd+eijb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb95c218a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2,) = plt.subplots(ncols=2, figsize=(12,12,))\n",
    "sns.barplot(\n",
    "    x=\"WeekendOverWeekday\",\n",
    "    y=\"Tag\",\n",
    "    data=highest_wday_plot_df,\n",
    "    color=sns.xkcd_rgb[\"coral\"],\n",
    "    ax=ax1,\n",
    ")\n",
    "sns.barplot(\n",
    "    x=\"WeekendOverWeekday\",\n",
    "    y=\"Tag\",\n",
    "    data=highest_wend_plot_df,\n",
    "    color=sns.xkcd_rgb[\"teal\"],\n",
    "    ax=ax2,\n",
    ")\n",
    "plt.subplots_adjust(wspace=0.2)\n",
    "plt.xticks([0, 50, 100, 150, 200, 250, 300,])\n",
    "plt.subplots_adjust(top=0.9)\n",
    "fig.suptitle(\n",
    "    \"Which tags have the biggest weekday/weekend differences?\\nFor tags with more than 20,000 questions\",\n",
    "    size=20,\n",
    ")\n",
    "ax1.set_title(\"Weekdays\")\n",
    "ax1.set_xlabel(\"\")\n",
    "ax1.set_ylabel(\"\")\n",
    "ax2.set_title(\"Weekends\")\n",
    "ax2.set_xlabel(\"\")\n",
    "ax2.set_ylabel(\"\")\n",
    "fig.text(0.5, 0.07, \"Relative frequency\", ha=\"center\", size=18,);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Nice. So that's one mystery solved. Now comes the harder part.\n",
    "\n",
    "For the next part, we will need to compute for each tag in tags with over 20,000 questions:\n",
    "1. The total number of questions with that tag (for each year)\n",
    "2. The total number of questions with that tag and were asked during weekends (for each year)\n",
    "\n",
    "And for each year, we need to compute the number of questions asked during weekends and weekdays. Let's do this first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Add year information to `questions`\n",
    "questions[\"Year\"] = questions[\"CreationDate\"].apply(\n",
    "    lambda datestring: datestring[:4]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>False</td>\n",
       "      <td>7506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>True</td>\n",
       "      <td>51254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "      <td>51778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>True</td>\n",
       "      <td>293509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>114376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Weekday   Total\n",
       "0  2008   False    7506\n",
       "1  2008    True   51254\n",
       "2  2009   False   51778\n",
       "3  2009    True  293509\n",
       "4  2010   False  114376"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_wday_counts = questions.copy()\n",
    "year_wday_counts = year_wday_counts.groupby(\n",
    "    [\"Year\", \"Weekday\"]\n",
    ").count()\n",
    "year_wday_counts = year_wday_counts.reset_index()\n",
    "year_wday_counts.pop(\"Id\")\n",
    "year_wday_counts.rename(\n",
    "    columns={\n",
    "        \"CreationDate\": \"Total\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "year_wday_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Seems correct enough to me. Moving on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Earlier we computed:\n",
    "\n",
    "`questions_with_tag_over_20k`: the set of all questions with some tag that has over 20,000 questions\n",
    "\n",
    "`tag_wday_counts`: the set of all tags where each tag has over 20,000 questions along with the breakdown of the number of questions asked during weekdays and weekends\n",
    "\n",
    "`question_tags_clean`: the cleaned data originally from `question_tags.csv`\n",
    "\n",
    "Let's take a look at them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2008-07-31T21:42:52Z</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>2008-07-31T22:08:08Z</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>2008-07-31T23:40:59Z</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>2008-07-31T23:55:37Z</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>2008-08-01T00:42:38Z</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id          CreationDate Weekday\n",
       "0   4  2008-07-31T21:42:52Z    True\n",
       "1   6  2008-07-31T22:08:08Z    True\n",
       "2   9  2008-07-31T23:40:59Z    True\n",
       "3  11  2008-07-31T23:55:37Z    True\n",
       "4  13  2008-08-01T00:42:38Z    True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_with_tag_over_20k.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.htaccess</td>\n",
       "      <td>10609</td>\n",
       "      <td>44636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.net</td>\n",
       "      <td>32613</td>\n",
       "      <td>214170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>7179</td>\n",
       "      <td>33219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>activerecord</td>\n",
       "      <td>4093</td>\n",
       "      <td>18690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ajax</td>\n",
       "      <td>28791</td>\n",
       "      <td>133425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Tag  Weekend  Weekday\n",
       "0       .htaccess    10609    44636\n",
       "1            .net    32613   214170\n",
       "2  actionscript-3     7179    33219\n",
       "3    activerecord     4093    18690\n",
       "4            ajax    28791   133425"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_wday_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>winforms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>482</td>\n",
       "      <td>winforms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1037</td>\n",
       "      <td>winforms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2770</td>\n",
       "      <td>winforms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2804</td>\n",
       "      <td>winforms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id       Tag\n",
       "0     4  winforms\n",
       "1   482  winforms\n",
       "2  1037  winforms\n",
       "3  2770  winforms\n",
       "4  2804  winforms"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_tags_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Time to add year information to `questions_with_tag_over_20k`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "questions_with_tag_over_20k[\"Year\"] = questions_with_tag_over_20k[\"CreationDate\"].apply(\n",
    "    lambda datestring: datestring[:4]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now let's breakdown the tags by year and counts during weekday, weekend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_over_20k_year_wend_counts = questions_with_tag_over_20k.merge(\n",
    "    question_tags_clean,\n",
    "    how=\"left\",\n",
    "    on=\"Id\",\n",
    ").groupby([\"Year\", \"Tag\", \"Weekday\"]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2008</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">.htaccess</th>\n",
       "      <th>False</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">.net</th>\n",
       "      <th>False</th>\n",
       "      <td>719</td>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>5202</td>\n",
       "      <td>5202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actionscript-3</th>\n",
       "      <th>False</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Id  CreationDate\n",
       "Year Tag            Weekday                    \n",
       "2008 .htaccess      False       5             5\n",
       "                    True       49            49\n",
       "     .net           False     719           719\n",
       "                    True     5202          5202\n",
       "     actionscript-3 False      32            32"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_over_20k_year_wend_counts.pop(\"CreationDate\")\n",
    "tag_over_20k_year_wend_counts.rename(\n",
    "    columns={\"Id\": \"WeekendTotal\"},\n",
    "    inplace=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_over_20k_year_wend_counts = tag_over_20k_year_wend_counts.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>WeekendTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>.htaccess</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>.htaccess</td>\n",
       "      <td>True</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>.net</td>\n",
       "      <td>False</td>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>.net</td>\n",
       "      <td>True</td>\n",
       "      <td>5202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year             Tag Weekday  WeekendTotal\n",
       "0  2008       .htaccess   False             5\n",
       "1  2008       .htaccess    True            49\n",
       "2  2008            .net   False           719\n",
       "3  2008            .net    True          5202\n",
       "4  2008  actionscript-3   False            32"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "My pandas skill isn't so awesome. So I'll extract the weekday counts from this dataframe, drop the rows we extracted, then do a merge to insert the weekday counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "weekday_counts = tag_over_20k_year_wend_counts.loc[\n",
    "    tag_over_20k_year_wend_counts[\"Weekday\"] == True\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>WeekendTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>.htaccess</td>\n",
       "      <td>True</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>.net</td>\n",
       "      <td>True</td>\n",
       "      <td>5202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008</td>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>True</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>activerecord</td>\n",
       "      <td>True</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008</td>\n",
       "      <td>ajax</td>\n",
       "      <td>True</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year             Tag Weekday  WeekendTotal\n",
       "1  2008       .htaccess    True            49\n",
       "3  2008            .net    True          5202\n",
       "5  2008  actionscript-3    True           276\n",
       "7  2008    activerecord    True            77\n",
       "9  2008            ajax    True           525"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Drop weekday counts\n",
    "tag_over_20k_year_wend_counts.drop(\n",
    "    tag_over_20k_year_wend_counts[\n",
    "        tag_over_20k_year_wend_counts[\"Weekday\"] == True\n",
    "    ].index,\n",
    "    inplace=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>WeekendTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>.htaccess</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>.net</td>\n",
       "      <td>False</td>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>activerecord</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>ajax</td>\n",
       "      <td>False</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year             Tag Weekday  WeekendTotal\n",
       "0  2008       .htaccess   False             5\n",
       "2  2008            .net   False           719\n",
       "4  2008  actionscript-3   False            32\n",
       "6  2008    activerecord   False             7\n",
       "8  2008            ajax   False            74"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>Weekday_l</th>\n",
       "      <th>WeekendTotal_l</th>\n",
       "      <th>Weekday_r</th>\n",
       "      <th>WeekendTotal_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>.htaccess</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>.net</td>\n",
       "      <td>False</td>\n",
       "      <td>719</td>\n",
       "      <td>True</td>\n",
       "      <td>5202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>False</td>\n",
       "      <td>32</td>\n",
       "      <td>True</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>activerecord</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>ajax</td>\n",
       "      <td>False</td>\n",
       "      <td>74</td>\n",
       "      <td>True</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year             Tag Weekday_l  WeekendTotal_l Weekday_r  WeekendTotal_r\n",
       "0  2008       .htaccess     False               5      True              49\n",
       "1  2008            .net     False             719      True            5202\n",
       "2  2008  actionscript-3     False              32      True             276\n",
       "3  2008    activerecord     False               7      True              77\n",
       "4  2008            ajax     False              74      True             525"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts = tag_over_20k_year_wend_counts.merge(\n",
    "    weekday_counts,\n",
    "    how=\"inner\",\n",
    "    on=[\"Year\", \"Tag\",],\n",
    "    suffixes=(\"_l\", \"_r\",),\n",
    ")\n",
    "tag_over_20k_year_wend_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>WeekendTotal</th>\n",
       "      <th>WeekdayTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>.htaccess</td>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>.net</td>\n",
       "      <td>719</td>\n",
       "      <td>5202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>actionscript-3</td>\n",
       "      <td>32</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>activerecord</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>ajax</td>\n",
       "      <td>74</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year             Tag  WeekendTotal  WeekdayTotal\n",
       "0  2008       .htaccess             5            49\n",
       "1  2008            .net           719          5202\n",
       "2  2008  actionscript-3            32           276\n",
       "3  2008    activerecord             7            77\n",
       "4  2008            ajax            74           525"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts.pop(\"Weekday_l\")\n",
    "tag_over_20k_year_wend_counts.pop(\"Weekday_r\")\n",
    "tag_over_20k_year_wend_counts.rename(\n",
    "    columns={\n",
    "        \"WeekendTotal_l\": \"WeekendTotal\",\n",
    "        \"WeekendTotal_r\": \"WeekdayTotal\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "tag_over_20k_year_wend_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Nice. Now we _almost_ have what we want in `tag_over_20k_year_wend_counts` and `year_wday_counts`.\n",
    "\n",
    "But we need to drop tags whose combined total count in any of its year of occurrence is 20 and below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "A small experiment below shows that the `Year` column is not of integer type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>WeekendTotal</th>\n",
       "      <th>WeekdayTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Year, Tag, WeekendTotal, WeekdayTotal]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts[\n",
    "    (tag_over_20k_year_wend_counts[\"Year\"] == 2008) &\n",
    "    (tag_over_20k_year_wend_counts[\"Tag\"] == \".htaccess\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year            object\n",
       "Tag             object\n",
       "WeekendTotal     int64\n",
       "WeekdayTotal     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's convert `Year` to integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_over_20k_year_wend_counts[\"Year\"] = tag_over_20k_year_wend_counts[\"Year\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>WeekendTotal</th>\n",
       "      <th>WeekdayTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>.htaccess</td>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year        Tag  WeekendTotal  WeekdayTotal\n",
       "0  2008  .htaccess             5            49"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_over_20k_year_wend_counts[\n",
    "    (tag_over_20k_year_wend_counts[\"Year\"] == 2008) &\n",
    "    (tag_over_20k_year_wend_counts[\"Tag\"] == \".htaccess\")\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Success!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'jenkins', 'python-2.7', 'swift', 'maven', 'express', 'symfony2', 'angularjs', 'pandas', 'hadoop', 'css3', 'razor', 'matrix', 'html5', 'numpy', 'unity3d', 'asp.net-web-api', 'asp.net-mvc-4', 'github', 'cordova', 'nginx', 'android-layout', 'web', 'opencv', 'c#-4.0', 'r', 'ruby-on-rails-4', 'core-data', 'azure', 'c++11', 'spring-mvc', 'if-statement', 'node.js'}\n"
     ]
    }
   ],
   "source": [
    "tags_to_remove = set()\n",
    "for tag in tag_over_20k_year_wend_counts[\"Tag\"].unique():\n",
    "    for year in range(2008, 2016 + 1):\n",
    "        tag_year_df = tag_over_20k_year_wend_counts[\n",
    "            (tag_over_20k_year_wend_counts[\"Year\"] == year) &\n",
    "            (tag_over_20k_year_wend_counts[\"Tag\"] == tag)\n",
    "        ]\n",
    "        if tag_year_df.shape[0] == 1:\n",
    "            if tag_year_df.iloc[0][\"WeekendTotal\"] + tag_year_df.iloc[0][\"WeekdayTotal\"] <= 20:\n",
    "                tags_to_remove.add(tag)\n",
    "                break\n",
    "print(tags_to_remove)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we remove those tags whose total number of posts in any year is <= 20:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_over_20k_year_wend_counts = tag_over_20k_year_wend_counts[\n",
    "    ~tag_over_20k_year_wend_counts[\"Tag\"].isin(tags_to_remove)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We'll also delete data for the year 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_over_20k_year_wend_counts = tag_over_20k_year_wend_counts[\n",
    "    tag_over_20k_year_wend_counts[\"Year\"] != 2017\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For the next part, we'll be extracting the `Year`, `WeekendTotal` and `WeekdayTotal` data for each tag, then perform the \"modeling\" (which is totally non-obvious and we'll go through in some detail later on) to find obtain the tags whose weekend proportion have changed the most over the years. To do this, we have to make use of the [statsmodel](http://www.statsmodels.org/stable/index.html) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import statsmodels\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.sandbox.stats.multicomp as sm_multicomp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "unique_tags = tag_over_20k_year_wend_counts[\"Tag\"].unique()\n",
    "trend_data_for_each_tag = {}\n",
    "for tag in unique_tags:\n",
    "    trend_data_for_each_tag[tag] = tag_over_20k_year_wend_counts[\n",
    "        tag_over_20k_year_wend_counts[\"Tag\"] == tag\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So this is part of the \"modeling\" Julia Silge mentioned in one sentence of her blog post. This is almost the equivalent of the `glm(cbind(nn, YearTagTotal) ~ Year, ., family=\"binomial\")` in her R kernel, except that I believe she made a bug and the 2nd element in the `cbind` should be the number of questions with that tag asked on weekdays for that year, not the total number of questions with that tag asked in the year.\n",
    "\n",
    "According to the R documentation for `glm`:\n",
    "\n",
    "     A typical predictor has the form ‘response ~ terms’ where\n",
    "     ‘response’ is the (numeric) response vector and ‘terms’ is a\n",
    "     series of terms which specifies a linear predictor for ‘response’.\n",
    "     For ‘binomial’ and ‘quasibinomial’ families the response can also\n",
    "     be specified as a ‘factor’ (when the first level denotes failure\n",
    "     and all others success) or as a two-column matrix with the columns\n",
    "     giving the numbers of successes and failures.  A terms\n",
    "     specification of the form ‘first + second’ indicates all the terms\n",
    "     in ‘first’ together with all the terms in ‘second’ with any\n",
    "     duplicates removed.\n",
    "\n",
    "We got a `PerfectSeparationError` during the model fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angular2\n"
     ]
    }
   ],
   "source": [
    "trend_models = {}\n",
    "for tag, df in trend_data_for_each_tag.items():\n",
    "    try:\n",
    "        trend_models[tag] = smf.glm(\n",
    "            formula=\"WeekendTotal + WeekdayTotal ~ Year\",\n",
    "            data=df,\n",
    "            family=sm.families.Binomial(),\n",
    "        ).fit()\n",
    "    except statsmodels.tools.sm_exceptions.PerfectSeparationError:\n",
    "        print(tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Tag</th>\n",
       "      <th>WeekendTotal</th>\n",
       "      <th>WeekdayTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>2015</td>\n",
       "      <td>angular2</td>\n",
       "      <td>218</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>2016</td>\n",
       "      <td>angular2</td>\n",
       "      <td>4575</td>\n",
       "      <td>24302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year       Tag  WeekendTotal  WeekdayTotal\n",
       "1715  2015  angular2           218           864\n",
       "1973  2016  angular2          4575         24302"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend_data_for_each_tag[\"angular2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "I do not know the exact reason for this. In fact, I do not know what the `glm` is doing in this case. It _seems_ to be doing logistic regression but for regression and not classification. I can't find any similar functionality in scikit-learn so I had to use the statsmodels library.\n",
    "\n",
    "The next part extracts the p-values for the `Year` of each model and then adjusts them, then only retains those tags whose model's adjusted pvalue is less than 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tags_with_year_pvalues = []\n",
    "for tag, model in trend_models.items():\n",
    "    tags_with_year_pvalues.append((tag, model.pvalues[\"Year\"],))\n",
    "\n",
    "adjusted_pvalues = sm_multicomp.multipletests(\n",
    "    list(map(lambda t: t[1], tags_with_year_pvalues))\n",
    ")[1]\n",
    "\n",
    "non_fluke_tags = list(\n",
    "    map(\n",
    "        lambda t: t[0],\n",
    "        filter(\n",
    "            lambda t: t[1] < 0.1,\n",
    "            list(\n",
    "                zip(\n",
    "                    map(lambda t: t[0], tags_with_year_pvalues),\n",
    "                    adjusted_pvalues\n",
    "                )\n",
    "            )\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we extract the 8 tags whose weekend activity relative to weekday activity has decreased the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_activity_trend = []\n",
    "for tag, model in trend_models.items():\n",
    "    if tag in non_fluke_tags:\n",
    "        tag_activity_trend.append((tag, model.params[\"Year\"],))\n",
    "tag_activity_trend.sort(key=lambda t: t[1])\n",
    "tags_most_decreased_wend_to_wday_activity = tag_activity_trend[:8]\n",
    "tags_most_increased_wend_to_wday_activity = tag_activity_trend[-8:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('asp.net-mvc-3', -0.098378606676268743),\n",
       " ('visual-studio-2012', -0.087244426169319045),\n",
       " ('ruby-on-rails-3', -0.07524473192117373),\n",
       " ('internet-explorer', -0.064543551021871454),\n",
       " ('scala', -0.062395774887527344),\n",
       " ('go', -0.054768205613629328),\n",
       " ('extjs', -0.051498290400011942),\n",
       " ('svn', -0.049350481499238874)]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_most_decreased_wend_to_wday_activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('jquery-mobile', 0.048516221470173815),\n",
       " ('swing', 0.050346843127050483),\n",
       " ('actionscript-3', 0.050591142495317057),\n",
       " ('listview', 0.054707989552278014),\n",
       " ('android-fragments', 0.056826010359758039),\n",
       " ('button', 0.057474225497405329),\n",
       " ('gridview', 0.063615078029569699),\n",
       " ('selenium', 0.078265141941906313)]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_most_increased_wend_to_wday_activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For the top 8 decreases, Julia Silge's analysis contains the tags `azure`, `ruby-on-rails-4` whereas ours don't. But we have the tags `go` and `extjs`. Still pretty similar.\n",
    "\n",
    "For the top 8 increases, Julia Silge's analysis contains the tags `android-layout`, `unity3d` whereas ours don't. But we have the tags `jquery-mobile` and `swing`. Pretty similar too.\n",
    "\n",
    "Let's plot the actual data for these tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Turns out the `Year` column on `year_wday_counts` is not int.\n",
    "# Let's convert it\n",
    "year_wday_counts[\"Year\"] = year_wday_counts[\"Year\"].astype(int)\n",
    "\n",
    "year_wday_qn_counts = year_wday_counts[year_wday_counts[\"Weekday\"]]\n",
    "year_wday_qn_counts.pop(\"Weekday\")\n",
    "\n",
    "year_wend_qn_counts = year_wday_counts[year_wday_counts[\"Weekday\"] != True].copy()\n",
    "year_wend_qn_counts.pop(\"Weekday\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TTyYzc+Y0/P39KVmyNKNGjeOPPzbx7bdfc+HCBUJCQmjRojXgxbx5M5ky5UMK\nFy7CwIFD+OqrL+w15OLW8UpOTvZ0DB4RExOXrTu+ont9LhyKsj8OKlqKtvO3yNWmG8LDg4mJibvx\njAKQ9sosaa/MkfbKHGmvzJH2ypx/214JCQlcunSR/PnT3i+8Y8f2FCtWnAkTJv+bEHMUT55f4eHB\nLhM+qRHPJtdizzg9vnTyMJdOHPJQNEIIIYS408XHx9OuXQs+/HC80/Q9e3Zz9OgRKle+y0OR3Tmk\nNCWbBJcox7Xzzsn42ajt5Cse6ZmAhBBCCHFHCwgIoHHjJixZ8gWJiQlUqKCIiTnF0qWLCQsrRIcO\nT3k6xNue9Ihnk9CKNdJMOxe1zQORCCGEEEIYr732Jt26PcOmTb8zYcI4li37inr16jN9+mxCQ0M9\nHd5tT3rEs0lBF4n42ajtHohECCGEEMIICAigR4/e9OjR29Oh3JGkRzybuO4R386dOlhWCCGEEOJO\nJ4l4NgkpXREf/wCnadfjYrl0Un65SgghhBDiTiSJeDbx9vGlQLm0o4/PSXmKEEIIIcQdSRLxbCR1\n4kIIIYQQwkYS8WyUXp24EEIIIYS480gino0KVqyZZtpZGbAphBBCCHFHkkQ8G4WUroi3n7/TtOsX\nznL5n6MeikgIIYQQd7KDBw+wceMGT4dxQzt2bOO553rQrFkjHnmkNVOmfER8fLynw/rXJBHPRt6+\nfi4HbJ6VH/YRQgghhAe8+upAdu/e5ekwMnTy5AkGDuxHlSp3MXv2Ql57bQSrVq1g6tSJng7tX5NE\nPJu5GrB5bu8OD0QihBBCiDtdbiiPPXHiOPfddz99+w4gIqIEd99dj6ZNm/PHH5s9Hdq/Jr+smc1C\nXdSJy4BNIYQQIi2vyxcJXrWYPEcO4JWYkO3bT/bx5XrJssS1fJzkwHxuLbN3bxSffDKJnTt3cPXq\nVYoVK063bs/QunVbdu78i8mTJ7B3bxR58vhTv34D+vcfREhIfrZs+YOXX36BESNGMXnyh5w/H0ut\nWnUYOHAIRYoUdbmtrl27UrFiFWJi/uGXX37Gx8eH5s1b0q/fQHx9TYq3fftWPv54IlFRmrCwQjRt\n2pz//vdZ/P39efHFXhw7dpSZM6ezcuW3LF683OV2Hn+8HY8+2oE///yDrVv/JCysEP37DyQxMYGP\nP57ImTOnqVGjFq+/PpL8+fPzxBMP06rVg06/1jl//hwWL17E4sXL8fLy4osv5rN06WJiYk5RsmRp\nevd+nvq/YideAAAgAElEQVT1G7ncfq1adahVq479sdZ7+OWXn2jSpKlbxyQnkx7xbFZQub6FYW64\nIhVCCCGyU/CqxfhHR3kkCQfwSkzAPzqK4FWL3Zr/ypUrDBjwIoUKhTNt2ixmz15IzZq1effdUZw9\ne4YhQwZQp849zJ37BePGfcju3X8zadIH9uUTExOZOnUSr7zyOpMnT+fChQsMHNiPhIT093/Ros8p\nWbI0M2d+Tv/+A/n6669Yt241AHv3agYMeJHGje9nzpyFDBnyOr/++jPjx48FYPTocRQrVpyOHbsw\nffqcDPdt5szpNGvWgrlzF1G+fHlGjhzG55/PYcSIUbzzzgR27drJ/Plz8PLyolWrB1m7dpXT8qtX\nf0/Llm3w9vbm889n89ln03j66R7MmbOI++9vytChgzlwYP8N27hVqyb06NGF4OBgnn66xw3nz+kk\nEc9m+SMr4e2Xx2natdjTXI457qGIhBBCiJzJ70TO+PVpd+O4evUKTz7ZiZdeGkypUpGULh1J167/\nJT4+nkOHojl/PpaCBcMoWrQYd91VldGj36NDh6ec1tG37wDuvrseFStWYtiwkURHH+DPP9MvwShf\nviLdu/ckIqIErVo9SLly5dm58y8AFiyYR/36jejUqSslSpSkTp27GTx4KCtWLOf06dOEhOTH29ub\ngIAAQkNDM9y3Ro3uo3XrtkRElKBdu/ZcvnyJPn1epFKlKtSuXZe7767HwYMmkW7V6kGOHDmM1nsA\nOHBgP/v2RdG69YMkJyfz5ZcLefLJTvb1Pf10D7p06c6VK1cyjCEpKYkJEyYzfvxErl69yuDB/XN9\nR6aUpmQzb18/ClWsxqldfzpNPxe1jaDCER6KSgghhMh54ouVwj86ytNhEF+slFvzhYYWpH37x/n+\n+2+JitIcPXqEvXtN/MnJyXTs2IX333+HGTM+oW7dejRseC/339/MaR21atW2/x8RUYICBULZv38f\n9erVd7nNUqWcYwsKykdCgrmbiInhMM2b32t/3pa4Hjp0kEKFCjktu3r1SsaNG21/3KJFawYPHgpA\niRIl7dPz5s0LQPHiJezT/P39OX/+vD3u6tVrsnbtKpSqxJo131O58l2UKhVJbGwsZ86cpkoV55tX\n2MpYBg7sx44dW+3T33vvI2rUqAWAt7c3lSub5V577U169+7Ozp07qFYtbbVBbiGJuAcUrlLbRSK+\ngxKNHvRQREIIIUTOE9fyccghNeLuOH06ht69/0t4eGEaNryXBg3upVChcHr27ArACy/059FHO7Bx\n4wY2b/6NMWNGsnz513z00VT7Omy13TZJSUl4e3ulu02/VN+yQ0qy7efnS+vWbenc+ek084SFFUoz\nrVGjxlSpUtX+OCgoyP6/j49Pmvkziqt167bMnDmd557ry9q1q3jqKdMGqfcvtSFDXufatWv2x+Hh\n4Rw8eIDTp09x993/sU8vV648ADExMRmuL6eTRNwDwu+qDV86T5OfuhdCCCGcJQfm40L77p4Ow21r\n1qzi8uXLTJ483Z64/v77/wFw/PhRfvhhDX37DuCxx57gsceeYN26NQwf/irnzp21r2PPnt32HuDD\nhw9x4cJ5KlasdFPxlClTjujog0692Tt37uDzz2czaNCrBAQE4OWVkkwHBgYRGBjkalWZ9sADzfjg\ng3EsWfIFp0/H0KxZCwDy5ctHWFgh9uzZ7TQ4s2/f3tSv35BOnbqlWdfGjb8wf/4clixZgb+/+T2W\nv//eCUBkZJksiddTpEbcAwpXqZ1m2jkZsCmEEELkaoULF+Hy5UusX/8DJ0+eYMOGn+ylHvnzF2Dd\nujWMHz+WQ4eiiY4+yA8/rCYiogT58xewr2P8+LH89dd29uz5m7feeoPKlatQs6bJGy5fvsyZM6fd\njqdz56f5+++dTJz4PocORbN165+8/fZw4uLi7D3igYGBHDlymNOns7ZnOSgoH/fe24Rp0z6mQYNG\nhITkd4irG4sWfc7atas4duwos2Z9yq5dO/nPfxq6XFerVqZiYMyYkRw6FM3mzb8xduxbNG3anLJl\ny2Vp3NlNesQ9IKzCXXj7+pGUkPKLUFfPneLK6RMEhhf3YGRCCCGEuFkPPNCM3bt38cEH47hy5TIR\nESXp3r0n8+bNYv/+fbz33kd8/PFH9Or1NElJydSqVZtx4z7E2zulX7RVq7a88carXLp0iQYNGvHS\nS4Ptzy9YMJeZM6ezYcMfbsVTrlx5xo37kOnTP2bp0sUEBeWjYcN7ef75/vZ5nnyyMxMmjGPz5t9Y\nvnyNUyz/VuvWbVm7dpU9kbZ5/PGOXL16lSlTPiI2NpayZcvxzjvvp5tUh4UV4sMPpzJx4gR69uxG\nQEAALVq0plev57MsVk/xulN7YWNi4jy24+Hhwcxrfzfn9jqXo9z79jwiGrb2UFQ5V3h4MDExcZ4O\nI9eQ9socaa/MkfbKHGmvzLmT22vLlj/o168PS5Z8R+HCRdxa5k5ur5vhyfYKDw92WVAvpSkeEuri\nFzalTlwIIYQQ4s4hibiHyE/dCyGEEELc2aRG3ENc/8LmNg9EIoQQQghPq127rtu13+L2IT3iHpK/\nTBW8ff2cpl098w9Xzpz0UERCCCGEECI7SSLuIT55/MlfpnKa6VInLoQQQghxZ5BE3INCK1RPM+2c\nJOJCCCGEEHcEScQ9SO6cIoQQQghx55JE3INc3jlFEnEhhBBCiDuCJOIeVKDcXXj5ON+45srpE1w9\ne8pDEQkhhBBCiOwiibgH+eTJS/7ISmmmS3mKEEIIIcTtTxJxD5M6cSGEEOL299lnn/Dkk49k2/Ya\nNarLqlUrbnr5nTt3sGPHv/t9k/79n2fUqBEAbNnyB40a1eXUqX/+1Tq/+moRnTo9RrNmjejSpQPL\nl3/t9PzRo0cYMKAvzZvfy6OPPsj8+XPSXde4caMZO/atTG8jK0ki7mEFXd45RX7YRwghhLidPPVU\nVz75ZJanw3DbCy88y9GjR7JsfdWq1WDZsu8pVCj8ptexdOlipk6dxNNP92D27IU8+WRnxo8fy/ff\nfwdAfHw8Awf2JTAwkGnTZtOnT19mzJjGN98sdVpPcnIyn346lWXLlmR6G1lNflnTw0Jd/sKm9IgL\nIYQQt5PAwEACAwM9HYbH+Pn5ERZW6F+t4+uvv6J9+w60bNkGgIiIEuzcuYMVK5bTqtWDrF+/jrNn\nzzB06HACAwMpU6YsR48eZv78uTz0UHsAjh07ytixb3Hw4H6KFCma6W1kNUnEPaxAuap4efuQnJRo\nn3Yl5jhXz8WQN/TmrxqFEEKI3C4x/iLn9y/h2vmDkJyQ/QF4+eKfvwz5yz2Kj1++G84+atQITpw4\nzqRJ0+zTdu/exbPPPk3z5q34+++dLFpkyhzmzZvFsmVLOX36FEWKFKNDh4489tgT9vWcOnWKDz+c\n4rRux2nr169j4cK5REXtxcsLKlZU9Os3kMqV73Jr1w4fjmbChHHs2rUTLy+oXbsu/foNpFix4jz+\neDsSExMZPfpNVqxYzqRJ02jUqC7Dho20J6iA07SkpCRmzpzON98s4fLlKzz00CMkOeQ2W7b8Qb9+\nfViy5DsKFy7C1atXmTlzOuvWrebs2TOUK1eB3r1foG7de9KN+aWXBqVJnr29vYmLuwDA9u3bUKqy\n0wVPrVp1mDFjGmfPniE8PJidO3dQuHARRowYxfDhQzO9jawmpSke5usfQEikSjP93N4dHohGCCGE\nyDnO71/Ctdi9nknCAZITuBa7l/P705YwuNK6dVt27NhGTEzK3c9Wr/6eqlWrU6JESfu0DRt+Zv78\nubzyymssWLCEzp278cEH49i2bYtb29m9exdvvPEqjz76KJ9//iWTJk0jORneeWeU27s2YsTrFC1a\njBkz5jF58qfExsYyZsxIAKZPn4OPjw/9+g1k9Ohxbq1vzpwZfPnlAl56aTDTp8/mwoULbN36Z7rz\nDx/+Kj/8sJbBg4cyc+Z87rqrGgMH9mXXrp3pLlOrVh2KF4+wPz558iRr166iXr0GAMTE/EN4eGGn\nZWylMLba9JYt2zBs2Mh0e+dvtI2sJol4DuDqfuJnpU5cCCHEHe56XNbVKP8b7sZRq1YdChcuwrp1\nqwFITExk3brVaUoajh07gp+fL0WLFqNo0WK0a/cIH3wwhdKlI93ajq+vLwMGvELnzp0pVqw4lSvf\nRbt2j3DgwD639+nYsSPkz1+AokWLUb58Bd544y169XoBgNDQUADy5ctHSEj+G64rOTmZJUu+pGPH\nLtx/fzMiI8vwv/+9liYptjl48AC//voLgwe/Sr169SldOpKXXhqEUpVZsGCuW/GfO3eO//2vPwUL\nhtGlS3cArl69Rp48eZzm8/Mzj69du+7Wem+0jawmpSk5QGjFGhz8foHTNPlhHyGEEHe6PMElTY94\nDojDHV5eXrRs2Ya1a1fTsWMX/vxzMxcvxtG0aQu++GK+fb4WLdrw7bfL6NixPeXKleeee+rTrFlL\nQkMLurWdChUU+fIF88knn7Br126OHDnCvn1RJCUlpZn35MmTdO3awf64SJFizJv3BT17PsekSRNY\nuvRLate+m4YNG9G0aUu3tp9abGwsZ8+eQamUWzL7+flRsWLab/wBDhzYD0DVqs43rKhRoxYbN/6S\nbsw2x44dZdCgfly7do2JEz8hXz5TNuTv7098fLzTOuPjTQIeEJA3U/uU3jaymiTiOUDBCjJgUwgh\nhEgtf7lHc0yNuLtatXqQ2bM/48iRw6xZ8z0NGzYmODjYaZ7Q0FBmz17Ijh3b+P33/+O3335lwYK5\nDB06nDZt2rlcb2JiSr31n39uZvDg/jRt2pRKle7iwQcf5vDhQ7z33pg0yxUqVIiZM1MuAnx9TerX\noUNHmjZtzsaNv7B58+989NEEFiyYx8yZ89P0KruSkJByPLy8vABITnaex8/Pz+Wy/v7+LqcnJSXi\n6+ubbswAWu9h0KB+hISE8PHHnznVcxcuXIQjRw45rfP06RiAdHvnXcloG1lNEvEcoED5qnh5e5Ps\ncCV7+Z+jXDt/Bv/8YR6MTAghhPAcH798FKzUzdNhZErJkqWoVq0669at5pdf1jNsWNr7VK9bt5rY\n2Fgee+wJatasTe/eLzBoUD9++GENbdq0w9fXj8uXLzotc+TIYQIDgwBYtGg+d99djw8++ICYmDgA\nNm36DTBlIrbEGEwS61ifDnD+fCwzZkyjc+enadv2Edq2fYS//95Jr17d2bcviipVqqaJ2dfXl0uX\nLtkfO97asECBAoSHF+avv7bToEEjAJKSkoiK0tSoUSvNusqUKQvAX39tp169+vbpO3ZsJzKyjMuY\nAQ4diubll18gIqIE7733IfnzF3B6vnr1mqxZs5KrV6+SN6/pAd+y5Q9KlSrt9rcNN9pGVpMa8RzA\nN28gIaUqppl+NkoGbAohhBC5TatWbZk/fy5+fnmcEk2b69evM3nyh6xevZKTJ0/wxx+biIrS9gS4\natVqREVp1q5dxfHjx5gxY5pT/XfhwkXYuzeKbdu2cfz4MRYvXsgXXyywr/tGgoND+O23jYwbN5p9\n+/Zy9OgRVqz4lnz5gilVKhKAwMAgoqMPcO7cWSum6nzzzVL27o1C6z28994Yp57zp57qwuLFC/n+\n++/sd2T555+TLrcfEVGCpk1b8N57Y9m06TcOHYpm4sT3iYraQ4cOT6Ub99tvv0GePHkYNmwkCQkJ\nnDlzmjNnThMbGwvAffc1ISQkP2+++RoHDuxjzZrvWbBgbqbqu2+0jawmPeI5RGjFGpyP3uM07VzU\nNordfb+HIhJCCCHEzXjggeZ8+OF42rZ9yKmswqZ167acO3eOzz77hFOn/iE0tCBt2rSjW7dnAHNn\nj717NePHv0NiYiIPPNCMJ57oxN9/7wKgZ88+nDkTQ48ePfDy8qZ8+Qq89toIhg9/lT17/nbZC+3I\n29ubceM+YOLECbz4Yi/i469TufJdvP/+RHstdJcuTzNr1qds3vw7M2fOZ+DAIYwfP5bevbsTFhbO\ns8/2cbo7zBNPdCIpKYlp06Zw/nwsTZo05d5770s3hldeeZ0pUz5i5MhhXLlymYoVFe+/PylN3bjN\n4cOH2L37bwA6dXrM6bmIiBIsWvQ1/v55GT/+I957byw9ez5NaGgovXq9kG65z81sI6t5Jacu6LlD\nxMTEeWzHw8OD7V8l2UR99QlbJjnfz7JE43Y0enNWNkaWM7lqL5E+aa/MkfbKHGmvzJH2yhxpr8yR\n9socT7ZXeHiwl6vpUpqSQ4RWrJlmmtw5RQghhBDi9iWJeA5RoPxd4OV8sXTp5GGunT/roYiEEEII\nIcStJIl4DuEXkI+QUhXSTD+3TwZsCiGEEELcjiQRz0FCXfzCppSnCCGEEELcniQRz0EKuqgTP6sl\nERdCCCGEuB1JIp6DuOoRP7tXEnEhhBBCiNuRJOI5SGj5qmkHbB6P5nrcrbmJvBBCCCGE8BxJxHMQ\nv8BggkuUSzP9rNSJCyGEEELcdiQRz2Fc1Ymf2yt3ThFCCCGEuN1IIp7DyJ1ThBBCiDvLqFEj6N//\neU+Hccs8/ng7Zs36FIDPPvuEJ5985KbXlZyczNy5s+jQ4SEeeKAhzzzThY0bN2RVqNnO19MBCGcF\nXQ3YlERcCCGEELnU9OlzyJs3b5asa+HCz/n881m89toIypYtz7p1q3n11YFMmzYbpSplyTayk/SI\n5zChFaqlmXbx2AGuX7zggWiEEEIIIf6d0NBQAgICsmRd165d5cUXX+Lee5sQEVGCbt2eISAgkG3b\n/syS9Wc36RHPYfyCQgguUY64o/udpp/bu50ite71UFRCCCFE9rsSH8P66Oc4HvczicnXsn37Pl7+\nFA9uTJPIjwnwC3drmUaN6tK9e0++++4bAD79dA4PP9yKYcNG0rJlG6f5HKclJiYwbtxoVq/+noCA\nANq0acezzz6Hj48P3bt3olq1Ggwc+Ip9+W+/Xca0aZNZsmQFvr5p07mEhAQWLfqc5cu/5tSpfyhR\noiRPP92Tpk2bA6ZEZNeunVSvXoOlS78kLu4iderU5ZVXXqdQIdf7euLEcTp0eIhevZ7niy8WkD9/\nfmbNWsBff21nxoxpaL2bhIQESpcuQ58+L/Kf/zQATGlK27YP0717zzTrnDdvFsuWLeX06VMUKVKM\nDh068thjT6Tbvo7ruHbtGt999w3Xrl2lVq066S6Tk+WKRFwp5QfMACIBf+BtrfU3Ds+/DPQEYqxJ\nvbXWOrvjzCqhFWukTcSjJBEXQghxZ1kf/RxHLqzx2PYTk69x5MIa1kc/R+sKi91ebvnypbz33kfE\nx8cTFlbIrWW2bdtCkSJFmT59NgcO7Oedd94iJCQ/nTp1pXXrB5k7dyb9+w+0J92rVq2gbdu2LpNw\ngEmTJrB27SoGDhxCuXIVWL9+HSNGDMXHx5smTZoCsHXrHwQGBjBhwhTi4uJ4440hfPrpVIYMGZZh\nrGvXrmLy5OlcvXqVs2fPMGhQfzp27Myrr77BlStXmD59Cm+/PZylS1fg5+eX7no2bPiZ+fPnMnLk\nGEqUKMnmzb/z7rujKFeuPDVr1s4whp9++oHXX3+F5ORkevbsQ8WKua8sBXJJIg50Ac5orbsqpQoC\n24BvHJ6vA3TTWufO7yVSKVixBod/WOI07WyU3DlFCCHEneWfS5s8HQKQ+That25HhQoqU8sULlyE\nIUOG4efnR2RkGaKjD/Dllwvo1KkrLVq0ZsqUj/jtt400atSYkydPsm3bFoYPd50wX7p0kaVLFzNg\nwCvcf38zALp1e4Z9+/Yyb95seyKelJTE0KHDCQwMAqBp0+Zs2vT7DWN99NEnKF06EoBjx47Ss2cf\nnnqqC17Wb6E8+WRn+vXrw9mzZyhSpGi66zl27Ah+fr4ULVqMokWL0a7dIxQvHmFfd0aqVKnKjBmf\ns2XLZqZM+YjQ0II8/PCjN1wup8ktifiXgO1S1AtISPV8HeBVpVRR4Dut9ZjsDC6rubxzyt5tHohE\nCCGE8JwiQfd4tEfcMY7MKF48ItPbqFSpilPvceXKVZgxYxpxcXGEhhakfv2GrF69kkaNGrNmzUrK\nli1P5cqVWbv2ZwYN6mdfrnr1WvTo0YvExESqVavutI0aNWqxYcPP9sdhYYXsSThAUFA+EhLiAawy\nmZX25wYPHkq1aiY/iYhI2b+IiBK0bv0gX3wxn/3793H06BH27jVFCUlJSRnuc4sWbfj222V07Nie\ncuXKc8899WnWrCWhoQU5efIkXbt2sM9bpEgx5s37wv44PLww4eGFqVChIkeOHGbBgnmSiN8qWuuL\nAEqpYExC/nqqWRYCk4ELwFKlVFut9bcZrTM0NBBfX59bEa5bwsOD030upH5Dfkw1Le7IfvIHJJMn\nX8itDSyHyqi9RFrSXpkj7ZU50l6ZI+2VOY7t9VjIXJb/9V+iz/5AYpIHasS9/Yks+ADtqs0kyN/9\n41ioUP40xz04OK99WkJCgtO0vHn9SErK47RMcHBevLy8KFbMDHTs2PEJBg0aRGCgN+vWreKJJ0yS\n2qjRPSxbtsy+XN68eYmNNb/IHRoa5LTOwEA//Px8CQ8PJijIn7x5/Z2eDwryx9vbi/DwYF55ZRAv\nvNDH/lxYWJh9vYULh9qXi4qKonPnztSoUYP69evz6KMPk5CQQJ8+fShY0Gzfx8eboCB/+3Z9fLwJ\nDw8mPDyYFSu+488//2TDhg389NNPLFgwlzFjxvDQQw857Zevr4l7/fr1lClThtKlS9ufq179Llat\nWuHWay2nvR5zRSIOoJQqCSwFpmit5ztM9wI+0Fqftx5/B9QCMkzEz527fAujzVh4eDAxMXEZzOFN\nvoiyXDx2wGnq3v/bSOGaDW9tcDnQjdtLOJL2yhxpr8yR9socaa/MSdteATQtvRBKp7tItrh8AS7j\n/nGMi7vqtB++vr6cPHnGPi06+qDTfFevxrNr19+cOnXBXt6xYcNvFC1anIsXE7h4MY6qVeuSN29e\npk79lIMHD9Kgwf3WOuIJDCzotP2gIG/8/Pz46aeNhIYWs0/fuPF3SpcuQ0xMHJcuXSMxMckpTudp\neZzWe+VKMmfPXgIgNvaKfblZs+YSHl6EMWMm2Of9+uuvADhz5iL+/nEkJiZx6dK1NNtdt241sbGx\nPPbYE0RGVqJLl54MGtSPZcuWc++9zdPsV0xMHGPGjKV27boMGJAycHXz5i32/cqIJ1+P6V0A5IpE\nXClVBFgNvKi1Xpfq6RBgp1KqMnAJeAAzsDNXC61QPU0ifjZq+x2ZiAshhBC5WdWq1fnmm6VUq1aD\npKQkJk58nzx58jjNc/z4McaNG02HDk+xZ8/fLF68kJdeGmR/3tfXl2bNWjF79mf85z8NCA0tmHoz\ndv7+eXnyyc58+unH5M+fn/LlK7J+/Q/89NMPjBgxOkv3rXDhIpw8eZzNm3+jZMnSbN++lWnTpgAQ\nHx+f4bLXr19n8uQPCQ4Opnr1mhw9eoSoKM0jjzyW7jJPPtmZ8ePHUqlSFapXr8nPP//I6tUrGTt2\nfJbuV3bJFYk4MBQIBYYppWwjE6YDQVrraUqpocCPwDVgndZ6hYfizDIFK9bgyPqvnabJL2wKIYQQ\nuc/AgUMYP34svXt3JywsnGef7UNMzCmneRo3bsL169fp2bMrISH56dGjD23bOv8CZatWD7J48UJa\nt257w2327NkHb29vPvrofc6fj6V06UhGjBjNAw80y9J9e/zxjkRHH+SNN4aSlJRI6dJlGDz4VUaP\nfpPdu3dlOPCydeu2nDt3js8++4RTp/4hNLQgbdq0o1u3Z9Jdpl27R0hMTGDu3Jn8889JSpYszahR\n71K/fqMs3a/s4pWcnOzpGDwiJibOYzvuzlcjJ//8ifWDnAcdhJSqQJvZv93K0HIk+Wo3c6S9Mkfa\nK3OkvTJH2itzpL0y9uuvvzBmzJssXboSPz8/aa9M8nBpiper6bmlR/yO4+qn7i8c2Uf85Tj8AnPW\nQAMhhBBC3DrR0QfZv38fM2Z8Qrt27TO8N7fIXeQn7nOoPMEFCCoe6TwxOZlz+3Z6JB4hhBBCeMah\nQ9GMGfMmxYoVz7BsQ+Q+0iOegxWsUINLx6Odpp2L2k7h6vU9E5AQQgghst19993Pffdt8HQY4haQ\nHvEcLLRi9TTTzkbJD/sIIYQQQtwOJBHPwVzVicudU4QQQgghbg+SiOdgoRVcDNg8vJeEK5c8EI0Q\nQgghhMhKkojnYP75CxJUtJTzRBmwKYQQQghxW5BEPIcLdVmeInXiQgghhBC5nSTiOVxoBVcDNqVO\nXAghhBAit5NEPIcrWLFmmmkyYFMIIYQQIveTRDyHK6hcDdiMIuHqZQ9EI4QQQoiMNGpUl1WrVrg9\n/8aNGzh48MAtjMg9Bw8eYONGz96rfNSoEfTv/7xHY8hukojncP75wwgsUsJpWnJSErH7d3koIiGE\nEEKkZ9my72nSpKlb88bEnOJ//3uJc+fO3uKobuzVVweye7fkFtlNEvFcwNX9xOWHfYQQQoicJyys\nEP7+/m7Nm5ycfIujcV9OiuVOIj9xnwuEVqzB0V++c5omdeJCCCFud7EJlxl/bA3bLh0lPjkx27fv\n5+VDzaASDIxoTgHfQLeWadSoLsOGjaRlyzaMGjUCb29vAgICWL36e+Ljr9OwYWP+97+hBAYG8eij\nDwLQr18fWrduy2uvjeCff04yceL7bNr0O/7+/tSuXYe+fQdQqFA4AC++2ItSpUqzZ89uTp48ztCh\nI1i4cB5Vq1YnJuYffvnlZ3x8fGjevCX9+g3E19eketu3b+XjjycSFaUJCytE06bN+e9/n8Xf358X\nX+zFsWNHmTlzOitXfsvixctd7tv169f55JPJrF37PVeuXKViRUWfPn2pWrUaCQkJPPtsN/Lk8efj\njz/D29ubbdu20K9fH958czT339+MRo3qMmjQqyxf/jUHDx6gTJmy9O37MjVr1na5vQMH9jFlykfs\n3Clhei4AACAASURBVPkXXl5eNGjQiL59B1CgQAF7W3fv3pPvvvsGgE8/nYOfXx4mTZrAhg0/k5yc\nzF13VaVfvwGUKhUJmPKXa9euce7cWaKi9tCnT1/at3/cvRPiFpAe8VzA1Q/7yJ1ThBBC3O7GH1vD\n5ouHPJKEA8QnJ7L54iHGH1tz0+tYvXoliYlJTJ06g5Ejx/Drrz/zxRcLAJgxYx4Ao0a9S//+g7hy\n5Qp9+/bG39+fqVM/4/33JxIfn0C/fn2Ij4+3r/Pbb5fRtWt35s6dS+3adQBYtOhzSpYszcyZn9O/\n/0C+/vor1q1bDcDevZoBA16kceP7mTNnIUOGvM6vv/7M+PFjARg9ehzFihWnY8cuTJ8+J919efvt\n4WzfvpWRI8fy6adzqF27Lv369ebw4UP4+vry+usjiYraw1dffcGlSxd5++3htG7dlvvvb2Zfx5Qp\nH/Hww48yc+bnKFWJAQP6cuzY0TTbOnHiOM8914OQkPxMmTKdsWPHs2/fXl5++XkSE1POh+XLl/Lu\nuxMYNepdQkMLMnhwf06fPs37709kypRPKVq0GM8/35Pz52Pty/zwwxoaN76fadNm07hxk8we0iwl\niXgu4Ko05UK0JuHaFQ9EI4QQQmSP3ZdPejoE4N/FERKSn5deGkSpUqWpX78RdevWY9euvwAoUCAU\ngODgEPLly8fatau4evUqQ4eOoGzZ8lSooBgxYhQxMTGsX7/Ovs7Kle/i/vubUalSJQIDgwAoX74i\n3bv3JCKiBK1aPUi5cuXZudNsZ8GCedSv34hOnbpSokRJ6tS5m8GDh7JixXJOnz5NSEh+e899aGio\ny/04evQIP/ywhqFDh1OjRi1KlSrNM8/0onr1mixcaC4oypUrT48evZk+/WNGjXoTX18/XnppsNN6\n2rV7hIceak/p0pEMHDiEsLBCLF/+dZrtLV36JfnyBTN06HDKli1PjRq1ePPN0ezdG8Xvv/+ffb7W\nrdtRoYKiSpWq/PnnZvbs+Zu33hpDpUpVKFOmLIMGvUq+fCF8881S+zJhYWF06NCR0qUjCQsrlOlj\nmpWkNCUXyBsaTkB4ca7EHLdPS05K5Pz+XYRVqevByIQQQohbp3JgUTZfPOTpMKgcWPSml42IKIGP\nj4/9cb58+YiJOeVy3qgoTWzsOVq1auI0/erVqxw6FG1/XLx4RJplS5Vy/iXuoKB8JCTE29d79Ohh\nmje/1/68rSb80KGDFCrknIyuXr2SceNG2x+3aNGaOnXuAaB37+5O816/ft2pt75Tp2789NOP/Pzz\nj0ydOoOAgACn+WvVSilD8fHxoVKlyhw4sC/N/hw4sJ/KlavYS2sAIiPLUKBAAQ4c2EeDBo3StMXe\nvZrExEQeeaR1mhijow/aH7tqP0+RRDyXKFixBsccEnEw5SmSiAshhLhdDYxonmNqxG96HX550kxL\nb1ykn58vZcqUZdSocWmey5cv2P6/q8GgrreTbF9v69Zt6dz56TTzuOoRbtSoMVWqVLU/DgoKYufO\nHQBMnTozzfb9/Pzs/58/f56TJ0/g4+PDpk2/UbWq8w8T+vg4p55JSYl4eXmliSG9Aa+JiUlOybnj\nfL6+foSE5GfatFlplnO8IMiTJ6/LdXuCJOK5RGjFmhz7daXTNKkTF0IIcTsr4BvIW6Uf9nQYt0zq\nBLRMmXIsX76MkJD8hISEAHDp0kVGjhzGk092pnbtm+t8K1OmHNHRBylRoqR92s6dO/j889kMGvQq\nAQEBTrEEBgbZS14c1wFw9uwZ7r67nn36+PHvEBkZyWOPPQnAuHGjCQ8Pp2/fAYwaNZwGDe6lUqXK\n9vm13k39+g0BSEhIYM+e3bRp0y5NzJGRZfn+++9ISEiwJ94HDx4gLu4CkZFl09nPsly4cB7Avq+J\niYmMHPk6jRs/QKVKZdxssewjNeK5hKs6cblzihBCCJF7BQaaO7Hs37+P8+djadGiFQUKFOCNN4aw\nZ8/fHDiwjzfffJ1du3ZSpozr5NMdnTs/zd9/72TixPc5dCiarVv/5O23hxMXF2fvEQ8MDOTIkcOc\nPv3/7N13fJRV9sfxzzOpEBJIIBCq9EuTJiKKvS2WdVF3wRV726Jrd0V/gi6ioriLuqhrWXQtrLq6\nttXFtXex0uEmIL1DAiEJpD6/PwaQYSY4kcw8U77v12tekpvJzJnHQE7unHvOxpCP0aFDR4477gTu\nuedOPv/8U1avXsUjjzzIq6++xAEH+BPcGTPe4NNPP2Ls2HGceOIIhg07jIkTb6Wqqmr34zz33DO8\n9947LF++jMmT76SsbBunnXZ60POdeeYoysvLuPPOP/H990uYPXsWEybcQvfuPRkyZGjIGIcMGUrf\nvgcyfvxYZs/+jhUrlnP33RP55JOP6Nq120++fpGkRDxO5PbsH7S2ddkiaqt2eBCNiIiI7K+srGb8\n8pejefjhvzJp0kQyMjKZMuVBMjMzufLK3/G7311MTU0tDzzwMLm5eT/5ebp1687kyfczZ85sLrzw\nbMaPv4mBAwdz55337r7P6NFj+OKLzzj//LOoq6sL+Tg33jiOQw89jLvumsC5545m5szPuOOOexgy\nZCgbNqznvvvu5de/PpeePXsBcN11Y9mwYT2PPvrQ7sc47bTTeeqpaVx44RhWr17FAw88Qn5+66Dn\nystryZQpD7Jhw3ouueQ8br75Onr0MNx330MBpSl7chyHu+66ly5dujJ27HVcdNEYVq5cwV/+MnW/\nfpGJJCdZG7hv3LjNsxeen5/Nxo3bGvx1r/6qL9s3BZ7cPuGh/9Gy90GNFVpM+qnXK1npejWMrlfD\n6Ho1jK5Xw+h6NUy8Xa89e6x7wcvrlZ+fHVwIj3bE40puz4FBayVFczyIRERERET2lxLxOBJ61L3q\nxEVERETikbqmxJFcHdgUERGROPXJJ197HULM0Y54HAm1I7516UJqqyo9iEZERERE9ocS8TjSpGUB\nmXltAtbqaqrZunSBRxGJiIiIyE+lRDzOhKwTtypPEREREYk3SsTjTMg6cXVOEREREYk7SsTjTOjO\nKbM8iERERERE9ocS8TgTakd869KF1FZXhbi3iIiIiMQqJeJxpkmrtmTmBo6CrauuYuvShR5FJCIi\nIpF2xRWXMWnS7V6HIY1MiXiccRyH3J79g9bVT1xEREQkvigRj0OhylM0YVNEREQkvmiyZhwKdWCz\npEiJuIiIJJYdJRuZefcVrP/2Y+qqoz+8zpeWQZvBR3DIjVPJzM0P62vefPN1nn32H6xZs5q8vJac\nfPLPufDCS/H5fHz++adMm/Yo33+/mNzcPM4441ecffZ5ABQVFfLII1OZN28OO3bsoG3bdpx33kWc\ndNKpIZ/nlVde5NVXX2L58uWkpKTSr9+BXHfdWDp06Nhor18iTzvicSi358CgtS1LFlBXU+1BNCIi\nIpEx8+4rWDvzHU+ScIC66krWznyHmXdfEdb9Fy8uYvLkO7nsst/zz3++zJVXXsf06U/x1ltvMm/e\nHG688RqGDh3GE09M5w9/uJYnnniM1157me3bt3PttVfQqlU+jz76JP/4x3MMHDiYe+65g+LizUHP\n8/777/DAA1P4/e9/z/TpL3HPPVNYt24tDz54X2NfAokw7YjHoab57cho0YrKLZt2r9VVV7J12SJy\nux/oYWQiIiKNZ9P8r7wOAQg/jtWrV+E4Dm3atKWgoICCggLuu+8h8vPb8PDDD9C//0AuvfR3AHTq\ndAAVFTeSkpLKjh3bGT36bH75y7PIzMwE4NxzL+T1119h5coV5OW1DHieFi1yuemmcZx88sls3LiN\ngoK2HH/8z3jrrTcb94VLxCkRj0OO45Dboz/rvnovYL3YzlYiLiIiCaNV34NZO/Mdr8OgVd+Dw7rf\nsGGH0qdPPy655Fw6dOjI0KHDOOaY4ykoKOD77xdz6KHDA+6/Z9nJ6af/khkz/kNhoWXVqpUUFRUC\nUFtbG/Q8gwYdxPffL2bq1KksWGBZuXI5S5YsJj+/ddB9JbapNCVOhawT12AfERFJIIfcOJW2hxyP\nLy3Dk+f3pWXQ9pDjOeTGqWHdPyMjk6lTH+Xxx59ixIhTKCqy/OEPv2HatEdJTa1/73PTpo2cd95Z\nzJjxJm3btmPUqLOZMuXBeu8/Y8YbXHTROaxZs4aBAwdzzTV/5JxzLmjoy5MYoB3xOKVR9yIikugy\nc/M5atLzXocRtq+++oL58+dxwQWX0KtXHy644BLuvXcS7733Nt2792TRosCZH48++hBLly6hf/9B\nVFRU8OCDj5GSkgLAzJmf1/s806c/xciRZ3LHHRPYuHEbAC+++Dyu60buxUlEKBGPU3khD2zOp662\nBl+K/reKiIhEW2pqGk888RhZWc0YPvwIios38913X9O374GceeZoLr30PJ588nGOP/5nLF5cyL/+\n9U+uuup6mjRpSkVFOR988B59+/Zj8eJC7rvvXgCqqoInZ7du3YY5c2axaNEiKipqefvtGbz33tvk\n5uZF+yXLflLGFqeatulAek4eVaXFu9dqq3ZQuszSoltfDyMTERFJToMGHcTYseOYPv0pHn74r2Rl\nZXHkkUdz+eVX0bRpFnfccQ+PP/4I//jH38nPb81ll13Oqaf+Atd1WbhwPvfdN5nt2yto374jF1xw\nCc888ySLFi1g2LDDAp7nmmv+yN13T+Sss84iIyOTPn36csMNNzN58p2sW7eOgoICj66ANJSTrG9j\nbNy4zbMXnp+fvfutpP3xwQ2/ZN3X7wesDf3jA3Q9acx+P3YsaazrlSx0vRpG16thdL0aRterYXS9\nGkbXq2G8vF75+dlOqHUd1oxjGnUvIiIiEr+UiMexUHXiGnUvIiIiEh+UiMexUJ1Tdh3YFBEREZHY\npkQ8jmUVdCI9u0XAWm3ldkpXFHkUkYiIiIiES4l4HHMcJ3Q/cavBPiIiIiKxTol4nAs1YVN14iIi\nIiKxT4l4nMvtEWrUvRJxERERkVinRDzOhSxNWTKPutpaD6IRERERkXApEY9zzdp1Jq1Z84C12h0V\nbFupA5siIiIisUyJeJxzHIfcHsGDfVQnLiIiIhLblIgngFCDfVQnLiIiIhLblIgnAHVOEREREYk/\nSsQTQG7P4NKULUVzdWBTREREJIaleh2A7L9m7bqQlpVNdfm23Ws1O8rZtmoxzQ8wHkYmIiLy022q\nhGu+hc82QmWdE/Xnz/C5HJYPUwZDq4zwvqa4eDN//vMkvvrqS5o0yWT06DG8+uq/Of/8izn55J/z\nn/+8yvPPP8vq1avJz89n1Khfc+aZoyP7QiRmKRFPAI7PR26PAWyY9UnAeknhHCXiIiISt675Ft5f\nH/0EfJfKOof318M137o8feiP37+uro4//vEafD4fDzzwMDU1Ndx77yTWrFkNwHPPPcNjjz3M1Vff\nwMCBg/nmm6944IG/UFVVza9/fU6EX43EIpWmJIiQ/cRVJy4iInHs22KvI/ALN45Zs75l0aIF3Hrr\nRHr16kO/fv0ZP34Cruviui7Tpz/NqFFn8/Ofj6Rjx06MHHkmv/zlaKZPfwrXdSP7IiQmKRFPEDqw\nKSIiiWZwntcR+IUbh7WLyM3No337DrvXunbtTrNmzdiypYTi4s306xd4rmvgwMGUlBRTUhIjv3VI\nVCkRTxCheomXLJ6DW1fnQTQiIiL7b8pgOKaNS4bPm93iDJ/LMW1cpgwO7/4pKSm4buifuxkZoYvM\n6+r8jRVSU1UtnIz0fz1BZHfoRmrTZtRUlO1eq6koY9uqJeR06uFhZCIiIj9Nqwz2qM2O/dKN7t17\nsGXLFlavXrV7V3zFimWUlZXRtGkWrVu3Ye7c2QwffsTur5kzZxYtW7YkOzvHq7DFQ9oRTxCOz0du\nd03YFBER8crgwUPo1asPEyeOZ9GihSxcOJ/bbx8P+Cdhn3feRfzrX//k9ddfYdWqlbz22su8+OIL\njB49Bsfx7lCqeEeJeAIJVSdeUjTHg0hERESS0x133EN2dnMuv/wSxo69lhNPPBnHcUhNTWXkyDP5\nzW8u5+mnn+Dcc0fx/PPP8oc/XMPZZ5/nddjiEZWmJBB1ThEREfHOli1bWLJkMXfddS8pKSkAbN68\nifvvv5fWrdsAMGrU2YwadbaXYUoMUSKeQPJMqB3x2bh1dTg+vfkhIiISST6fj3HjbmT06DGccspp\nbN++nccff5gOHTrSt++BXocnMUjZWQJp1r4bqZlZAWvV5dsoW7PUo4hERESSR05ODnffPYVvvvmK\n888/iyuuuIyUlBSmTHlQXVEkJH1XJBBfSgotehzIprlfBKwXF84mu0M3j6ISERFJHkOGDGXIkKFe\nhyFxQjviCSbkgc1CHdgUERERiTVKxBNMqAObxYWzPIhERERERPZFiXiCqa+FoevG/iAEERERkWSi\nRDzBZHfsQUpm04C16rKtlK1Z5k1AIiIiIhKSEvEE40tJIbdbv6B19RMXERERiS1KxBOQ6sRFRERE\nYp8S8QSkUfciIiLxZ968OcyZ88PG2eGHD+Gtt970MCKJNCXiCSjXDAxaKymcrQObIiIiMezyyy9l\n1aqVuz9+9dUZHH30cR5GJJGmRDwB5XTqQUpGk4C1qm1bKF+3wqOIREREpKFatmxFRkaG12FIBGmy\nZgLypaTSons/Ns//KmC9pHA2zdoe4FFUIiIiDVNTAatn+Chf4eDWOlF/fifFJauTS/sRdaQ2/fH7\nA5SWljJ16hQ++eQjXNelb99+XHnlteTmtuS880bTr19/br99EgAzZrzBXXdN4MEHH+e2226mtraW\nO+/8E2+++TpTpz7K4YcPYdy4CfzsZyezYsUypkyZzPz583AcGDx4CLfdNp6MjOYRvAISadoRT1B5\nPfoHrenApoiIxJPVM3yULfV5koQDuLUOZUt9rJ4RXrpUV1fHDTdcxaZNm/jLX/7KQw89TkFBW37/\n+0uoq6tl7NhxvP/+O3zyyYesW7eO++6bzIUXXkq/fgfy2GNPkZKSwpVXXsedd04OeuzbbruFgoK2\nTJv2DA8++Dhbtmzh5ptvbuyXLFGmHfEEFbpziloYiohI/KhY400Cvrdw4/jmm69YtGgBb775LllZ\nzQC4/vqb+Prrr3jttZc599wLGTnyTKZMmUy7du3p2rU75557IQC5ubkANGvWjJyc4F3u1atXMnTo\nMAoK2pKamsr48bdTV7e9kV6heEWJeILK6xnqwKZ/wqbjxMY/bCIiIvvStJ1L2VLvf2Y1bRdes4Oi\nIkttbS0jR54UsF5VVcWyZUsBuPzyq/n880+ZO3c2zz33MikpKWE99iWX/I6pU6fw8sv/YvDggxk+\n/HBGjz6T8vLahr0YiSlKxBNUTmdDSnomtVU7dq9VlRZTsX4VWQUdPYxMREQkPO1H1LF6BjFRIx6O\n1NQ0cnKa8+ijTwZ9rkkTfxOFDRvWU1JSguu6fPfdN5x00qlhPfavfnUWxx13Ap999jFffTWTBx6Y\nwgsvTOfxx58hPT097NcksUWJeILypaTSoltfNi/8JmC9uHCWEnEREYkLqU3hgDPCS4JjQZcuXSkt\n3QpAhw7+n7W1tbVMmHALRx55LEcddQwTJ45nyJChDBgwkPvvv5fBg4fQpk3BPh9369YtTJv2KGPG\nnM+pp47k1FNHsmDBPC677AIWLy6kT5/gidoSH3RYM4GFqhPXqHsREZHIGDJkKH37Hsj48WOZPfs7\nVqxYzt13T+STTz6ia9duPPPMk6xYsZwbbriJ0aPH0K5de+68c8LuOR9Nm2axbNn3lJQUBzxudnYO\nX3zxGZMn38nixUWsWrWSN9/8Dzk5OXTq1NmDVyqNRYl4AsvtoQObIiIi0eI4DnfddS9dunRl7Njr\nuOiiMaxcuYK//GUq1dVVPPnk4/zud1eSn9+a1NRUbrxxHLNmfcO///0CAOeccz4vv/wi1157RcDj\n+nw+Jk++D4ArrriM888/i2XLvufvf/87zZo1i/rrlMaj0pQElmdC74jrwKaIiEhk5OW1ZNy420N+\n7oMPvgj42JhefPjhzN0fjxlzPmPGnA9ATU0N4K87B+jUqTOTJ98f8PX5+dls3Lit0WKX6NOOeAJr\n3rkXvrTAiVyVWzdTsXGNRxGJiIjIj9m0aSMff/wBwI/Wj0t8UyKewHypabTo1idovUSDfURERGLW\niy8+z513/omf/exk+vbVQcxEpkQ8weVpsI+IiEhc+e1vr+Dttz9m3LgJKiVNcErEE1yoA5vqnCIi\nIiLiPSXiCa6+Ufe7WiWJiIiIiDeUiCe45l1640sLnLhVWbKR7Zt0YFNERETES0rEE1xKWjrNu/QO\nWi8pnONBNCIiIiKyixLxJJDXc2DQmg5sioiIiHhLiXgS0Kh7ERERkdijRDwJ5PXsH7SmHXERERER\nbykRTwLNu/TBt3NE7i47itezfdNajyISERERESXiSSAlPSPkgc3iIh3YFBEREfGKEvEkoTpxERER\nkdiS6nUAEh15PQfw/RtPB6ypTlxERGKZU1FN9vvLSV+9Dac2+oPo3BSHqvbZbDvmANymaT/+BcCb\nb77Os8/+gzVrVpOX15KTT/45I0acwllnnc799z/M4MFDdt/3hhuuIjs7h/Hjb+fww4cwduw4/vvf\n/7Bw4XxatMjl/PMv5he/OCNSL09igHbEk0ToHfFZHkQiIiISnuz3l5OxotSTJBzAqXXJWFFK9vvL\nw7r/4sVFTJ58J5dd9nv++c+XufLK65g+/SnmzJnFgAGDeOedt3bft6SkhC+//IIRI07Zvfa3v/2V\nM84YxdNPv8BRRx3Ln/88iXXrdJ4rkSkRTxItuvbBSQl8A2T7pnVsL17vUUQiIiL7lrau3OsQgPDj\nWL16FY7j0KZNWwoKCjjqqGO4776HGDRoCCNGnMIHH7xHTU0NAO+99zZ5eS0ZMmTo7q8/+eTTOO64\nE2jfvgMXX/wb6urqWLBgfkRek8QGJeJJIiU9k+adewWtl1iVp4iISGyqLsjyOgQg/DiGDTuUPn36\ncckl53LWWafzl7/cTU1NDQUFBRx77PFUVVXy5ZdfAPD22zM44YQR+Hw/pGKdOnXa/edmzZoBUFNT\n3YivRGKNEvEkEqo8RZ1TREQkVm075gAqO+XgpjiePL+b4lDZKYdtxxwQ1v0zMjKZOvVRHn/8KUaM\nOIWiIssf/vAbpk17lKZNszjyyGN45523WLNmNfPmzeGkk04N+Pq0tPTgGFxvynIkOnRYM4nk9RzA\n0v8+G7CmOnEREYlVbtM0Sk/p7nUYYfvqqy+YP38eF1xwCb169eGCCy7h3nsn8d57b3PRRZdx0kmn\ncMstN9Kp0wEY05suXbp6HbJ4TIl4EskLtSOuzikiIiKNIjU1jSeeeIysrGYMH34ExcWb+e67r+nb\n90AADjpoKE2bZjF9+tNcdtnvPI5WYoFKU5JI8259cXwpAWvbN65hR8lGjyISERFJHIMGHcTYseN4\n7bV/c845o7jppusZOHAwV199PQA+n4+f/exkqqoqOf74ER5HK7HASdbao40bt3n2wvPzs9m4cZsn\nz/3fi49g6/cLAtaOnPQ87Q453pN4wuHl9YpHul4No+vVMLpeDaPr1TC6Xg2j69UwXl6v/PzskAcd\ntCOeZEKVp6hOXERERCT6lIgnmZCDfdQ5RURERCTqlIgnmbyeA4PWdGBTREREJPqUiCeZFt364vgC\n/7dXrF9F5dbNHkUkIiIikpyUiCeZ1Mym5HTqGbRerAmbIiIiIlGlRDwJhZywqQObIiIiIlGlRDwJ\nhe6coh1xERERkWhSIp6EckMc2FTnFBEREZHoUiKehHK79ws6sFm+bgWVW4s9ikhEREQk+SgRT0Kp\nTbLI7tgjaL2kSOUpIiIiItGiRDxJ5fbsH7SmfuIiIiIi0aNEPEmFGuyjA5siIiIi0aNEPEmFbmGo\nRFxEREQkWlK9DiAcxpg0YBrQGcgAJlprX9vj8z8HxgM1wDRr7WNexBlPcnscCI4Drrt7rXztcqq2\nbSE9u4WHkYmIiIgkh3jZET8H2GytPQIYAUzd9YmdSfoU4ETgKOAyY0wbT6KMI2lNmpHTsXvQunbF\nJZmsKIeKGvfH7ygiIhIB8ZKI/wsYt/PPDv6d7116A4uttSXW2irgE+DIKMcXl0KVp6hOXJLBynI4\n82M47G2Hlk+W8+wyryMSEZFkFBelKdbaMgBjTDbwInDLHp/OAbbu8fE2oPmPPWZublNSU1MaM8wG\nyc/P9uy5d+k4aCjL33kxYK18+fyYiG1vsRhTLNP1qt/czbWc8ekO1lb4d8LrXLhxlsPBHZswvMC7\nfxPiib6/GkbXq2F0vRpG16thYu16xUUiDmCM6Qi8DDxkrZ2+x6dKgT2vajaw5ccer6SkonEDbID8\n/Gw2btzm2fPvkt6uV9DaurnfxERse4qV6xUvdL3qN3MTXPgFlNY4QZ+74sMK3jwaUuPlfUKP6Pur\nYXS9GkbXq2F0vRrGy+tV3y8AcfEjZ2fN9/+AG6210/b69EKghzEmzxiTjr8s5fNoxxiPcnscGLRW\ntmYpVWVbQ9xbJL69tRbO/ix0Eg6woNThmWXRjUlERJJbXCTiwM1ALjDOGPPBztsYY8xl1tpq4Frg\nLfwJ+DRr7Wovg40XaVk5ZHfsFrReUjTHg2hEImf6Mrh0JlTWhU7Cd5m8EDZXRicmERGRuChNsdZe\nBVy1j8+/DrwevYgSR26PgWxbuSRgraRwNm0GHeFRRCKNx3VhaiHcvXDfCfguW6sdJi1wmTwowoGJ\niIjQgETcGJMLXIa/S8kf8ZeAzLPWLopQbBIFeWYAK957KWBNLQwlEdS5cNtcmPZ96CR8YK5Lhybw\nnzWBn39uOZzdGQblRiFIERFJamGVphhjegKLgIuAs4FmwJnA18aYwyIXnkRabo/+QWtqYSjxrqoO\n/vB1/Un40a1dXhgOt/eH5umBn3NxuGW2P5EXERGJpHBrxKcAL1prDbCrgnIM8AIwKRKBSXTkobAQ\nSAAAIABJREFU9gjuJb5t1RKqyko9iEZk/5VVwwWfw6urQyfhp3dweWIYNE2F/EwYf1B60H1mb3F4\nbnmkIxURkWQXbiI+DPjrngvW2jr8SbiqKeNYerMcmrXvGrS+ZfFcD6IR2T+bK2H0p/DRxtBJ+KXd\nXO4/CNL2+Jfvt33S6JUTvP191wIoqYpUpCIiIuEn4i7QJMR6a37YIZc4lRdiwqbqxCXerCyH0z/2\n72aHclMfl/H9wLfXp1N9DhODK7QoqXK4d2EEAhUREdkp3ET8NWCiMabZzo9dY0xX4D7gjYhEJlGj\nUfcS7xZuhZEfw/dlwUl4iuPy50Eul/cEp57mKcNawcgOwbviTy+FeT86HkxEROSnCTcRvxbIA4qB\nLOBLoAioAq6PTGgSLaF3xGd5EIlIw83cBGd+DOt3BGfZGT6Xx4fC6AN+/HFu6QtZqYHJeB0Ot8zR\nwU0REYmMsBJxa+0W4DDgZPytC28HTrTWHmat3RjB+CQKQnVO2bZqCdUVGpsrse1/+5iW2TzN5bnh\ncELb8B6roAlcbYLXvy52+PfK/QxUREQkhLAna1prXWvtO9bae4F/ArnGmC6RC02iJT27BVntOgcu\nui4lRTqwKbHrn8vgknqmZbbJdHnpCDi4ZcMe8+Ju0L1Z8Pb3HfOhtPqnxSkiIlKfcPuIDzDGFBlj\njjTG5AGz8bcuXGiMOTGiEUpU5IVoY6hR9xKLdk3LvGGWQx3BSXi3Zi6vHgm9chr+2Ok+mBDi4ObG\nSocpGl0mIiKNLNwd8XuBucAC/P3DU4A2wMSdN4lzeUZ14hL7dk3LnLSg/mmZLx8BHZr+9Oc4sjWc\n3C54V3za97BI7fVFRKQRhZuIHwrcaK3dBJwE/GdnbfgzQL9IBSfRo84pEuuq6uAP38Df65mWeVRr\nl+eHQ17G/j/Xrf0gMyUwGa91HcbN8e/Ii4iINIZwE/FKwDHGZABHAW/vXG8N6ERfAsjtHvx+fOmK\nIqq3l3kQjUig8hq48At4dVXoJHzkzmmZWamN83ztm8KVPYPXP9/k8PrqxnkOERGRcBPxD4DJwKM7\nP37TGDMAuB94NwJxSZRlNM8jq6BT4KLrsmXxPG8CEtlpcyWM+gQ+3BA6Cb+km8sDB/nruxvTZd3h\ngKzg7e8J8/y/GIiIiOyvcH90/Q6oAQYA51lrS4FzgArg6gjFJlEWqjyl2Ko8RbyzqmLf0zLH9nG5\nNcS0zMaQmQITDgxeX7fD4X7b+M8nIiLJJ6w3cq21G4Az91oea62tbfyQxCt5PQew6qPXA9ZKipSI\nizcWboVzPg89qMeHyz2D4KwwBvXsj+MK4IQCl7fXBcbw2GIY3Qm6ZUf2+UVEJLGFlYgbY26uZx0A\na+2djRiTeCS358CgNR3YFC98udlfE761OvS0zIcPhhPDHNSzv247ED7a4Ab0K692HcbPdXnmUHAi\nsBsvIiLJIdyjTZeG+Lo2QDXwKaBEPAHk9Qx1YLOQmu3lpDbJ8iAiSUb/Wwu/+yr0oJ7maf5DmUMb\nOKhnfxyQBb/rAfftVY7y4QaHGWtdTmoXvVhERCSxhFuaEjRB0xiTAzwBfNLYQYk3Mpq3pGmbDlSs\nX7V7za2rY8uS+bTqN9TDyCRZPLccbpzlbxW4tzaZ/h3o3s2jH9flPeDFFS6rtgfG9ae5cHRraNJI\n3VpERCS5/OQ+AzsPbI4Hrmu8cMRreaEObGqwj0TYrmmZ13/nhEzCu+6clulFEg7+RPvWEAc3V213\neLAo+vGIiEhi2N+GX9lAi8YIRGJDyM4pqhOXCKpz4U/z6p+WOaDF/k/LbAwj2vqHBu3t4SJYVu5B\nQCIiEvf257BmDvBr4L1GjUg8lRfqwGbRHA8ikWRQVQfXfguv1DOo56jWLo8ObbxBPfvDcfztDI9/\nz6V6j137yjqHP831166LiIg0xE89rAlQBbwPhOyoIvEp1I546TJLTeV2UjOaeBCRJKryGrjsy/oH\n9fyig8uUwY0/qGd/dMuGS7vDQ3uVo7y9zuHddS7HFXgTl4iIxKeffFhTElNmi1Y0bd2eig0/zPF2\n62rZsmQerfoc7GFkkkiKK+G8L2BWSegk/KKuLrcdGJlBPfvrKgP/Xumybq/+5uPnwvB8/yAgERGR\ncMTQXpPEitwewW0MSzRhUxrJrmmZ9SXhY/u4/ClGk3Dwl8mM7xe8vrzc4dHF0Y9HRETilxJxCRJq\nsI8ObEpjWFQKIz+CJWWhp2VOHuhyRc/YH5Lz8/ZwaKvgg5sPFMLqCg8CEhGRuKREXIKEamGoA5uy\nv77aDGd+TFBJB/inZT52CPy6c/Tj+ikcB27vDylOYDK+o9ZhwjyPghIRkbijRFyChErEty5bRG3V\nDg+ikUTw9lo469PQI+tzUl2mHwY/i9LI+sbSKwcu6hq8/sYah483RD8eERGJP2El4saYfxtjfm6M\n0TGkJJCZ15omrQKzIre2hi1L5nsUkcSz55fDJV+GHlnfJtPlpSPgkFYeBNYIrukF+RnBJSrj5vhb\nM4qIiOxLuDviZcB0YI0xZooxJvg0nySU0BM2VScu4XNdeLAQrtvHtMxXjvBuWmZjyEmD/+sbvL64\nzGHakujHIyIi8SWsRNxaex7QBv84+97AN8aYWcaYq4wx+ZEMULwRqp94iRJxCVOdCxPmwV0/Mi2z\nY1aUA4uAMzrCkLzgXfEpFtZt9yAgERGJG2HXiFtrK6y1z1hrRwAdgH8BdwKrjDGvGGOOjVSQEn0a\ndS8/VVUdXPUNPLYkdBJ+ZL7L88OhZUaUA4sQnwN39Pd3fdlTeY3DRFVziYjIPjTosKYxpoUx5jfA\nC8CfgCLgpp3/fcUYM7HxQxQvhDywuXQhtVWVHkQj8aKiBi78Al6uZ2T9L9q7PHkoNEuLcmAR1rcF\nnBti7Nkrqxy+2BT9eEREJD6Ee1jzTGPMy8A64HbgO2CItXagtfYv1tobgOuBqyIXqkRTk5YFZLZs\nE7Dm1tawdekCjyKSWFdcCaM+rX9k/YVdXf46JLZG1jem63tDbnpwicotc6BGBzdFRCSEcH8kTgcc\n4CygnbX2amvtrL3uMx/4W2MGJ97KCzXYRxM2JYTVPzIt88beLhNieFpmY8hNh5v6BK8vKnX4x9Lo\nxyMiIrEvNcz7tbfW7vMNVmvtp8Cn+x+SxIrcHv1Z8/lbAWuqE5e92VIY81noQT0+XCYNhLM7Rz8u\nL5x1ADy7zGX2lsBrce9COK095Gd6FJiIiMSksBJxa+0mY8xpwIHArl7iDpABHGytPSFC8YmHQk7Y\nVCIue/h6M5z/RehBPRk+lweHwIh2HgTmEZ8DEwfAaR+6uPxwTbbVOExa4PLnwR4GJyIiMSesRNwY\ncw/+1oUrgY7AcqAtkA48E7HoxFOhOqdsXbqA2qpKUtITpOWF/GTvrIPffuUf6763nFSXacNgWJwO\n6tkfg3L9O+P/XB64/vwKh7M7uxyU501cIiISe8KtER8DXG6t7QysBo7F31f8A2BVRCITzzVp1ZbM\n3NYBa3U11WxdtsijiCRWvLAcLp4ZOglvneHy4hHJmYTvMrYPNE8LcXBzNtQGL4uISJIKNxHPB/67\n889zgKHW2lLgFmBUJAIT7zmOo8E+EsB14aEiuLaeaZldslxeORL6xPG0zMbQMgNu6B28Pnerwz+X\nRTsaERGJVeEm4puAXW+oFuKvFQdYA7Rv7KAkdmjUvexS58Lt8+DO+aFbn/Rv4fLykdApAaZlNoZz\nOkOfnODt70kLoKQq+vGIiEjsCTcRnwE8aIzpDXwMjDHGDAB+i79URRJUbs/+QWslhXt3rpREV10H\nV38Dj9YzLfOIfJcXhkMrHR3YLdUHdwT/HsuWaoe71Y5fREQIPxG/DtgMHAO8in+S5nfAtcCtkQlN\nYkFuiF7iW75fQG21tvSSxa5pmf+uZ1rmae1dnhyWeNMyG8PBLeHMjsG74s8ugzlboh+PiIjElnDb\nF5YAP9/1sTFmBDAIWGutXRuh2CQGNM1vR0aLVlRu+aGNfF11FaXLFpHbI3i3XBJLSRWc9zl8V8+g\nngu6uEzon9iDevbXzX3hrbUuZTU/XCQXh/+b7fLqkbp2IiLJrN5E3BjT6Ue+dhOQZozpZK1d0bhh\nSaxwHIe8ngNY++W7AevFhXOUiCe41RX+QT2Ly0Jnin/s7fKHnuAokdynNplwXS/407zA9e9KHP61\nwmX0Ad7EJSIi3ttXacoyYGmYN0lgoTunqE48kdlSGPlx6CTch8vdA12uNErCw3VBV+iZHVyicud8\n2KoqLxGRpLWvRPwI4Midt2vw74BfAQwB+gMX4+8hfnmEYxSPqXNKcvl6M5zxMazdHnpa5iNDYUzn\n6McVz9J8cHuIN5A2Vzn8WW35RUSSVr2lKdbaT3f92RjzMHCJtfb1Pe4y3xizFvgr8LfIhShey+0R\nnIhvWTKfuppqfKk6oZdI3l0Hv6lnWmb2zmmZhybxoJ79MTzff7D1tdWB1/bJ7/2TOJO997qISDIK\nt2tKN/ydUva2CmjXeOFILGrapgPpOYFzueuqK9m6zHoUkUTCiyvgon1My3zpCCXh++uWftAkJbBE\npQ6HcXP8w5JERCS5hJuIfwWMN8Y02bVgjGkO3IW/r7gksF0HNvemCZuJ4+EiuPrb0NMyO2taZqNp\n1wSuMsHrMzc7vLIq+vGIiIi3wk3ErwKOB9YYY74wxswEVuKvFVeNeBIIeWCzSIl4vNs1LfOOeqZl\nHtjcn4RrWmbjubQbdG0WvP09cT5sq/YgIBER8UxYibi1djbQE7gZ+Br4Ev8Bzn7W2u8jF57ECh3Y\nTDzVdXDNt/DI4tBJ+OH5Li8crmmZjS0jBSYcGLy+fofDfar2EhFJKmEN9DHG9LTWFgIP77Weboy5\n01p7c0Sik5iRa0JM2Fwyn7raGnwpYX0bSQypqIHffgXvrQ+dhP+8vct9g/1JozS+o9vAz9q6vLU2\n8Pr/fYn/4GaPbI8CExGRqAq3NOU9Y0z3PReMMYcBc/C3NJQEl9WmI+nZLQLWaiu3U7q80KOI5Kcq\nqYKzPq0/CT+/i8vUIUrCI+22fv52kHuqcXVwU0QkmYSbiL8FfGCM6WaMaWqMuR//Ic0lQIg3WSXR\nOI4Tsk68WIN94sqaCn+P8G/rGVl/fS+Xif0hRYN6Iq5jFlzRM3j9k40Ob6yJfjwiIhJ94daIXwy8\nCnwIzANGAb+21p5irV0ewfgkhqhzSnwrLIVffAxF20JPy5w00OXqXpqWGU2/7QGdmgZvf0+Y5y8f\nEhGRxBbujjjW2suB6UAnYKS19oWIRSUxKbdncJ14SeEcDyKRhvqmeN/TMv82FM7pHP24kl2TFLgt\nxHuKa7Y7TFXVl4hIwqv3lJ0x5n/1fKoaeM0Ys3sr1Fp7YmMHJrEn5I74knnU1dbiS1FBcaz6sWmZ\nfz8EDsv3IDAB4IQCOLaNG1Sz/7fF8MtO0LWZR4GJiEjE7WtHfHU9t+eAN/dakySQ1fYA0poFTnWp\n3VHBthWhhq5KLNjXtMz8DJcXD1cS7jXH8e+Kp+91cLOqzuHWuTq4KSKSyOrdEbfWXhjNQCT2OY5D\nbo/+bPgucJhqceEsmnfp5VFUUp+/FcHEegb1HJDlMv0wOECDemJC12bwm+7w173KUd5f7/DOOpcT\n2noTl4iIRFbYNeLGmFxjzI3GmCeMMa2NMb80xij7SjJ5IevEdWAzltS5LrfPqz8J79fc5ZUjlITH\nmj/0hHZNgre/b50L22s9CEhERCIurETcGNMTWARcBIwBmgFnAl/v7CcuSSLkhM0iHdiMFdV1cOmH\nlfVOyxzeyuVfh0N+ZpQDkx/VNBXG9wteX1Hh8DdVf4mIJKRwd8SnAC9aaw1QuXNtDPACMCkSgUls\nyjXBifiWornU1WrLzmsVNXDxTHimKHTfu1PbuTx1KGSnRTkwCdsp7eDw/OBd8amFsLLcg4BERCSi\nwk3EhwF/3XPBWluHPwkf1NhBSexq1q4LaVk5AWs1O8rZtmqxRxElp1oXvi+DN9fAXxbBZV/C0e/u\ne1rmgwdrWmascxy4vT+kOoHJeGWdw23zPApKREQipt7DmntxgSYh1lvzww65JIHdBzZnfRKwXlI4\nm+YHGI+iSlyuCxsrYVFp4K1wW+hOKKFc18vlaqNBPfGiRzZc3A0e2et327fWOry/3uWYNt7EJSIi\njS/cRPw1YKIxZvTOj11jTFfgPuCNiEQmMSu354CgRLy4cDadTxjlUUSJobwmOOG2pVBc9dMyaAeX\nOwfAuV0aOVCJuKsNvLLKZf2OwP/34+fAO8fqnQ0RkUQRbiJ+LfBfoHjn13wJ5AEzgesjE5rEKo26\n3z/Vdf6ykl2J9q6ke0VF421Zp/tc/noQnNK+0R5Soig7Df6vL1z5TeD60nKHx5e4XN7Tm7hERKRx\nhZWIW2u37OyOchwwEKgC5ltr341kcBKbckMl4kVzcOvqcHxhd8RMeK4La7fDwr12uZeU+Ye1RMrw\nNj7+aGo5KC9iTyFRcHoHeHaZy8zNgd8r91s4vSO0C1UsKCIicSXcHXGsta4xZi5Qg38nPDtiUUlM\ny27fldSmzaipKNu9VrO9nG2rlpDTqYeHkXlna1XospLSmsgl3M3TXHrlEHAzOdCtXVM2btwWseeV\n6Nh1cHPE+y51/PB9VFHrMHGey0MHexiciIg0irAScWNMBvAQcCFQB/QE/myMyQHOsNZujVyIEmsc\nn4/cHgPYOPvTgPXiwtkJn4hX1sLibT/scttt/v+u3R65hDvD59I9m6CkuyBTBzATXZ/mcH5XeOL7\nwPXXVjuM6ewyPN+buEREpHGEuyN+K3AwcDjwv51r9wD/2Pnf3zR+aBLL8nr0D0rESwpn0/n4X3oU\nUeOqc2FFBSza+kOyvajUX9td60Ym+3Vw6ZT1Q6Lde+d/O2dBqip+ktb1veC1VS6b9zq0O24OvHUM\npOl7Q0QkboWbiI8CLrHWfmaMcQGstZ8bYy4FnkOJeNIJVSdeHKcHNjfvbA+4Zy13Yam/BCBSWmW4\nQSUlJts/XVFkT83T4ea+cN13geuF2xye/N7l0u7exCUiIvsv3B/77YDlIdbXAc0bLxyJF3khJmyW\nFM2O6QOb22sCd7d33TZVRi7hbpLiYnYl3NnQq7n/z60yIvaUkoB+1QmeWebyXUng9+qfF8EvOkDr\nTI8CExGR/RJuIv4dMBL/qHvwD/gBuAyIz21Q2S/ZHbqT2iSLmu0/zN2uqSijbM1Ssjt08zAyqKmD\nZeWBu9y2FJaXg0tkku4Ux6VL1l513M2hU1PwqY5b9pPPgTsGwCkfuAHfw2U1DnfMd7n/IA+DExGR\nnyzcRHwsMMMYMwxIA8YaY3oDhwCnRCo4iV3+A5v92Tjn84D14sLZUUvEXRfW7Qjux120zT8SPFIK\nMgPLSnrnQLdsyNSQFYmg/i1gTGd4Zlng+ksr/Qc3h7b0IioREdkf4fYR/9gYMxy4DliM/+DmfOD3\n1tp5EYxPYlhuzwFBiXhJ4WwOOPaMRn+u0mp/3fbetdxbqyOXcGenhm4P2CI9Yk8psk839oH/rHbZ\nstf3/S2z4b/HQIrefRERiSvhti/8BfA/a+25EY5H4khuj/5Ba8WFsxrlsV0X3lgDL66AwvJyVpRF\nLsNIc35oD2j26FbSronaA0psyU33J+M37VUQuKDU4ZmlLud39SYuERH5acItTXkSSDfGvAe8Crxu\nrV0fsagkLoQedT8H13Vx9jODfeJ7GD9312O4+7xvQ3RqusfhyZ23rs3UAk7ix9mdYfoyl7lbA/+O\n3bMQTm0PLXUQWEQkboSbiLcChgMjgCuAR4wxXwOvAa9Za+dGKD6JYdkde5CamUXNjh8ObFaXl/oP\nbLbfv625F1fuX2y56SHKSrKhWdr+Pa6I11IcmDgAfvFR4PrWaodJC1wmD/ImLhERabhwa8RrgY92\n3m42xhwA3Ab8CZgA6JhaEvKlpNCiez82zZsZsF5SOGe/E/HmYSbMGb7gHW6TA60zVFYiieugPBjV\nyeWFFYHf5M8t9++YD8r1Ji4REWmYcGvEU4ChwFHA0cBhgA94G3g3UsFJ7MvrOSAoES8unEWnY0bu\n1+Pe0BsWbP1hmqAPl87NAne3ezeHA7J0QE2S0019YMYal9KaH/4CuDjcMtvl9aPUNlNEJB6EW5qy\nFcgEPtx5uwP4wlpbHanAJD6EmrBZ0ggTNgfnwYfHQ+E2l7Ytm9KqtoImet9FZLf8TLiuN9y6V2Hg\n7C0Ozy13ObuzJ2GJiEgDhHtE7d/4p2getPM2GDCRCkriR8hEvMh/YHN/tUiHoS1hcH6KknCREM7v\nAr1ygv+u3bUASqo8CEhERBokrETcWnuetbYDMAz4H/4SlfeNMRuMMc9HMkCJbTmdepCS0SRgrWrb\nFsrXLvcoIpHkkeqDicFdRCmpcrh3YfTjERGRhmlQ0zZr7SLgX8BLwH+BbPydVCRJ+VJSadG9X9B6\ncSOUp4jIjxvWCkZ2CN4Vf3opzNviQUAiIhK2sBJxY8wIY8y9xpjZwFr8HVNKgNPwtzaUJBayn3iR\nEnGRaLmlL2SlBibjdTjcMsc/HEtERGJTuIc1Xwc+A54B/mOt1ZuesluoOnHtiItET0ETuNrAHfMD\n178udnhppcsvO3kTl4iI7Fu9ibgx5llgxs5bvrVWb3JKSHk9QndOaYwJmyISnou7wfPLXRaXBf6d\nu2M+nNgWcjTMSkQk5uyrNOUj4EygCHjHGDPRGDPcGKNh4BIgp7MhJT0zYK2qtITy9fs5HlNEwpbu\ngwkhDm5urHSYsij68YiIyI+rN6m21j5irR0J5AM3AGnAQ8AmY8y/jDEXGWPaRSlOiWG+lFRadOsb\ntF5iZ3kQjUjyOrI1nNwuuCh82vdgSz0ISERE9ulHd7ettdXW2vettTdaawcAB+LvmDICmGeMmRPp\nICX21ddPXESi69Z+kJkSmIzXujq4KSISixpcZmKtXW2tnWatHYW/Y8rvGj8siTd5PQcGrenApkj0\ntW8KV/YMXv98k8Prq6Mfj4iI1G9fhzXHN+BxPm2EWCSO1TfqXgc2RaLvsu7w/AqX5eWBf/cmzIPj\nCiAr3H5ZIiISUfv65/jcvT7uCuwAFgNVQE+gCTATmBCR6CRuNO9s8KVlUFdduXutcutmKjasJqtN\nBw8jE0k+mSkw4UA4/4vA9XU7HO63LjcHH+kQEREP7OuwZo9dN+Ax4C2go7V2gLX2YKAD8Cr+RFyS\nnC81jRbd+gStlxTqwKaIF44rgBMKgovCH1sMS7Z5EJCIiAQJt0b8j8AN1triXQvW2m3ArcAlkQhM\n4k+oCZuqExfxzq0HQoYvMBmvdh3Gz9XBTRGRWNCQw5ptQqx1BSpDrEsSyg1xYFOdU0S80zkLftsj\neP3DDQ5vrY1+PCIiEijcIzvPAU8YY24CvgUc4DBgIvB4hGKTOFPfjrgObIp454oe8NIKl1XbA/8O\n3jYXjmoNTXRwU0TEM+HuiF8HvAM8AczfeZuKP0FvSHcVSWA5nXvhS0sPWKss2cj2TWs8ikhEmqT6\nS1T2tmq7w4NF0Y9HRER+EFYibq2ttNZejL9v+CHAwUAra+011traSAYo8SMlLZ3mXXoHrRdb1YmL\neGlEWziqdXBR+MNFsLzcg4BERARoQI24MaYJ/paFaUAGMMAYc5gx5rBIBSfxJ9RgnxId2BTxlOP4\n2xmmOYHJeGWdw21zPQpKRETCqxE3xvwCeBLIwV8fvicXSGncsCRehRrso84pIt7rlg2XdoeH9ipH\neXudw7vrXI4r8CYuEZFkFu6O+N34+4gPArrsdesamdAkHoU6sKnOKSKx4SoDBZnBJSrj58IOFRmK\niERduOflOwMnW2u/j2AskgCad+mNLzWNuprq3Ws7itezfdNamrRq62FkIpKVCuP7we+/DlxfXu7w\n6GKXK403cYmIJKtwd8TnA90iGYgkhpT0DJp3CZ6wqfIUkdjw8/ZwaKvgXfEHCmF1hQcBiYgksXB3\nxO8AHjbG3AMUsdcQH2vtZ40dmMSv3J79KSkKTLyLC2fT/rARHkUkIrs4DtzeH372vkut+8ORnx21\nDhPmuTwy1MPgRESSTLg74i/irwX/G/Au8Mket48jE5rEq5B14toRF4kZvXLgohCne95Y4/DxhujH\nIyKSrMLdEe8S0SiSRNkyh+3rIMXUQq7X0URO6M4pszyIRETqc00veGWVy8bKwEZY4+bA/46F9LCb\n24qIyE8VViJurV2+68/GmM7AKsCx1lbX+0USYPM3Dus+8Hd53PBpFW2OdGh1cHCdZiJo0bUPTkoq\nbm3N7rUdm9ezffM6mrRUjzSRWJCTBv/XF67+NnB9cZnDtCUuv+3hTVwiIskkrD0PY4xjjBlnjCkH\nFgOdgKeNMX83xqRFNMIEsfmbwEu98QsfdTX13DnOpaRnhpywWVKoNoYiseSMjjAkL3hDYIqFdds9\nCEhEJMmE++bjtcAlwKX8cFDzeeBUYGIE4kp4dVUO29d6HUXk5PboH7SmzikiscXnwB39wUdgMl5e\n4zBxvkdBiYgkkXAT8YuBy62104E6AGvty8BFwK8jFFtCadI2eNepfEXiFmGGPrCpOnGRWNO3BZwb\n4hTQK6scvtgU/XhERJJJuJlgF/y9xPdmgfzGCydxZXUKTsTLljsh7pkYQiXi2hEXiU3X94bc9OB/\no26ZAzV1HgQkIpIkwk3ELXBkiPUzd35OfkSoRHz7OqitDHHnBNC8W18cX0rA2vZNa9lRrN5oIrEm\nNx1uCp7DxaJSh38sjX48IiLJItxE/DbgQWPM3fg7rYwxxjyFvz58UoRiSyjpLSAtZ69k3HWoWJWY\nu+KpGU1o3rlX0HpxkQ5sisSisw6AAS2CNwzuXQgbd3gQkIhIEggrEbfWvgKMAg4DaoFr8JernGqt\nfS5y4SUOx6mnPGVFYibiELqfuOrERWKTz4GJA8DZ6+DmthqHSQs8CkpEJMGF1UfcGONLYrA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bg7bOr7KpwCJA4AQSMjO059Tk6LgKUaEnMjKFiQ0RlTMeYX0VJx1GO84dBuciaeGSGpYpuaXVvY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DfGjbbT1fTsJ8EvRyAXMph2ctiy+dxZ/PErQz+FVnzft9GD250sOTKz3M+XJ86dgsf3Z0lgV/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\nkCAuhBBCCCFEG/z/1bRS/1ftc+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8ef74ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12,))\n",
    "trend_plot_colors = [\n",
    "    sns.xkcd_rgb[\"salmon\"],\n",
    "    sns.xkcd_rgb[\"tan\"],\n",
    "    sns.xkcd_rgb[\"dark lime\"],\n",
    "    sns.xkcd_rgb[\"greenish teal\"],\n",
    "    sns.xkcd_rgb[\"brown red\"],\n",
    "    sns.xkcd_rgb[\"azure\"],\n",
    "    sns.xkcd_rgb[\"light purple\"],\n",
    "    sns.xkcd_rgb[\"pink\"],\n",
    "]\n",
    "idx = 0\n",
    "for tag, _ in tags_most_decreased_wend_to_wday_activity:\n",
    "    df = trend_data_for_each_tag[tag].copy()\n",
    "    df = df.merge(\n",
    "        year_wend_qn_counts,\n",
    "        on=\"Year\",\n",
    "        how=\"left\",\n",
    "    )\n",
    "    df[\"WeekendTotal\"] /= df[\"Total\"]\n",
    "    df.pop(\"Total\")\n",
    "    df = df.merge(\n",
    "        year_wday_qn_counts,\n",
    "        on=\"Year\",\n",
    "        how=\"left\",\n",
    "    )\n",
    "    df[\"WeekdayTotal\"] /= df[\"Total\"]\n",
    "    df.pop(\"Total\")\n",
    "    df[\"Ratio\"] = df[\"WeekendTotal\"] / df[\"WeekdayTotal\"]\n",
    "    ax.plot(\n",
    "        df[\"Year\"],\n",
    "        df[\"Ratio\"],\n",
    "        label=tag,\n",
    "        linewidth=5,\n",
    "        color=trend_plot_colors[idx],\n",
    "    )\n",
    "    idx += 1\n",
    "leg = ax.legend(fontsize=15,)\n",
    "leg.set_title(\"Tag\", prop={\"size\": 18,})\n",
    "ax.set_ylabel(\"Relative weekend/weekday use\", fontsize=15, labelpad=20,)\n",
    "ax.set_title(\n",
    "    \"Which tags' weekend activity has decreased the most?\",\n",
    "    fontsize=18,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "ttl = ax.title\n",
    "ttl.set_position([0.5, 1.02])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Of note in this plot:\n",
    "\n",
    "- Scala relative weekend over weekday usage sharply declined from 2008 to 2009. From 2011 onwards it is almost a steady downward trend towards 1.\n",
    "- Golang has also seen a steady decrease in weekend over weekday relative activity from 2011 to 2016\n",
    "- There are 3 Microsoft technologies on this list. asp.net-mvc3, visual-studio-2012 and internet-explorer. Very curious."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Time to do the same plot but for tags whose weekend activity have increased the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZN9+cyvvvv0VkZCTNmjVn0qTnWb16JV9//QXp6ekkJCQwePAwwMbMme8zbdpk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\nZq7oDT/1B9X3ho/Gr80l0hsuhGjynI0VD/I20jlsPFlZxR6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XrsYqwbpXr95V2kyYcDVL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7LbvG4TEB7FuEf/QcrFV7frXL2FIxXvKLqj6jTBDIm7q5si6lu0Oe3WuIjzSeBL4B8dF5KKSt+l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8ef74ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12,))\n",
    "idx = 0\n",
    "for tag, _ in tags_most_increased_wend_to_wday_activity:\n",
    "    df = trend_data_for_each_tag[tag].copy()\n",
    "    df = df.merge(\n",
    "        year_wend_qn_counts,\n",
    "        on=\"Year\",\n",
    "        how=\"left\",\n",
    "    )\n",
    "    df[\"WeekendTotal\"] /= df[\"Total\"]\n",
    "    df.pop(\"Total\")\n",
    "    df = df.merge(\n",
    "        year_wday_qn_counts,\n",
    "        on=\"Year\",\n",
    "        how=\"left\",\n",
    "    )\n",
    "    df[\"WeekdayTotal\"] /= df[\"Total\"]\n",
    "    df.pop(\"Total\")\n",
    "    df[\"Ratio\"] = df[\"WeekendTotal\"] / df[\"WeekdayTotal\"]\n",
    "    ax.plot(\n",
    "        df[\"Year\"],\n",
    "        df[\"Ratio\"],\n",
    "        label=tag,\n",
    "        linewidth=5,\n",
    "        color=trend_plot_colors[idx],\n",
    "    )\n",
    "    idx += 1\n",
    "leg = ax.legend(fontsize=15,)\n",
    "leg.set_title(\"Tag\", prop={\"size\": 18,})\n",
    "ax.set_ylabel(\"Relative weekend/weekday use\", fontsize=15, labelpad=20,)\n",
    "ax.set_title(\n",
    "    \"Which tags' weekend activity has increased the most?\",\n",
    "    fontsize=18,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "ttl = ax.title\n",
    "ttl.set_position([0.5, 1.02])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Of note in this plot:\n",
    "\n",
    "- A good number of tags seem to be mobile oriented. `jquery-mobile`, `listview`, `android-fragments`, `button`, `gridview`.\n",
    "- `swing` is seeing increasing weekend usage. Could it also be mobile related?\n",
    "- `selenium` experienced a decline from 2008 to 2012, then seen some resurgence from 2012 to 2016\n",
    "- `gridview` and `selenium`, despite being on this plot, are still weekday dominant technologies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Time for a final plot that shows all the tags with over 20,000 questions and their relative weekend / weekday activity, overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tag_wend_wday_rel_activity = tag_wday_counts.copy()\n",
    "tag_wend_wday_rel_activity[\"Total\"] = \\\n",
    "    tag_wend_wday_rel_activity[\"Weekday\"] + tag_wend_wday_rel_activity[\"Weekend\"]\n",
    "tag_wend_wday_rel_activity[\"Weekend\"] /= nr_wend_qns\n",
    "tag_wend_wday_rel_activity[\"Weekday\"] /= nr_wday_qns\n",
    "tag_wend_wday_rel_activity[\"WeekendOverWeekday\"] = \\\n",
    "    tag_wend_wday_rel_activity[\"Weekend\"] / tag_wend_wday_rel_activity[\"Weekday\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y36Lk/Fd7L++tKaXtS7/3A5rr2zcXv11HSynu+8CQVOogR6qqqJ5+OzngXj7V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0YMplWrdgQHBxIW9hgDA0N+/XUc9vaV8PFZSVhYKGFhjylevCRjx0/LlU/TtIQjNi7dkCY94Oyp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\n3TrSsqUf169fwd+/H1euXDJup1m7diUXLpxFIpFQp059R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08i9sF9HJxL0adnB2pWUiCT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552ZiYmJwc/Oge/cfqVixEmZm5rRr1xJnZxcqVPiUSZNmsG3bFqys7jNs2Ogs1ypbtjyr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\nziULAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8ef6ba3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 15,))\n",
    "ax.scatter(\n",
    "    tag_wend_wday_rel_activity[\"Total\"],\n",
    "    tag_wend_wday_rel_activity[\"WeekendOverWeekday\"],\n",
    ")\n",
    "\n",
    "for tag, coord in zip(\n",
    "        tag_wend_wday_rel_activity[\"Tag\"],\n",
    "        zip(\n",
    "            tag_wend_wday_rel_activity[\"Total\"],\n",
    "            tag_wend_wday_rel_activity[\"WeekendOverWeekday\"]\n",
    "        )\n",
    "    ):\n",
    "    ax.annotate(tag, coord)\n",
    "\n",
    "plt.axhline(y=1, linewidth=4, color=\"red\", linestyle=\"dashed\",)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"Total # of questions\", fontsize=15, labelpad=20,)\n",
    "ax.set_ylabel(\"Relative use on weekends vs. weekdays\", fontsize=15,)\n",
    "ax.set_xticks([1e4, 1e5, 1e6])\n",
    "ax.set_xticklabels([\"1e+04\", \"1e+05\", \"1e+06\",])\n",
    "ax.set_yticks([0.5, 1, 2])\n",
    "ax.set_yticklabels([\"1/2 on Weekends\", \"Same\", \"2x on Weekends\"])\n",
    "fig.suptitle(\n",
    "    \"Which tags have the biggest weekend/weekday differences?\",\n",
    "    fontsize=20,\n",
    "    fontweight=\"bold\",\n",
    "    x=0.44,\n",
    ")\n",
    "ttl = ax.set_title(\n",
    "    \"For tags with more than 20,000 questions\",\n",
    "    loc=\"left\",\n",
    "    fontsize=15,\n",
    ")\n",
    "ttl.set_y(1.01)\n",
    "plt.subplots_adjust(top=0.93)"
   ]
  },
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   "source": [
    "Imho not a very useful plot because of the clustering in between 10,000 and 100,000 questions. This is probably best for identifying \"outliers\". We can _easily_ spot the very popular technologies on the right side and technologies which are very weekend or weekday dominant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Conclusion\n",
    "\n",
    "This was pretty instructive. Initially we didn't take into account that we should exclude deleted questions. Moreover, we tried too hard to not load everything into memory but ironically ended up using even more memory. Lol. I am very thankful for AWS. The relatively small size of this data set also means that a m4.xlarge instance with 16GB memory is sufficient for our purposes and we have a pretty small bill.\n",
    "\n",
    "### What we've picked up from this\n",
    "\n",
    "1. Pandas. I believe I am much better at using it now. But it is quite a beast and I don't know if it has an equivalent of `spread` in R's dplyr package.\n",
    "2. Reading R code. Imho R can be quite a horrible language with confusing documentation meant for pros. But I have to admit that it has some really neat dataframe manipulation functionality, both built-in and from libraries.\n",
    "3. Simplifying some of the excessive data computation and storage made in Julia Selge's original blog post. This is done by going through her code and seeing what is actually needed, then making the necessary adjustments.\n",
    "4. matplotlib. More experience making plots now.\n",
    "\n",
    "The downside is that there is almost no machine learning involved. Which is the area I want to get the most hands on experience in.\n",
    "\n",
    "### Follow up questions / items\n",
    "\n",
    "1. What algorithm is the `glm(cbind(nn, YearTagTotal) ~ Year, data, family=\"binomial\")` using? And what do the p values represent?\n",
    "2. During the process, we created a lot of pandas dataframes but they're like temporary database tables that are very hard to keep track of and for which we have to do a `.head()` call or print them out to get the schema. Is there a better way to manage these dataframes?"
   ]
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