Popular DS Questions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popular Data Science Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What the project is about:** In this project, I'll be working for a hypothetical company that creates data science content - be it books, online articles, videos or interactive text-based platforms.\n",
    "\n",
    "I've been tasked with figuring out the best content to write about. And since I'm passionate about helping people learn, I've decided to scour the internet in search for the answer to the question, \"What is it that people want to learn about in data science?\" (as opposed to determining the most profitable content, for instance).\n",
    "\n",
    "Thinking back on my own experience when I first started learning programming, it occured to me that if I wanted to figure out what programming content to write, I could consult Stack Overflow (a question and answer website about programming) and see what kind of content is more popular.\n",
    "\n",
    "With that in mind, I decided to investigate Stack Overflow a little more and found out that it is part of a question and answer website network called Stack Exchange.\n",
    "\n",
    "Stack Exchange hosts sites on a multitude of fields and subjects, including mathematics, physics, philosophy, and (of course) data science! Stack Exchange also employs a reputation award system for its questions and answers. Each post — each question/answer — is a post that is subject to upvotes and downvotes. This ensures that good posts are easily identifiable.\n",
    "\n",
    "**Goal of the project:** My goal in this project is to use the [Data Science Stack Exchange](https://datascience.stackexchange.com/) to determine what content should a data science education company create, based on interest by subject."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack Exchange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What kind of questions are welcome on this site?**\n",
    "\n",
    "On DSSE's help center's [section on questions](https://datascience.stackexchange.com/help/asking), it reads that one should:\n",
    "\n",
    "- Avoid subjective questions.\n",
    "- Ask practical questions about Data Science — there are adequate sites for theoretical questions.\n",
    "- Ask specific questions.\n",
    "- Make questions relevant to others.\n",
    "\n",
    "All of these characteristics, if employed, should be helpful attributes to my goal.\n",
    "\n",
    "In the help center I also see that in addition to the sites mentioned in the *Learn* section, there are other two sites that are relevant:\n",
    "\n",
    "- [Open Data](https://opendata.stackexchange.com/help/on-topic) (Dataset requests)\n",
    "- [Computational Science](https://scicomp.stackexchange.com/help/on-topic) (Software packages and algorithms in applied mathematics)\n",
    "\n",
    "**What, other than questions, does DSSE's home subdivide into?**\n",
    "\n",
    "On the [home page](https://datascience.stackexchange.com/), I can see that there are four sections:\n",
    "\n",
    "- [Questions](https://datascience.stackexchange.com/questions) — a list of all questions asked;\n",
    "- [Tags](https://datascience.stackexchange.com/tags) — a list of tags (keywords or labels that categorize questions);\n",
    "- [Users](https://datascience.stackexchange.com/users) — a list of users;\n",
    "- [Unanswered](https://datascience.stackexchange.com/unanswered) — a list of unanswered questions;\n",
    "\n",
    "The tagging system used by Stack Exchange looks just like what I need to solve this problem as it allow me to quantify how many questions are asked about each subject.\n",
    "\n",
    "Something else I can learn from exploring the help center, is that Stack Exchange's sites are heavily moderated by the community; this gives me some confidence in using the tagging system to derive conclusions.\n",
    "\n",
    "**What information is available in each post?**\n",
    "\n",
    "Looking, just as an example, at [this question](https://datascience.stackexchange.com/questions/19141/linear-model-to-generate-probability-of-each-possible-output?rq=1), some of the information I see is:\n",
    "\n",
    "- For both questions and answers:\n",
    "    - The posts's score;\n",
    "    - The posts's title;\n",
    "    - The posts's author;\n",
    "    - The posts's body;\n",
    "- For questions only:\n",
    "    - How many users have it on their \"\n",
    "    - The last time the question as active;\n",
    "    - How many times the question was viewed;\n",
    "    - Related questions;\n",
    "    - The question's tags;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack Exchange Data Explorer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perusing the table names, a few stand out as relevant for my goal:\n",
    "\n",
    "- Posts\n",
    "- PostTags\n",
    "- Tags\n",
    "- TagSynonyms\n",
    "\n",
    "Running a few exploratory queries, leads me to focus my efforts on the `Posts` table. For example, the `Tags` table looked very promising - as it tells me how many times each tag was used - but there's no way to tell just from this if the interest in these tags is recent or a thing from the past."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the relevant data, I run the following query:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`SELECT Id, CreationDate,\n",
    "       Score, ViewCount, Tags,\n",
    "       AnswerCount, FavoriteCount\n",
    "  FROM posts\n",
    " WHERE PostTypeId = 1 AND YEAR(CreationDate) = 2019;`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can read in the data while immediately making sure `CreationDate` will be stored as a datetime object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I import everything that I'll use\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "questions = pd.read_csv(\"2019_questions.csv\", parse_dates=[\"CreationDate\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running `questions.info()` should provide me with a lot of useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8839 entries, 0 to 8838\n",
      "Data columns (total 7 columns):\n",
      "Id               8839 non-null int64\n",
      "CreationDate     8839 non-null datetime64[ns]\n",
      "Score            8839 non-null int64\n",
      "ViewCount        8839 non-null int64\n",
      "Tags             8839 non-null object\n",
      "AnswerCount      8839 non-null int64\n",
      "FavoriteCount    1407 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(4), object(1)\n",
      "memory usage: 483.5+ KB\n"
     ]
    }
   ],
   "source": [
    "questions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I see that only `FavoriteCount` has missing values. A missing value on this column probably means that the question was is not present in any users' favorite list, so I can replace the missing values with zero.\n",
    "\n",
    "The types seem adequate for every column, however, after I fill in the missing values on `FavoriteCount`, there is no reason to store the values as floats.\n",
    "\n",
    "Since the `object` dtype is a catch-all type, let's see what types the objects in `questions[\"Tags\"]` are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<class 'str'>], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions[\"Tags\"].apply(lambda value: type(value)).unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I see that every value in this column is a string. On Stack Exchange, each question can only have a maximum of five tags ([source](https://meta.stackexchange.com/a/18879)), so one way to deal with this column is to create five columns in `questions` called `Tag1`, `Tag2`, `Tag3`, `Tag4`, and `Tag5` and populate the columns with the tags in each row.\n",
    "\n",
    "However, since it doesn't help relating tags from one question to another, I'll just keep them as a list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll start by fixing `FavoriteCount`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                        int64\n",
       "CreationDate     datetime64[ns]\n",
       "Score                     int64\n",
       "ViewCount                 int64\n",
       "Tags                     object\n",
       "AnswerCount               int64\n",
       "FavoriteCount             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions.fillna(value={\"FavoriteCount\": 0}, inplace=True)\n",
    "questions[\"FavoriteCount\"] = questions[\"FavoriteCount\"].astype(int)\n",
    "questions.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I'll modify `Tags` to make it easier to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>Score</th>\n",
       "      <th>ViewCount</th>\n",
       "      <th>Tags</th>\n",
       "      <th>AnswerCount</th>\n",
       "      <th>FavoriteCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6397</th>\n",
       "      <td>63826</td>\n",
       "      <td>2019-11-27 03:57:32</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>[apache-spark, pyspark]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>57031</td>\n",
       "      <td>2019-08-06 07:11:23</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>[neural-network, deep-learning, cnn]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8161</th>\n",
       "      <td>65263</td>\n",
       "      <td>2019-12-22 05:35:38</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>[svm, mini-batch-gradient-descent, hinge-loss]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id        CreationDate  Score  ViewCount  \\\n",
       "6397  63826 2019-11-27 03:57:32      0        103   \n",
       "1732  57031 2019-08-06 07:11:23      1         62   \n",
       "8161  65263 2019-12-22 05:35:38      0          8   \n",
       "\n",
       "                                                Tags  AnswerCount  \\\n",
       "6397                         [apache-spark, pyspark]            1   \n",
       "1732            [neural-network, deep-learning, cnn]            2   \n",
       "8161  [svm, mini-batch-gradient-descent, hinge-loss]            0   \n",
       "\n",
       "      FavoriteCount  \n",
       "6397              0  \n",
       "1732              0  \n",
       "8161              0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions[\"Tags\"] = questions[\"Tags\"].str.replace(\"^<|>$\", \"\").str.split(\"><\")\n",
    "questions.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Used and Most Viewed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I can go ahead and count how many times each tag was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tag_count = dict()\n",
    "\n",
    "for tags in questions[\"Tags\"]:\n",
    "    for tag in tags:\n",
    "        if tag in tag_count:\n",
    "            tag_count[tag] += 1\n",
    "        else:\n",
    "            tag_count[tag] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For improved aesthetics, I'll transform `tag_count` in a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distribution</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regex</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>software-recommendation</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>julia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apache-hadoop</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>implementation</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiments</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openai-gym</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h2o</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categorical-data</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Count\n",
       "distribution                57\n",
       "regex                        8\n",
       "software-recommendation      4\n",
       "julia                        2\n",
       "apache-hadoop               13\n",
       "implementation               9\n",
       "experiments                  3\n",
       "openai-gym                  17\n",
       "h2o                          4\n",
       "categorical-data            81"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_count = pd.DataFrame.from_dict(tag_count, orient=\"index\")\n",
    "tag_count.rename(columns={0: \"Count\"}, inplace=True)\n",
    "tag_count.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I'll sort this dataframe by `Coun`t and visualize the top 20 results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>machine-learning-model</th>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistics</th>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clustering</th>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predictive-modeling</th>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regression</th>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lstm</th>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time-series</th>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn</th>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nlp</th>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scikit-learn</th>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorflow</th>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classification</th>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras</th>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural-network</th>\n",
       "      <td>1055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deep-learning</th>\n",
       "      <td>1220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>1814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine-learning</th>\n",
       "      <td>2693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Count\n",
       "machine-learning-model    224\n",
       "statistics                234\n",
       "clustering                257\n",
       "predictive-modeling       265\n",
       "r                         268\n",
       "dataset                   340\n",
       "regression                347\n",
       "pandas                    354\n",
       "lstm                      402\n",
       "time-series               466\n",
       "cnn                       489\n",
       "nlp                       493\n",
       "scikit-learn              540\n",
       "tensorflow                584\n",
       "classification            685\n",
       "keras                     935\n",
       "neural-network           1055\n",
       "deep-learning            1220\n",
       "python                   1814\n",
       "machine-learning         2693"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_used = tag_count.sort_values(by=\"Count\").tail(20)\n",
    "most_used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The threshold of 20 is somewhat arbitrary and I can experiment with others; however, the popularity of the tags rapidly declines, so looking at these tags should be enough to help me with my goal. Let's visualize these data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a980d6748>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBkAAAHdCAYAAABYPqH3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wHEmpA1qul/oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a98839e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_used.plot(kind=\"barh\", figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some tags are very, very broad and are unlikely to be useful (e.g. `python`, `dataset`, `r`). But before I investigate the tags further, I'll repeat the same process for views.\n",
    "\n",
    "To do this, I'll use pandas's `pandas.DataFrame.iterrows()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a98362ac8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/0AAAHdCAYAAABPDBl/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtYVWXe//EPbNwqx0DBUcoHQcCUTIVK7Uzq00HwMOmk\nhY1paYcxy3RCM09jTmk1JB57qjEnpVBBUdO5zJypGU0tfyaZkeYobUzITchJiM3+/eHlHk0kDwsW\nbN6vv/ZhrXt99uZ76fVd973W9nA6nU4BAAAAAAC342l2AAAAAAAAUDdo+gEAAAAAcFM0/QAAAAAA\nuCmafgAAAAAA3BRNPwAAAAAAbsrL7ACoWVWVQ4WFZWbHgBsJDPSmpmAoagpGop5gNGoKRqOmYLTg\nYL96OQ4z/Q2Ul5fF7AhwM9QUjEZNwUjUE4xGTcFo1BQaK5p+AAAAAADcFE0/AAAAAABuiqYfAAAA\nAAA3xY38GqicnBzZ7SVmx4AbKSz0paZgKGoKRqKeYDRqCkZz95oKCwuXxcJ9C9wRTX8DlZS8Qt4B\nIWbHAAAAAODmyorylTIxURERkWZHQR2g6b8IS5Ys0ZgxYyRJNptNY8eOVVZWVp0e0zsgRL6BoXV6\nDAAAAACAe+Oa/ouwePFisyMAAAAAAHDJmuRMv81m0+jRo9WlSxft379fkZGRGjx4sD744AMtWLBA\nkvTvf/9bK1asUIcOHVRRUaFBgwapY8eOGj9+vBwOh6ZOnao9e/aoTZs2WrRokaxWqw4cOKBp06bp\n1KlTat++vV566SX5+fkpKSlJ119/vT777DMVFxdr9uzZio2NNflbAAAAAAC4uyY703/48GE99NBD\n2rhxo3x9fXXw4EEdPnxYhYWFkqTVq1dryJAhmjBhglq0aKGMjAzNnTtXknTkyBE99NBDWr9+vfz8\n/LR582ZJ0qRJkzRx4kStXbtWkZGRSk1NdR3P4XAoPT1dycnJ57wOAAAAAEBdabJNf7t27dStWzdJ\nUkJCgr744gsNGDBAa9euVXFxsfbu3atbb721xn2vvvpqRUdHS5K6dOkim82mkpISlZSUKC4uTpI0\naNAg7dq1y7VPv379JEkxMTHKy8ury48GAAAAAICkJrq8vyaenp4aPHiwxowZo+bNm+vuu++Wp+fp\ncyJOp/Ocba1Wq+uxxWJRRUVFjdvVtI+np6eqqqqMjg8AAAAAly0oyFfBwX5mx0AdaLJNf15envbu\n3avrr79e69evV2xsrIKDgxUSEqLFixfrnXfecW1rtVrlcDhq/d1KX19fBQQE6PPPP1dsbKzWrl2r\nG2+8scZtazs5AAAAAAD1zW4vUUFBsdkxmpT6OsnSZJf3d+jQQe+9957uvfdenTx5UsOGDZMkJSYm\nqm3btgoPD3dtO3ToUCUkJGjixIm1jvnnP/9Zr7zyigYMGKADBw7oySeflCR5eHics90vnwMAAAAA\nUBc8nE1w2tlms2ns2LHKyso6771Zs2apc+fO+u1vf2tCsv+685GF8g0MNTUDAAAAAPdXUmjTnMd6\nKiIi0uwoTUp9zfQ32eX9NRk8eLB8fHz0/PPPmx1FZUX5ZkcAAAAA0ATQe7i3JjnT3xjk5OTIbi8x\nOwbcSFCQLzUFQ1FTMBL1BKNRUzCau9dUWFh4rfcwg/Hqa6afpr8B40YaMFJwsB81BUNRUzAS9QSj\nUVMwGjUFo3EjPwAAAAAAcEVo+gEAAAAAcFM0/QAAAAAAuCmafgAAAAAA3BRNPwAAAAAAboqmHwAA\nAAAAN0XTDwAAAACAm6LpBwAAAADATXmZHQA1y8nJkd1eYnYMuJHCQl9qCoaipmAk6glnCwsLl8Vi\nMTsGALgFmv4GKil5hbwDQsyOAQAAUK/KivKVMjFRERGRZkcBALdA099AeQeEyDcw1OwYAAAAAIBG\nzJRr+uPj4/XTTz+d9/rWrVv15ptvGnKMjIwMzZo1y5CxajN16lQdOnSozo8DAAAAAMClMmWm38PD\no8bX4+PjFR8fX+fHuRQOh6PWa8rq48QCAAAAAACX41ebfpvNptGjR6tbt2764osvFBMTo8GDB2v+\n/PkqLCzUvHnz5HQ69dJLL6myslLNmzfXnDlzFBYWpurqas2dO1effvqpPD09NXToUD344INyOp1a\nvny5Pv74Y1VVVSklJUUdOnRQRkaGsrOzNXXqVCUnJ8vHx0fZ2dk6ceKEJk6cqH79+kmS3nrrLX34\n4Yf6+eef1bdvXz311FO1fga73a7p06fr2LFjkqTJkyere/fu+vLLL2vMnZGRob///e8qKytTdXW1\n/vCHP2j+/PkKDAzUt99+q5iYGM2dO1eSlJSUpOeff15dunRR9+7dNWLECG3btk0tW7bUwoULFRQU\npNzcXD333HMqLy9XfHy8li1bpj179lzp3w4AAAAAgFpd1PL+3NxcjRo1Sps3b9bhw4e1YcMGpaWl\nadKkSVq8eLEiIiK0YsUKrVmzRuPGjdNrr70mSUpLS1NeXp7WrVuntWvXKiEhwTVmUFCQ1qxZowce\neEBvv/226/WzZ+d//PFHpaWlafHixZo3b54k6V//+peOHDmiVatWKTMzU9nZ2dq9e3et+WfPnq3f\n//73Sk9P1xtvvKEpU6ZI0gVzS9LXX3+t1NRULV++XJJ04MABvfDCC9q4caNyc3P1xRdfnHec8vJy\n9ejRQ2vXrlVsbKw++OAD1/EffvhhrVu3Tr/5zW8MWYEAAAAAAMCvuajl/aGhoerYsaMkKTIyUr17\n95YkRUVFKS8vT8XFxfrjH/+oI0eOSDq9JF6SduzYoWHDhrmaXH9/f9eYffv2lSTFxMRoy5YtNR63\nT58+kk435ydOnJAkffrpp/rXv/6lQYMGyel0qry8XEeOHFFcXNwF82/fvl3fffednE6nJKmsrEzl\n5eUXzC1JvXv3lp+fn+t5165dFRJy+m76nTp1ks1mU48ePc45jtVq1e233y5J6tKli7Zv3y5J2rNn\njxYuXChJ6t+/v1555ZULZgUAAGjqgoJ8FRzs9+sb/gojxgDORk2hMbqopt9qtboee3p6up57enq6\nluf37NlTqampstlsGjFixEWPeWaMXzvumYZdksaMGaOhQ4ees+17772n9PR0eXh4aOnSpee853Q6\n9cEHH6hZs2bnvD5z5swL5vb29j5n27P3tVgs55wgOMPLy+ucbc58Lmb2AQAALp7dXqKCguIrGiM4\n2O+KxwDORk3BaPV1EsmQu/eXlJSoTZs2kqQ1a9a4Xu/du7fS0tJcDXJRUdFlH+NM03/LLbdo9erV\nKisrkyQdP35cdrtdDz74oDIzM5WRkaHg4OBz9r355pv17rvvup4fOHCg1txXmvGXunXrpk2bNkmS\nNmzYcMXHAQAAAADgYhjS9I8ePVrz5s3T4MGDVV1d7Xp9yJAhatu2rRITEzVw4ECtX79e0uXNfJ/Z\n5+abb1b//v31u9/9TgkJCXr66adVWlpa675TpkxRdna2EhMT1b9/f6WlpUmSRo0aVWPui81S2+Oz\nJScn669//asGDBigo0ePnnPZAAAAAAAAdcXDeaHpaRjm1KlTatGihSRp48aN2rBhgxYsWFDrPjf9\ndrq8A0LqIx4AAECDUVaUr5SJiYqIiLyicViKDaNRUzBafS3vv6hr+nFlsrOzNWvWLDmdTgUEBOil\nl1761X2Wzxkuu72kHtKhqQgK8qWmYChqCkainnC2sLBwsyMAgNtgpr8B40wijMTZaRiNmoKRqCcY\njZqC0agpGK1R3cgPAAAAAAA0PDT9AAAAAAC4KZp+AAAAAADcFE0/AAAAAABuiqYfAAAAAAA3RdMP\nAAAAAICboukHAAAAAMBN0fQDAAAAAOCmaPoBAAAAAHBTXmYHQM1ycnJkt5eYHQNupLDQl5qCoaip\nhiksLFwWi8XsGAAAoIGg6W+gkpJXyDsgxOwYAIBGpKwoXykTExUREWl2FAAA0EDQ9NfAZrNp7Nix\nysrKMi2Dd0CIfANDTTs+AAAAAKDx45p+AzkcDrMjAAAAAADgwkz/r8jNzdW4ceM0Y8YMbdq0Sbt2\n7VJlZaUefPBBDR06VDt37lRKSor8/f11+PBhbdq0SU8++aR++OEHVVZWasSIERoyZIiqq6s1ZcoU\nZWdny8PDQ7/97W/18MMPm/3xAAAAAABujKa/FocPH9azzz6rP//5z9q7d6/8/f2Vnp6uyspKDRs2\nTDfffLMkaf/+/dqwYYPatWsnSZozZ478/f1VUVGh+++/X/369dP333+v48ePuy4ZKCnh5lcAAAAA\ngLpF038BdrtdTz75pFJTUxUeHq4FCxYoJydHmzZtknS6aT9y5Ii8vLzUtWtXV8MvScuWLdOWLVsk\nST/88IOOHDmisLAwff/99/rTn/6k22+/XbfccospnwsA4N6CgnwVHOxndozL0lhzo+GipmA0agqN\nEU3/Bfj6+qpt27bavXu3wsPDJUlTp051ze6fsXPnTrVs2fKc5zt27FB6erqsVquSkpJUUVEhf39/\nrV27Vp9++qnef/99ffjhh3rppZfq9TMBANyf3V6igoJis2NcsuBgv0aZGw0XNQWjUVMwWn2dROJG\nfhdgtVq1YMECZWZmav369brlllu0YsUKVVVVSZL+85//qLy8/Lz9iouL5e/vL6vVqkOHDmnv3r2S\npMLCQjkcDvXt21dPP/20vv7663r9PAAAAACApoeZ/lq0aNFCS5Ys0SOPPKInnnhCHTt21KBBgyRJ\nQUFBWrBgwXn73HrrrUpLS9N9992nDh06qFu3bpKk48ePa/LkyaqurpaHh4cmTJhQr58FAAAAAND0\neDidTqfZIXC+Ox9ZKN/AULNjAAAakZJCm+Y81lMREZFmR7lkLJuF0agpGI2agtHqa3k/M/0NVFlR\nvtkRAACNDP93AACAX6Lpb6CWzxkuu52f9YNxgoJ8qSkYippqmMLCws2OAAAAGhCa/gYqKiqK5UMw\nFEvSYDRqCgAAoOHj7v0AAAAAALgpmn4AAAAAANwUTT8AAAAAAG6Kph8AAAAAADdF0w8AAAAAgJui\n6QcAAAAAwE3R9AMAAAAA4KZo+gEAAAAAcFNeZgdAzXJycmS3l5gdA26ksNCXmoKh3KWmwsLCZbFY\nzI4BAABQJ2j6G6ik5BXyDggxOwYAuLWyonylTExURESk2VEAAADqhGlNf2pqqnx8fDRy5MhGOf4Z\nY8aM0auvvipfX19Dx/UOCJFvYKihYwIAAAAAmhZm+n+Fw+GoddnnkiVL6jENAAAAAAAXr16b/kWL\nFikzM1OtW7fWb37zG8XExCg3N1czZsxQYWGhWrZsqVmzZqlDhw6y2+2aPn26jh07JkmaPHmyunfv\nrtTUVB09elRHjx5VYWGhRo8erSFDhtR63Asd4+OPP9aiRYtUVVWlq666SvPmzVNQUJDrGLm5uWrX\nrp1uueUWbd26VeXl5crNzVWfPn00ceJESVJ8fLzWrFmj0tJSPfroo4qNjdWePXvUpk0bLVq0SFar\nVV9++aVeeOEFWSwW9erVS5988omysrLq/PsGAAAAADRt9Xb3/q+++koffvihsrKytGTJEu3bt0+S\nNHXqVL344otavXq1Jk2apOnTp0uSZs+erd///vdKT0/XG2+8oSlTprjGysnJ0bvvvqu0tDQtWLBA\nBQUFtR77QseIi4vTBx98oDVr1uiee+7Rm2++6drn0KFDWrZsmV599VVJ0oEDB5SSkqKsrCx9+OGH\nOn78uCTJw8PDtc/Ro0f10EMPaf369fLz89PmzZslSVOmTNGf/vQnZWRkcLMoAAAAAEC9qbeZ/t27\nd6tv376yWq2yWq266667dOrUKe3Zs0dPP/20nE6nJKmqqkqStH37dn333Xeu18vKylReXi5Juuuu\nu1zj9OzZU19++aXuuuuuGo9bVlZ2wWMcO3ZM48ePV35+vqqqqnT11Ve79ouPj5fVanU979Wrl3x8\nfCRJERERstlsatOmjWtMSQoNDVV0dLQkqUuXLrLZbCouLlZpaam6du0qSerfv7+2bdt2ZV8mAAAA\nAAAXwbRr+p1Op6qrq+Xv76+MjIwa3//ggw/UrFmz8947e3bd6XTKw8NDr7/+uv7xj3/Iw8PjnPFq\nO8asWbM0atQo3XHHHdq5c6dSU1Nd73l7e5+z7dknACwWixwOx3nj/XKbioqKC318AEADERTkq+Bg\nP7NjQOLvAMNRUzAaNYXGqN6a/htuuEHJyckaM2aMKisr9fHHH+uBBx7Q1VdfrU2bNunuu++WdHoZ\nfadOnXTzzTfr3Xff1ahRo855XZI++ugjPfbYYyotLdWuXbv03HPPKT4+Xs8888x5x/X19b3gMUpL\nSxUScvpn8Wo6KWAEPz8/+fj46Msvv1TXrl21cePGOjkOAODy2O0lKigoNjtGkxcc7MffAYaipmA0\nagpGq6+TSPXW9Hfu3Fn33nuvEhIS1Lp1a1133XWSpHnz5mnatGlatGiRHA6H7r33XnXq1ElTpkzR\nzJkzlZiYqOrqasXFxbmuxY+OjtaIESNUWFioJ554QsHBwbUee+7cuZo+ffp5x3jyySc1btw4BQQE\nqGfPnrLZbJf8uc5edXAhs2fPdt3I74YbbpCfH2cIAQAAAAB1z8N59kXpjUBqaqp8fHw0cuRIs6Nc\ntLKyMtflAkuXLtWPP/6oyZMn17rPnY8slG9gaH3EA4Amq6TQpjmP9VRERKTZUZo8ZtBgNGoKRqOm\nYDS3m+lvyrZt26alS5fK4XAoNDRUc+bM+dV9yory6yEZADRt/FsLAADcXaOb6W8qcnJyZLeXmB0D\nbiQoyJeagqHcpabCwsL5OdUGgBk0GI2agtGoKRiNmf4mLioqin9UYCj+o4LRqCkAAICGz9PsAAAA\nAAAAoG7Q9AMAAAAA4KZo+gEAAAAAcFM0/QAAAAAAuCmafgAAAAAA3BRNPwAAAAAAboqmHwAAAAAA\nN0XTDwAAAACAm/IyOwBqlpOTI7u9xOwYcCOFhb7UFAx1pqbCwsJlsVjMjgMAAIAa0PQ3UEnJK+Qd\nEGJ2DACoVVlRvlImJioiItLsKAAAAKgBTX8D5R0QIt/AULNjAAAAAAAaMa7pN0BSUpK++uors2MA\nAAAAAHAOmn4AAAAAANxUk1reb7PZNHr0aHXp0kX79+9XZGSkXn75Zb311lvatm2bTp06pe7du2vm\nzJmSTs/gX3/99frss89UXFys2bNnKzY2VhUVFUpOTtY333yjDh06qLKy0nWM6dOnKzs7WxUVFfrf\n//1fPfXUU5KkefPmadu2bbJYLLr55ps1adIkU74DAAAAAEDT0aSafkk6fPiw5syZo27dumny5Mla\nuXKlkpKS9OSTT0qSJk2apG3btumOO+6QJDkcDqWnp+sf//iHUlNT9c4772jlypVq2bKlNmzYoG++\n+UaDBw92jf/ss8/K399f1dXVevjhh9WvXz+FhIRoy5Yt2rRpkySppIQ7qAMAAAAA6l6Ta/rbtWun\nbt26SZISExO1fPlyhYaG6v/+7/9UXl6ukydPKjIy0tX09+vXT5IUExOjvLw8SdKuXbs0YsQISVJ0\ndLSio6Nd42/YsEHp6emqqqrSjz/+qIMHDyoiIkItWrTQlClTdMcdd7jGBgB3EBTkq+BgP7NjwA1Q\nRzAaNQWjUVNojJpc0/9LHh4emjlzplavXq02bdooNTVVFRUVrvetVqskydPTU1VVVbWO9f333+ud\nd97RmjVr5Ovrq+TkZFVWVspisSg9PV3bt2/Xpk2b9Le//U3Lli2r088FAPXFbi9RQUGx2THQyAUH\n+1FHMBQ1BaNRUzBafZ1EanI38svLy9PevXslSevXr1dcXJwkKTAwUKWlpdq8efOvjnHDDTcoKytL\nkpSTk6NvvvlG0ull+97e3vLx8dGPP/6of/7zn5Kk8vJyFRcX67bbbnPdCwAAAAAAgLrW5Gb6O3To\noPfee0/JycmKjIzUsGHD9NNPP+m+++5TcHCwrrvuOte2Hh4eNY4xbNgwJScn67777lNERIRiYmIk\nSZ06ddK1116re+65R23btlVsbKyk0ycDnnjiCdcKguTk5Dr+lAAAAAAASB5Op9Npdoj6YrPZNHbs\nWNcsfUN202+nyzsgxOwYAFCrsqJ8pUxMVEREpNlR0MixbBZGo6ZgNGoKRquv5f1Nbqa/sVg+Z7js\ndu7yD+MEBflSUzDUmZoKCws3OwoAAAAuoEnN9Dc2nEmEkTg7DaNRUzAS9QSjUVMwGjUFo3EjPwAA\nAAAAcEVo+gEAAAAAcFM0/QAAAAAAuCmafgAAAAAA3BRNPwAAAAAAboqmHwAAAAAAN0XTDwAAAACA\nm6LpBwAAAADATdH0AwAAAADgprzMDoCa5eTkyG4vMTsG3EhhoS811USFhYXLYrGYHQMAAAAmoOlv\noJKSV8g7IMTsGAAaubKifKVMTFRERKTZUQAAAGACt2j64+PjtWbNGl111VVXPNaSJUs0ZswYA1Kd\n61IzegeEyDcw1PAcAAAAAICmo1Fc0+9wOGp938PDw7BjLV682LCxzqiurjY0IwAAAAAAF8PwmX6b\nzaZHH31UsbGx2rNnj9q0aaNFixbp+PHjmjFjhgoLC9WyZUvNmjVLHTp0UHJysu68807169dPktS9\ne3ft2bNHO3fuVEpKivz9/XX48GFt2rRJTz75pH744QdVVlZqxIgRGjJkiCTJ6XTWmCU1NVV5eXnK\nzc3VDz/8oBEjRigpKUmStG7dOi1fvlxVVVXq2rWrpk2bptdff10VFRUaNGiQOnbsqE6dOslqtSop\nKUkvvfSSvvnmGy1btkw7duzQqlWrNG/ePK1fv15LliyRJN1+++167rnnXJ/jgQce0Pbt2zV16lRX\nplOnTukPf/iD+vXr58oPAAAAAEBdqJOZ/qNHj+qhhx7S+vXr5e/vr02bNmnq1Kl68cUXtXr1ak2a\nNEnTp0+vcd+zZ8T379+vqVOnatOmTZKkOXPmaPXq1Vq1apXeffddFRUV/WqWw4cP65133tEHH3yg\n1NRUORwOHTp0SBs3blRaWpoyMjLk6emprKwsTZgwQS1atFBGRobmzp2ruLg4ff7555Kkr776SuXl\n5XI4HNq9e7duvPFG5efn69VXX9Xy5cu1du1a7du3Tx999JEkqby8XN26dVNmZqZiY2MlSaWlpXr8\n8ceVkJBAww8AAAAAqHN1ck1/aGiooqOjJUmdO3eWzWbTnj179PTTT7tm5auqqn51nK5du6pdu3au\n58uWLdOWLVskST/88IOOHDmirl271jrGHXfcIS8vLwUGBqp169b68ccftWPHDu3fv1/333+/nE6n\nKioq1Lp1a0nnrhro0qWLvvrqK5WUlMhqtapLly7at2+fPv/8c73wwgvat2+fbrrpJtd1+gkJCdq9\ne7fuuusuWSwW1+qFM+M++eSTGj16tPr3738xXyMAGCIoyFfBwX51MnZdjYumiXqC0agpGI2aQmNU\nJ02/1Wp1PbZYLDpx4oT8/f2VkZFx3rYWi0XV1dWSTjfGP//8s+u9li1buh7v3LlTO3bsUHp6umvJ\nfUVFhaT/rg547733lJ6eLg8PDy1duvS8LJ6ennI4HHI6nRo0aJCeeeaZWj+Hl5eXQkNDlZGRoR49\neig6OlqfffaZjh49qoiICP3nP/+54KUFzZs3P+86/h49euiTTz6h6QdQr+z2EhUUFBs+bnCwX52M\ni6aJeoLRqCkYjZqC0errJFK93MjP19dXV199tWuZviQdOHBA0ulVAdnZ2ZKkjz766IIrAIqLi+Xv\n7y+r1apDhw5p7969rvfONN4PPvigMjMzlZGRoeDg4Avm6dWrlzZt2iS73S5JKioq0rFjxySdPklw\ndoa4uDi9/fbbiouLU2xsrNLS0tS5c2dJp1ci7Nq1Sz/99JMcDoc2bNigG2+88ZxMZxs3bpz8/Pw0\nY8aMX/nGAAAAAAC4cvV29/558+Zp1apVGjBggPr376+tW7dKkoYOHapdu3Zp4MCB+n//7/+dM7t/\ntltvvVVVVVW677779Prrr6tbt26u9y71zvgREREaP368HnnkESUmJuqRRx5RQUGBK09iYqImTpwo\nSYqNjVVBQYG6d++uVq1aqXnz5oqLi5MkBQcH67nnnlNSUpIGDhyomJgY3XnnnTVmOvP8hRdeUEVF\nhebNm3dJmQEAAAAAuFQezgutT4ep7nxkoXwDQ82OAaCRKym0ac5jPRUREWn42CxzhJGoJxiNmoLR\nqCkYrb6W99fJNf24cmVF+WZHAOAG+LcEAACgaaPpb6CWzxkuu73E7BhwI0FBvtRUExUWFm52BAAA\nAJiEpr+BioqKYvkQDMWSNAAAAKDpqbcb+QEAAAAAgPpF0w8AAAAAgJui6QcAAAAAwE3R9AMAAAAA\n4KZo+gEAAAAAcFM0/QAAAAAAuCmafgAAAAAA3BRNPwAAAAAAbsrL7ACoWU5Ojuz2ErNjwI0UFvo2\nipoKCwuXxWIxOwYAAADgFmj6G6ik5BXyDggxOwZQr8qK8pUyMVEREZFmRwEAAADcgts0/dnZ2Vq7\ndq2mTJmi1NRU+fj4aOTIkeds88Ybb+iGG25Qr169tGzZMj3wwANq3rz5eWPt3LlTb7/9thYvXlxf\n8c/jHRAi38BQ044PAAAAAGj83Kbpj4mJUUxMTK3bjBs3zvV42bJlGjBgQI1Nv1EcDgfLlAEAAAAA\npmnwTX95ebnGjx+v48ePy+Fw6IknntDVV1+t2bNnq7y8XM2bN9df//pXZWdn1zg7/8EHH2jLli1K\nTU3VtGnTdOedd+r48ePKz8/XiBEjFBgYqGXLltV6/FmzZungwYOqqqrSU089pfj4eNlsNk2aNEnl\n5eWSpBdffFHdunXTzp07lZKSIn9/fx0+fFhvvfWWHn30UcXGxmrPnj1q06aNFi1aJKvVWqffGwAA\nAAAADb7p/+STT9SmTRstWbJEklRSUqKBAwcqJSVFXbp0UWlpqVq0aHHefk6nU++9957+/e9/a+HC\nhfLy+u9HTUpK0jvvvKPly5crICCg1uMvXrxYvXr10ksvvaTi4mLdf//96t27t1q3bq133nlHVqtV\nR44c0bP64KTWAAAgAElEQVTPPqvVq1dLkvbv368NGzaoXbt2stlsOnr0qF5//XXNmjVL48eP1+bN\nm5WQkGDgtwQAAAAAwPkafNMfFRWll19+Wa+++qpuv/12+fv7KyQkRF26dJEk+fj41LhfZmam2rVr\npwULFlxwib3T6fzV43/66afaunWr3nrrLUnSzz//rLy8PIWEhGjmzJn6+uuvZbFYdOTIEdc+Xbt2\nVbt27VzPQ0NDFR0dLUnq0qWLbDbbxX14oAkKCvJVcLCf2TFwkfhbwUjUE4xGTcFo1BQaowbf9IeF\nhSkjI0P/+Mc/lJKSoptuuumi9ouOjtaBAwd07NgxXX311bVue2b5v4eHh/70pz+d857T6dT8+fMV\nFhZ2zuupqalq3bq1srKy5HA4dP3117vea9my5Tnbnr2U32KxqKKi4qI+A9AU2e0lKigoNjsGLkJw\nsB9/KxiGeoLRqCkYjZqC0errJJJnvRzlCuTn56tFixZKSEjQqFGj9OWXX6qgoED79u2TJJWWlsrh\ncJy3X+fOnTVjxgw9/vjjKigoOO99X19flZSc/s3yPn36KDMzUxkZGa4VBGfceuutWr58uev5119/\nLUkqLi5WSMjpn9TLzMysMQMAAAAAAGZq8DP9OTk5euWVV+Tp6almzZpp+vTpcjqdmjVrlk6dOqWW\nLVvqnXfeqXHfHj166I9//KPGjBmjt99++5z3hg4dqtGjR6tNmza13sjviSee0OzZs13X4IeGhmrx\n4sUaPny4/vCHPygzM1O33nrrebP7AAAAAACYzcN5MRe2o97d+chC+QaGmh0DqFclhTbNeaynIiIi\nzY6Ci8AyRxiJeoLRqCkYjZqC0epreX+Dn+lvqsqK8s2OANQ76h4AAAAwFk1/A7V8znDZ7SVmx4Ab\nCQrybRQ1FRYWbnYEAAAAwG3Q9DdQUVFRLB+CoViSBgAAADQ9Df7u/QAAAAAA4PLQ9AMAAAAA4KZo\n+gEAAAAAcFM0/QAAAAAAuCmafgAAAAAA3BRNPwAAAAAAboqmHwAAAAAAN0XTDwAAAACAm/IyOwBq\nlpOTI7u9xOwYcCOFhb6m1lRYWLgsFotpxwcAAACaIpr+BiopeYW8A0LMjgEYoqwoXykTExUREWl2\nFAAAAKBJoelvoLwDQuQbGGp2DAAAAABAI9aorukvLi7WihUrTDv+s88+qwEDBmjZsmVKTk7W3//+\nd9OyAAAAAADwaxpV019UVKSVK1fW+3EdDocKCgqUnZ2ttWvX6uGHH673DAAAAAAAXKpGtbz/tdde\nU25urgYNGqTevXsrKChIH374oX7++Wf17dtXTz31lGw2mx599FHFxsZqz549atOmjRYtWiSr1ap3\n331X77//vry8vNSxY0e9+uqrKioq0uTJk5Wbmytvb2/NnDlTUVFRSk1N1dGjR/X999+rbdu2+vbb\nb3X8+HENGjRIU6ZMOSfX9u3b9corr8jhcOi6667T9OnTdeDAAS1dulTz58/Xli1bNGHCBH3++eeq\nrq7Wvffeqy1btpj0LQIAAAAAmopGNdM/YcIEXXPNNcrIyFDv3r115MgRrVq1SpmZmcrOztbu3bsl\nSUePHtVDDz2k9evXy8/PT5s3b5Ykvfnmm8rMzNTatWs1Y8YMSdL8+fPVuXNnrVu3TuPHj9ekSZNc\nxzt06JD++te/6tVXX9WiRYvUvn17ZWRkKC4uzrVNZWWlkpOTlZKSonXr1qmqqkorV65U586ddeDA\nAUnS559/rqioKO3bt0979+7V9ddfX19fGQAAAACgCWtUM/1n+/TTT/Wvf/1LgwYNktPpVHl5uY4c\nOaK2bdsqNDRU0dHRkqQuXbrIZrNJkjp16qQJEyaoT58+6tOnj6TTDfn8+fMlST179lRRUZFKS0sl\nSfHx8bJarbXm+O6773TNNdeoffv2kqSBAwdq5cqVGjFihNq3b69Dhw5p3759GjlypHbt2iWHw3HO\nSQOgqQgK8lVwsJ/ZMWAw/qYwEvUEo1FTMBo1hcao0Tb9kjRmzBgNHTr0nNdsNts5jbrFYlFFRYUk\naenSpdq1a5e2bt2qxYsXKysrq9bxvb29LyqH0+ms8fXY2Fh98sknatasmXr16qXnn39e1dXV56wm\nAJoKu71EBQXFZseAgYKD/fibwjDUE4xGTcFo1BSMVl8nkRrV8n4fHx/XLPwtt9yi1atXq6ysTJJ0\n/Phx2e32WvfPy8vTjTfeqAkTJqikpETl5eWKi4vTunXrJEmfffaZAgMD5ePjc9GZwsPDlZeXp9zc\nXEnSunXrdMMNN0iS4uLitGzZMnXv3l2BgYH66aefdPjwYUVG8lvlAAAAAIC616hm+q+66ir16NFD\nCQkJuu2229S/f3/97ne/k3T6hMDcuXPl6VnzeYyqqipNnDhRJSUlcjqdGjFihHx9ffXUU09p8uTJ\nSkxMlLe3t15++eVLymS1WvXSSy9p3Lhxrhv5PfDAA5Kk66+/XidOnHCdBIiOjtaJEyeu4BsAAAAA\nAODieTgvtDYdprrpt9PlHRBidgzAEGVF+UqZmKiICFa5uBOWOcJI1BOMRk3BaNQUjFZfy/sb1Ux/\nU7J8znDZ7SVmx4AbCQryNbWmwsLCTTs2AAAA0FTR9DdQUVFRnEmEoTg7DQAAADQ9jepGfgAAAAAA\n4OLR9AMAAAAA4KZo+gEAAAAAcFM0/QAAAAAAuCmafgAAAAAA3BRNPwAAAAAAboqmHwAAAAAAN0XT\nDwAAAACAm6LpBwAAAADATXmZHQA1y8nJkd1eYnYMuJHCQt8L1lRYWLgsFks9JwIAAABQ12j6G6ik\n5BXyDggxOwaagLKifKVMTFRERKTZUQAAAAAYrEE1/ampqfLx8dHIkSMNGW/YsGFauXKlJOnll1/W\np59+qttuu03t27dXixYtNGDAgEsar7i4WFlZWRo+fLgkKT8/X7Nnz1ZKSoohec/mHRAi38BQw8cF\nAAAAADQdDarpN9qZhl+S0tPTtWvXLnl4eFz2eEVFRVq5cqWr6Q8JCamThh8AAAAAACOY2vRnZmbq\n7bfflqenp6Kjo3XNNde43ktPT9f777+vqqoqtW/fXnPnzlXz5s314YcfauHChbJYLPLz89Py5ct1\n8OBBJScnq6qqStXV1Zo/f77at2+v7t27a8+ePXr88cdVVlamwYMH67HHHtOhQ4dcKwqOHj2qadOm\nyW63y2KxKCUlRa1atdITTzyhkydPqqqqSuPHj1d8fLxee+015ebmatCgQerdu7eGDx+usWPHKisr\nS5WVlZo2bZqys7PVrFkz/fGPf9RNN92kjIwMbd26VeXl5crNzVWfPn00ceJEE791AAAAAEBTYVrT\nf/DgQS1evFjvv/++AgICdPLkSb377ruu9/v166chQ4ZIkv7yl79o1apVevDBB7Vw4UK99dZbCgkJ\nUUnJ6ZuSpaWl6eGHH1b//v1djb8k16z+okWL1KNHD2VkZEg6fRnBGc8995zGjBmju+66S5WVlXI6\nnWrWrJkWLFggHx8fFRYW6ne/+53i4+M1YcIEffvtt65xbDaba5z33ntPnp6eysrK0nfffadRo0Zp\n8+bNkqQDBw4oMzNTzZo10913360RI0aoTZs2dfXVAgAAAAAgycSmf8eOHbr77rsVEBAgSfL39z/n\n/W+++UYpKSk6efKkysvLdcstt0iSYmNj9fzzz+uee+5R3759JUndunXT4sWL9cMPP6hv3776n//5\nH0mS0+msNUNpaany8/N11113SZKsVqskqaqqSq+99pp27dolT09P5efn68SJE7WO9fnnnyspKUmS\nFB4ertDQUP3nP/+RJPXq1Us+Pj6SpIiICNlsNpp+NChBQb4KDvYzOwYaIeoGRqKeYDRqCkajptAY\nNdhr+pOTk7Vo0SJFRUUpIyNDO3fulCRNnz5dX375pbZt26bBgwcrIyND/fv31/XXX69t27bpscce\n08yZM3XTTTdd1HFqOjGQlZWlwsJCZWZmytPTU/Hx8aqoqLik/GePe+ZkgiRZLBY5HI5LGguoa3Z7\niQoKis2OgUYmONiPuoFhqCcYjZqC0agpGK2+TiJ51stRatCzZ09t2rRJP/30k6TTN8k7W1lZmVq3\nbq2ff/5ZWVlZrtdzc3PVtWtXjRs3Tq1atdKxY8eUm5ura665RklJSYqPj9c333xz3vFqau59fHzU\ntm1bbdmyRZJUWVmpU6dOqbi4WEFBQfL09NSOHTuUl5fn2r60tLTGzxMXF+fKefjwYR07dkwdOnS4\njG8GAAAAAABjmDbT37FjR40dO1ZJSUmyWCy69tprFRr635+oGzdunIYMGaJWrVqpa9eurmb7lVde\ncS2b7927tzp16qSlS5dq3bp18vLyUnBwsB5//HFJOudO/Re6a//LL7+sF198UW+88YaaNWumlJQU\nJSQk6PHHH1diYqJiYmIUEREhSbrqqqvUo0cPJSQk6LbbbnPdxV+Shg8frmnTpikhIUHNmjXTyy+/\nrGbNmhn6nQEAAAAAcCk8nL924TtMcecjC+UbGPrrGwJXqKTQpjmP9VRERKTZUdDIsMwRRqKeYDRq\nCkajpmC0+lre32Cv6W/qyoryzY6AJoJaAwAAANwXTX8DtXzOcNntJWbHgBsJCvK9YE2FhYXXcxoA\nAAAA9YGmv4GKiopi+RAMxZI0AAAAoOkx7e79AAAAAACgbtH0AwAAAADgpmj6AQAAAABwUzT9AAAA\nAAC4KZp+AAAAAADcFE0/AAAAAABuiqYfAAAAAAA3RdMPAAAAAICb8jI7AGqWk5Mju73E7BhoQMLC\nwmWxWMyOAQAAAKARoelvoJKSV8g7IMTsGGggyorylTIxURERkWZHAQAAANCINOqmPzU1VT4+Pho5\ncmSN72/ZskUdOnRQRERErePY7XaNHTtWVVVVmjJlimJjY+si7iXxDgiRb2Co2TEAAAAAAI2YW1/T\n/9FHH+ngwYO/ut327dsVHR2tNWvWnNfwV1dX11U8AAAAAADqVKOb6V+0aJEyMzPVunVr/eY3v1FM\nTIzS09P1/vvvq6qqSu3bt9fcuXO1f/9+bd26Vbt27dLixYv1xhtvaMeOHedtd/jwYc2bN0+nTp1S\ndna20tLS1LNnTz3wwAPavn27XnzxRW3fvl0ff/yxKioq1L17d82cOVOSlJSUpM6dO2v37t0qLy/X\nn//8Zy1dulTffvut7rnnHo0fP16StG7dOi1fvlxVVVXq2rWrpk+fLg8PDzO/RgAAAABAE9CoZvq/\n+uorffjhh8rKytKSJUu0b98+SVK/fv20atUqZWZmKjw8XKtWrVL37t0VHx+vSZMmKSMjQ9dcc02N\n23Xq1Enjxo3Tfffdp4yMDDVv3lzl5eXq1q2bMjMz1aNHDyUlJWnVqlXKysrSqVOntG3bNlcmq9Wq\n1atX64EHHtATTzyhGTNmKCsrSxkZGSoqKtKhQ4e0ceNGpaWlKSMjQ56enlq3bp1J3yAAAAAAoClp\nVDP9u3fvVt++fWW1WmW1WhUfHy/p9J3u//KXv+jkyZMqLy/XLbfcUuP+F7udl5eX+vXr53q+fft2\nvfXWWyovL9fJkycVGRmpO+64Q5JcGaKiohQdHa1WrVpJktq3b69jx47p888/1/79+3X//ffL6XSq\noqLCtQ1wKYKCfBUc7HdFY1zp/sAvUVMwEvUEo1FTMBo1hcaoUTX9NXE6nXr++ee1aNEiRUVFKSMj\nQzt37qxx24vdzmq1upbfV1ZWaubMmVqzZo3atGmj1NRUVVRUnLOtJHl6eqpZs2bnjONwOOR0OjVo\n0CA988wzRnxcNGF2e4kKCoove//gYL8r2h/4JWoKRqKeYDRqCkajpmC0+jqJ1KiW999www3asmWL\nKisrVVJSoo8//liSVFZWptatW+vnn39WVlaWa3sfHx+VlPz3t+4vtN0vOZ1O1+OKigp5eHgoMDBQ\npaWl2rx58yVl7tWrlzZt2iS73S5JKioqUl5e3iWNAQAAAADA5WhUM/2dO3fWvffeq4SEBLVu3VrX\nXXedPDw89PTTT2vIkCFq1aqVunbtqtLSUknSvffeq6lTp+pvf/ubUlJSLrjdL519kz0/Pz8NGTJE\n9913n4KDg3XdddfVuN2FxoiIiND48eP1yCOPqLq6Ws2aNdO0adPUrl07I74SAAAAAAAuyMN59rQ2\nGow7H1ko38BQs2OggSgptGnOYz0VERF52WOwJA1Go6ZgJOoJRqOmYDRqCkarr+X9jWqmvykpK8o3\nOwIaEOoBAAAAwOWg6W+gls8ZLru95Nc3RJMRFhZudgQAAAAAjQxNfwMVFRXF8iEAAAAAwBVpVHfv\nBwAAAAAAF4+mHwAAAAAAN0XTDwAAAACAm6LpBwAAAADATdH0AwAAAADgpmj6AQAAAABwUzT9AAAA\nAAC4KZp+AAAAAADclJfZAVCznJwc2e0lZsfAZQgLC5fFYjE7BgAAAADQ9DdUSckr5B0QYnYMXKKy\nonylTExURESk2VEAAAAAgKa/ofIOCJFvYKjZMQAAAAAAjRjX9F+k7t271/r+kiVL6ikJAAAAAAAX\nh6b/Inl4eNT6/uLFi+spCQAAAAAAF4fl/ZeooKBAzzzzjEpLS1VVVaXp06dr27Ztqqio0KBBg9Sx\nY0eNHz9eo0ePVrdu3fTFF18oJiZGgwcP1vz581VYWKh58+bpuuuuM/ujAAAAAADcHDP9l2j9+vW6\n9dZblZGRoXXr1unaa6/VhAkT1KJFC2VkZGju3LmSpNzcXI0aNUqbN2/W4cOHtWHDBqWlpWnSpEms\nCgAAAAAA1Atm+i/RddddpylTpujnn39Wnz591KlTpxq3Cw0NVceOHSVJkZGR6t27tyQpKipKeXl5\n9ZYX9S8oyFfBwX5mx6hRQ82FxouagpGoJxiNmoLRqCk0RjT9lyguLk5/+9vftG3bNj3//PMaOXKk\nBgwYIKfTec52VqvV9djT09P13NPTU1VVVfWaGfXLbi9RQUGx2THOExzs1yBzofGipmAk6glGo6Zg\nNGoKRquvk0gs779IZ5r6vLw8tWrVSkOGDNGQIUO0f/9+SaebfIfDYWZEAAAAAADOwUz/RTpz9/6d\nO3fqrbfekpeXl3x8fPTyyy9LkoYOHaqEhAR16dJF48ePNzMqAAAAAACSJA/nL9elo0G46bfT5R0Q\nYnYMXKKyonylTExURESk2VHOw5I0GI2agpGoJxiNmoLRqCkYrb6W9zPT30AtnzNcdnuJ2TFwGcLC\nws2OAAAAAACSaPobrKioKM4kAgAAAACuCDfyAwAAAADATdH0AwAAAADgpmj6AQAAAABwUzT9AAAA\nAAC4KZp+AAAAAADcFE0/AAAAAABuiqYfAAAAAAA3RdMPAAAAAICboukHAAAAAMBNeZkdADXLycmR\n3V5idgz8irCwcFksFrNjAAAAAECNaPobqKTkFfIOCDE7BmpRVpSvlImJioiINDsKAAAAANSIpr+O\nJCcn684771S/fv0ua3/vgBD5BoYanAoAAAAA0JRwTT8AAAAAAG6Kmf4rZLPZ9Oijjyo2NlZ79uxR\nmzZttHDhwnO2iY+P1z333KN//vOfatmypV599VVdc801JiUGAAAAADQVzPQb4OjRo3rooYe0fv16\n+fv7a/PmzedtExAQoKysLA0fPlyzZ882ISUAAAAAoKlhpt8AoaGhio6OliR17txZNpvtvG3uvfde\nSVL//v01Z86ces2HuhMU5KvgYD+zY1y0xpQVjQM1BSNRTzAaNQWjUVNojGj6DWC1Wl2PLRaLKioq\nztvGw8PD9djTkwUW7sJuL1FBQbHZMS5KcLBfo8mKxoGagpGoJxiNmoLRqCkYrb5OItF91pONGzdK\nkjZs2KBu3bqZnAYAAAAA0BQw02+ws2f0z3by5EklJiaqefPmeu211+o5FQAAAACgKaLpv0KhoaHK\nyspyPR85cmSN240aNUoTJkyor1gAAAAAAND014cLzf7Xpqwovw6SwEj8jQAAAAA0dDT99eCjjz66\n5H2Wzxkuu72kDtLASGFh4WZHAAAAAIALoulvoKKiorg7KAAAAADginD3fgAAAAAA3BRNPwAAAAAA\nboqmHwAAAAAAN0XTDwAAAACAm6LpBwAAAADATdH0AwAAAADgpmj6AQAAAABwUzT9AAAAAAC4KS+z\nA6BmOTk5sttLzI6BWoSFhctisZgdAwAAAAAuiKa/gUpKXiHvgBCzY+ACyorylTIxURERkWZHAQAA\nAIALoulvoLwDQuQbGGp2DAAAAABAI8Y1/QAAAAAAuClm+i9TZmam3n77bXl6eio6Olqenp7y8fFR\ndna2Tpw4oYkTJ6pfv37auXOn5s+fr8DAQH377beKiYnR3LlzzY4PAAAAAGgCaPovw8GDB7V48WK9\n//77CggI0MmTJzVnzhz9+OOPSktL06FDh/T444+rX79+kqQDBw5ow4YNCg4O1rBhw/TFF1+oR48e\nJn8KAAAAAIC7o+m/DDt27NDdd9+tgIAASZK/v78kqU+fPpKkiIgInThxwrV9165dFRJy+qZ8nTp1\nks1mo+l3A0FBvgoO9jM7xiVpbHnR8FFTMBL1BKNRUzAaNYXGiKbfQFar1fXY6XS6Hjdr1sz12GKx\nyOFw1Gsu1A27vUQFBcVmx7howcF+jSovGj5qCkainmA0agpGo6ZgtPo6icSN/C5Dz549tWnTJv30\n00+SpKKiovO2ObvpBwAAAADADMz0X4aOHTtq7NixSkpKksVi0bXXXnveNh4eHjXue6HXAQAAAAAw\nGk3/ZRo4cKAGDhx4wfe/+OILSdKNN96oG2+80fX6Cy+8UOfZAAAAAACQaPobrLKifLMjoBb8fQAA\nAAA0BjT9DdTyOcNlt5eYHQO1CAsLNzsCAAAAANSKpr+BioqK4u6gAAAAAIArwt37AQAAAABwUzT9\nAAAAAAC4KZp+AAAAAADcFE0/AAAAAABuiqYfAAAAAAA3RdMPAAAAAICboukHAAAAAMBN0fQDAAAA\nAOCmvMwOgJrl5OTIbi8xO0aTFRYWLovFYnYMAAAAALgiNP0NVFLyCnkHhJgdo0kqK8pXysRERURE\nmh0FAAAAAK4ITX8D5R0QIt/AULNjAAAAAAAasQZ9TX9xcbFWrFghScrPz9fTTz9tcqLzbd26VW++\n+abZMQAAAAAAOE+DbvqLioq0cuVKSVJISIhSUlJMTnQuh8Oh+Ph4Pfroo2ZHAQAAAADgPA16ef9r\nr72m3NxcDRo0SO3bt9d3332nrKwsZWRkaMuWLSovL9eRI0c0cuRIVVZWKisrS82bN9fSpUvl7++v\n3NxczZgxQ4WFhWrZsqVmzZqlDh06nHOM6v/f3r3HVVXn+x9/b0Aa7oGCJmkoine8ZTrhZKKAF0BA\n0bFC8nLSmtEsfx4Vx5NZ6oxMFztaamOdsbxbUJbajDZW5mjZeCQlQzIDURHbiiCMCKzfH077xHjJ\nZMnebF7Px6PHQ9b+rrU+a/mG/KzLl+pqzZ49WwcPHpTFYtHw4cOVmpp6zXVnzZold3d3HT58WD16\n9FBYWJgOHjyoOXPmyGq1au7cuTp58qQkKS0tTd27d9dnn32mBQsWyGKxyGKx6M0335Snp6c9TikA\nAAAAoAFx6KZ/2rRpOnLkiDIyMlRQUKBJkybZPsvNzVVmZqbKy8sVFRWlGTNmKCMjQwsXLlRmZqbG\njBmjOXPmaN68eWrZsqWysrI0d+5c/fnPf66xj6+++kqFhYXavHmzJKm09PKM+ddbt7CwUOvXr5ck\nZWRkyGKxSJLmz5+vhx9+WD169NDJkyc1fvx4bdmyRa+99pqeeuopde/eXeXl5brttttu+bkDAAAA\nAMChm/7r6d27tzw8POTh4SE/Pz/df//9kqSwsDDl5OSorKxM+/fv1+OPPy7DMCRJlZWVV2ynRYsW\nOn78uJ599ln169dPffv2/cl1Bw0adNWa/v73v+vo0aO2dcrKylReXq4ePXpo4cKFiouLU3R0tJo2\nbWrmqcAtEBDgrcBAH3uXYTpnPCbYF5mCmcgTzEamYDYyhfqo3jb97u7uV/3axcVFVVVVqq6ulq+v\nrzIyMmqMq66uVlJSkiwWiyIjIzV58mS988472rVrl9atW6dt27Zp1qxZV133B9d6NN8wDG3YsEGN\nGjWqsfyRRx5R//79tXPnTo0ePVorV6684jUDOBartVRFRSX2LsNUgYE+TndMsC8yBTORJ5iNTMFs\nZApmq6uLSA49kZ+Xl5cuXLggSba75zfK29tbd955p7Zt22ZbdvjwYbm4uCgzM1MZGRmaPHmyzp49\nq6qqKkVFRWnq1KnKzs6+5ro/JSIiQqtWrbpinfz8fLVt21b/8R//oc6dO+vo0aM/61gAAAAAALgZ\nDn2n//bbb1ePHj0UFxen1q1b296d/3fXWp6enq65c+fqlVdeUVVVlYYMGaL27dvXGFNYWKi0tDRV\nV1fLYrFo2rRpN7zuv5s9e7bmzZun+Ph4VVdX6+6777bNBbB37165uLioTZs2uu+++27ibAAAAAAA\n8PNYjJ97Cx11ovfwufL0C7J3GQ1SWfFpLZ4er9DQtvYuxVQ8kgazkSmYiTzBbGQKZiNTMFtdPd7v\n0Hf6G7I3Fj4gq7XU3mU0WCEhre1dAgAAAADUGk2/gwoLC+NKIgAAAACgVhx6Ij8AAAAAAHDzaPoB\nAAAAAHBSNP0AAAAAADgpmn4AAAAAAJwUTT8AAAAAAE6Kph8AAAAAACdF0w8AAAAAgJOi6QcAAAAA\nwEm52bsAXF1OTo6s1lJ7l+HwQkJay9XV1d5lAAAAAIBDoul3UCmz1sjTL8jeZTi0suLTWjw9XqGh\nbe1dCgAAAAA4JJp+B+XpFyRv/2B7lwEAAAAAqMd4px8AAAAAACdF0w8AAAAAgJNqkE1/QUGBhgwZ\nojlz5ig2Nlbjx4/XxYsXlZKSokOHDkmSzp49q8jISElSRkaGfvOb32jcuHEaMGCA3nzzTb322mtK\nTN7M5HYAACAASURBVEzUr3/9a50/f16SlJKSovnz5yshIUFxcXH68ssvZRiGYmJidPbsWUmSYRiK\njo62fQ0AAAAAwK3SIJt+ScrLy9NDDz2k9957T76+vvrggw9ksVhqjPnx17m5uVq6dKk2btyoF154\nQd7e3srIyFDXrl2VmZlpG3fx4kVlZmbqqaeeUlpamiwWi+Lj4/Xuu+9Kknbv3q327dvL39+/bg4U\nAAAAANBgNdiJ/IKDg9WuXTtJUseOHVVQUHDd8b1795aHh4c8PDzk5+en+++/X5IUFhamnJwc27ih\nQ4dKku6++25duHBBpaWlGj58uB577DGlpqbqrbfe0vDhw2/NQTVAAQHeCgz0sXcZ9QbnCmYjUzAT\neYLZyBTMRqZQHzXYpt/d3d32Z1dXV128eFFubm6qrq6WJFVUVFxz/I+/dnFxUVVVlW35j58OMAxD\nktSsWTM1adJEe/bs0ZdffqnnnnvO3INpwKzWUhUVldi7jHohMNCHcwVTkSmYiTzBbGQKZiNTMFtd\nXURqsI/3X01wcLAOHjwoSdq6detNbWPLli2SpH379snHx0fe3t6SpBEjRmj69OkaPHjwFa8RAAAA\nAABwK9D0/8i4ceO0du1aJSUlqbi4+Jrjrte033bbbUpMTNTTTz+tBQsW2JZHRkaqvLxciYmJptYM\nAAAAAMC1WIwfnkFHraWkpGjmzJnq1KnTFZ99+eWX+sMf/qA333zzhrbVf9zL8vYPNrtEp1J6tkAL\nH+mj0NC29i6lXuCRNJiNTMFM5AlmI1MwG5mC2erq8f4G+07/rXCtJwBWrFihdevW/ax3+cuKT5tV\nltPiHAEAAADA9XGn30Hl5OTIai21dxkOLySktVxdXe1dRr3A1WmYjUzBTOQJZiNTMBuZgtm409/A\nhYWF8UMFAAAAAFArTOQHAAAAAICToukHAAAAAMBJ0fQDAAAAAOCkaPoBAAAAAHBSNP0AAAAAADgp\nmn4AAAAAAJwUTT8AAAAAAE6Kph8AAAAAACflZu8CcHU5OTmyWkvtXYbDCwlpLVdXV3uXAQAAAAAO\niabfQaXMWiNPvyB7l+HQyopPa/H0eIWGtrV3KQAAAADgkGj6a+mll15Sr1699Mtf/tLU7Xr6Bcnb\nP9jUbQIAAAAAGpZ63fQbhiGLxXJT61ZVVZnyWPiUKVNqvQ0AAAAAAG6FetX0FxQUaPz48eratauy\ns7M1fvx4rVu3ThUVFWrZsqUWLlwoDw8PffTRR/r9738vT09Pde/eXcePH9eyZcu0ZMkS5eXlKT8/\nX82bN1d6err++Mc/6vPPP1dFRYUefPBBjRw5UkVFRXriiSd04cIFVVZWau7cuerevbtmz56tgwcP\nymKxaPjw4UpNTdWsWbPUv39/RUdH6+9//7sWLVqkqqoqdenSRXPnzlWjRo0UGRmpxMRE/e1vf1Nl\nZaUWL16sVq1a2ft0AgAAAACcXL1q+iUpLy9PixYtUosWLTR58mT9z//8j37xi1/o1Vdf1euvv64J\nEyboqaee0po1a9S8eXNNmzatxvrffPON1q5dK3d3d23YsEG+vr7auHGjKioqNHr0aEVEROgvf/mL\nfvWrX2nixIkyDEPl5eX66quvVFhYqM2bN0uSSktrTrJXUVGhWbNmadWqVWrZsqVmzJihtWvXasyY\nMZKkgIAAvf3221qzZo1WrlypZ599tm5OGAAAAACgwap3TX/z5s0VHh6unTt3Kjc3V6NHj5ZhGKqs\nrFS3bt109OhRtWjRQs2bN5ckDR06VBs2bLCtHxkZKXd3d0nSrl27lJOTo23btkm63Mh/99136tKl\ni9LS0nTp0iUNHDhQ7du3V4sWLXT8+HE9++yz6tevn/r27Vujrh/227JlS0lSQkJCjaY/KipKktS5\nc2dt37791p6kBiQgwFuBgT72LqPe4FzBbGQKZiJPMBuZgtnIFOqjetf0e3h4SLr8Pn9ERISee+65\nGp8fPnz4uut7enrW+HrOnDmKiIi4Ytzq1au1c+dOzZw5U2PHjtWwYcP0zjvvaNeuXVq3bp22bdum\n+fPn11jHMIxr7veHCw0uLi6qrKy8bo24cVZrqYqKSuxdRr0QGOjDuYKpyBTMRJ5gNjIFs5EpmK2u\nLiK51MleboGuXbtq//79ysvLkySVl5fr2LFjatWqlY4fP64TJ05IkrZs2XLNbfTt21dr1qyxNeHH\njh1TeXm5Tpw4ocaNGys5OVnJycnKzs7WuXPnVFVVpaioKE2dOlXZ2dk1ttW6dWudOHFC+fn5kqR3\n331X99xzz604dAAAAAAAbki9u9P/g4CAAC1cuFBPPvmkKioqZLFYNHXqVIWEhOipp57S+PHj5enp\nqS5dulxzhv/k5GQVFBQoMTHRts2lS5fqs88+08qVK+Xm5iYvLy/94Q9/0KlTp5SWlqbq6mpZLJYr\n5gpwd3fXggULNGXKFNtEfqNGjZKkm/4NAwAAAAAA1IbFuN4z6fVUWVmZ7TH+p59+WiEhIUpNTbVz\nVT9P/3Evy9s/2N5lOLTSswVa+EgfhYa2tXcp9QKPpMFsZApmIk8wG5mC2cgUzFZXj/fX2zv917Nh\nwwZlZmbq0qVL6tixo37961/bu6Sfraz4tL1LcHicIwAAAAC4Pqe80+8McnJyZLWW/vTABi4kpLVc\nXV3tXUa9wNVpmI1MwUzkCWYjUzAbmYLZuNPfwIWFhfFDBQAAAABQK/V29n4AAAAAAHB9NP0AAAAA\nADgpmn4AAAAAAJwUTT8AAAAAAE6Kph8AAAAAACdF0w8AAAAAgJOi6QcAAAAAwEnR9AMAAAAA4KTc\n7F0Ari4nJ0dWa6m9y7CbkJDWcnV1tXcZAAAAAFCv0fQ7qJRZa+TpF2TvMuyirPi0Fk+PV2hoW3uX\nAgAAAAD1WoNt+pcsWSIvLy+NHTv2qp9v375drVq1UmhoqGn7LCgo0P79+xUbG/uTYz39guTtH2za\nvgEAAAAADQ/v9F/Djh07lJuba+o2jx8/rvfee8/UbQIAAAAAcC0N6k7/K6+8oszMTDVp0kTNmjVT\n586dtXHjRq1fv16VlZVq2bKl0tPTlZ2drQ8//FCff/65li1bppdeekl79uy5Ytxtt92mrVu36uWX\nX5arq6t8fHz0xhtvqLq6Wn/84x/1+eefq6KiQg8++KBGjhyp559/XkePHlViYqISEhKUmppq71MC\nAAAAAHBiDabpP3TokLZu3arNmzeroqJCSUlJ6ty5s6Kjo5WcnCxJevHFF7Vp0yY9+OCDioyMVP/+\n/RUdHS1J8vX1veq4l19+WStXrlRQUJBKSy9PvLdp0yb5+vpq48aNqqio0OjRoxUREaFp06bptdde\n07Jly+xzEgAAAAAADUqDafr37dunqKgoubu7y93dXZGRkZIuz5L/4osv6vz58yovL1ffvn2vuv61\nxvXs2VMzZ87U4MGDFRUVJUnatWuXcnJytG3bNklSaWmpvvvuO7m5NZjTDQAAAABwAA26CzUMQzNn\nztQrr7yisLAwZWRk6LPPPrvq2GuNmzt3rrKysrRz504lJSXp7bffliTNmTNHERERNbZxrW3jSgEB\n3goM9LF3GU6HcwqzkSmYiTzBbGQKZiNTqI8aTNPfq1cvzZo1SxMnTlRFRYX+9re/adSoUSorK1OT\nJk106dIlbd68WU2bNpUkeXl52R7Xl3TNcfn5+QoPD1d4eLg++eQTnTp1Sn379tWaNWvUu3dvubm5\n6dixY2rWrJm8vLx04cIFuxx/fWO1lqqoqMTeZTiVwEAfzilMRaZgJvIEs5EpmI1MwWx1dRGpwTT9\nHTt21JAhQxQXF6cmTZqoS5cuslgsevzxx5WcnKzGjRsrPDzc1pQPGTJEc+bM0ZtvvqnFixdfc9yi\nRYt07NgxSdIvf/lLtW/fXu3atVNBQYESExMlSQEBAVq6dKnatWsnFxcXJSQkKDExkYn8AAAAAAC3\nlMUwDMPeReBK/ce9LG//YHuXYRelZwu08JE+Cg1ta+9SnApXp2E2MgUzkSeYjUzBbGQKZuNOfwNX\nVnza3iXYTUM+dgAAAAAwE02/g3pj4QOyWkt/eqCTCglpbe8SAAAAAKDeo+l3UGFhYTw+BAAAAACo\nFRd7FwAAAAAAAG4Nmn4AAAAAAJwUTT8AAAAAAE6Kph8AAAAAACdF0w8AAAAAgJOi6QcAAAAAwEnR\n9AMAAAAA4KRo+gEAAAAAcFJu9i4AV5eTkyOrtdTeZdSZkJDWcnV1tXcZAAAAAOBUaPodVMqsNfL0\nC7J3GXWirPi0Fk+PV2hoW3uXAgAAAABOhabfQXn6BcnbP9jeZQAAAAAA6jHe6b+G5cuXm7atJUuW\n6PXXXzdtewAAAAAA3Aia/mtYtmzZz17HMIxbUAkAAAAAADenwTzeX1BQoAkTJqhTp07Kzs5W27Zt\nlZSUpA0bNmjp0qWSpN27d2vNmjVq1aqVLl68qMTERLVp00bp6el6/fXX9fbbb0uSRowYodTUVBUU\nFGj8+PHq2rWrsrOztWLFCh05ckQvvviiqqqqFBAQYLvDf+TIEaWkpOjUqVMaM2aMUlJS7HYuAAAA\nAAANQ4Np+iXp22+/1cKFC9WtWzfNnj1bubm5+vbbb3X27Fn5+/vrrbfeUnJysvr166fVq1crIyND\nknTo0CFlZGRo06ZNqqqq0siRI9W7d2/5+PgoLy9PixYtUnh4uKxWq/7rv/5La9asUfPmzXX+/Pka\n+37jjTdUUlKiQYMG6YEHHmC2egAAAADALdWgmv7mzZurW7dukqS4uDi98cYbGjZsmN555x0NHz5c\nBw4cUHp6+hXrffHFF4qKitJtt90mSYqKitK+ffvUv39/NW/eXOHh4ZKkAwcOqFevXmrevLkkydfX\n17aN+++/X25ubvL391eTJk105swZNW3a9FYfcr0REOCtwEAfe5fh9DjHMBuZgpnIE8xGpmA2MoX6\nqEE1/f/OxcVFSUlJmjhxom677TYNGjRILi6Xpzm40ffzPTw8anx9rfXc3d1r7Leqquomq3ZOVmup\niopK7F2GUwsM9OEcw1RkCmYiTzAbmYLZyBTMVlcXkRrURH4nTpzQgQMHJEnvvfeeevbsqcDAQAUF\nBWnZsmVKSkqyjXV3d7c15nfffbe2b9+uixcvqqysTNu3b9fdd999xfa7du2qL774QgUFBZKk4uLi\nOjgqAAAAAACurkHd6W/VqpVWr16tWbNmqU2bNho9erQkKT4+XufOnVPr1q1tY0eOHKm4uDh16tRJ\n6enpSkxM1IgRI2yftW/f3tbc/yAgIEDz5s3Tb3/7WxmGocaNG2vlypV1d4AAAAAAAPyIxWggv2eu\noKBAkyZN0ubNm6/47JlnnlHHjh01fPhwO1R2df3HvSxv/2B7l1EnSs8WaOEjfRQa2tbepTg1HkmD\n2cgUzESeYDYyBbORKZitrh7vb1B3+q8mKSlJXl5emjlzpr1LqaGs+LS9S6gzDelYAQAAAKAuNZg7\n/fVNTk6OrNZSe5dRZ0JCWvMrDG8xrk7DbGQKZiJPMBuZgtnIFMzGnf4GLiwsjB8qAAAAAIBaaVCz\n9wMAAAAA0JDQ9AMAAAAA4KRo+gEAAAAAcFI0/QAAAAAAOCmafgAAAAAAnBRNPwAAAAAAToqmHwAA\nAAAAJ0XTDwAAAACAk3KzdwG4upycHFmtpfYu45YLCWktV1dXe5cBAAAAAE6Jpt9BpcxaI0+/IHuX\ncUuVFZ/W4unxCg1ta+9SAAAAAMAp0fQ7KE+/IHn7B9u7DAAAAABAPcY7/QAAAAAAOCma/lrKzMxU\nfHy8EhISNGPGDG3btk1xcXFKSEhQSkqKJGnUqFH65ptvbOukpKTo0KFD9ioZAAAAANBA8Hh/LeTm\n5mrZsmVav369/Pz8VFxcrIceekgrV65UUFCQSksvT8Q3ZMgQbdmyRZMnT1ZRUZHOnDmjTp062bl6\nAAAAAICzo+mvhT179mjQoEHy8/OTJPn5+alnz56aOXOmBg8erKioKEnSoEGDNGHCBE2ePFlbt25V\nTEyMPct2KAEB3goM9LF3GQ0G5xpmI1MwE3mC2cgUzEamUB/R9Jts7ty5ysrK0s6dO5WUlKSMjAw1\nbdpUt99+u77++mtt2bJF8+bNs3eZDsNqLVVRUYm9y2gQAgN9ONcwFZmCmcgTzEamYDYyBbPV1UUk\n3umvhT59+mjbtm06d+6cJKm4uFj5+fkKDw/XlClT1LhxY508eVKSNHjwYP3pT3/ShQsXFBYWZs+y\nAQAAAAANBHf6a6FNmzaaNGmSUlJS5Orqqg4dOqi0tFTHjh2TJN17771q3769JCkmJkYLFizQY489\nZseKAQAAAAANCU1/LSUkJCghIeEnxzVu3FgHDx6sg4oAAAAAALiMpt9BlRWftncJt1xDOEYAAAAA\nsCeafgf1xsIHZLWW2ruMWy4kpLW9SwAAAAAAp0XT76DCwsKYHRQAAAAAUCvM3g8AAAAAgJOi6QcA\nAAAAwEnR9AMAAAAA4KRo+gEAAAAAcFI0/QAAAAAAOCmafgAAAAAAnBRNPwAAAAAATsrN3gUAAAAA\nAOyvqqpKx44dNXWbISGt5erqauo28fPQ9DuonJwcWa2l9i7DFHyjAwAAAI7v2LGjejz9XXn6BZmy\nvbLi01o8PV6hoW2vOWbKlElKSXlYvXr1sS3bsGGtcnNzVF5ermee+X2tarBav9fixc/p66+/kre3\njwICAjRlyjTdeWeLWm33x/bv/0KNGjVS587hpm3TTDT9Dipl1hrTvtns6Ua+0QEAAAA4Bk+/IHn7\nB9fZ/qKiBumvf/2gRtO/Y8df9Nhjj6tr12613n5a2nQNGRKnp59eIEn65ptcWa3fm970e3h40vTX\nhSVLlsjLy0tjx479WeuVlJRo8+bNeuCBB372PidOnKjnnntO3t7eP3vd66nrbzYAAAAAqGv9+kXq\n1VdfUWVlpdzc3HTq1El9//0ZBQUFacyYUVq1ar2qq6u1bNl/a//+f+jSpUtKSkpWfHyinn/+D+rd\n+15FRPxKs2b9P/n5+WnmzDl6//13deJEgXr27CU3NzfFxyfa9hca2sb256VLF2vv3t2yWFw0Zsw4\nDRgQpf37v9DatW9q0aIXJEkvvLBI7dt31ODBsUpOjtegQUP16aefqKqqSs8883u5u7vrnXfekqur\nm/76162aOnW6wsNrf7HCTEzkJ6m4uFhr16792esZhqHly5eb3vADAAAAQEPg6+urDh06as+e3ZKk\n7ds/UGTkQFksFlksFknSe++9I29vH7366p/16qt/1rvvZujUqZPq2rW7DhzYL0k6c6ZIx459K0k6\ncGC/unXrrqNHc9WuXYer7vejjz7UN98c0apV6/Xii0v18suLZbV+L0n6126vyt8/QK+99qYSEoZr\n7do31azZHRo2bLhGjhyt115b7XANv1TPm/7MzEzFx8crISFBM2bMqPFZSkqKDh06JEk6e/asIiMj\nJUm5ublKTk5WYmKihg0bpry8PD3//PPKz89XYmKi0tPTJUkrV67UiBEjNGzYMC1ZskSSVFBQoEGD\nBmnGjBmKi4vTyZMnFRkZqXPnzqmgoEBDhgzRnDlzFBsbq/Hjx6uiokKSlJWVpfj4eCUmJmrRokWK\ni4urq1MEAAAAAA5twIAY7djxF0mXH+0fODCmxueffbZH27a9r7FjH9Ajj6Tq/Pli5efn2Zr+Y8e+\nVatWreXvH6Dvvz+jQ4e+VOfOXa+7z6ys/7Xtx98/QN2799RXX2X/ZK333ddfktSuXXudOnXiZg63\nztXbx/tzc3O1bNkyrV+/Xn5+fjp//rxWrVp1zfE/XCVat26dUlNTFRsbq8rKSlVXV2vatGk6cuSI\nMjIyJEmffvqpvvvuO23atEmGYejRRx/Vvn37dMcddygvL0+LFi1SeHh4je1KUl5enl544QU988wz\nmjp1qj744APFxcVp9uzZmj9/vsLDw/Xcc8/dwrMCAAAAAPXLr37VT0uWvKCcnMO6ePGiwsLa69Sp\nkz8aYeiJJ6bXeO//B6WlJfrss7+rW7ceOn++WB9+uF2enp7y8PBQq1ah2rnzwxuqwTAMSZKrq6uq\nq6tty3+4kfsDd/dGkiQXF1dVVVX9zCO1j3p7p3/Pnj0aNGiQ/Pz8JF1+LORGdOvWTcuWLdOrr76q\ngoICubu7XzFm165d+vTTT5WYmKjExER9++23+u677yRJzZs3tzX80v+FQ5KCg4PVrl07SVKnTp1U\nUFCgkpISXbhwwbZObGzszR0wAAAAANxiZcWnVXq2wJT/yopP39A+PTw81L17Dy1cOO+Ku/ySdM89\nv9Tbb29SZWWlJCk/P08XL/5TktSpUxetX79GXbt2V3h4N61d+4bCw7tLknr27KVLly5p8+ZM27a+\n+SZXBw78r8LDu2vHjr+qurpaZ8+eVVbW/6pjx05q1uwOHTt2TJWVlSopKdG+fZ//ZP2enp66cOHC\nDR2rPdTbO/0/xc3NzXaF5sdXZ2JjY9W1a1ft3LlTjzzyiObNm6c777zzivUnTpyokSNH1lhWUFAg\nDw+Pa+7zxxcQXF1ddfHixdoehlMICPBWYKCPvcuAxN8DTEemYCbyBLORKZjN2TMVENBVbyw0d76y\n0NDQG/r13UlJCZo8ebL++79fUmCgjyoqzsvV1UWBgT4aNy5F589/r0ceGfOvOgO0dOlSeXt7KyKi\nj7744jN169ZBlZWVKi0t0X333Wv7u1q+/BXNnz9fa9eu0i9+8QsFBwcrLS1NLVv+SkePHtaECQ/J\nYrFo5swZCgu7S5IUGztEDz/8a915550KD+8sX18PBQb6yNXVRY0be+v2231UWOipRo1cFRjoo7i4\nwZoyZYr27v1Uv/vd79SzZ09Tz2Ft1dumv0+fPvrtb3+rhx9+WLfffruKi4trfB4cHKyDBw+qS5cu\n2rp1q215fn6+WrRooZSUFJ04cUJff/212rVrV+PKTN++ffXSSy8pNjZWnp6eKiwsVKNGjW6qTh8f\nH3l5eSkrK0vh4eHasmXLzR1wPWa1lqqoqMTeZTR4gYE+/D3AVGQKZiJPMBuZgtkaSqb8/e8wdXtW\na9kNjevatbc+/vgzSVJRUYnc3X312mtrbOf8oYcm6KGHJtjGl5cbKi8vUb9+MerXL8Y27i9/+di2\njct+odmzn7lif0VFJRo79lGNHftojWWSlJo6UampE68Yv25dpi5duvznpk3v0nPPLVVRUYm8vBpr\n5crVV2znp9TVRaR62/S3adNGkyZNUkpKilxdXdWhQwcFB//fr7gbN26cpk6dqo0bN6pfv3625Vu3\nbtW7774rNzc3BQYG6tFHH5Wvr6969OihuLg43XfffZo+fbq++eYbjRo1SpLk5eWl9PR0ubhc+TaE\n5XpTO/7L/Pnz9bvf/U6urq7q1auXfHyc+wohAAAAAMAxWIwfv5SOW6KsrEyenp6SpBUrVujMmTNK\nS0u77jr9x70sb//g646pD0rPFmjhI30UGtrW3qU0eA3l6jTqDpmCmcgTzEamYDYyBbNxp9+J7Ny5\nUytWrFBVVZWCg4O1cOHCn1znRie9cHTOchwAAAAAUB9xp99B5eTkyGottXcZpggJaX1Dk3fg1uLq\nNMxGpmAm8gSzkSmYjUzBbNzpb+DCwsL4oQIAAAAAqJUrZ6YDAAAAAABOgaYfAAAAAAAnRdMPAAAA\nAICToukHAAAAAMBJMXs/AAAAAABOijv9AAAAAAA4KZp+AAAAAACcFE0/AAAAAABOiqYfAAAAAAAn\nRdMPAAAAAICToukHAAAAAMBJ0fQDAAAAAOCkaPod0Mcff6xBgwYpJiZGK1assHc5sLO0tDTde++9\niouLsy0rLi7WuHHjFBMTo/Hjx6ukpMT22fLlyxUdHa3Bgwdr165dtuWHDh1SXFycYmJiNH/+fNvy\niooKPfHEE4qOjtaoUaN04sQJ22cZGRmKiYlRTEyMMjMzb/GRoi6cOnVKY8aM0dChQxUXF6dVq1ZJ\nIlO4eRUVFUpOTlZCQoKGDh2q559/XhKZQu1VV1crMTFRkyZNkkSmUDuRkZGKj49XQkKCRowYIYlM\n4eaVlJRoypQpGjx4sIYOHaoDBw44dp4MOJSqqipj4MCBxvHjx42KigojPj7eyM3NtXdZsKPPP//c\nyM7ONmJjY23LFi1aZKxYscIwDMNYvny5kZ6ebhiGYRw5csQYNmyYcenSJSM/P98YOHCgUV1dbRiG\nYYwYMcI4cOCAYRiGMWHCBOPjjz82DMMwVq9ebTz11FOGYRjG+++/b0ydOtUwDMM4d+6cMWDAAOP8\n+fNGcXGx7c+o306fPm1kZ2cbhmEYpaWlRnR0tJGbm0umUCtlZWWGYRhGZWWlkZycbOzbt49ModZe\nf/11Y9q0acbEiRMNw+D/faidyMhI49y5czWWkSncrBkzZhibNm0yDMMwLl26ZJw/f96h88SdfgeT\nlZWlu+66S8HBwWrUqJGGDh2qHTt22Lss2NHdd98tX1/fGst27NihxMRESVJiYqK2b98uSfrwww81\nZMgQubm56c4779Rdd92lrKwsFRUV6cKFCwoPD5ckJSQk2Nb58bZiYmK0Z88eSdKuXbsUEREhHx8f\n+fr6KiIiQp988kmdHDNuncDAQHXo0EGS5OXlpdDQUBUWFpIp1IqHh4eky3cmqqur5efnR6ZQK6dO\nndJHH32k5ORk2zIyhdowDEPV1dU1lpEp3IzS0lLt27dPw4cPlyS5ubnJx8fHofNE0+9gCgsLdccd\nd9i+btq0qU6fPm3HiuCIrFarmjRpIulyE2e1WiVdPT+FhYUqLCxUs2bNrlguSadPn7Z95urqKh8f\nH507d+6a24LzOH78uA4fPqyuXbvq+++/J1O4adXV1UpISFBERITuuecetWnThkyhVhYsWKD//M//\nlMVisS0jU6gNi8WicePGafjw4dq4caMkMoWbc/z4cfn7+2vWrFlKTEzUnDlzVF5e7tB5oukHfEXE\nggAAA3ZJREFUnMCP/1FUW4ZhmLYtOK4LFy5oypQpSktLk5eX1xUZIlP4OVxcXJSZmamPP/5YX3zx\nhfbu3UumcNN27typJk2aqEOHDtf9uyZT+DnWrl2rjIwMvfrqq1q9erX27dvHzynclMrKSmVnZ+uB\nBx5QRkaGPDw8tGLFCofOE02/g2natGmNiRoKCwsVFBRkx4rgiBo3bqwzZ85IkoqKihQQECDpcn5O\nnjxpG3fq1Ck1bdr0iuWFhYVq2rSpJCkoKEinTp2SJFVVVam0tFS33377FVn8YVuo/yorKzVlyhQN\nGzZMAwcOlESmYA5vb2/dd999OnjwIJnCTfvHP/6hDz/8UAMGDNC0adO0d+9eTZ8+XU2aNCFTuGk/\n/Hs6ICBAAwcOVFZWFj+ncFOaNWumZs2aqUuXLpKk6OhoZWdnO3SeaPodTJcuXZSXl6eCggJVVFTo\n/fff14ABA+xdFuzs36/uRUZG6u2335Z0eQbPHzISGRmpLVu2qKKiQvn5+crLy1N4eLgCAwPl4+Oj\nrKwsGYahzMzMGutkZGRIkrZt26Y+ffpIkvr27avdu3erpKRExcXF2r17t/r27VtXh4xbKC0tTW3a\ntFFqaqptGZnCzbJarbYZiv/5z39q9+7d6tixI5nCTXvyySe1c+dO7dixQ88//7x69+6t9PR09e/f\nn0zhppSXl+vChQuSpLKyMu3atUthYWH8nMJNadKkie644w59++23kqQ9e/aoTZs2jp2nWkxaiFvk\no48+MqKjo42oqChj+fLl9i4Hdvbkk08aERERRqdOnYx+/foZmzZtMs6dO2ekpqYa0dHRxtixY43i\n4mLb+GXLlhkDBw40Bg0aZHzyySe25V9++aURGxtrREVFGc8884xt+cWLF40pU6YYUVFRRnJyspGf\nn2/77K233jKioqKM6OhoIyMjo24OGLfUvn37jPbt2xvx8fHGsGHDjISEBOOjjz4yzp49S6ZwUw4f\nPmwkJCQYw4YNM+Li4ow//elPhmEYZAqm2Lt3r232fjKFm5WXl2f7/15sbKzt39dkCjfrq6++MpKS\nkoz4+HjjN7/5jXH+/HmHzpPFMHjhBAAAAAAAZ8Tj/QAAAAAAOCmafgAAAAAAnBRNPwAAAAAAToqm\nHwAAAAAAJ0XTDwAAAACAk6LpBwAAAADASdH0AwAAAADgpP4/CA9Bso/Nw88AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a976e23c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag_view_count = dict()\n",
    "\n",
    "for index, row in questions.iterrows():\n",
    "    for tag in row['Tags']:\n",
    "        if tag in tag_view_count:\n",
    "            tag_view_count[tag] += row['ViewCount']\n",
    "        else:\n",
    "            tag_view_count[tag] = row['ViewCount']\n",
    "            \n",
    "tag_view_count = pd.DataFrame.from_dict(tag_view_count, orient=\"index\")\n",
    "tag_view_count.rename(columns={0: \"ViewCount\"}, inplace=True)\n",
    "\n",
    "most_viewed = tag_view_count.sort_values(by=\"ViewCount\").tail(20)\n",
    "\n",
    "most_viewed.plot(kind=\"barh\", figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view them side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f1a97e27470>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABd4AAAJcCAYAAADn17YPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAAAT/QF0Sx5VPZQY1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a9768d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "fig.set_size_inches((24, 10))\n",
    "most_used.plot(kind=\"barh\", ax=axes[0], subplots=True)\n",
    "most_viewed.plot(kind=\"barh\", ax=axes[1], subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "in_used = pd.merge(most_used, most_viewed, how=\"left\", left_index=True, right_index=True)\n",
    "in_viewed = pd.merge(most_used, most_viewed, how=\"right\", left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relations Between Tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way of trying to gauge how pairs of tags are related to each other is to count how many times each pair appears together. So, let's go ahead and do this.\n",
    "\n",
    "I'll begin by creating a list of all tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_tags = list(tag_count.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I'll create a dataframe where each row will represent a tag, and each column as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distribution</th>\n",
       "      <th>regex</th>\n",
       "      <th>software-recommendation</th>\n",
       "      <th>julia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distribution</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regex</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>software-recommendation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>julia</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        distribution regex software-recommendation julia\n",
       "distribution                     NaN   NaN                     NaN   NaN\n",
       "regex                            NaN   NaN                     NaN   NaN\n",
       "software-recommendation          NaN   NaN                     NaN   NaN\n",
       "julia                            NaN   NaN                     NaN   NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "associations = pd.DataFrame(index=all_tags, columns=all_tags)\n",
    "associations.iloc[0:4,0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will now fill this dataframe with zeroes and then, for each lists of tags in `questions[\"Tags\"]`, I will increment the intervening tags by one. The end result will be a dataframe that, for each pair of tags, tells me how many times they were used together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "associations.fillna(0, inplace=True)\n",
    "\n",
    "for tags in questions[\"Tags\"]:\n",
    "    associations.loc[tags, tags] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe is quite large, so I'll focus my attention on the most used tags. I'll also add some colors to make it easier to talk about the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col0 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col1 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col2 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col3 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col4 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col9 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col5 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col6 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col7 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col8 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col4 {\n",
       "            background-color:  yellow;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col9 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col10 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col11 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col12 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col13 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col14 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col15 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col16 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col17 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col18 {\n",
       "            color:  blue;\n",
       "        }    #T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col19 {\n",
       "            color:  blue;\n",
       "        }</style>  \n",
       "<table id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >machine-learning-model</th> \n",
       "        <th class=\"col_heading level0 col1\" >statistics</th> \n",
       "        <th class=\"col_heading level0 col2\" >clustering</th> \n",
       "        <th class=\"col_heading level0 col3\" >predictive-modeling</th> \n",
       "        <th class=\"col_heading level0 col4\" >r</th> \n",
       "        <th class=\"col_heading level0 col5\" >dataset</th> \n",
       "        <th class=\"col_heading level0 col6\" >regression</th> \n",
       "        <th class=\"col_heading level0 col7\" >pandas</th> \n",
       "        <th class=\"col_heading level0 col8\" >lstm</th> \n",
       "        <th class=\"col_heading level0 col9\" >time-series</th> \n",
       "        <th class=\"col_heading level0 col10\" >cnn</th> \n",
       "        <th class=\"col_heading level0 col11\" >nlp</th> \n",
       "        <th class=\"col_heading level0 col12\" >scikit-learn</th> \n",
       "        <th class=\"col_heading level0 col13\" >tensorflow</th> \n",
       "        <th class=\"col_heading level0 col14\" >classification</th> \n",
       "        <th class=\"col_heading level0 col15\" >keras</th> \n",
       "        <th class=\"col_heading level0 col16\" >neural-network</th> \n",
       "        <th class=\"col_heading level0 col17\" >deep-learning</th> \n",
       "        <th class=\"col_heading level0 col18\" >python</th> \n",
       "        <th class=\"col_heading level0 col19\" >machine-learning</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row0\" class=\"row_heading level0 row0\" >machine-learning-model</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col0\" class=\"data row0 col0\" >224</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col1\" class=\"data row0 col1\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col2\" class=\"data row0 col2\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col3\" class=\"data row0 col3\" >21</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col4\" class=\"data row0 col4\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col5\" class=\"data row0 col5\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col6\" class=\"data row0 col6\" >8</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col7\" class=\"data row0 col7\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col8\" class=\"data row0 col8\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col9\" class=\"data row0 col9\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col10\" class=\"data row0 col10\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col11\" class=\"data row0 col11\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col12\" class=\"data row0 col12\" >18</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col13\" class=\"data row0 col13\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col14\" class=\"data row0 col14\" >21</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col15\" class=\"data row0 col15\" >17</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col16\" class=\"data row0 col16\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col17\" class=\"data row0 col17\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col18\" class=\"data row0 col18\" >37</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row0_col19\" class=\"data row0 col19\" >139</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row1\" class=\"row_heading level0 row1\" >statistics</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col0\" class=\"data row1 col0\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col1\" class=\"data row1 col1\" >234</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col2\" class=\"data row1 col2\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col3\" class=\"data row1 col3\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col4\" class=\"data row1 col4\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col5\" class=\"data row1 col5\" >17</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col6\" class=\"data row1 col6\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col7\" class=\"data row1 col7\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col8\" class=\"data row1 col8\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col9\" class=\"data row1 col9\" >22</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col10\" class=\"data row1 col10\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col11\" class=\"data row1 col11\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col12\" class=\"data row1 col12\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col13\" class=\"data row1 col13\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col14\" class=\"data row1 col14\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col15\" class=\"data row1 col15\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col16\" class=\"data row1 col16\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col17\" class=\"data row1 col17\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col18\" class=\"data row1 col18\" >35</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row1_col19\" class=\"data row1 col19\" >89</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row2\" class=\"row_heading level0 row2\" >clustering</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col1\" class=\"data row2 col1\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col2\" class=\"data row2 col2\" >257</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col3\" class=\"data row2 col3\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col4\" class=\"data row2 col4\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col5\" class=\"data row2 col5\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col6\" class=\"data row2 col6\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col7\" class=\"data row2 col7\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col8\" class=\"data row2 col8\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col9\" class=\"data row2 col9\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col10\" class=\"data row2 col10\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col11\" class=\"data row2 col11\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col12\" class=\"data row2 col12\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col13\" class=\"data row2 col13\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col14\" class=\"data row2 col14\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col15\" class=\"data row2 col15\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col16\" class=\"data row2 col16\" >8</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col17\" class=\"data row2 col17\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col18\" class=\"data row2 col18\" >45</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row2_col19\" class=\"data row2 col19\" >61</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row3\" class=\"row_heading level0 row3\" >predictive-modeling</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col0\" class=\"data row3 col0\" >21</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col1\" class=\"data row3 col1\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col2\" class=\"data row3 col2\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col3\" class=\"data row3 col3\" >265</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col4\" class=\"data row3 col4\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col5\" class=\"data row3 col5\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col6\" class=\"data row3 col6\" >28</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col7\" class=\"data row3 col7\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col8\" class=\"data row3 col8\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col9\" class=\"data row3 col9\" >31</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col10\" class=\"data row3 col10\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col11\" class=\"data row3 col11\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col12\" class=\"data row3 col12\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col13\" class=\"data row3 col13\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col14\" class=\"data row3 col14\" >27</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col15\" class=\"data row3 col15\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col16\" class=\"data row3 col16\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col17\" class=\"data row3 col17\" >32</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col18\" class=\"data row3 col18\" >35</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row3_col19\" class=\"data row3 col19\" >123</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row4\" class=\"row_heading level0 row4\" >r</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col0\" class=\"data row4 col0\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col1\" class=\"data row4 col1\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col2\" class=\"data row4 col2\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col3\" class=\"data row4 col3\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col4\" class=\"data row4 col4\" >268</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col5\" class=\"data row4 col5\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col6\" class=\"data row4 col6\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col7\" class=\"data row4 col7\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col8\" class=\"data row4 col8\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col9\" class=\"data row4 col9\" >22</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col10\" class=\"data row4 col10\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col11\" class=\"data row4 col11\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col12\" class=\"data row4 col12\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col13\" class=\"data row4 col13\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col14\" class=\"data row4 col14\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col15\" class=\"data row4 col15\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col16\" class=\"data row4 col16\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col17\" class=\"data row4 col17\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col18\" class=\"data row4 col18\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row4_col19\" class=\"data row4 col19\" >63</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row5\" class=\"row_heading level0 row5\" >dataset</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col0\" class=\"data row5 col0\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col1\" class=\"data row5 col1\" >17</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col2\" class=\"data row5 col2\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col3\" class=\"data row5 col3\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col4\" class=\"data row5 col4\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col5\" class=\"data row5 col5\" >340</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col6\" class=\"data row5 col6\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col7\" class=\"data row5 col7\" >14</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col8\" class=\"data row5 col8\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col9\" class=\"data row5 col9\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col10\" class=\"data row5 col10\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col11\" class=\"data row5 col11\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col12\" class=\"data row5 col12\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col13\" class=\"data row5 col13\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col14\" class=\"data row5 col14\" >28</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col15\" class=\"data row5 col15\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col16\" class=\"data row5 col16\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col17\" class=\"data row5 col17\" >32</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col18\" class=\"data row5 col18\" >53</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row5_col19\" class=\"data row5 col19\" >99</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row6\" class=\"row_heading level0 row6\" >regression</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col0\" class=\"data row6 col0\" >8</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col1\" class=\"data row6 col1\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col2\" class=\"data row6 col2\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col3\" class=\"data row6 col3\" >28</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col4\" class=\"data row6 col4\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col5\" class=\"data row6 col5\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col6\" class=\"data row6 col6\" >347</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col7\" class=\"data row6 col7\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col8\" class=\"data row6 col8\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col9\" class=\"data row6 col9\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col10\" class=\"data row6 col10\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col11\" class=\"data row6 col11\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col12\" class=\"data row6 col12\" >37</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col13\" class=\"data row6 col13\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col14\" class=\"data row6 col14\" >34</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col15\" class=\"data row6 col15\" >31</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col16\" class=\"data row6 col16\" >42</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col17\" class=\"data row6 col17\" >21</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col18\" class=\"data row6 col18\" >59</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row6_col19\" class=\"data row6 col19\" >119</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row7\" class=\"row_heading level0 row7\" >pandas</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col0\" class=\"data row7 col0\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col1\" class=\"data row7 col1\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col2\" class=\"data row7 col2\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col3\" class=\"data row7 col3\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col4\" class=\"data row7 col4\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col5\" class=\"data row7 col5\" >14</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col6\" class=\"data row7 col6\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col7\" class=\"data row7 col7\" >354</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col8\" class=\"data row7 col8\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col9\" class=\"data row7 col9\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col10\" class=\"data row7 col10\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col11\" class=\"data row7 col11\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col12\" class=\"data row7 col12\" >37</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col13\" class=\"data row7 col13\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col14\" class=\"data row7 col14\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col15\" class=\"data row7 col15\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col16\" class=\"data row7 col16\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col17\" class=\"data row7 col17\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col18\" class=\"data row7 col18\" >244</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row7_col19\" class=\"data row7 col19\" >62</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row8\" class=\"row_heading level0 row8\" >lstm</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col0\" class=\"data row8 col0\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col1\" class=\"data row8 col1\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col2\" class=\"data row8 col2\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col3\" class=\"data row8 col3\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col4\" class=\"data row8 col4\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col5\" class=\"data row8 col5\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col6\" class=\"data row8 col6\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col7\" class=\"data row8 col7\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col8\" class=\"data row8 col8\" >402</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col9\" class=\"data row8 col9\" >87</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col10\" class=\"data row8 col10\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col11\" class=\"data row8 col11\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col12\" class=\"data row8 col12\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col13\" class=\"data row8 col13\" >43</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col14\" class=\"data row8 col14\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col15\" class=\"data row8 col15\" >133</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col16\" class=\"data row8 col16\" >69</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col17\" class=\"data row8 col17\" >103</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col18\" class=\"data row8 col18\" >61</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row8_col19\" class=\"data row8 col19\" >71</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row9\" class=\"row_heading level0 row9\" >time-series</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col0\" class=\"data row9 col0\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col1\" class=\"data row9 col1\" >22</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col2\" class=\"data row9 col2\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col3\" class=\"data row9 col3\" >31</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col4\" class=\"data row9 col4\" >22</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col5\" class=\"data row9 col5\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col6\" class=\"data row9 col6\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col7\" class=\"data row9 col7\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col8\" class=\"data row9 col8\" >87</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col9\" class=\"data row9 col9\" >466</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col10\" class=\"data row9 col10\" >8</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col11\" class=\"data row9 col11\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col12\" class=\"data row9 col12\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col13\" class=\"data row9 col13\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col14\" class=\"data row9 col14\" >25</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col15\" class=\"data row9 col15\" >51</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col16\" class=\"data row9 col16\" >33</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col17\" class=\"data row9 col17\" >44</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col18\" class=\"data row9 col18\" >105</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row9_col19\" class=\"data row9 col19\" >131</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row10\" class=\"row_heading level0 row10\" >cnn</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col0\" class=\"data row10 col0\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col1\" class=\"data row10 col1\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col2\" class=\"data row10 col2\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col3\" class=\"data row10 col3\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col4\" class=\"data row10 col4\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col5\" class=\"data row10 col5\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col6\" class=\"data row10 col6\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col7\" class=\"data row10 col7\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col8\" class=\"data row10 col8\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col9\" class=\"data row10 col9\" >8</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col10\" class=\"data row10 col10\" >489</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col11\" class=\"data row10 col11\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col12\" class=\"data row10 col12\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col13\" class=\"data row10 col13\" >57</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col14\" class=\"data row10 col14\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col15\" class=\"data row10 col15\" >116</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col16\" class=\"data row10 col16\" >118</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col17\" class=\"data row10 col17\" >160</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col18\" class=\"data row10 col18\" >62</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row10_col19\" class=\"data row10 col19\" >124</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row11\" class=\"row_heading level0 row11\" >nlp</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col0\" class=\"data row11 col0\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col1\" class=\"data row11 col1\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col2\" class=\"data row11 col2\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col3\" class=\"data row11 col3\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col4\" class=\"data row11 col4\" >4</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col5\" class=\"data row11 col5\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col6\" class=\"data row11 col6\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col7\" class=\"data row11 col7\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col8\" class=\"data row11 col8\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col9\" class=\"data row11 col9\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col10\" class=\"data row11 col10\" >7</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col11\" class=\"data row11 col11\" >493</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col12\" class=\"data row11 col12\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col13\" class=\"data row11 col13\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col14\" class=\"data row11 col14\" >35</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col15\" class=\"data row11 col15\" >23</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col16\" class=\"data row11 col16\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col17\" class=\"data row11 col17\" >72</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col18\" class=\"data row11 col18\" >71</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row11_col19\" class=\"data row11 col19\" >113</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row12\" class=\"row_heading level0 row12\" >scikit-learn</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col0\" class=\"data row12 col0\" >18</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col1\" class=\"data row12 col1\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col2\" class=\"data row12 col2\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col3\" class=\"data row12 col3\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col4\" class=\"data row12 col4\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col5\" class=\"data row12 col5\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col6\" class=\"data row12 col6\" >37</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col7\" class=\"data row12 col7\" >37</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col8\" class=\"data row12 col8\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col9\" class=\"data row12 col9\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col10\" class=\"data row12 col10\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col11\" class=\"data row12 col11\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col12\" class=\"data row12 col12\" >540</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col13\" class=\"data row12 col13\" >15</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col14\" class=\"data row12 col14\" >47</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col15\" class=\"data row12 col15\" >34</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col16\" class=\"data row12 col16\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col17\" class=\"data row12 col17\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col18\" class=\"data row12 col18\" >235</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row12_col19\" class=\"data row12 col19\" >188</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row13\" class=\"row_heading level0 row13\" >tensorflow</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col0\" class=\"data row13 col0\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col1\" class=\"data row13 col1\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col2\" class=\"data row13 col2\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col3\" class=\"data row13 col3\" >6</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col4\" class=\"data row13 col4\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col5\" class=\"data row13 col5\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col6\" class=\"data row13 col6\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col7\" class=\"data row13 col7\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col8\" class=\"data row13 col8\" >43</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col9\" class=\"data row13 col9\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col10\" class=\"data row13 col10\" >57</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col11\" class=\"data row13 col11\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col12\" class=\"data row13 col12\" >15</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col13\" class=\"data row13 col13\" >584</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col14\" class=\"data row13 col14\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col15\" class=\"data row13 col15\" >256</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col16\" class=\"data row13 col16\" >108</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col17\" class=\"data row13 col17\" >136</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col18\" class=\"data row13 col18\" >167</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row13_col19\" class=\"data row13 col19\" >106</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row14\" class=\"row_heading level0 row14\" >classification</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col0\" class=\"data row14 col0\" >21</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col1\" class=\"data row14 col1\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col2\" class=\"data row14 col2\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col3\" class=\"data row14 col3\" >27</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col4\" class=\"data row14 col4\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col5\" class=\"data row14 col5\" >28</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col6\" class=\"data row14 col6\" >34</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col7\" class=\"data row14 col7\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col8\" class=\"data row14 col8\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col9\" class=\"data row14 col9\" >25</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col10\" class=\"data row14 col10\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col11\" class=\"data row14 col11\" >35</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col12\" class=\"data row14 col12\" >47</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col13\" class=\"data row14 col13\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col14\" class=\"data row14 col14\" >685</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col15\" class=\"data row14 col15\" >58</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col16\" class=\"data row14 col16\" >65</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col17\" class=\"data row14 col17\" >59</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col18\" class=\"data row14 col18\" >98</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row14_col19\" class=\"data row14 col19\" >259</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row15\" class=\"row_heading level0 row15\" >keras</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col0\" class=\"data row15 col0\" >17</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col1\" class=\"data row15 col1\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col2\" class=\"data row15 col2\" >0</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col3\" class=\"data row15 col3\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col4\" class=\"data row15 col4\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col5\" class=\"data row15 col5\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col6\" class=\"data row15 col6\" >31</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col7\" class=\"data row15 col7\" >3</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col8\" class=\"data row15 col8\" >133</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col9\" class=\"data row15 col9\" >51</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col10\" class=\"data row15 col10\" >116</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col11\" class=\"data row15 col11\" >23</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col12\" class=\"data row15 col12\" >34</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col13\" class=\"data row15 col13\" >256</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col14\" class=\"data row15 col14\" >58</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col15\" class=\"data row15 col15\" >935</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col16\" class=\"data row15 col16\" >235</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col17\" class=\"data row15 col17\" >247</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col18\" class=\"data row15 col18\" >280</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row15_col19\" class=\"data row15 col19\" >195</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row16\" class=\"row_heading level0 row16\" >neural-network</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col0\" class=\"data row16 col0\" >10</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col1\" class=\"data row16 col1\" >11</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col2\" class=\"data row16 col2\" >8</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col3\" class=\"data row16 col3\" >13</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col4\" class=\"data row16 col4\" >9</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col5\" class=\"data row16 col5\" >20</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col6\" class=\"data row16 col6\" >42</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col7\" class=\"data row16 col7\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col8\" class=\"data row16 col8\" >69</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col9\" class=\"data row16 col9\" >33</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col10\" class=\"data row16 col10\" >118</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col11\" class=\"data row16 col11\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col12\" class=\"data row16 col12\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col13\" class=\"data row16 col13\" >108</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col14\" class=\"data row16 col14\" >65</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col15\" class=\"data row16 col15\" >235</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col16\" class=\"data row16 col16\" >1055</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col17\" class=\"data row16 col17\" >305</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col18\" class=\"data row16 col18\" >137</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row16_col19\" class=\"data row16 col19\" >366</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row17\" class=\"row_heading level0 row17\" >deep-learning</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col0\" class=\"data row17 col0\" >19</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col1\" class=\"data row17 col1\" >12</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col2\" class=\"data row17 col2\" >2</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col3\" class=\"data row17 col3\" >32</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col4\" class=\"data row17 col4\" >5</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col5\" class=\"data row17 col5\" >32</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col6\" class=\"data row17 col6\" >21</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col7\" class=\"data row17 col7\" >1</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col8\" class=\"data row17 col8\" >103</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col9\" class=\"data row17 col9\" >44</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col10\" class=\"data row17 col10\" >160</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col11\" class=\"data row17 col11\" >72</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col12\" class=\"data row17 col12\" >16</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col13\" class=\"data row17 col13\" >136</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col14\" class=\"data row17 col14\" >59</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col15\" class=\"data row17 col15\" >247</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col16\" class=\"data row17 col16\" >305</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col17\" class=\"data row17 col17\" >1220</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col18\" class=\"data row17 col18\" >160</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row17_col19\" class=\"data row17 col19\" >429</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row18\" class=\"row_heading level0 row18\" >python</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col0\" class=\"data row18 col0\" >37</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col1\" class=\"data row18 col1\" >35</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col2\" class=\"data row18 col2\" >45</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col3\" class=\"data row18 col3\" >35</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col4\" class=\"data row18 col4\" >24</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col5\" class=\"data row18 col5\" >53</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col6\" class=\"data row18 col6\" >59</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col7\" class=\"data row18 col7\" >244</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col8\" class=\"data row18 col8\" >61</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col9\" class=\"data row18 col9\" >105</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col10\" class=\"data row18 col10\" >62</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col11\" class=\"data row18 col11\" >71</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col12\" class=\"data row18 col12\" >235</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col13\" class=\"data row18 col13\" >167</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col14\" class=\"data row18 col14\" >98</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col15\" class=\"data row18 col15\" >280</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col16\" class=\"data row18 col16\" >137</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col17\" class=\"data row18 col17\" >160</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col18\" class=\"data row18 col18\" >1814</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row18_col19\" class=\"data row18 col19\" >499</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010level0_row19\" class=\"row_heading level0 row19\" >machine-learning</th> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col0\" class=\"data row19 col0\" >139</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col1\" class=\"data row19 col1\" >89</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col2\" class=\"data row19 col2\" >61</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col3\" class=\"data row19 col3\" >123</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col4\" class=\"data row19 col4\" >63</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col5\" class=\"data row19 col5\" >99</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col6\" class=\"data row19 col6\" >119</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col7\" class=\"data row19 col7\" >62</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col8\" class=\"data row19 col8\" >71</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col9\" class=\"data row19 col9\" >131</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col10\" class=\"data row19 col10\" >124</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col11\" class=\"data row19 col11\" >113</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col12\" class=\"data row19 col12\" >188</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col13\" class=\"data row19 col13\" >106</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col14\" class=\"data row19 col14\" >259</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col15\" class=\"data row19 col15\" >195</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col16\" class=\"data row19 col16\" >366</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col17\" class=\"data row19 col17\" >429</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col18\" class=\"data row19 col18\" >499</td> \n",
       "        <td id=\"T_3a7f36d2_ccb9_11ec_8a03_0242ac110010row19_col19\" class=\"data row19 col19\" >2693</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1a97f2fac8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relations_most_used = associations.loc[most_used.index, most_used.index]\n",
    "\n",
    "def style_cells(x):\n",
    "    helper_df = pd.DataFrame('', index=x.index, columns=x.columns)\n",
    "    helper_df.loc[\"time-series\", \"r\"] = \"background-color: yellow\"\n",
    "    helper_df.loc[\"r\", \"time-series\"] = \"background-color: yellow\"\n",
    "    for k in range(helper_df.shape[0]):\n",
    "        helper_df.iloc[k,k] = \"color: blue\"\n",
    "    \n",
    "    return helper_df\n",
    "\n",
    "relations_most_used.style.apply(style_cells, axis=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cells highlighted in yellow tell me that `time-series` was used together with `r` 22 times. The values in blue tell me how many times each of the tags was used. I saw earlier that `machine-learning` was used 2693 times and I can confirm that in this dataframe.\n",
    "\n",
    "It's hard for a human to understand what is going on in this dataframe. To make things visually easier, I'll create a heatmap. But before I do, I'll get rid of the values in blue, otherwise the colors will be too skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(relations_most_used.shape[0]):\n",
    "    relations_most_used.iloc[i,i] = pd.np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a945a9828>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvkAAAJOCAYAAAAtcxi1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a97b19518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(relations_most_used, cmap=\"Greens\", annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most used tags also seem to have the strongest relationships, as given by the dark concentration in the bottom right corner. However, this could simply be because each of these tags is used a lot, and often end up being used together without possibly having any strong relation between them.\n",
    "\n",
    "A more intuitive manifestation of this phenomenon is the following: A lot of people buy bread, a lot of people buy toilet paper, so they end up being purchased together a lot, but purchasing one of them doesn't increase the chances of purchasing the other.\n",
    "\n",
    "Another shortcoming of this attempt is that it only looks at relations between pairs of tags and not between multiple groups of tags. For example, it could be the case that when used together, `dataset` and `scikit-learn` have a \"strong\" relation to `pandas`, but each by itself does not.\n",
    "\n",
    "So how do I attack both these problems? There is a powerful data mining technique that allows data scientists to handle this: [association rules](https://en.wikipedia.org/wiki/Association_rule_learning). Association rules allow me to analytically spot relations like \"people who purchase milk, also purchase eggs\". Moreover, I can also measure how strong these relations are on several fronts: how common each relation is, how strong it is, and how independent the components of the relationship are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enter Domain Knowledge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras, scikit-learn, and TensorFlow are all Python libraries that allow their users to employ deep learning (a type of neural network).\n",
    "\n",
    "Most of the top tags are all intimately related with one central machine learning theme: deep learning. If I want to be extra specific, I can suggest the creation of Python content that uses deep learning for classification problems (and other variations of this suggestion).\n",
    "\n",
    "At a glance, someone with sufficient domain knowledge can tell that the most popular topic at the moment, as shown by my analysis, is deep learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Just a Fad?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read in the file into a dataframe called `all_q`. I'll parse the dates at read-time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_q = pd.read_csv(\"all_questions.csv\", parse_dates=[\"CreationDate\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can use the same technique as before to clean the tags column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_q[\"Tags\"] = all_q[\"Tags\"].str.replace(\"^<|>$\", \"\").str.split(\"><\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before deciding which questions should be classified as being deep learning questions, I should decide which tags are deep learning tags.\n",
    "\n",
    "The definition of what constitutes a deep learning tag is a tag that belongs to the list `[\"lstm\", \"cnn\", \"scikit-learn\", \"tensorflow\", \"keras\", \"neural-network\", \"deep-learning\"]`.\n",
    "\n",
    "This list was obtained by looking at all the tags in `most_used` and seeing which ones had any relation to deep learning.\n",
    "\n",
    "I'll now create a function that assigns `1` to deep learning questions and `0` otherwise; and then use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def class_deep_learning(tags):\n",
    "    for tag in tags:\n",
    "        if tag in [\"lstm\", \"cnn\", \"scikit-learn\", \"tensorflow\",\n",
    "                   \"keras\", \"neural-network\", \"deep-learning\"]:\n",
    "            return 1\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_q[\"DeepLearning\"] = all_q[\"Tags\"].apply(class_deep_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>Tags</th>\n",
       "      <th>DeepLearning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12289</th>\n",
       "      <td>30210</td>\n",
       "      <td>2018-04-12 06:22:25</td>\n",
       "      <td>[dataset, visualization, data]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4299</th>\n",
       "      <td>57245</td>\n",
       "      <td>2019-08-08 15:33:39</td>\n",
       "      <td>[python, seaborn]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6582</th>\n",
       "      <td>17695</td>\n",
       "      <td>2017-03-19 17:17:02</td>\n",
       "      <td>[python, dataset, regression]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16199</th>\n",
       "      <td>45054</td>\n",
       "      <td>2019-02-04 22:45:45</td>\n",
       "      <td>[scikit-learn, outlier]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21205</th>\n",
       "      <td>66551</td>\n",
       "      <td>2020-01-16 06:45:13</td>\n",
       "      <td>[cnn, image-preprocessing]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id        CreationDate                            Tags  DeepLearning\n",
       "12289  30210 2018-04-12 06:22:25  [dataset, visualization, data]             0\n",
       "4299   57245 2019-08-08 15:33:39               [python, seaborn]             0\n",
       "6582   17695 2017-03-19 17:17:02   [python, dataset, regression]             0\n",
       "16199  45054 2019-02-04 22:45:45         [scikit-learn, outlier]             1\n",
       "21205  66551 2020-01-16 06:45:13      [cnn, image-preprocessing]             1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_q.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good!\n",
    "\n",
    "The data-science-techonology landscape isn't something as dynamic to merit daily, weekly, or even monthly tracking. Let's track it quarterly.\n",
    "\n",
    "Since I don't have all the data for the first quarter of 2020, I'll get rid of those dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_q = all_q[all_q[\"CreationDate\"].dt.year < 2020]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I'll create a column that identifies the quarter in which a question was asked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fetch_quarter(datetime):\n",
    "    year = str(datetime.year)[-2:]\n",
    "    quarter = str(((datetime.month-1) // 3) + 1)\n",
    "    return \"{y}Q{q}\".format(y=year, q=quarter)\n",
    "\n",
    "all_q[\"Quarter\"] = all_q[\"CreationDate\"].apply(fetch_quarter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>Tags</th>\n",
       "      <th>DeepLearning</th>\n",
       "      <th>Quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45416</td>\n",
       "      <td>2019-02-12 00:36:29</td>\n",
       "      <td>[python, keras, tensorflow, cnn, probability]</td>\n",
       "      <td>1</td>\n",
       "      <td>19Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45418</td>\n",
       "      <td>2019-02-12 00:50:39</td>\n",
       "      <td>[neural-network]</td>\n",
       "      <td>1</td>\n",
       "      <td>19Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45422</td>\n",
       "      <td>2019-02-12 04:40:51</td>\n",
       "      <td>[python, ibm-watson, chatbot]</td>\n",
       "      <td>0</td>\n",
       "      <td>19Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45426</td>\n",
       "      <td>2019-02-12 04:51:49</td>\n",
       "      <td>[keras]</td>\n",
       "      <td>1</td>\n",
       "      <td>19Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45427</td>\n",
       "      <td>2019-02-12 05:08:24</td>\n",
       "      <td>[r, predictive-modeling, machine-learning-mode...</td>\n",
       "      <td>0</td>\n",
       "      <td>19Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id        CreationDate  \\\n",
       "0  45416 2019-02-12 00:36:29   \n",
       "1  45418 2019-02-12 00:50:39   \n",
       "2  45422 2019-02-12 04:40:51   \n",
       "3  45426 2019-02-12 04:51:49   \n",
       "4  45427 2019-02-12 05:08:24   \n",
       "\n",
       "                                                Tags  DeepLearning Quarter  \n",
       "0      [python, keras, tensorflow, cnn, probability]             1    19Q1  \n",
       "1                                   [neural-network]             1    19Q1  \n",
       "2                      [python, ibm-watson, chatbot]             0    19Q1  \n",
       "3                                            [keras]             1    19Q1  \n",
       "4  [r, predictive-modeling, machine-learning-mode...             0    19Q1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_q.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For the final stretch, I'll group by quarter and:\n",
    "\n",
    "- Count the number of deep learning questions\n",
    "- Count the total number of questions\n",
    "- Compute the ratio between the two numbers above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quarter</th>\n",
       "      <th>DeepLearningQuestions</th>\n",
       "      <th>TotalQuestions</th>\n",
       "      <th>DeepLearningRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16Q1</td>\n",
       "      <td>110</td>\n",
       "      <td>516</td>\n",
       "      <td>0.213178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16Q4</td>\n",
       "      <td>177</td>\n",
       "      <td>527</td>\n",
       "      <td>0.335863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17Q3</td>\n",
       "      <td>247</td>\n",
       "      <td>714</td>\n",
       "      <td>0.345938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17Q2</td>\n",
       "      <td>226</td>\n",
       "      <td>647</td>\n",
       "      <td>0.349304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17Q1</td>\n",
       "      <td>242</td>\n",
       "      <td>698</td>\n",
       "      <td>0.346705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Quarter  DeepLearningQuestions  TotalQuestions  DeepLearningRate\n",
       "7     16Q1                    110             516          0.213178\n",
       "10    16Q4                    177             527          0.335863\n",
       "13    17Q3                    247             714          0.345938\n",
       "12    17Q2                    226             647          0.349304\n",
       "11    17Q1                    242             698          0.346705"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarterly = all_q.groupby('Quarter').agg({\"DeepLearning\": ['sum', 'size']})\n",
    "quarterly.columns = ['DeepLearningQuestions', 'TotalQuestions']\n",
    "quarterly[\"DeepLearningRate\"] = quarterly[\"DeepLearningQuestions\"]\\\n",
    "                                /quarterly[\"TotalQuestions\"]\n",
    "# The following is done to help with visualizations later.\n",
    "quarterly.reset_index(inplace=True)\n",
    "quarterly.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABWYAAALhCAYAAADcon1pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABhxmAWAAAAAAAAAMLs/wMKIHaMPy4N7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a94223f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = quarterly.plot(x=\"Quarter\", y=\"DeepLearningRate\",\n",
    "                    kind=\"line\", linestyle=\"-\", marker=\"o\", color=\"orange\",\n",
    "                    figsize=(24,12)\n",
    "                    )\n",
    "\n",
    "ax2 = quarterly.plot(x=\"Quarter\", y=\"TotalQuestions\",\n",
    "                     kind=\"bar\", ax=ax1, secondary_y=True, alpha=0.7, rot=45)\n",
    "\n",
    "for idx, t in quarterly[\"TotalQuestions\"].iteritems():\n",
    "    ax2.text(idx, t, str(t), ha=\"center\", va=\"bottom\")\n",
    "xlims = ax1.get_xlim()\n",
    "\n",
    "ax1.get_legend().remove()\n",
    "\n",
    "handles1, labels1 = ax1.get_legend_handles_labels()\n",
    "handles2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(handles=handles1 + handles2,\n",
    "           labels=labels1 + labels2,\n",
    "           loc=\"upper left\", prop={\"size\": 12})\n",
    "\n",
    "\n",
    "for ax in (ax1, ax2):\n",
    "    for where in (\"top\", \"right\"):\n",
    "        ax.spines[where].set_visible(False)\n",
    "        ax.tick_params(right=False, labelright=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "It seems that deep learning questions were a high-growth trend since the start of DSSE, but it looks like it is plateauing. There is no evidence to suggest that interest in deep learning is decreasing and so I maintain my previous idea of proposing that my client create deep learning content."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}