Projects/step_4/5.Data_analysis_in_business/4.Guided Project_Popular Data Science Questions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popular Data Science Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Our goal in this project is to use [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": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You're tasked with figuring out what is best content to write about. Because you took this course, you know that given the lack of instructions there's some leeway in what \"best\" means here.\n",
    "\n",
    "Since you're passionate about helping people learn, you decide to scower 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 to your experience when you first started learning programming, it occurs to you that if you wanted to figure out what programming content to write, you could consult Stack Overflow (a question and answer website about programming) and see what kind of content is more popular.\n",
    "\n",
    "You decide to investigate Stack Overflow a little more and find out that it is part of a question and answer website network called [Stack Exchange](https://en.wikipedia.org/wiki/Stack_Exchange)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Stack Exchange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack Exchange hosts sites on a multitude of fields and subjects, including mathematics, physics, philosophy, and data science! Here's a sample of the most popular sites:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='se_sites.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack Exchange 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Being a multidisciplinary field, there a few Stack Exchange websites there are relevant to our goal here:\n",
    "\n",
    "* [Data Science](https://datascience.stackexchange.com/)\n",
    "* [Cross Validated — a statistics site](https://stats.stackexchange.com/)\n",
    "* [Artificial Intelligence](https://ai.stackexchange.com/)\n",
    "* [Mathematics](https://math.stackexchange.com/)\n",
    "* [Stack Overflow](https://stackoverflow.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What kind of questions are welcome on this site?\n",
    "On DSSE's help center's [section on questions](https://datascience.stackexchange.com/help/asking) , we can read that we 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",
    "All of these characteristics, if employed, should be helpful attributes to our goal.\n",
    "\n",
    "In the help center we also learned 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",
    "What, other than questions, does DSSE's home subdivide into?\n",
    "On the home page we can see that we have 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);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Users](https://datascience.stackexchange.com/users) — a list of users;\n",
    "* [Unanswered](https://datascience.stackexchange.com/unanswered) — a list of unanswered questions;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tagging system used by Stack Exchange looks just like what we need to solve this problem as it allow us to quantify how many questions are asked about each subject.\n",
    "\n",
    "Something else we can learn from exploring the help center, is that Stack Exchange's sites are heavily moderated by the community; this gives us some confidence in using the tagging system to derive conclusions.\n",
    "\n",
    "What information is available in each post?\n",
    "Looking, just as an example, at this question, some of the information we 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": [
    "# 3. Stack Exchange Data Explorer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perusing the table names, a few stand out as relevant for our goal:\n",
    "\n",
    "* Posts\n",
    "* PostTags\n",
    "* Tags\n",
    "* TagSynonyms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now comes the challenge of accessing the data en masse. One potential solution would be to scrape the site. However, because we still haven't learned how to web scrape, and because we have an easier alternative (mostly the second reason), we're going to do something else.\n",
    "\n",
    "Stack Exchange provides a public data base for each of its websites. [Here](https://data.stackexchange.com/datascience/query/new)'s a link to query and explore Data Science Stack Exchange's database."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read more about Stack Exchange Data Explorer (SEDE) on its [help section](https://data.stackexchange.com/help) and on [this](https://data.stackexchange.com/tutorial) tutorial link."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Note that SEDE uses a different dialect (Transact-SQL — Microsoft's SQL) than SQLite` , which you learned earlier. Most things are the same, but some are different. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the relevant data we run the following query.\n",
    "\n",
    "`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": [
    "# 5. Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We import everything that we'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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = pd.read_csv(\"2019_questions.csv\", parse_dates=[\"CreationDate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8115 entries, 0 to 8114\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Id             8115 non-null   int64         \n",
      " 1   CreationDate   8115 non-null   datetime64[ns]\n",
      " 2   Score          8115 non-null   int64         \n",
      " 3   ViewCount      8115 non-null   int64         \n",
      " 4   Tags           8115 non-null   object        \n",
      " 5   AnswerCount    8115 non-null   int64         \n",
      " 6   FavoriteCount  1513 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(4), object(1)\n",
      "memory usage: 443.9+ KB\n"
     ]
    }
   ],
   "source": [
    "questions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We 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 we can replace the missing values with zero.\n",
    "\n",
    "The types seem adequate for every column, however, after we 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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<class 'str'>], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions[\"Tags\"].apply(lambda value: type(value)).unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We see that every value in this column is a string. On Stack Exchange, each question can only have a maximum of five tags (source), 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 doesn't help is relating tags from one question to another, we'll just keep them as a list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Cleaning the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                        int64\n",
       "CreationDate     datetime64[ns]\n",
       "Score                     int64\n",
       "ViewCount                 int64\n",
       "Tags                     object\n",
       "AnswerCount               int64\n",
       "FavoriteCount             int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "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": "code",
   "execution_count": 7,
   "metadata": {},
   "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>4411</th>\n",
       "      <td>51941</td>\n",
       "      <td>2019-05-14 11:04:52</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>[decision-trees, cost-function]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7850</th>\n",
       "      <td>53878</td>\n",
       "      <td>2019-06-16 01:28:20</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>[machine-learning, recommender-system]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>58842</td>\n",
       "      <td>2019-09-07 21:08:05</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>[decision-trees]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id        CreationDate  Score  ViewCount  \\\n",
       "4411  51941 2019-05-14 11:04:52      1         20   \n",
       "7850  53878 2019-06-16 01:28:20      1         35   \n",
       "2858  58842 2019-09-07 21:08:05      1         61   \n",
       "\n",
       "                                        Tags  AnswerCount  FavoriteCount  \n",
       "4411         [decision-trees, cost-function]            1              0  \n",
       "7850  [machine-learning, recommender-system]            1              0  \n",
       "2858                        [decision-trees]            0              0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions[\"Tags\"] = questions[\"Tags\"].str.replace(\"^<|>$\", \"\").str.split(\"><\")\n",
    "questions.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Most Used and Most Viewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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, let's transform tag_count in a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>python</th>\n",
       "      <td>1679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predictive-modeling</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-forest</th>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categorical-data</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pca</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine-learning</th>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word-embeddings</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convolution</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Count\n",
       "python                1679\n",
       "pandas                 341\n",
       "predictive-modeling    250\n",
       "data                   195\n",
       "random-forest          147\n",
       "categorical-data        77\n",
       "pca                     82\n",
       "machine-learning      2499\n",
       "word-embeddings        112\n",
       "convolution             91"
      ]
     },
     "execution_count": 9,
     "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": [
    "Let's now sort this dataframe by Count and visualize the top 20 results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistics</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clustering</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predictive-modeling</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regression</th>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lstm</th>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time-series</th>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn</th>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nlp</th>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scikit-learn</th>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tensorflow</th>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classification</th>\n",
       "      <td>639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keras</th>\n",
       "      <td>855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural-network</th>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deep-learning</th>\n",
       "      <td>1114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>1679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine-learning</th>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Count\n",
       "machine-learning-model    209\n",
       "statistics                214\n",
       "clustering                250\n",
       "predictive-modeling       250\n",
       "r                         252\n",
       "dataset                   308\n",
       "regression                321\n",
       "pandas                    341\n",
       "lstm                      355\n",
       "time-series               419\n",
       "cnn                       449\n",
       "nlp                       457\n",
       "scikit-learn              495\n",
       "tensorflow                531\n",
       "classification            639\n",
       "keras                     855\n",
       "neural-network            982\n",
       "deep-learning            1114\n",
       "python                   1679\n",
       "machine-learning         2499"
      ]
     },
     "execution_count": 10,
     "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 we can experiment with others, however, popularity of the tags rapidly declines, so looking at these tags should be enough to help us with our goal. Let's visualize these data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac20e7d808>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Cq5vbC6ATEswDvl9V/w480LRRVf8GvAO4ArgZuLGqvjrB8mcAuyR5/9bWL0mSJEmanjLGbnTp18yZt6DmnXxur8uQpCljZHCg1yVIkiRttiQrq6p/dPtMeIaBttHC+XMZ8n9+JUmSJGlGmam3JEiSJEmSpE0wMJAkSZIkSS0GBpIkSZIkqcXAQJIkSZIktRgYSJIkSZKkFgMDSZIkSZLUYmAgSZIkSZJaDAwkSZIkSVKLgYEkSZIkSWoxMJAkSZIkSS2ze12AJr/htevoW7Ks12VImuFGBgd6XYIkSdKM4g4DSZIkSZLUYmAwiSXpS7Km13VIkiRJkmYeA4NpKMmsXtcgSZIkSZraDAymiCRPSXJTkmcmOSfJiiSrk/xhc/6YJFck+Tww3LR9JcnKJLckWdy0zUpyYZI1SYaTvL2HlyVJkiRJmqR86OEUkGQ/4AvA64HDgXVVdViSOcC1SZY3XQ8HDqyqO5r3b6iqnyXZFViR5BKgD5hfVQc2c++1M69FkiRJkjQ1uMNg8tsH+CrwmqpaBbwAeF2SVcD1wGOBBU3fG7rCAoDTk9wMXAc8oen3I+ApST6a5HeBe8ZaNMniJENJhjasX7dDLkySJEmSNHkZGEx+64B/AZ7TvA/wtqpa1Pw8uao27jC4b+OgJMcAxwJHVNXBwE3ALlX1n8DBwJXAW4BPjLVoVS2tqv6q6p+129wdcFmSJEmSpMnMWxImv/8CjgcuT3IvcDnwpiTfqaqHkjwNWDvGuLnAf1bV+iT7A88CSLI38F9VdUmSfwYu3ClXIUmSJEmaUgwMpoCqui/J7wPfBP4SuBW4MUmAn9IJFEb7BnBaktXAP9G5LQFgPvCpJBt3l7xjhxYvSZIkSZqSDAwmsaoaAQ5sju8GDmtOfRV456juVzY/G8c+CPzeOFM/YzuWKUmSJEmahgwMNKGF8+cyNDjQ6zIkSZIkSTuRDz2UJEmSJEktBgaSJEmSJKnFwECSJEmSJLUYGEiSJEmSpBYDA0mSJEmS1GJgIEmSJEmSWgwMJEmSJElSi4GBJEmSJElqMTCQJEmSJEktBgaSJEmSJKlldq8L0OQ3vHYdfUuW9boMadoaGRzodQmSJElSizsMJEmSJElSi4HBOJKcneTM7Tjf97qOz0lyS/N6WpLXbcV8eyV5c9f7xye5eHvVK0mSJEma2bwlYSepqmd3vf1DYJ+qenAbptwLeDNwfjP/ncAJ2zCfJEmSJEm/5A6DRpLXJVmd5OYknx117tQkK5pzlyTZrWl/eZI1TftVTdsBSW5IsqqZb0HTfm/zehnwaOD6JCd272RI8tQk32rmuzHJvkl2T/Lt5v1wkuOasgaBfZt1zknSl2RNM88uST7V9L8pyXOb9lOSXJrkG0l+mOT9O/6TlSRJkiRNRe4woPMlH3gX8JyquivJY4DTu7pcWlUXNH3/Engj8FHgLOCFVbU2yV5N39OAD1fVRUkeBczqXquqXpzk3qpa1Mx3dtfpi4DBqvpykl3oBDr/Bbykqu5JsjdwXRM6LAEO7Jqnr2uetzRrLUyyP7A8ydOac4uAQ4AHgX9K8tGq+pet+dwkSZIkSdOXOww6ngdcXFV3AVTVz0adPzDJ1UmGgZOAA5r2a4ELk5zKr4KB7wPvTPKnwJOq6v7NKSDJHsD8qvpyU8MDVbUeCPC/k6wGvgXMBx43wXRHAp9t5rkd+DGwMTD4dlWtq6oHgFuBJ41Tz+IkQ0mGNqxftzmXIEmSJEmaRgwMOgLUJs5fCLy1qhYC7wZ2Aaiq04A/A54ArEry2Kr6PPBi4H7g8iTP24IaxnISsA9waLOb4N83rr8Vc0FnZ8FGGxhnl0lVLa2q/qrqn7Xb3AmWkyRJkiRNNwYGHd8GXpHksQDNLQnd9gD+Lckj6XyBp+m3b1VdX1VnAXcBT0jyFOBHVfUR4DLgoM0poKruAf41yfHN3HOaZyXMBX5SVQ81zyLYuCPg501dY7lqY53NrQhPBP5pc+qQJEmSJAkMDACoqluAvwK+m+Rm4IOjuvw5cD3wTeD2rvZzmgcLrqHzJf1m4ERgTZJVwP7AZ7aglNcCpze3H3wP+E06zzXoTzJEJwS4van5P4Brm4cunjNqnvOBWc0tFF8ETtnGv8ggSZIkSZphUrWpnfgSzJm3oOadfG6vy5CmrZHBgV6XIEmSpBksycqq6h/d7l9J0IQWzp/LkF9oJEmSJGlG8ZYESZIkSZLUYmAgSZIkSZJaDAwkSZIkSVKLgYEkSZIkSWoxMJAkSZIkSS0GBpIkSZIkqcXAQJIkSZIktRgYSJIkSZKkFgMDSZIkSZLUYmAgSZIkSZJaZve6AE1+w2vX0bdkWa/LkLaLkcGBXpcgSZIkTQnuMJAkSZIkSS0GBlspyV5J3tzD9f8+yeokb09yYZITelWLJEmSJGn6MTDYensBOz0wSDI7yW8Cz66qg6rqQzu7BkmSJEnS9GdgsPUGgX2TrEpyTpI/TrKi+a3/uwGS9CW5LckFSW5JsjzJrs2505Pc2vT/QtP2mCRfadquS3JQ0352kqVJlgOfAZYDv9GsfVR3UUmen+SmJMNJPplkTpLDk1zanD8uyf1JHpVklyQ/2nkfmSRJkiRpqjAw2HpLgH+uqkXAN4EFwOHAIuDQJEc3/RYA51XVAcDdwMu6xh9SVQcBpzVt7wZuatreSScc2OhQ4LiqejXw4o1rV9XVGzsk2QW4EDixqhbSeajlm4AbgUOabkcBa4DDgGcC1491cUkWJxlKMrRh/bot/nAkSZIkSVObgcH28YLm5yY6X873pxMUANxRVaua45VAX3O8GrgoyWuAXzRtRwKfBaiq7wCPTTK3OXdZVd0/QR37Nev9oHn/aeDoqvoF8H+T/BadUOODwNF0woOrx5qoqpZWVX9V9c/abe5YXSRJkiRJ05iBwfYR4K+b3/gvqqqnVtXfNece7Oq3gV/9KcsB4Dw6OwdWJpndzDNaNa/3bWYd47ka+D3gIeBbdMKJI4GrNmNeSZIkSdIMY2Cw9X4O7NEcXw68IcnuAEnmJ/mN8QYmeQTwhKq6AvgTOg9Q3J3Ol/eTmj7HAHdV1T1bUNPtQF+SpzbvXwt8tzm+CjgD+H5V/RR4LJ2dELdswfySJEmSpBli9sRdNJaq+o8k1yZZA3wd+Dzw/SQA9wKvobOjYCyzgM81txsE+FBV3Z3kbOBTSVYD64GTt7CmB5K8HvhSs2NhBfA3zenrgcfxqx0Fq4GfVFW1Z5IkSZIkzXTx+6ImMmfegpp38rm9LkPaLkYGB3pdgiRJkjSpJFlZVf2j291hoAktnD+XIb9kSZIkSdKM4jMMJEmSJElSi4GBJEmSJElqMTCQJEmSJEktBgaSJEmSJKnFwECSJEmSJLUYGEiSJEmSpBYDA0mSJEmS1GJgIEmSJEmSWgwMJEmSJElSi4GBJEmSJElqmd3rAjT5Da9dR9+SZb0uQxrTyOBAr0uQJEmSpiV3GOwASfqTfKQ5PjvJmWP0eU+SY5vjM5LsNs5cxyT52o6tWJIkSZKkX+cOgx2gqoaAoQn6nNX19gzgc8D6HVVTkllVtWFHzS9JkiRJml7cYbAFkjw6ybIkNydZk+TEJIcl+V7TdkOSPcbbFZDk1CRfT7JrkguTnJDkdODxwBVJrtiM9T+ZZEWSm5Ic17T3Jbk6yY3Nz7Ob9mOSXJHk88Bw0++2JBckuSXJ8iS77oCPSpIkSZI0xbnDYMv8LnBnVQ0AJJkL3AScWFUrkuwJ3D/WwCRvBV4AHF9VDyYBoKo+kuSPgOdW1V0TrP8u4DtV9YYkewE3JPkW8BPgd6rqgSQLgL8H+psxhwMHVtUdSfqABcCrqurUJP8HeBmd3Q2SJEmSJP2SgcGWGQY+kOR9wNeAu4F/q6oVAFV1D8DGMKDLa4F/pRMWPLQN678AeHHXMxF2AZ4I3Al8LMkiYAPwtK4xN1TVHV3v76iqVc3xSqBvrIWSLAYWA8zac59tKFmSJEmSNBV5S8IWqKofAIfSCQ7+GngJUJsxdA2dL+b/faKOSV6SZFXz0z/6NPCyqlrU/Dyxqm4D3g78O3AwnZ0Fj+oac9+oOR7sOt7AOKFRVS2tqv6q6p+129yJypYkSZIkTTMGBlsgyeOB9VX1OeADwLOAxyc5rDm/R5KxvoDfBPwhcFkzx2g/B/YAqKovdwUCox+ceDnwtjRbGJIc0rTPpbPT4WE6uxlmbdOFSpIkSZJmPG9J2DILgXOSPAw8BLyJzm/9P9o8PPB+4NixBlbVNc2tBMuS/M6o00uBryf5t6p67ibWfy9wLrC6CQ1GgN8HzgcuSfJy4ArauwokSZIkSdoiqdqcHfWayebMW1DzTj6312VIYxoZHOh1CZIkSdKUlmRlVY2+Jd4dBprYwvlzGfJLmSRJkiTNKD7DQJIkSZIktRgYSJIkSZKkFgMDSZIkSZLUYmAgSZIkSZJaDAwkSZIkSVKLgYEkSZIkSWoxMJAkSZIkSS0GBpIkSZIkqcXAQJIkSZIktRgYSJIkSZKkltm9LkCT3/DadfQtWdbrMjQDjAwO9LoESZIkSQ13GEiSJEmSpBYDgxkgyYVJTuh1HZIkSZKkqcPAQJIkSZIktRgYTCNJ+pLcluSCJLckWZ5k11F9RpK8L8kNzc9Te1WvJEmSJGnyMjCYfhYA51XVAcDdwMvG6HNPVR0OfAw4d2cWJ0mSJEmaGgwMpp87qmpVc7wS6Bujz993vR4x1iRJFicZSjK0Yf267V+lJEmSJGlSMzCYfh7sOt7A2H86s8Y5/lVj1dKq6q+q/lm7zd2e9UmSJEmSpgADg5npxK7X7/eyEEmSJEnS5DTWb581/c1Jcj2dwOhVvS5GkiRJkjT5GBhMI1U1AhzY9f4D43Q9r6revVOKkiRJkiRNSQYGmtDC+XMZGhzodRmSJEmSpJ3IwGCGqaq+XtcgSZIkSZr8fOihJEmSJElqMTCQJEmSJEktBgaSJEmSJKnFwECSJEmSJLUYGEiSJEmSpBYDA0mSJEmS1GJgIEmSJEmSWgwMJEmSJElSi4GBJEmSJElqmd3rAjT5Da9dR9+SZb0uQ9PUyOBAr0uQJEmSNAZ3GEiSJEmSpBYDA0mSJEmS1GJgIEmSJEmSWnyGwTSQ5HXAmUABq4ENwD1AP/CbwJ9U1cVJjgHOBu4CDgRWAq+pqupB2ZIkSZKkSczAYIpLcgDwLuA5VXVXkscAHwTmAUcC+wOXARc3Qw4BDgDuBK4FngNcM8a8i4HFALP23GcHX4UkSZIkabLxloSp73nAxVV1F0BV/axp/0pVPVxVtwKP6+p/Q1X9a1U9DKwC+saatKqWVlV/VfXP2m3uDixfkiRJkjQZGRhMfaFzK8JoD47qM1b7BtxlIkmSJEkag4HB1Pdt4BVJHgvQ3JIgSZIkSdI28bfLU1xV3ZLkr4DvJtkA3NTrmiRJkiRJU5+BwTRQVZ8GPr2J87s3r1cCV3a1v3VH1yZJkiRJmpoMDDShhfPnMjQ40OsyJEmSJEk7kc8wkCRJkiRJLQYGkiRJkiSpxcBAkiRJkiS1GBhIkiRJkqQWAwNJkiRJktRiYCBJkiRJkloMDCRJkiRJUouBgSRJkiRJajEwkCRJkiRJLQYGkiRJkiSpZXavC9DkN7x2HX1LlvW6DE0xI4MDvS5BkiRJ0jZwh4EkSZIkSWoxMNhMSfZK8ubm+PFJLu51TaMleXGSJb2uQ5IkSZI09RkYbL69gDcDVNWdVXVCj+v5NUlmV9VlVTXY61okSZIkSVOfzzDYfIPAvklWAT8EfquqDkxyCnA8MAs4EPh/gEcBrwUeBF5UVT9Lsi9wHrAPsB44tapu714gySzg74B+oIBPVtWHxhub5ELgZ8AhwI1JhoH+qnprkn2AvwGe2Ex/RlVdm+S3gQ83bQUcXVU/366flCRJkiRpyjMw2HxLgAOralGSPuBrXecOpPOlfRfg/wJ/WlWHJPkQ8DrgXGApcFpV/TDJM4HzgeeNWmMRML+qDoTObRBN+6bGPg04tqo2NOHFRh8GPlRV1yR5InA58FvAmcBbmvBgd+CBsS42yWJgMcCsPffZ3M9IkiRJkjRNGBhsH1c0v6X/eZJ1wD807cPAQc0X82cDX0qyccycMQqp0IoAACAASURBVOb5EfCUJB8FlgHLN2Psl6pqwxhzHQs8vWvMnkn2AK4FPpjkIuDSqvrXsS6oqpbSCSqYM29BbfLqJUmSJEnTjoHB9vFg1/HDXe8fpvMZPwK4u6oWdQ9qbkFY2by9rKrOSnIw8ELgLcArgDPGGtvlvnHaHwEcUVX3j2ofTLIMeBFwXZJjR98aIUmSJEmSDz3cfD8H9tiagVV1D3BHkpcDpOPgqtpQVYuan7OS7A08oqouAf4ceMZ4Yzdj2eXAWze+SbKoed23qoar6n3AELD/1lyTJEmSJGl6MzDYTFX1H8C1SdYA52zFFCcBb0xyM3ALcNwYfeYDVzYPVrwQeMcWjB3tdKA/yeoktwKnNe1nJFnTzHU/8PWtuBZJkiRJ0jSXKm9P16bNmbeg5p18bq/L0BQzMjjQ6xIkSZIkbYYkK6uqf3S7zzDQhBbOn8uQX/4kSZIkaUbxlgRJkiRJktRiYCBJkiRJkloMDCRJkiRJUouBgSRJkiRJajEwkCRJkiRJLQYGkiRJkiSpxcBAkiRJkiS1GBhIkiRJkqQWAwNJkiRJktRiYCBJkiRJklpm97oATX7Da9fRt2RZr8vQDjIyONDrEiRJkiRNQu4wmIKS3DvB+XfurFokSZIkSdOTgcH0ZGAgSZIkSdomBgZTWJJ5Sa5KsirJmiRHJRkEdm3aLkrSl+T2JJ9o+lyU5Ngk1yb5YZLDe30dkiRJkqTJx8Bgans1cHlVLQIOBlZV1RLg/qpaVFUnNf2eCnwYOAjYvxl3JHAm7kaQJEmSJI3Bhx5ObSuATyZ5JPCVqlo1Tr87qmoYIMktwLerqpIMA31jDUiyGFgMMGvPfbZ74ZIkSZKkyc0dBlNYVV0FHA2sBT6b5HXjdH2w6/jhrvcPM05oVFVLq6q/qvpn7TZ3e5UsSZIkSZoiDAymsCRPAn5SVRcAfwc8ozn1ULPrQJIkSZKkrWJgMLUdA6xKchPwMjrPKQBYCqxOclGvCpMkSZIkTW2pql7XoEluzrwFNe/kc3tdhnaQkcGBXpcgSZIkqYeSrKyq/tHtPvRQE1o4fy5DfqmUJEmSpBnFWxIkSZIkSVKLgYEkSZIkSWoxMJAkSZIkSS0GBpIkSZIkqcXAQJIkSZIktRgYSJIkSZKkFgMDSZIkSZLUYmAgSZIkSZJaDAwkSZIkSVKLgYEkSZIkSWqZ3esCNPkNr11H35JlvS5D28nI4ECvS5AkSZI0BbjDQJIkSZIktRgYTDNJrkzS3+s6JEmSJElTm4GBJEmSJElqMTDokSR9SW5P8ukkq5NcnGS3JGclWZFkTZKlSdL0vzLJ+5LckOQHSY5q2ndN8oVmji8Cu3at8fEkQ0luSfLurvbBJLc2Yz6w0y9ekiRJkjTpGRj01n7A0qo6CLgHeDPwsao6rKoOpPPl//e7+s+uqsOBM4C/aNreBKxv5vgr4NCu/u+qqn7gIOC3kxyU5DHAS4ADmjF/uQOvT5IkSZI0RRkY9Na/VNW1zfHngCOB5ya5Pskw8DzggK7+lzavK4G+5vjoZixVtRpY3dX/FUluBG5q5nk6nWDiAeATSV4KrB+rsCSLm90JQxvWr9u2q5QkSZIkTTkGBr1VY7w/HzihqhYCFwC7dJ1/sHndwK//SczR85DkycCZwPObnQTLgF2q6hfA4cAlwPHAN8YsrGppVfVXVf+s3eZu8YVJkiRJkqY2A4PeemKSI5rjVwHXNMd3JdkdOGEz5rgKOAkgyYF0bj8A2BO4D1iX5HHA7zV9dgfmVtU/0rm1YdH2uBBJkiRJ0vQye+Iu2oFuA05O8rfAD4GPA/8NGAZGgBWbMcfHgU8lWQ2sAm4AqKqbk9wE3AL8CNh468MewFeT7AIEePt2uxpJkiRJ0rRhYNBbD1fVaaPa/qz5+TVVdUzX8V00zzCoqvuBV441eVWdMs66h295qZIkSZKkmcTAQBNaOH8uQ4MDvS5DkiRJkrQTGRj0SFWNAAf2ug5JkiRJksbiQw8lSZIkSVKLgYEkSZIkSWoxMJAkSZIkSS0GBpIkSZIkqcXAQJIkSZIktRgYSJIkSZKkFgMDSZIkSZLUYmAgSZIkSZJaDAwkSZIkSVLL7F4XoMlveO06+pYs63UZ2gIjgwO9LkGSJEnSFOcOA0mSJEmS1GJgMI0keU+SY3tdhyRJkiRp6vOWhO0gSYBU1cNbOX52Vf1iW+uoqrO2dQ5JkiRJksAdBlstSV+S25KcD9wIvDbJ95PcmORLSXZv+r0oye1JrknykSRfa9rPTrI0yXLgM0lmJTknyYokq5P8YdNvXpKrkqxKsibJUU3fC5v3w0ne3vS9MMkJzfHzk9zUnP9kkjlN+0iSdzd1DifZvwcfnyRJkiRpkjMw2Db7AZ8Bfgd4I3BsVT0DGAL+KMkuwN8Cv1dVRwL7jBp/KHBcVb26Gb+uqg4DDgNOTfJk4NXA5VW1CDgYWAUsAuZX1YFVtRD4VPekzboXAic252cDb+rqcldT58eBM8e6sCSLkwwlGdqwft3WfDaSJEmSpCnMwGDb/LiqrgOeBTwduDbJKuBk4EnA/sCPquqOpv/fjxp/WVXd3xy/AHhdM/564LHAAmAF8PokZwMLq+rnwI+ApyT5aJLfBe4ZNe9+wB1V9YPm/aeBo7vOX9q8rgT6xrqwqlpaVf1V1T9rt7mb8VFIkiRJkqYTn2Gwbe5rXgN8s6pe1X0yySGbOX7jHG+rqstHd0pyNDAAfDbJOVX1mSQHAy8E3gK8AnjDqLk25cHmdQP+G5AkSZIkjcEdBtvHdcBzkjwVIMluSZ4G3E5nJ0Bf0+/ETcxxOfCmJI9s5nhakkcneRLwk6q6APg74BlJ9gYeUVWXAH8OPGPUXLcDfRvrAV4LfHdbL1KSJEmSNHP42+XtoKp+muQU4O83PlwQ+LOq+kGSNwPfSHIXcMMmpvkEndsDbmz+6sJPgeOBY4A/TvIQcC/wOmA+8KkkGwOfd4yq54Ekrwe+lGQ2ndsa/mbbr1SSJEmSNFOkqnpdw7SWZPequrcJAc4DflhVH+p1XVtizrwFNe/kc3tdhrbAyOBAr0uQJEmSNEUkWVlV/aPb3WGw452a5GTgUcBNdP5qwpSycP5chvwCKkmSJEkzioHBDtbsJphSOwokSZIkSfKhh5IkSZIkqcXAQJIkSZIktRgYSJIkSZKkFgMDSZIkSZLUYmAgSZIkSZJaDAwkSZIkSVKLgYEkSZIkSWoxMJAkSZIkSS0GBpIkSZIkqWV2rwvQ5De8dh19S5b1ugyNY2RwoNclSJIkSZqG3GHQY0nOTnLmJs4fn+Tp23nNviSv3p5zSpIkSZKmFwODye94YLsGBkAfYGAgSZIkSRqXgUEPJHlXkn9K8i1gv6bt1CQrktyc5JIkuyV5NvBi4Jwkq5LsO1a/ZvzLk6xp2q9q2mYlOafpvzrJHzYlDAJHNXO+vQcfgSRJkiRpkjMw2MmSHAq8EjgEeClwWHPq0qo6rKoOBm4D3lhV3wMuA/64qhZV1T+P1a8Zfxbwwqb9xU3bG4F1VXVYs86pSZ4MLAGubub80A6/aEmSJEnSlONDD3e+o4AvV9V6gCSXNe0HJvlLYC9gd+DyccaP1+9a4MIk/we4tGl7AXBQkhOa93OBBcB/TVRkksXAYoBZe+6z+VcnSZIkSZoWDAx6o8ZouxA4vqpuTnIKcMw4Y8fsV1WnJXkmMACsSrIICPC2qvq18CHJeHP/qsCqpcBSgDnzFoxVryRJkiRpGvOWhJ3vKuAlSXZNsgfwB037HsC/JXkkcFJX/58359hUvyT7VtX1VXUWcBfwBDq7D97U9CXJ05I8eow5JUmSJEn6Ne4w2Mmq6sYkXwRWAT8Grm5O/TlwfdM2zK++0H8BuCDJ6cAJm+h3TpIFdHYVfBu4GVhN5y8i3JgkwE/p/NWF1cAvktwMXOhzDCRJkiRJo6XK3ebatDnzFtS8k8/tdRkax8jgQK9LkCRJkjSFJVlZVf2j270lQZIkSZIktXhLgia0cP5chvwttiRJkiTNKO4wkCRJkiRJLQYGkiRJkiSpxcBAkiRJkiS1GBhIkiRJkqQWAwNJkiRJktRiYCBJkiRJkloMDCRJkiRJUouBgSRJkiRJajEwkCRJkiRJLQYGkiRJkiSpZXavC9DkN7x2HX1LlvW6jGllZHCg1yVIkiRJ0ia5w0CSJEmSJLUYGIh0+G9BkiRJkvRLfkmcoZL0JbktyfnAjcATel2TJEmSJGnyMDCY2fYDPlNVh1TVj3tdjCRJkiRp8jAwmNl+XFXXjXUiyeIkQ0mGNqxft7PrkiRJkiT1mIHBzHbfeCeqamlV9VdV/6zd5u7MmiRJkiRJk4CBgSRJkiRJajEwkCRJkiRJLbN7XYB6o6pGgAN7XYckSZIkaXIyMNCEFs6fy9DgQK/LkCRJkiTtRN6SIEmSJEmSWgwMJEmSJElSi4GBJEmSJElqMTCQJEmSJEktBgaSJEmSJKnFwECSJEmSJLUYGEiSJEmSpBYDA0mSJEmS1GJgIEmSJEmSWgwMJEmSJElSy+xeF6DJb3jtOvqWLOt1GdPKyOBAr0uQJEmSpE1yh4EkSZIkSWqZNoFBknub18cnuXiCvmck2a3r/T8m2WtH17glkhyT5Gub2yfJi5Ms2TnVSZIkSZKmu0kdGCSZtaVjqurOqjphgm5nAL8MDKrqRVV195auNZlU1WVVNdjrOiRJkiRJ00PPAoMkfUluT/LpJKuTXJxktyQjSc5Kcg3w8iT7JvlGkpVJrk6yfzP+yUm+n2RFkveOmndNczwryQeSDDdrvC3J6cDjgSuSXNH0G0myd5L3JXlz11xnJ/lfzfEfN2utTvLuca7p7OZ6ljdzvjTJ+5v1v5HkkU2/5ye5qWn/ZJI5TfvvNp/JNcBLu+Z9dNNvRTPuuDHWPiXJx5rjC5N8JMn3kvwoyQlN+yOSnJ/kliRfa3ZWTBSuSJIkSZJmoF7vMNgPWFpVBwH3ABu/rD9QVUdW1ReApcDbqupQ4Ezg/KbPh4GPV9VhwP83zvyLgScDhzRrXFRVHwHuBJ5bVc8d1f8LwIld718BfCnJC4AFwOHAIuDQJEePs+a+wABwHPA5/v/27j3Kzrq+9/j7Y5KSo0m5BMVwKUljEEkJwQRaRBFzFC3Ueuei1aBWTnrE6umCVbwsi7qsMeSceqmKQD3eBbzQCkiB0kQEUXMhIQkgeiTaICIXTUGFhvA9f+xncDt79swEJtmTmfdrrVn72d/9e37P99n55Vmzv/N7fhuWV9WhwG+AE5JMBj4NnNTEJwJ/1cTPB14MPAd4aluf7wT+vTnX5wHnJHlSl+P3mQ48G/gzoG/mwcuBGcChwF8CRw3RhyRJkiRpnOp1weA/qur6ZvvztD7gAlwEkGQK8CxaH9rXAp+k9UEY4GjgS83257r0/3zg3Kp6GKCq7hssmaq6EXhKsw7CYcAvquonwHHNz43AGuBgWgWEgVxRVVuB9cAE4F+b+HpaH9afDtxeVbc18c8AxzR93l5VP6iqat6PPscBZzXvwQpgMvAHg50L8M9V9UhV3Qzs08SeDXy5if8MWN5t5ySnJVmVZNW2X28Z4lCSJEmSpLGm11+rWF2e/6p5fALwy6qaN8z9+8sw2vT3FeCVtP7Cf2FbPx+oqk/+TufJm4E3NU+Pbx4fAqiqR5JsbT78AzxC6/3OIMfulmuAV1TV9/sdf58u7R/No23/9schVdV5tGZ3sNv02dv7HkqSJEmSdnG9nmHwB0n6psWfAlzX/mJV/Sdwe5JXAaTlsObl64GTm+3XdOn/KmBxkonN/ns18fuBqV32ubDp95W0igcAVwJvaGY8kGS/JE+pqo9V1bzm56fDO2VuBWYkeVrz/LXAN5v4zCSzmvgpbftcCbwlSZrjHz7MY/V3HfCKZi2DfYBjH2M/kiRJkqQxrtcFg1uARUluAvYCPjFAm9cAb0yyDthIa20AgLcCb06yEti9S/8XAD8Bbmr2f3UTPw+4om/Rw3ZVtZFWMeGOqrqziV0FfBG4Icl6WoWEbgWHQVXVg8Drad1msZ7WzINzm/hpwOXNooc/btvtfcCk5jw2NM8fi68Cm4ENtG7v+C7g/QaSJEmSpA757Yz5nXzgZAZwWVX9UU8SGKeSTKmqB5JMA74HHN2sZ9DVbtNn1/RFH9o5CY4Tm5ac0OsUJEmSJAmAJKurakH/eK/XMNDOd1mSPYDfA943VLEA4ND9dmeVH3AlSZIkaVzpWcGgqjYBzi7Yyarq2F7nIEmSJEka/Xq9hoEkSZIkSRqFLBhIkiRJkqQOFgwkSZIkSVIHCwaSJEmSJKmDBQNJkiRJktTBgoEkSZIkSepgwUCSJEmSJHWwYCBJkiRJkjpYMJAkSZIkSR0m9joBjX7r79jCjLMu73UaY8qmJSf0OgVJkiRJGpQzDCRJkiRJUgcLBjtAkrOTnPEY9tsjyf98jMf8RpI9Hsu+kiRJkiT1Z8FgdNkD2K6CQVqeUFXHV9Uvd1BekiRJkqRxxoLBCEjyuiQ3JVmX5HP9XluRZEGzvXeSTc32nCTfS7K22Xc2sASY1cTOadqdmWRl0+Y9TWxGkluSfBxYAxyQZFPTf99r5yfZmOSqJP+t2e+Ipp8bkpyTZMNOe5MkSZIkSbsUCwaPU5I5wDuBhVV1GPDWYe66GPhwVc0DFgCbgbOA/1dV86rqzCTHAbOBI4F5wPwkxzT7Px34bFUdXlU/7tf3bOBjVTUH+CXwiib+f4HFVXUUsG2I8zotyaokq7b9esswT0mSJEmSNFZYMHj8FgJfqap7AKrqvmHudwPwjiR/CxxYVb8ZoM1xzc+NtGYSHEyrGADw46r6Tpe+b6+qtc32amBGs77B1Kr6dhP/4mDJVdV5VbWgqhZMeOLuwzwlSZIkSdJYYcHg8QtQg7z+ML99nyf3Bavqi8CfA78BrkyysEvfH2hmHMyrqqdV1T81r/1qkGM+1La9jdbXZ2bw05AkSZIk6bcsGDx+1wAnJpkGkGSvfq9vAuY326/sCyb5Q+BHVfUR4OvAXOB+YGrbvlcCb0gypdlnvyRPeSxJVtUvgPuT/EkTOvmx9CNJkiRJGh8m9jqBXV1VbUzyfuCbSbbRun1gU1uTZcDFSV4L/Htb/CTgL5JsBX4GvLeq7ktyfbMY4RXNOgbPAG5IAvAA8BcMsf7AIN4InJ/kV8AKwMUJJEmSJEkDStVgs+k1liSZUlUPNNtnAdOrashFGnebPrumL/rQDs9vPNm05IRepyBJkiRJACRZXVUL+sedYTC+nJDk7bT+3X8MnDqcnQ7db3dW+QFXkiRJksYVCwbjSFVdBFzU6zwkSZIkSaOfix5KkiRJkqQOFgwkSZIkSVIHCwaSJEmSJKmDBQNJkiRJktTBgoEkSZIkSepgwUCSJEmSJHWwYCBJkiRJkjpYMJAkSZIkSR0sGEiSJEmSpA4Te52ARr/1d2xhxlmX9zqNUW3TkhN6nYIkSZIkjShnGOxESd6W5Inb2y7JN5LsMVLtJUmSJEkaigWDnettwJAFg/7tqur4qvrlCLaXJEmSJGlQFgx2kCRPSnJ5knVJNiT5O2BfYHmS5U2bTyRZlWRjkvc0sb8eoN2mJHsP0OdJg7Vvtl+X5KZmn881sVc1+69Lcu3Ofm8kSZIkSaOfaxjsOC8CflpVJwAk2R14PfC8qrqnafPOqrovyQTgmiRzq+ojSf6mX7uufVbVlm7tk8wB3gkcXVX3JNmreendwAur6g5vXZAkSZIkDcQZBjvOeuD5ST6Y5DlVtWWANicmWQPcCMwBDhmBPtstBL7SV0ioqvua+PXAp5O8CZgw0I5JTmtmP6za9uuhDiNJkiRJGmssGOwgVXUbMJ/Wh/wPJHl3++tJZgJnAP+9quYClwOTH0+fAwhQA/SzGHgXcACwNsm0AdqcV1ULqmrBhCfuPsRhJEmSJEljjQWDHSTJvsCvq+rzwDLgmcD9wNSmye8DvwK2JNkH+NO23dvbDdVn1/bANbRmMUxr9t+reZxVVd+tqncD99AqHEiSJEmS9CjXMNhxDgXOSfIIsBX4K+Ao4Iokd1bV85LcCGwEfkTrNoE+57W3G6LPru2ramOS9wPfTLKN1q0PpzZ9zKY1A+EaYN1In7wkSZIkadeWqo4Z69Lv2G367Jq+6EO9TmNU27TkhF6nIEmSJEmPSZLVVbWgf9xbEiRJkiRJUgdvSdCQDt1vd1b5F3RJkiRJGlecYSBJkiRJkjpYMJAkSZIkSR0sGEiSJEmSpA6uYSBJkiRJGvO2bt3K5s2befDBB3udSs9MnjyZ/fffn0mTJg2rvQUDSZIkSdKYt3nzZqZOncqMGTNI0ut0drqq4t5772Xz5s3MnDlzWPt4S4IkSZIkacx78MEHmTZt2rgsFgAkYdq0ads1w8KCgSRJkiRpXBivxYI+23v+FgwkSZIkSdpJfvazn3HyyScza9YsDjnkEI4//nhuu+22Eet/xYoVfPvb3x6RvlzDQJIkSZI07sw46/IR7W/TkhOGbFNVvOxlL2PRokVceOGFAKxdu5a77rqLgw46aETyWLFiBVOmTOFZz3rW4+7LgoGGtP6OLSP+n2msGM5FQZIkSZIAli9fzqRJk1i8ePGjsXnz5lFVnHnmmVxxxRUk4V3vehcnnXQSK1asYNmyZVx22WUAnH766SxYsIBTTz2VGTNmsGjRIi699FK2bt3Kl7/8ZSZPnsy5557LhAkT+PznP89HP/pRnvOc5zzmfC0YSJIkSZK0E2zYsIH58+d3xL/2ta+xdu1a1q1bxz333MMRRxzBMcccM2R/e++9N2vWrOHjH/84y5Yt44ILLmDx4sVMmTKFM84443Hn25M1DJJsSrL3APE/T3LWCB3j1CT/OBJ9DXGcC5IcsqOPs72Gc/476z2SJEmSJHV33XXXccoppzBhwgT22Wcfnvvc57Jy5coh93v5y18OwPz589m0adOI5zWqFj2sqq9X1ZJe59EuyaCzMKrqL6vq5p2VjyRJkiRp1zRnzhxWr17dEa+qAdtPnDiRRx555NHn/b8ScbfddgNgwoQJPPzwwyOYacuQBYMkM5Lc2vwlfUOSLyR5fpLrk/wgyZHNz7eT3Ng8Pr3Zd0KSZUnWJ7kpyVvaun5LkjXNawc37R/9i3eSTyf5SNPfj5K8si2nM5OsbPp8zzDO4clJvtrsszLJ0U28W96nJvlykkuBq5Icm2RFkq8078UX0nwfRRNf0Gw/kOT9SdYl+U6SfZr4rOb5yiTvTfJAlzw/neQTSZY35/zcJJ9KckuST7e1O6V53zYk+WBb/PVJbkvyTeDooc5fkiRJkrTzLFy4kIceeojzzz//0djKlSvZc889ueiii9i2bRt333031157LUceeSQHHnggN998Mw899BBbtmzhmmuuGfIYU6dO5f777x+RfIc7w+BpwIeBucDBwKuBZwNnAO8AbgWOqarDgXcDf9/sdxowEzi8quYCX2jr856qeibwiaafgUxvjvNnwBKAJMcBs4EjgXnA/CRD3dzxYeAfquoI4BXABU28W94ARwGLqmph8/xw4G3AIcAf0vaBvM2TgO9U1WHAtcCb2o7/4eb4Px0i1z2BhcD/Ai4F/gGYAxyaZF6SfYEPNm3mAUckeWmS6cB7mrxe0OQ51PlLkiRJknaSJFxyySVcffXVzJo1izlz5nD22Wfz6le/mrlz53LYYYexcOFCli5dylOf+lQOOOAATjzxRObOnctrXvMaDj/88CGP8eIXv5hLLrmEefPm8a1vfetx5TvcRQ9vr6r1AEk2AtdUVSVZD8wAdgc+k2Q2UMCkZr/nA+dW1cMAVXVfW59fax5XAy/vctx/rqpHgJv7/loPHNf83Ng8n0KrgHDtIPk/HzikmRQA8PtJpg6SN8DV/fL9XlVtbt6Dtc15X9fvOP8FXNZ2Xi9oto8CXtpsfxFYNkiul7a9t3f1e99nAAcCK6rq7ib+BaCvYNIevwjo+16ObuffVZLTaBV8mPD7Tx6sqSRJkiTtcnr1jWf77rsvF198cUf8nHPO4ZxzzumIL126lKVLl3bE29csWLBgAStWrADgoIMO4qabbhqRXIdbMHiobfuRtuePNH28D1heVS9LMgNY0bweWh/EB+tz2yB5tB83bY8fqKpPtjdM8mZ++xf94/v18wTgqKr6Tb99Ptolb4BfDZJLt5y31m9vPhnsvPqO/37gBICqmtfvOO3vc9/zicBgN6Z0e6+7nX/3jqrOA84D2G367G79SpIkSZLGqJFa9HB34I5m+9S2+FXA4r6FA5PsNQLHuhJ4Q5IpTZ/7JXlKVX2squY1P/2n/V8FnN73JEnfh/NueY+079C6FQDg5L5gVb2zL+ft6Ou7wHOT7J1kAnAK8M0mfmySaUkmAa9q26fb+UuSJEmSNKCRKhgsBT6Q5HpgQlv8AuAnwE1J1tFa++BxqaqraE3rv6GZtv8VYNDp9cBfAwuaRRJvBhYPkfdIexvwN0m+R2tdhi2PtaOquhN4O7AcWAesqap/aeJnAzcA/wasadut2/lLkiRJkjSgdPv6Bo2cJE8EftOsTXAycEpVvaTXeQ3XbtNn1/RFH+p1GqNSr+57kiRJkrR9brnlFg4++OBBb80e66qKW2+9lWc84xm/E0+yuqoW9G8/3DUM9PjMB/6x+SrGXwJv6HE+2+XQ/XZnlR+MJUmSJO3CJk+ezL333su0adPGZdGgqrj33nuZPHnysPexYLATVNW3gMN6nYckSZIkjVf7778/mzdv5u677+51Kj0zefJk9t9//2G3t2AgSZIkSRrzJk2axMyZM3udxi5lpBY9lCRJkiRJY4gFA0mSJEmS1MGCgSRJkiRJ6uDXKmpISe4Hvt/rPKQh7A3c0+skpCE4TrUrcJxqV+A41a5gVxqnB1bVk/sHXfRQw/H9gb6TUxpNkqxynGq0c5xqV+A4Mx2I5AAABD1JREFU1a7AcapdwVgYp96SIEmSJEmSOlgwkCRJkiRJHSwYaDjO63UC0jA4TrUrcJxqV+A41a7AcapdwS4/Tl30UJIkSZIkdXCGgSRJkiRJ6mDBQF0leVGS7yf5YZKzep2Pxrckm5KsT7I2yaomtleSq5P8oHncs4knyUeasXtTkmf2NnuNZUk+leTnSTa0xbZ7bCZZ1LT/QZJFvTgXjV1dxunZSe5orqtrkxzf9trbm3H6/SQvbIv7u4F2iCQHJFme5JYkG5O8tYl7PdWoMcg4HbPXU29J0ICSTABuA14AbAZWAqdU1c09TUzjVpJNwIKquqctthS4r6qWNBfaPavqb5uL9FuA44E/Bj5cVX/ci7w19iU5BngA+GxV/VET266xmWQvYBWwAChgNTC/qn7Rg1PSGNRlnJ4NPFBVy/q1PQT4EnAksC/wb8BBzcv+bqAdIsl0YHpVrUkyldZ18KXAqXg91SgxyDg9kTF6PXWGgbo5EvhhVf2oqv4LuBB4SY9zkvp7CfCZZvsztC7YffHPVst3gD2aC7w04qrqWuC+fuHtHZsvBK6uqvuaX2qvBl6047PXeNFlnHbzEuDCqnqoqm4Hfkjr9wJ/N9AOU1V3VtWaZvt+4BZgP7yeahQZZJx2s8tfTy0YqJv9gP9oe76Zwf8zSDtaAVclWZ3ktCa2T1XdCa0LOPCUJu74Va9t79h0zKpXTm+mc3+qb6o3jlP1WJIZwOHAd/F6qlGq3ziFMXo9tWCgbjJAzPtX1EtHV9UzgT8F3txMr+3G8avRqtvYdMyqFz4BzALmAXcC/7uJO07VM0mmAF8F3lZV/zlY0wFijlPtFAOM0zF7PbVgoG42Awe0Pd8f+GmPcpGoqp82jz8HLqE1leuuvlsNmsefN80dv+q17R2bjlntdFV1V1Vtq6pHgPNpXVfBcaoeSTKJ1oewL1TV15qw11ONKgON07F8PbVgoG5WArOTzEzye8DJwNd7nJPGqSRPahaWIcmTgOOADbTGZN/qx4uAf2m2vw68rllB+U+ALX3TGaWdZHvH5pXAcUn2bKYxHtfEpB2m39ouL6N1XYXWOD05yW5JZgKzge/h7wbagZIE+Cfglqr6P20veT3VqNFtnI7l6+nEXieg0amqHk5yOq0L7ATgU1W1scdpafzaB7ikdY1mIvDFqvrXJCuBi5O8EfgJ8Kqm/TdorZr8Q+DXwOt3fsoaL5J8CTgW2DvJZuDvgCVsx9isqvuSvI/WLxAA762q4S5QJw2pyzg9Nsk8WtNgNwH/A6CqNia5GLgZeBh4c1Vta/rxdwPtKEcDrwXWJ1nbxN6B11ONLt3G6Slj9Xrq1ypKkiRJkqQO3pIgSZIkSZI6WDCQJEmSJEkdLBhIkiRJkqQOFgwkSZIkSVIHCwaSJEmSJKmDBQNJkiRJktTBgoEkSZIkSepgwUCSJEmSJHX4/9tAuK9MHBszAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_used.plot(kind=\"barh\", figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Some tags are very, very broad and are unlikely to be useful; e.g.: python, dataset, r. Before we investigate the tags a little deeper, let's repeat the same process for views.\n",
    "\n",
    "We'll use pandas's pandas.DataFrame.iterrows()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac21507c88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001AC215EF7C8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 14,
   "metadata": {},
   "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": [
    "# 8. Relations Between Tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "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. Let's do this.\n",
    "\n",
    "We'll begin by creating a list of all tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tags = list(tag_count.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>python</th>\n",
       "      <th>pandas</th>\n",
       "      <th>predictive-modeling</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predictive-modeling</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</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": [
       "                    python pandas predictive-modeling data\n",
       "python                 NaN    NaN                 NaN  NaN\n",
       "pandas                 NaN    NaN                 NaN  NaN\n",
       "predictive-modeling    NaN    NaN                 NaN  NaN\n",
       "data                   NaN    NaN                 NaN  NaN"
      ]
     },
     "execution_count": 16,
     "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": [
    "\n",
    "We will now fill this dataframe with zeroes and then, for each lists of tags in questions[\"Tags\"], we will increment the intervening tags by one. The end result will be a dataframe that for each pair of tags, it tells us how many times they were used together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "associations.fillna(0, inplace=True)\n",
    "\n",
    "for tags in questions[\"Tags\"]:\n",
    "    associations.loc[tags, tags] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col10 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col11 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col12 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col13 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col14 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col15 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col16 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col17 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col18 {\n",
       "            color:  blue;\n",
       "        }    #T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col19 {\n",
       "            color:  blue;\n",
       "        }</style><table id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >machine-learning-model</th>        <th class=\"col_heading level0 col1\" >statistics</th>        <th class=\"col_heading level0 col2\" >clustering</th>        <th class=\"col_heading level0 col3\" >predictive-modeling</th>        <th class=\"col_heading level0 col4\" >r</th>        <th class=\"col_heading level0 col5\" >dataset</th>        <th class=\"col_heading level0 col6\" >regression</th>        <th class=\"col_heading level0 col7\" >pandas</th>        <th class=\"col_heading level0 col8\" >lstm</th>        <th class=\"col_heading level0 col9\" >time-series</th>        <th class=\"col_heading level0 col10\" >cnn</th>        <th class=\"col_heading level0 col11\" >nlp</th>        <th class=\"col_heading level0 col12\" >scikit-learn</th>        <th class=\"col_heading level0 col13\" >tensorflow</th>        <th class=\"col_heading level0 col14\" >classification</th>        <th class=\"col_heading level0 col15\" >keras</th>        <th class=\"col_heading level0 col16\" >neural-network</th>        <th class=\"col_heading level0 col17\" >deep-learning</th>        <th class=\"col_heading level0 col18\" >python</th>        <th class=\"col_heading level0 col19\" >machine-learning</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row0\" class=\"row_heading level0 row0\" >machine-learning-model</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col0\" class=\"data row0 col0\" >209</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col1\" class=\"data row0 col1\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col2\" class=\"data row0 col2\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col3\" class=\"data row0 col3\" >20</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col4\" class=\"data row0 col4\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col5\" class=\"data row0 col5\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col6\" class=\"data row0 col6\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col7\" class=\"data row0 col7\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col8\" class=\"data row0 col8\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col9\" class=\"data row0 col9\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col10\" class=\"data row0 col10\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col11\" class=\"data row0 col11\" >4</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col12\" class=\"data row0 col12\" >17</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col13\" class=\"data row0 col13\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col14\" class=\"data row0 col14\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col15\" class=\"data row0 col15\" >14</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col16\" class=\"data row0 col16\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col17\" class=\"data row0 col17\" >17</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col18\" class=\"data row0 col18\" >35</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row0_col19\" class=\"data row0 col19\" >131</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row1\" class=\"row_heading level0 row1\" >statistics</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col0\" class=\"data row1 col0\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col1\" class=\"data row1 col1\" >214</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col2\" class=\"data row1 col2\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col3\" class=\"data row1 col3\" >14</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col4\" class=\"data row1 col4\" >15</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col5\" class=\"data row1 col5\" >15</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col6\" class=\"data row1 col6\" >14</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col7\" class=\"data row1 col7\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col8\" class=\"data row1 col8\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col9\" class=\"data row1 col9\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col10\" class=\"data row1 col10\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col11\" class=\"data row1 col11\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col12\" class=\"data row1 col12\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col13\" class=\"data row1 col13\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col14\" class=\"data row1 col14\" >16</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col15\" class=\"data row1 col15\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col16\" class=\"data row1 col16\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col17\" class=\"data row1 col17\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col18\" class=\"data row1 col18\" >33</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row1_col19\" class=\"data row1 col19\" >86</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row2\" class=\"row_heading level0 row2\" >clustering</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col2\" class=\"data row2 col2\" >250</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col4\" class=\"data row2 col4\" >16</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col5\" class=\"data row2 col5\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col6\" class=\"data row2 col6\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col7\" class=\"data row2 col7\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col8\" class=\"data row2 col8\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col9\" class=\"data row2 col9\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col10\" class=\"data row2 col10\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col11\" class=\"data row2 col11\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col12\" class=\"data row2 col12\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col13\" class=\"data row2 col13\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col14\" class=\"data row2 col14\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col15\" class=\"data row2 col15\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col16\" class=\"data row2 col16\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col17\" class=\"data row2 col17\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col18\" class=\"data row2 col18\" >45</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row2_col19\" class=\"data row2 col19\" >59</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row3\" class=\"row_heading level0 row3\" >predictive-modeling</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col0\" class=\"data row3 col0\" >20</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col1\" class=\"data row3 col1\" >14</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col2\" class=\"data row3 col2\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col3\" class=\"data row3 col3\" >250</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col4\" class=\"data row3 col4\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col5\" class=\"data row3 col5\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col6\" class=\"data row3 col6\" >25</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col7\" class=\"data row3 col7\" >4</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col8\" class=\"data row3 col8\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col9\" class=\"data row3 col9\" >27</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col10\" class=\"data row3 col10\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col11\" class=\"data row3 col11\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col12\" class=\"data row3 col12\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col13\" class=\"data row3 col13\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col14\" class=\"data row3 col14\" >26</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col15\" class=\"data row3 col15\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col16\" class=\"data row3 col16\" >13</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col17\" class=\"data row3 col17\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col18\" class=\"data row3 col18\" >35</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row3_col19\" class=\"data row3 col19\" >119</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row4\" class=\"row_heading level0 row4\" >r</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col0\" class=\"data row4 col0\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col1\" class=\"data row4 col1\" >15</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col2\" class=\"data row4 col2\" >16</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col3\" class=\"data row4 col3\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col4\" class=\"data row4 col4\" >252</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col5\" class=\"data row4 col5\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col6\" class=\"data row4 col6\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col7\" class=\"data row4 col7\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col8\" class=\"data row4 col8\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col9\" class=\"data row4 col9\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col10\" class=\"data row4 col10\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col11\" class=\"data row4 col11\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col12\" class=\"data row4 col12\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col13\" class=\"data row4 col13\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col14\" class=\"data row4 col14\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col15\" class=\"data row4 col15\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col16\" class=\"data row4 col16\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col17\" class=\"data row4 col17\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col18\" class=\"data row4 col18\" >23</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row4_col19\" class=\"data row4 col19\" >58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row5\" class=\"row_heading level0 row5\" >dataset</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col0\" class=\"data row5 col0\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col1\" class=\"data row5 col1\" >15</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col2\" class=\"data row5 col2\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col3\" class=\"data row5 col3\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col4\" class=\"data row5 col4\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col5\" class=\"data row5 col5\" >308</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col6\" class=\"data row5 col6\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col7\" class=\"data row5 col7\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col8\" class=\"data row5 col8\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col9\" class=\"data row5 col9\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col10\" class=\"data row5 col10\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col11\" class=\"data row5 col11\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col12\" class=\"data row5 col12\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col13\" class=\"data row5 col13\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col14\" class=\"data row5 col14\" >24</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col15\" class=\"data row5 col15\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col16\" class=\"data row5 col16\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col17\" class=\"data row5 col17\" >27</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col18\" class=\"data row5 col18\" >50</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row5_col19\" class=\"data row5 col19\" >93</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row6\" class=\"row_heading level0 row6\" >regression</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col0\" class=\"data row6 col0\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col1\" class=\"data row6 col1\" >14</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col2\" class=\"data row6 col2\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col3\" class=\"data row6 col3\" >25</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col4\" class=\"data row6 col4\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col5\" class=\"data row6 col5\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col6\" class=\"data row6 col6\" >321</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col7\" class=\"data row6 col7\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col8\" class=\"data row6 col8\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col9\" class=\"data row6 col9\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col10\" class=\"data row6 col10\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col11\" class=\"data row6 col11\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col12\" class=\"data row6 col12\" >37</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col13\" class=\"data row6 col13\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col14\" class=\"data row6 col14\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col15\" class=\"data row6 col15\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col16\" class=\"data row6 col16\" >42</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col17\" class=\"data row6 col17\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col18\" class=\"data row6 col18\" >57</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row6_col19\" class=\"data row6 col19\" >114</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row7\" class=\"row_heading level0 row7\" >pandas</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col0\" class=\"data row7 col0\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col1\" class=\"data row7 col1\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col2\" class=\"data row7 col2\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col3\" class=\"data row7 col3\" >4</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col4\" class=\"data row7 col4\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col5\" class=\"data row7 col5\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col6\" class=\"data row7 col6\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col7\" class=\"data row7 col7\" >341</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col8\" class=\"data row7 col8\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col9\" class=\"data row7 col9\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col10\" class=\"data row7 col10\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col11\" class=\"data row7 col11\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col12\" class=\"data row7 col12\" >36</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col13\" class=\"data row7 col13\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col14\" class=\"data row7 col14\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col15\" class=\"data row7 col15\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col16\" class=\"data row7 col16\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col17\" class=\"data row7 col17\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col18\" class=\"data row7 col18\" >235</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row7_col19\" class=\"data row7 col19\" >60</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row8\" class=\"row_heading level0 row8\" >lstm</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col0\" class=\"data row8 col0\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col1\" class=\"data row8 col1\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col2\" class=\"data row8 col2\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col3\" class=\"data row8 col3\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col4\" class=\"data row8 col4\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col5\" class=\"data row8 col5\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col6\" class=\"data row8 col6\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col7\" class=\"data row8 col7\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col8\" class=\"data row8 col8\" >355</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col9\" class=\"data row8 col9\" >76</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col10\" class=\"data row8 col10\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col11\" class=\"data row8 col11\" >17</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col12\" class=\"data row8 col12\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col13\" class=\"data row8 col13\" >32</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col14\" class=\"data row8 col14\" >15</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col15\" class=\"data row8 col15\" >116</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col16\" class=\"data row8 col16\" >60</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col17\" class=\"data row8 col17\" >94</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col18\" class=\"data row8 col18\" >54</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row8_col19\" class=\"data row8 col19\" >57</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row9\" class=\"row_heading level0 row9\" >time-series</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col0\" class=\"data row9 col0\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col1\" class=\"data row9 col1\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col2\" class=\"data row9 col2\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col3\" class=\"data row9 col3\" >27</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col4\" class=\"data row9 col4\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col5\" class=\"data row9 col5\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col6\" class=\"data row9 col6\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col7\" class=\"data row9 col7\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col8\" class=\"data row9 col8\" >76</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col9\" class=\"data row9 col9\" >419</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col10\" class=\"data row9 col10\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col11\" class=\"data row9 col11\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col12\" class=\"data row9 col12\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col13\" class=\"data row9 col13\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col14\" class=\"data row9 col14\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col15\" class=\"data row9 col15\" >44</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col16\" class=\"data row9 col16\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col17\" class=\"data row9 col17\" >39</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col18\" class=\"data row9 col18\" >93</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row9_col19\" class=\"data row9 col19\" >120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row10\" class=\"row_heading level0 row10\" >cnn</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col0\" class=\"data row10 col0\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col1\" class=\"data row10 col1\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col2\" class=\"data row10 col2\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col3\" class=\"data row10 col3\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col4\" class=\"data row10 col4\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col5\" class=\"data row10 col5\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col6\" class=\"data row10 col6\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col7\" class=\"data row10 col7\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col8\" class=\"data row10 col8\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col9\" class=\"data row10 col9\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col10\" class=\"data row10 col10\" >449</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col11\" class=\"data row10 col11\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col12\" class=\"data row10 col12\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col13\" class=\"data row10 col13\" >49</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col14\" class=\"data row10 col14\" >18</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col15\" class=\"data row10 col15\" >107</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col16\" class=\"data row10 col16\" >111</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col17\" class=\"data row10 col17\" >149</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col18\" class=\"data row10 col18\" >54</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row10_col19\" class=\"data row10 col19\" >114</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row11\" class=\"row_heading level0 row11\" >nlp</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col0\" class=\"data row11 col0\" >4</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col1\" class=\"data row11 col1\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col2\" class=\"data row11 col2\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col3\" class=\"data row11 col3\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col4\" class=\"data row11 col4\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col5\" class=\"data row11 col5\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col6\" class=\"data row11 col6\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col7\" class=\"data row11 col7\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col8\" class=\"data row11 col8\" >17</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col9\" class=\"data row11 col9\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col10\" class=\"data row11 col10\" >7</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col11\" class=\"data row11 col11\" >457</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col12\" class=\"data row11 col12\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col13\" class=\"data row11 col13\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col14\" class=\"data row11 col14\" >34</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col15\" class=\"data row11 col15\" >20</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col16\" class=\"data row11 col16\" >24</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col17\" class=\"data row11 col17\" >65</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col18\" class=\"data row11 col18\" >68</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row11_col19\" class=\"data row11 col19\" >106</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row12\" class=\"row_heading level0 row12\" >scikit-learn</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col0\" class=\"data row12 col0\" >17</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col1\" class=\"data row12 col1\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col2\" class=\"data row12 col2\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col3\" class=\"data row12 col3\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col4\" class=\"data row12 col4\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col5\" class=\"data row12 col5\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col6\" class=\"data row12 col6\" >37</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col7\" class=\"data row12 col7\" >36</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col8\" class=\"data row12 col8\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col9\" class=\"data row12 col9\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col10\" class=\"data row12 col10\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col11\" class=\"data row12 col11\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col12\" class=\"data row12 col12\" >495</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col13\" class=\"data row12 col13\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col14\" class=\"data row12 col14\" >42</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col15\" class=\"data row12 col15\" >27</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col16\" class=\"data row12 col16\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col17\" class=\"data row12 col17\" >13</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col18\" class=\"data row12 col18\" >216</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row12_col19\" class=\"data row12 col19\" >175</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row13\" class=\"row_heading level0 row13\" >tensorflow</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col0\" class=\"data row13 col0\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col1\" class=\"data row13 col1\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col2\" class=\"data row13 col2\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col3\" class=\"data row13 col3\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col4\" class=\"data row13 col4\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col5\" class=\"data row13 col5\" >8</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col6\" class=\"data row13 col6\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col7\" class=\"data row13 col7\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col8\" class=\"data row13 col8\" >32</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col9\" class=\"data row13 col9\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col10\" class=\"data row13 col10\" >49</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col11\" class=\"data row13 col11\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col12\" class=\"data row13 col12\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col13\" class=\"data row13 col13\" >531</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col14\" class=\"data row13 col14\" >18</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col15\" class=\"data row13 col15\" >240</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col16\" class=\"data row13 col16\" >102</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col17\" class=\"data row13 col17\" >123</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col18\" class=\"data row13 col18\" >146</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row13_col19\" class=\"data row13 col19\" >96</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row14\" class=\"row_heading level0 row14\" >classification</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col0\" class=\"data row14 col0\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col1\" class=\"data row14 col1\" >16</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col2\" class=\"data row14 col2\" >12</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col3\" class=\"data row14 col3\" >26</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col4\" class=\"data row14 col4\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col5\" class=\"data row14 col5\" >24</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col6\" class=\"data row14 col6\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col7\" class=\"data row14 col7\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col8\" class=\"data row14 col8\" >15</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col9\" class=\"data row14 col9\" >22</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col10\" class=\"data row14 col10\" >18</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col11\" class=\"data row14 col11\" >34</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col12\" class=\"data row14 col12\" >42</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col13\" class=\"data row14 col13\" >18</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col14\" class=\"data row14 col14\" >639</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col15\" class=\"data row14 col15\" >54</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col16\" class=\"data row14 col16\" >62</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col17\" class=\"data row14 col17\" >55</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col18\" class=\"data row14 col18\" >92</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row14_col19\" class=\"data row14 col19\" >242</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row15\" class=\"row_heading level0 row15\" >keras</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col0\" class=\"data row15 col0\" >14</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col1\" class=\"data row15 col1\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col2\" class=\"data row15 col2\" >0</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col3\" class=\"data row15 col3\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col4\" class=\"data row15 col4\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col5\" class=\"data row15 col5\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col6\" class=\"data row15 col6\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col7\" class=\"data row15 col7\" >3</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col8\" class=\"data row15 col8\" >116</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col9\" class=\"data row15 col9\" >44</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col10\" class=\"data row15 col10\" >107</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col11\" class=\"data row15 col11\" >20</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col12\" class=\"data row15 col12\" >27</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col13\" class=\"data row15 col13\" >240</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col14\" class=\"data row15 col14\" >54</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col15\" class=\"data row15 col15\" >855</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col16\" class=\"data row15 col16\" >222</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col17\" class=\"data row15 col17\" >231</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col18\" class=\"data row15 col18\" >256</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row15_col19\" class=\"data row15 col19\" >178</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row16\" class=\"row_heading level0 row16\" >neural-network</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col0\" class=\"data row16 col0\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col1\" class=\"data row16 col1\" >10</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col2\" class=\"data row16 col2\" >6</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col3\" class=\"data row16 col3\" >13</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col4\" class=\"data row16 col4\" >9</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col5\" class=\"data row16 col5\" >19</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col6\" class=\"data row16 col6\" >42</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col7\" class=\"data row16 col7\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col8\" class=\"data row16 col8\" >60</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col9\" class=\"data row16 col9\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col10\" class=\"data row16 col10\" >111</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col11\" class=\"data row16 col11\" >24</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col12\" class=\"data row16 col12\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col13\" class=\"data row16 col13\" >102</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col14\" class=\"data row16 col14\" >62</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col15\" class=\"data row16 col15\" >222</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col16\" class=\"data row16 col16\" >982</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col17\" class=\"data row16 col17\" >288</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col18\" class=\"data row16 col18\" >131</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row16_col19\" class=\"data row16 col19\" >343</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row17\" class=\"row_heading level0 row17\" >deep-learning</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col0\" class=\"data row17 col0\" >17</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col1\" class=\"data row17 col1\" >11</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col2\" class=\"data row17 col2\" >2</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col3\" class=\"data row17 col3\" >31</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col4\" class=\"data row17 col4\" >5</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col5\" class=\"data row17 col5\" >27</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col6\" class=\"data row17 col6\" >21</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col7\" class=\"data row17 col7\" >1</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col8\" class=\"data row17 col8\" >94</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col9\" class=\"data row17 col9\" >39</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col10\" class=\"data row17 col10\" >149</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col11\" class=\"data row17 col11\" >65</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col12\" class=\"data row17 col12\" >13</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col13\" class=\"data row17 col13\" >123</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col14\" class=\"data row17 col14\" >55</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col15\" class=\"data row17 col15\" >231</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col16\" class=\"data row17 col16\" >288</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col17\" class=\"data row17 col17\" >1114</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col18\" class=\"data row17 col18\" >144</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row17_col19\" class=\"data row17 col19\" >393</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row18\" class=\"row_heading level0 row18\" >python</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col0\" class=\"data row18 col0\" >35</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col1\" class=\"data row18 col1\" >33</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col2\" class=\"data row18 col2\" >45</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col3\" class=\"data row18 col3\" >35</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col4\" class=\"data row18 col4\" >23</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col5\" class=\"data row18 col5\" >50</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col6\" class=\"data row18 col6\" >57</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col7\" class=\"data row18 col7\" >235</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col8\" class=\"data row18 col8\" >54</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col9\" class=\"data row18 col9\" >93</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col10\" class=\"data row18 col10\" >54</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col11\" class=\"data row18 col11\" >68</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col12\" class=\"data row18 col12\" >216</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col13\" class=\"data row18 col13\" >146</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col14\" class=\"data row18 col14\" >92</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col15\" class=\"data row18 col15\" >256</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col16\" class=\"data row18 col16\" >131</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col17\" class=\"data row18 col17\" >144</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col18\" class=\"data row18 col18\" >1679</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row18_col19\" class=\"data row18 col19\" >468</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323level0_row19\" class=\"row_heading level0 row19\" >machine-learning</th>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col0\" class=\"data row19 col0\" >131</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col1\" class=\"data row19 col1\" >86</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col2\" class=\"data row19 col2\" >59</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col3\" class=\"data row19 col3\" >119</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col4\" class=\"data row19 col4\" >58</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col5\" class=\"data row19 col5\" >93</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col6\" class=\"data row19 col6\" >114</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col7\" class=\"data row19 col7\" >60</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col8\" class=\"data row19 col8\" >57</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col9\" class=\"data row19 col9\" >120</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col10\" class=\"data row19 col10\" >114</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col11\" class=\"data row19 col11\" >106</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col12\" class=\"data row19 col12\" >175</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col13\" class=\"data row19 col13\" >96</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col14\" class=\"data row19 col14\" >242</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col15\" class=\"data row19 col15\" >178</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col16\" class=\"data row19 col16\" >343</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col17\" class=\"data row19 col17\" >393</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col18\" class=\"data row19 col18\" >468</td>\n",
       "                        <td id=\"T_3e7af122_b1d0_11ea_a1b3_98fa9b647323row19_col19\" class=\"data row19 col19\" >2499</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ac2198d508>"
      ]
     },
     "execution_count": 18,
     "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": [
    "\n",
    "The cells highlighted in yellow tell us that time-series was used together with r 22 times. The values in blue tell us how many times each of the tags was used. We saw earlier that machine-learning was used 2693 times and we confirm it in this dataframe.\n",
    "\n",
    "It's hard for a human to understand what is going on in this dataframe. Let's create a heatmap. But before we do it, let's get rid of the values in blue, otherwise the colors will be too skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "for i in range(relations_most_used.shape[0]):\n",
    "    relations_most_used.iloc[i,i] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac21b24888>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": [
    "\n",
    "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 so end up being used together a lot without possibly even 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 doesn't.\n",
    "\n",
    "So how do we attack both these problems? There is a powerful data mining technique that allows us to handle this: association rules. Association rules allow us to analytically spot relations like \"people who purchase milk, also purchase eggs\". Moreover, we can also measure how strong this relations are on several fronts: how common the relation is, how strong it is, and how independent the components of the relationship are (toilet paper and bread are probably more independent than eggs and milk — you'll learn more about [statistical independence](https://en.wikipedia.org/wiki/Independence_(probability_theory)) in the next step).\n",
    "\n",
    "We won't get into the details of it, as the technique is out of scope for this course, but it is a path worth investigating!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.Enter Domain Knowledge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras, scikit-learn, 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 we want to be very specific, we can suggest the creation of Python content that uses deep learning for classification problems (and other variations of this suggestion).\n",
    "\n",
    "At the glance of an eye, someone with sufficient domain knowledge can tell that the most popular topic at the moment, as shown by our analysis, is deep learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10.Just a Fad?\n",
    "Let's read in the file into a dataframe called all_q. We'll parse the dates at read-time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`SELECT Id, CreationDate, Tags\n",
    "  FROM posts\n",
    " WHERE PostTypeId = 1;`\n",
    " \n",
    " Run this query to get all_questions.csv on SEDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_q = pd.read_csv(\"all_questions.csv\", parse_dates=[\"CreationDate\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can use the same technique as before to clean the tags column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_q[\"Tags\"] = all_q[\"Tags\"].str.replace(\"^<|>$\", \"\").str.split(\"><\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Before deciding which questions should be classified as being deep learning questions, we should decide what tags are deep learning tags.\n",
    "\n",
    "The definition of what constitutes a deep learning tag we'll use 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. You can use Google and read the tags descriptions to reach similar results.\n",
    "\n",
    "We'll now create a function that assigns 1 to deep learning questions and 0 otherwise; and we use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_q[\"DeepLearning\"] = all_q[\"Tags\"].apply(class_deep_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19710</th>\n",
       "      <td>74868</td>\n",
       "      <td>2020-05-26 10:30:50</td>\n",
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       "    <tr>\n",
       "      <th>22721</th>\n",
       "      <td>53809</td>\n",
       "      <td>2019-06-14 16:45:21</td>\n",
       "      <td>[sequence-to-sequence]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21018</th>\n",
       "      <td>31201</td>\n",
       "      <td>2018-05-04 07:02:07</td>\n",
       "      <td>[python, deep-learning, nlp, keras, text-mining]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4564</th>\n",
       "      <td>745</td>\n",
       "      <td>2014-07-16 00:06:02</td>\n",
       "      <td>[machine-learning, python]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20850</th>\n",
       "      <td>44202</td>\n",
       "      <td>2019-01-18 14:09:33</td>\n",
       "      <td>[text-mining]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id        CreationDate  \\\n",
       "19710  74868 2020-05-26 10:30:50   \n",
       "22721  53809 2019-06-14 16:45:21   \n",
       "21018  31201 2018-05-04 07:02:07   \n",
       "4564     745 2014-07-16 00:06:02   \n",
       "20850  44202 2019-01-18 14:09:33   \n",
       "\n",
       "                                                   Tags  DeepLearning  \n",
       "19710           [dataset, feature-scaling, definitions]             0  \n",
       "22721                            [sequence-to-sequence]             0  \n",
       "21018  [python, deep-learning, nlp, keras, text-mining]             1  \n",
       "4564                         [machine-learning, python]             0  \n",
       "20850                                     [text-mining]             0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_q.sample(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "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 we don't have all the data for the first quarter of 2020, we'll get rid of those dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_q = all_q[all_q[\"CreationDate\"].dt.year < 2020]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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": 28,
   "metadata": {},
   "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>17555</td>\n",
       "      <td>2017-03-13 14:03:10</td>\n",
       "      <td>[python]</td>\n",
       "      <td>0</td>\n",
       "      <td>17Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17557</td>\n",
       "      <td>2017-03-13 14:07:24</td>\n",
       "      <td>[deep-learning, tensorflow, keras, word-embedd...</td>\n",
       "      <td>1</td>\n",
       "      <td>17Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17559</td>\n",
       "      <td>2017-03-13 17:27:14</td>\n",
       "      <td>[tensorflow, gpu]</td>\n",
       "      <td>1</td>\n",
       "      <td>17Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17561</td>\n",
       "      <td>2017-03-13 18:38:15</td>\n",
       "      <td>[python, classification, time-series]</td>\n",
       "      <td>0</td>\n",
       "      <td>17Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17565</td>\n",
       "      <td>2017-03-13 20:46:57</td>\n",
       "      <td>[python, logistic-regression]</td>\n",
       "      <td>0</td>\n",
       "      <td>17Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id        CreationDate  \\\n",
       "0  17555 2017-03-13 14:03:10   \n",
       "1  17557 2017-03-13 14:07:24   \n",
       "2  17559 2017-03-13 17:27:14   \n",
       "3  17561 2017-03-13 18:38:15   \n",
       "4  17565 2017-03-13 20:46:57   \n",
       "\n",
       "                                                Tags  DeepLearning Quarter  \n",
       "0                                           [python]             0    17Q1  \n",
       "1  [deep-learning, tensorflow, keras, word-embedd...             1    17Q1  \n",
       "2                                  [tensorflow, gpu]             1    17Q1  \n",
       "3              [python, classification, time-series]             0    17Q1  \n",
       "4                      [python, logistic-regression]             0    17Q1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_q.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For the final stretch of this screen, we'll group by quarter and:\n",
    "\n",
    "Count the number of deep learning questions.\n",
    "\n",
    "Count the total number of questions.\n",
    "\n",
    "Compute the ratio between the two numbers above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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>20</th>\n",
       "      <td>19Q2</td>\n",
       "      <td>745</td>\n",
       "      <td>1956</td>\n",
       "      <td>0.380879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15Q2</td>\n",
       "      <td>28</td>\n",
       "      <td>284</td>\n",
       "      <td>0.098592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17Q2</td>\n",
       "      <td>225</td>\n",
       "      <td>645</td>\n",
       "      <td>0.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15Q3</td>\n",
       "      <td>45</td>\n",
       "      <td>311</td>\n",
       "      <td>0.144695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18Q1</td>\n",
       "      <td>554</td>\n",
       "      <td>1219</td>\n",
       "      <td>0.454471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Quarter  DeepLearningQuestions  TotalQuestions  DeepLearningRate\n",
       "20    19Q2                    745            1956          0.380879\n",
       "4     15Q2                     28             284          0.098592\n",
       "12    17Q2                    225             645          0.348837\n",
       "5     15Q3                     45             311          0.144695\n",
       "15    18Q1                    554            1219          0.454471"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 was a high-growth trend since the start of DSSE and it looks like it is plateauing. There is no evidence to suggest that interest in deep learning is decreasing and so we maintain our previous idea of proposing that we 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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}