news_data_mining.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "1. Data Source\n",
    "2. Data Preparation\n",
    "3. So What's Next\n",
    " - 3.1 Converting Dictionary into Pandas dataframe\n",
    " - 3.2 Familiarizing yourself with the Data\n",
    "4. Data Mining using Pandas\n",
    " - 4.1 Dealing with Missing Values\n",
    " - 4.2 Dealing with Duplicate Data\n",
    "5. Data Preprocessing\n",
    " - 5.1 Sampling\n",
    " - 5.2 Feature Creation\n",
    " - 5.3 Feature Subset Selection\n",
    " - 5.4 Dimensionality Reduction\n",
    " - 5.5 Atrribute Transformation / Aggregation\n",
    " - 5.6 Discretization and Binarization\n",
    "6. Conclusion\n",
    "7. References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "In this notebook I will explore a text-based, document-based [dataset](http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html) using scientific computing tools such as Pandas and Numpy. In addition, several fundamental Data Mining concepts will be explored and explained in details, ranging from calculating distance measures to computing term frequency vectors to visualizing embeddings. Coding examples, visualizations and demonstrations will be provided where necessary. Furthermore, additional exercises are provided after special topics. These exercises are geared towards testing the understanding of students and also for students to explore beyond, through programming practises, the techniques covered in the notebook. "
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements\n",
    "Here are the computing and software requirements\n",
    "\n",
    "#### Computing Resources\n",
    "- Operating system: Preferably Linux or MacOS\n",
    "- RAM: 8GB\n",
    "- Disk space: Mininium 8GB\n",
    "\n",
    "#### Software Requirements\n",
    "Here is a list of the required programs and libraries necessary for this lab session:\n",
    "- [Python 3+](https://www.python.org/download/releases/3.0/) (Note: coding will be done strictly on Python 3)\n",
    "    - Install latest version of Python 3\n",
    "- [Anaconda](https://www.anaconda.com/download/) environemnt or any other environement (recommended but not required)\n",
    "    - Install anaconda environment\n",
    "- [Jupyter](http://jupyter.org/) (Strongly recommended but not required)\n",
    "    - Install jupyter\n",
    "- [Scikit Learn](http://scikit-learn.org/stable/index.html)\n",
    "    - Install `sklearn` latest python library\n",
    "- [Pandas](http://pandas.pydata.org/)\n",
    "    - Install `pandas` python library\n",
    "- [Numpy](http://www.numpy.org/)\n",
    "    - Install `numpy` python library\n",
    "- [Matplotlib](https://matplotlib.org/)\n",
    "    - Install `maplotlib` for python\n",
    "- [Plotly](https://plot.ly/)\n",
    "    - Install and signup for `plotly`\n",
    "- [NLTK](http://www.nltk.org/)\n",
    "    - Install `nltk` library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary for when working with external scripts\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data source\n",
    "In this notebook we will explore the popular 20 newsgroup dataset, originally provided [here](http://qwone.com/~jason/20Newsgroups/). The dataset is called \"Twenty Newsgroups\", which means there are 20 categories of news articles available in the entire dataset. A short description of the dataset, provided by the authors, is provided below:\n",
    "\n",
    "- *The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of our knowledge, it was originally collected by Ken Lang, probably for his paper “Newsweeder: Learning to filter netnews,” though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering.*\n",
    "\n",
    "If you need more information about the dataset please refer to the reference provided above. Below is a snapshot of the dataset already converted into a table. Keep in mind that the original dataset is not in this nice pretty format. That work is left to us. That is one of the tasks that will be covered in this notebook: how to convert raw data into convenient tabular formats using Pandas. \n",
    "\n",
    "![atl txt](https://docs.google.com/drawings/d/e/2PACX-1vRd845nNXa1x1Enw6IoEbg-05lB19xG3mfO2BjnpZrloT0pSnY89stBV1gS9Iu6cgRCTq3E5giIT5ZI/pub?w=835&h=550)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data Preparation\n",
    "Now let us begin to explore the data. The original dataset can be found on the link provided above or you can directly use the version provided by scikit learn. Here we will use the scikit learn version. \n",
    "\n",
    "In this demonstration we are only going to look at 4 categories. This means we will not make use of the complete dataset, but only a subset of it, which includes the 4 categories defined below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categories\n",
    "categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the documents containing the categories provided\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "twenty_train = fetch_20newsgroups(subset='train', categories=categories, \\\n",
    "                                  shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take at look some of the records that are contained in our subset of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['From: sd345@city.ac.uk (Michael Collier)\\nSubject: Converting images to HP LaserJet III?\\nNntp-Posting-Host: hampton\\nOrganization: The City University\\nLines: 14\\n\\nDoes anyone know of a good way (standard PC application/PD utility) to\\nconvert tif/img/tga files into LaserJet III format.  We would also like to\\ndo the same, converting to HPGL (HP plotter) files.\\n\\nPlease email any response.\\n\\nIs this the correct group?\\n\\nThanks in advance.  Michael.\\n-- \\nMichael Collier (Programmer)                 The Computer Unit,\\nEmail: M.P.Collier@uk.ac.city                The City University,\\nTel: 071 477-8000 x3769                      London,\\nFax: 071 477-8565                            EC1V 0HB.\\n',\n",
       " \"From: ani@ms.uky.edu (Aniruddha B. Deglurkar)\\nSubject: help: Splitting a trimming region along a mesh \\nOrganization: University Of Kentucky, Dept. of Math Sciences\\nLines: 28\\n\\n\\n\\n\\tHi,\\n\\n\\tI have a problem, I hope some of the 'gurus' can help me solve.\\n\\n\\tBackground of the problem:\\n\\tI have a rectangular mesh in the uv domain, i.e  the mesh is a \\n\\tmapping of a 3d Bezier patch into 2d. The area in this domain\\n\\twhich is inside a trimming loop had to be rendered. The trimming\\n\\tloop is a set of 2d Bezier curve segments.\\n\\tFor the sake of notation: the mesh is made up of cells.\\n\\n\\tMy problem is this :\\n\\tThe trimming area has to be split up into individual smaller\\n\\tcells bounded by the trimming curve segments. If a cell\\n\\tis wholly inside the area...then it is output as a whole ,\\n\\telse it is trivially rejected. \\n\\n\\tDoes any body know how thiss can be done, or is there any algo. \\n\\tsomewhere for doing this.\\n\\n\\tAny help would be appreciated.\\n\\n\\tThanks, \\n\\tAni.\\n-- \\nTo get irritated is human, to stay cool, divine.\\n\"]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.data[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note** the `twenty_train` is just a bunch of objects that can be accessed as python dictionaries; so, you can do the following operations on `twenty_train`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'comp.graphics', 'sci.med', 'soc.religion.christian']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(twenty_train.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(twenty_train.filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can also print an example from the subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: sd345@city.ac.uk (Michael Collier)\n",
      "Subject: Converting images to HP LaserJet III?\n",
      "Nntp-Posting-Host: hampton\n",
      "Organization: The City University\n",
      "Lines: 14\n",
      "\n",
      "Does anyone know of a good way (standard PC application/PD utility) to\n",
      "convert tif/img/tga files into LaserJet III format.  We would also like to\n",
      "do the same, converting to HPGL (HP plotter) files.\n",
      "\n",
      "Please email any response.\n",
      "\n",
      "Is this the correct group?\n",
      "\n",
      "Thanks in advance.  Michael.\n",
      "-- \n",
      "Michael Collier (Programmer)                 The Computer Unit,\n",
      "Email: M.P.Collier@uk.ac.city                The City University,\n",
      "Tel: 071 477-8000 x3769                      London,\n",
      "Fax: 071 477-8565                            EC1V 0HB.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# An example of what the subset contains\n",
    "print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and determine the label of the example via `target_names` key value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comp.graphics\n"
     ]
    }
   ],
   "source": [
    "print(twenty_train.target_names[twenty_train.target[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... we can also get the category of 10 documents via `target` key value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 3, 3, 3, 3, 3, 2, 2, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# category of first 10 documents.\n",
    "twenty_train.target[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: As you can observe, both approaches above provide two different ways of obtaining the `category` value for the dataset. Ideally, we want to have access to both types -- numerical and nominal -- in the event some particular library favors a particular type. \n",
    "\n",
    "As you may have already noticed as well, there is no **tabular format** for the current version of the data. As data miners, we are interested in having our dataset in the most convenient format as possible; something we can manipulate easily and is compatible with our algorithms, and so forth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is one way to get access to the *text* version of the label of a subset of our training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comp.graphics\n",
      "comp.graphics\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "sci.med\n",
      "sci.med\n",
      "sci.med\n",
      "soc.religion.christian\n",
      "comp.graphics\n",
      "alt.atheism\n",
      "alt.atheism\n",
      "comp.graphics\n",
      "comp.graphics\n",
      "sci.med\n",
      "alt.atheism\n",
      "soc.religion.christian\n",
      "alt.atheism\n",
      "soc.religion.christian\n",
      "alt.atheism\n",
      "soc.religion.christian\n",
      "comp.graphics\n",
      "comp.graphics\n",
      "comp.graphics\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "sci.med\n",
      "sci.med\n",
      "sci.med\n",
      "soc.religion.christian\n",
      "sci.med\n",
      "soc.religion.christian\n",
      "sci.med\n",
      "soc.religion.christian\n",
      "alt.atheism\n",
      "alt.atheism\n",
      "alt.atheism\n",
      "comp.graphics\n",
      "soc.religion.christian\n",
      "alt.atheism\n",
      "comp.graphics\n",
      "comp.graphics\n",
      "sci.med\n",
      "alt.atheism\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "comp.graphics\n",
      "sci.med\n"
     ]
    }
   ],
   "source": [
    "for t in twenty_train.target[:50]:\n",
    "    print(twenty_train.target_names[t])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. So What's next?\n",
    "So we want to explore and understand our data a little bit better. Before we do that we definitely need to make some conversions here and there just so we can have our dataset in a nice format to be able to explore it freely. Lucky for us, there are efficient scientific tools to transform our data into that tabular format we are so farmiliar with. So that is what we will do in the next section--transform our data into a nice table to play with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Converting Dictionary into Pandas dataframe\n",
    "**Note:** This part is not provided in the original scikit learn tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['From: sd345@city.ac.uk (Michael Collier)\\nSubject: Converting images to HP LaserJet III?\\nNntp-Posting-Host: hampton\\nOrganization: The City University\\nLines: 14\\n\\nDoes anyone know of a good way (standard PC application/PD utility) to\\nconvert tif/img/tga files into LaserJet III format.  We would also like to\\ndo the same, converting to HPGL (HP plotter) files.\\n\\nPlease email any response.\\n\\nIs this the correct group?\\n\\nThanks in advance.  Michael.\\n-- \\nMichael Collier (Programmer)                 The Computer Unit,\\nEmail: M.P.Collier@uk.ac.city                The City University,\\nTel: 071 477-8000 x3769                      London,\\nFax: 071 477-8565                            EC1V 0HB.\\n',\n",
       " \"From: ani@ms.uky.edu (Aniruddha B. Deglurkar)\\nSubject: help: Splitting a trimming region along a mesh \\nOrganization: University Of Kentucky, Dept. of Math Sciences\\nLines: 28\\n\\n\\n\\n\\tHi,\\n\\n\\tI have a problem, I hope some of the 'gurus' can help me solve.\\n\\n\\tBackground of the problem:\\n\\tI have a rectangular mesh in the uv domain, i.e  the mesh is a \\n\\tmapping of a 3d Bezier patch into 2d. The area in this domain\\n\\twhich is inside a trimming loop had to be rendered. The trimming\\n\\tloop is a set of 2d Bezier curve segments.\\n\\tFor the sake of notation: the mesh is made up of cells.\\n\\n\\tMy problem is this :\\n\\tThe trimming area has to be split up into individual smaller\\n\\tcells bounded by the trimming curve segments. If a cell\\n\\tis wholly inside the area...then it is output as a whole ,\\n\\telse it is trivially rejected. \\n\\n\\tDoes any body know how thiss can be done, or is there any algo. \\n\\tsomewhere for doing this.\\n\\n\\tAny help would be appreciated.\\n\\n\\tThanks, \\n\\tAni.\\n-- \\nTo get irritated is human, to stay cool, divine.\\n\"]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.data[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 3, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# my functions\n",
    "import helpers.data_mining_helpers as dmh\n",
    "\n",
    "X = pd.DataFrame.from_records(dmh.format_rows(twenty_train), columns= ['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: sd345@city.ac.uk (Michael Collier) Subje...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...</td>\n",
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      "text/plain": [
       "                                                text\n",
       "0  From: sd345@city.ac.uk (Michael Collier) Subje...\n",
       "1  From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add category to the dataframe\n",
    "X['category'] = twenty_train.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add category label also\n",
    "X['category_name'] = X.category.apply(lambda t: dmh.format_labels(t, twenty_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can print screen and see what our table looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>category</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: sd345@city.ac.uk (Michael Collier) Subje...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>From: s0612596@let.rug.nl (M.M. Zwart) Subject...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>From: stanly@grok11.columbiasc.ncr.com (stanly...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>From: jodfishe@silver.ucs.indiana.edu (joseph ...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>From: aldridge@netcom.com (Jacquelin Aldridge)...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>From: geb@cs.pitt.edu (Gordon Banks) Subject: ...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>From: libman@hsc.usc.edu (Marlena Libman) Subj...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  category  \\\n",
       "0  From: sd345@city.ac.uk (Michael Collier) Subje...         1   \n",
       "1  From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...         1   \n",
       "2  From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...         3   \n",
       "3  From: s0612596@let.rug.nl (M.M. Zwart) Subject...         3   \n",
       "4  From: stanly@grok11.columbiasc.ncr.com (stanly...         3   \n",
       "5  From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...         3   \n",
       "6  From: jodfishe@silver.ucs.indiana.edu (joseph ...         3   \n",
       "7  From: aldridge@netcom.com (Jacquelin Aldridge)...         2   \n",
       "8  From: geb@cs.pitt.edu (Gordon Banks) Subject: ...         2   \n",
       "9  From: libman@hsc.usc.edu (Marlena Libman) Subj...         2   \n",
       "\n",
       "            category_name  \n",
       "0           comp.graphics  \n",
       "1           comp.graphics  \n",
       "2  soc.religion.christian  \n",
       "3  soc.religion.christian  \n",
       "4  soc.religion.christian  \n",
       "5  soc.religion.christian  \n",
       "6  soc.religion.christian  \n",
       "7                 sci.med  \n",
       "8                 sci.med  \n",
       "9                 sci.med  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice! Isn't it? With this format of the data we can conduct many operations, easily and efficiently, since Pandas dataframes provide us with a wide range of built-in features. These features are operations which can directly and quickly be applied to the dataset. These operations may include standard operations like removing records with missing values and aggregating new fields to the current state of the table (hereinafter referred to as a dataframe), which is desirable in almost every data mining project. If you are not covinced head on over to Kaggle -- one of the largest data science community on the web -- and see for yourself some of the top kernels and what they make use of. First thing you will notice is the use of Pandas to transform and explore data. Take a look at this [kernel](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python) for example.\n",
    "\n",
    "I want to also add here that in this notebook we are not following conventions, but we are very interested in using tools and techniques that make our lives easier. Go Pandas!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Familiarizing yourself with the Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin to show you the magnificence of Pandas dataframes, let us look at how to run a simple query on our dataset. We want to query for the first 10 rows (news articles), and we only want to keep the `text` and `category_name` attributes or fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>text</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: sd345@city.ac.uk (Michael Collier) Subje...</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>From: s0612596@let.rug.nl (M.M. Zwart) Subject...</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>From: stanly@grok11.columbiasc.ncr.com (stanly...</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>From: jodfishe@silver.ucs.indiana.edu (joseph ...</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>From: aldridge@netcom.com (Jacquelin Aldridge)...</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>From: geb@cs.pitt.edu (Gordon Banks) Subject: ...</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>From: libman@hsc.usc.edu (Marlena Libman) Subj...</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text           category_name\n",
       "0  From: sd345@city.ac.uk (Michael Collier) Subje...           comp.graphics\n",
       "1  From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...           comp.graphics\n",
       "2  From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...  soc.religion.christian\n",
       "3  From: s0612596@let.rug.nl (M.M. Zwart) Subject...  soc.religion.christian\n",
       "4  From: stanly@grok11.columbiasc.ncr.com (stanly...  soc.religion.christian\n",
       "5  From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...  soc.religion.christian\n",
       "6  From: jodfishe@silver.ucs.indiana.edu (joseph ...  soc.religion.christian\n",
       "7  From: aldridge@netcom.com (Jacquelin Aldridge)...                 sci.med\n",
       "8  From: geb@cs.pitt.edu (Gordon Banks) Subject: ...                 sci.med\n",
       "9  From: libman@hsc.usc.edu (Marlena Libman) Subj...                 sci.med"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:10][[\"text\", \"category_name\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at a few more interesting queries to familiarize ourselves with the efficiency and conveniency of Pandas dataframes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We want the last 10 records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>text</th>\n",
       "      <th>category</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>From: lmvec@westminster.ac.uk (William Hargrea...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>From: daniels@math.ufl.edu (TV's Big Dealer) S...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248</th>\n",
       "      <td>From: \"danny hawrysio\" &lt;danny.hawrysio@canrem....</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>From: shellgate!llo@uu4.psi.com (Larry L. Over...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>From: ingles@engin.umich.edu (Ray Ingles) Subj...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>From: Mark-Tarbell@suite.com Subject: Amniocen...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>From: roos@Operoni.Helsinki.FI (Christophe Roo...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>From: mhollowa@ic.sunysb.edu (Michael Holloway...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>From: sasghm@theseus.unx.sas.com (Gary Merrill...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>From: Dan Wallach &lt;dwallach@cs.berkeley.edu&gt; S...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  category  \\\n",
       "2246  From: lmvec@westminster.ac.uk (William Hargrea...         3   \n",
       "2247  From: daniels@math.ufl.edu (TV's Big Dealer) S...         3   \n",
       "2248  From: \"danny hawrysio\" <danny.hawrysio@canrem....         1   \n",
       "2249  From: shellgate!llo@uu4.psi.com (Larry L. Over...         3   \n",
       "2250  From: ingles@engin.umich.edu (Ray Ingles) Subj...         0   \n",
       "2251  From: Mark-Tarbell@suite.com Subject: Amniocen...         2   \n",
       "2252  From: roos@Operoni.Helsinki.FI (Christophe Roo...         2   \n",
       "2253  From: mhollowa@ic.sunysb.edu (Michael Holloway...         2   \n",
       "2254  From: sasghm@theseus.unx.sas.com (Gary Merrill...         2   \n",
       "2255  From: Dan Wallach <dwallach@cs.berkeley.edu> S...         2   \n",
       "\n",
       "               category_name  \n",
       "2246  soc.religion.christian  \n",
       "2247  soc.religion.christian  \n",
       "2248           comp.graphics  \n",
       "2249  soc.religion.christian  \n",
       "2250             alt.atheism  \n",
       "2251                 sci.med  \n",
       "2252                 sci.med  \n",
       "2253                 sci.med  \n",
       "2254                 sci.med  \n",
       "2255                 sci.med  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[-11:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ready for some sourcery? Brace yourselves! Let us see if we can query every 10th record in our dataframe. In addition, our query must only contain the first 10 records. For this we will use the build-in function ``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>text</th>\n",
       "      <th>category</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: sd345@city.ac.uk (Michael Collier) Subje...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>From: anasaz!karl@anasazi.com (Karl Dussik) Su...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>From: dotsonm@dmapub.dma.org (Mark Dotson) Sub...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>From: vgwlu@dunsell.calgary.chevron.com (greg ...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>From: david-s@hsr.no (David A. Sjoen) Subject:...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>From: ab@nova.cc.purdue.edu (Allen B) Subject:...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>From: Nanci Ann Miller &lt;nm0w+@andrew.cmu.edu&gt; ...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>From: weaver@chdasic.sps.mot.com (Dave Weaver)...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>From: annick@cortex.physiol.su.oz.au (Annick A...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Subject: Vonnegut/atheism From: dmn@kepler.unh...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 text  category  \\\n",
       "0   From: sd345@city.ac.uk (Michael Collier) Subje...         1   \n",
       "10  From: anasaz!karl@anasazi.com (Karl Dussik) Su...         3   \n",
       "20  From: dotsonm@dmapub.dma.org (Mark Dotson) Sub...         3   \n",
       "30  From: vgwlu@dunsell.calgary.chevron.com (greg ...         2   \n",
       "40  From: david-s@hsr.no (David A. Sjoen) Subject:...         3   \n",
       "50  From: ab@nova.cc.purdue.edu (Allen B) Subject:...         1   \n",
       "60  From: Nanci Ann Miller <nm0w+@andrew.cmu.edu> ...         0   \n",
       "70  From: weaver@chdasic.sps.mot.com (Dave Weaver)...         3   \n",
       "80  From: annick@cortex.physiol.su.oz.au (Annick A...         2   \n",
       "90  Subject: Vonnegut/atheism From: dmn@kepler.unh...         0   \n",
       "\n",
       "             category_name  \n",
       "0            comp.graphics  \n",
       "10  soc.religion.christian  \n",
       "20  soc.religion.christian  \n",
       "30                 sci.med  \n",
       "40  soc.religion.christian  \n",
       "50           comp.graphics  \n",
       "60             alt.atheism  \n",
       "70  soc.religion.christian  \n",
       "80                 sci.med  \n",
       "90             alt.atheism  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[::10, :][0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Exercise 0:** Experiment with other querying techniques using pandas dataframes. Refer to the their documentation for more information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** Exercise 0:**  \n",
    "Try to fect records belong to ```sci.med``` category, and query every 10th record. Only show the first 5 records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>text</th>\n",
       "      <th>category</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>From: I3150101@dbstu1.rz.tu-bs.de (Benedikt Ro...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Subject: Re: A visit from the Jehovah's Witnes...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>From: keith@cco.caltech.edu (Keith Allan Schne...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>From: sandvik@newton.apple.com (Kent Sandvik) ...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>From: keith@cco.caltech.edu (Keith Allan Schne...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text  category category_name\n",
       "12   From: I3150101@dbstu1.rz.tu-bs.de (Benedikt Ro...         0   alt.atheism\n",
       "52   Subject: Re: A visit from the Jehovah's Witnes...         0   alt.atheism\n",
       "95   From: keith@cco.caltech.edu (Keith Allan Schne...         0   alt.atheism\n",
       "128  From: sandvik@newton.apple.com (Kent Sandvik) ...         0   alt.atheism\n",
       "178  From: keith@cco.caltech.edu (Keith Allan Schne...         0   alt.atheism"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer example\n",
    "X[X['category_name']=='alt.atheism'].iloc[::10][0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Data Mining using Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's stop playing around and let's do some serious work. Let's learn to program some of the ideas and concepts related to the data mining course. This is the only way we can be convinced of the true power of Pandas dataframes for data mining tasks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let us consider that our dataset has some *missing values* and we want to remove those values. In its current state our dataset has no missing values, but for practice sake we will add some records with missing values and then write to code to deal with these object that contain missing value. You will see for yourself how easy it is to deatl with missing values once you have your data transformed into a Pandas dataframe.\n",
    "\n",
    "First let us do a quick review of what we learned in the Data Mining course. Specifically, how to deal with missing values and how they come about. \n",
    "\n",
    "The most common of cases for having missing values in datasets has to do with how the data was initially collected. A good example of this is when a patients comes into the ER room, the data is collected as quickly as possible and depending on the conditions of the patients, the personal data being collected it either incomplete or partially complete. In the former and latter cases, we are presented with a case of \"missing values\". Knowing that patients data is particularly critical and can be used by the health authorities to conduct some interesting analysis, we as the data miners are left with the tough task of deciding what to do with these missing and incomplete records. We need to deal with these records because they are definitely going to affect our analysis or algorithms. So what do we do? There are several ways to handle missing values, and some of the more effective ways are presented below (Note: You can reference the slides - Session 1 Handout for the additional information).\n",
    "\n",
    "- **Eliminate Data Objects** - Here we completely discard records once they contain some missing values. This is the easiest approach and the one we will be using in this notebook. The immediate drawback of going with this approach is that you loose information. Now imagine that half of the records have at least one or more missing values. Here you are presented with the tough decision of quantity vs quality. In any event, this decision must be made carefully, hence the reason for emphasizing it here in this notebook. \n",
    "\n",
    "- **Estimate Missing Values** - Here we try to estimate the missing values based on some criteria. Although this approach may be proven to be effective, it is not always the case, especially when we are dealing with sensitive data, like **Gender** or **Names**. For fields like **Address**, there could be ways to obtain these missing addresses using some data aggregation technique or obtain the information directly from other databases or public data sources.\n",
    "\n",
    "- **Ignore the missing value during analysis** - Here we basically ignore the missing values and proceed with our analysis. Although this is the most naive way to handle missing values it may proof effective, especially when the missing values includes information that is not important to the analysis being conducted. But think about it for a while. Would you be ignore missing values, especially when in this day and age it is difficult to obtain high quality databases. Again, there are some tradeoffs, which will talk about later in the notebook.\n",
    "\n",
    "- **Replace with all possible values** - As an efficient and responsible data miner, we sometimes just need to put in the hard hours of work and find ways to makes up for these missing values. This last option is a very wise option for cases where data is scarce (which is almost always) or when dealing with sensitive data. Imagine that our dataset has an **Age** field, which contains many missing values. Since **Age** is a continuous variable, it means that we can build a separate model for calculating the age for the incomplete records based on some rule-based appraoch or probabilistic approach.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned earlier, we are going to go with the first option but you may be asked to compute missing values, using a different approach, as an exercise. So let's get to it!\n",
    "\n",
    "First we want to add the dummy records with missing values since the dataset we have is perfectly composed and cleaned that it contains no missing values. First let us check for ourselves that indeed the dataset doesn't contain any missing values. We can do that easily by using the following built-in function provided by Pandas.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2229</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2230</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2242</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2243</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2244</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2257 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       text  category  category_name\n",
       "0     False     False          False\n",
       "1     False     False          False\n",
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       "5     False     False          False\n",
       "6     False     False          False\n",
       "7     False     False          False\n",
       "8     False     False          False\n",
       "9     False     False          False\n",
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       "11    False     False          False\n",
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       "19    False     False          False\n",
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       "23    False     False          False\n",
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       "25    False     False          False\n",
       "26    False     False          False\n",
       "27    False     False          False\n",
       "28    False     False          False\n",
       "29    False     False          False\n",
       "...     ...       ...            ...\n",
       "2227  False     False          False\n",
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       "2231  False     False          False\n",
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       "2233  False     False          False\n",
       "2234  False     False          False\n",
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       "2238  False     False          False\n",
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       "2240  False     False          False\n",
       "2241  False     False          False\n",
       "2242  False     False          False\n",
       "2243  False     False          False\n",
       "2244  False     False          False\n",
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       "2246  False     False          False\n",
       "2247  False     False          False\n",
       "2248  False     False          False\n",
       "2249  False     False          False\n",
       "2250  False     False          False\n",
       "2251  False     False          False\n",
       "2252  False     False          False\n",
       "2253  False     False          False\n",
       "2254  False     False          False\n",
       "2255  False     False          False\n",
       "2256  False     False          False\n",
       "\n",
       "[2257 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `isnull` function looks through the entire dataset for null values and return `True` wherever it finds any missing field or record. As you will see above, and as we anticipated, our dataset looks clean and all values are present, since `isnull` return **False** for all fields and records. But let us start to get our hands dirty and build a nice little function to check each of the records, column by column, and return a nice little message telling us the amount of missing records collected. This excerice will also encourage us to explore the possibilities of pandas dataframes. In reality, the build function are good enough, but as you saw above when the entire table was printed, it is impossible really to tell if there are missing records just by looking at the records manually, especially in cases where the dataset is huge. Let's get to it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text             (The amoung of missing records is: , 0)\n",
       "category         (The amoung of missing records is: , 0)\n",
       "category_name    (The amoung of missing records is: , 0)\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().apply(lambda x: dmh.check_missing_values(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, a lot happened there in that one line of code, so let's break it down. First, with the `isnull` we tranformed our table into the **True/False** table you see above, where **True** in this case means that the data is missing and **False** means that the data is present. We then take the transformed table and apply a function to each that essentially counts to see if there are missing values in each records and how much missing values are there. In other words the `check_missing_values` function looks through each field in the dataset and counts how many missing values there are. \n",
    "\n",
    "There are many other clever ways to check for missing data, and that is what makes Pandas so beautiful to work with. You get the control you need as a data scientist or just a person working in data mining projects. Indeed, Pandas makes your life easy!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Exercise 1:** \n",
    "Code for how to calculate the missing values for every record instead of every column.  \n",
    "$Hint$ : `axis` parameter. Check the documentation for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       (The amoung of missing records is: , 0)\n",
       "1       (The amoung of missing records is: , 0)\n",
       "2       (The amoung of missing records is: , 0)\n",
       "3       (The amoung of missing records is: , 0)\n",
       "4       (The amoung of missing records is: , 0)\n",
       "5       (The amoung of missing records is: , 0)\n",
       "6       (The amoung of missing records is: , 0)\n",
       "7       (The amoung of missing records is: , 0)\n",
       "8       (The amoung of missing records is: , 0)\n",
       "9       (The amoung of missing records is: , 0)\n",
       "10      (The amoung of missing records is: , 0)\n",
       "11      (The amoung of missing records is: , 0)\n",
       "12      (The amoung of missing records is: , 0)\n",
       "13      (The amoung of missing records is: , 0)\n",
       "14      (The amoung of missing records is: , 0)\n",
       "15      (The amoung of missing records is: , 0)\n",
       "16      (The amoung of missing records is: , 0)\n",
       "17      (The amoung of missing records is: , 0)\n",
       "18      (The amoung of missing records is: , 0)\n",
       "19      (The amoung of missing records is: , 0)\n",
       "20      (The amoung of missing records is: , 0)\n",
       "21      (The amoung of missing records is: , 0)\n",
       "22      (The amoung of missing records is: , 0)\n",
       "23      (The amoung of missing records is: , 0)\n",
       "24      (The amoung of missing records is: , 0)\n",
       "25      (The amoung of missing records is: , 0)\n",
       "26      (The amoung of missing records is: , 0)\n",
       "27      (The amoung of missing records is: , 0)\n",
       "28      (The amoung of missing records is: , 0)\n",
       "29      (The amoung of missing records is: , 0)\n",
       "                         ...                   \n",
       "2227    (The amoung of missing records is: , 0)\n",
       "2228    (The amoung of missing records is: , 0)\n",
       "2229    (The amoung of missing records is: , 0)\n",
       "2230    (The amoung of missing records is: , 0)\n",
       "2231    (The amoung of missing records is: , 0)\n",
       "2232    (The amoung of missing records is: , 0)\n",
       "2233    (The amoung of missing records is: , 0)\n",
       "2234    (The amoung of missing records is: , 0)\n",
       "2235    (The amoung of missing records is: , 0)\n",
       "2236    (The amoung of missing records is: , 0)\n",
       "2237    (The amoung of missing records is: , 0)\n",
       "2238    (The amoung of missing records is: , 0)\n",
       "2239    (The amoung of missing records is: , 0)\n",
       "2240    (The amoung of missing records is: , 0)\n",
       "2241    (The amoung of missing records is: , 0)\n",
       "2242    (The amoung of missing records is: , 0)\n",
       "2243    (The amoung of missing records is: , 0)\n",
       "2244    (The amoung of missing records is: , 0)\n",
       "2245    (The amoung of missing records is: , 0)\n",
       "2246    (The amoung of missing records is: , 0)\n",
       "2247    (The amoung of missing records is: , 0)\n",
       "2248    (The amoung of missing records is: , 0)\n",
       "2249    (The amoung of missing records is: , 0)\n",
       "2250    (The amoung of missing records is: , 0)\n",
       "2251    (The amoung of missing records is: , 0)\n",
       "2252    (The amoung of missing records is: , 0)\n",
       "2253    (The amoung of missing records is: , 0)\n",
       "2254    (The amoung of missing records is: , 0)\n",
       "2255    (The amoung of missing records is: , 0)\n",
       "2256    (The amoung of missing records is: , 0)\n",
       "Length: 2257, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer example\n",
    "X.isnull().apply(lambda x: dmh.check_missing_values(x), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have our function to check for missing records, now let us do some bad deed and insert some dummy data into the dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_series = pd.Series([\"dummy_record\", 1], index=[\"text\", \"category\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text        dummy_record\n",
       "category               1\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_with_series = X.append(dummy_series, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2258"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if the records was commited into result\n",
    "len(result_with_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we that we have added the record (specifically a `Series` or what we would usually now as lists) with some missing values. Let try our function and see if it can detect that there is a missing value on the resulting dataframe called ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text             (The amoung of missing records is: , 0)\n",
       "category         (The amoung of missing records is: , 0)\n",
       "category_name    (The amoung of missing records is: , 1)\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_with_series.isnull().apply(lambda x: dmh.check_missing_values(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed there is a missing value in this new dataframe. Specificall, the missing value comes from the `category_name` attribute. As I mentioned before, there are many ways to conduct specific operations on the dataframes. In this case let us use a simple dictionary and try to insert it into our original dataframe `X`. Notice also that we are note changing the `X` dataframe as results are directly applied to the assignment variable provided. But in the event that we just want to keep things simple, we can just directly apply the changes to `X` and assign it to itself as we will do below. This exercise will challenge to us for the need to remove this dummy records later on, which means that we need to learn more about Pandas dataframes. This is getting intense! But just relax, everything will be fine!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_dict = [{'text': 'dummy_record',\n",
    "               'category': 1\n",
    "              }]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nini/py_venv/lib/python3.6/site-packages/pandas/core/frame.py:5186: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  other = other.loc[:, self.columns]\n",
      "/Users/nini/py_venv/lib/python3.6/site-packages/pandas/core/indexing.py:1367: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return self._getitem_tuple(key)\n"
     ]
    }
   ],
   "source": [
    "X = X.append(dummy_dict, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2258"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text             (The amoung of missing records is: , 0)\n",
       "category         (The amoung of missing records is: , 0)\n",
       "category_name    (The amoung of missing records is: , 1)\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().apply(lambda x: dmh.check_missing_values(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now that we can see that our data has missing values, we want to remove the records with missing values. The code to drop the record with missing that we just added, is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and now let us test to see if we gotten rid of the records with missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text             (The amoung of missing records is: , 0)\n",
       "category         (The amoung of missing records is: , 0)\n",
       "category_name    (The amoung of missing records is: , 0)\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().apply(lambda x: dmh.check_missing_values(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we are back with our original dataset, clean and tidy as we want it. That's enough on how to deal with missing values, let us now move unto something more fun. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But just in case you want to learn more about how to deal with missing data, go to reference the official [Pandas documentation](http://pandas.pydata.org/pandas-docs/stable/missing_data.html#missing-data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Exercise 2** \n",
    "There is an old saying that goes, \"The devil is in the details.\" When we face to deal  with extremely large data, it's difficult to check records one by one. Thus, most \"debug\" skills get better at the same time as you solve bugs.   \n",
    "\n",
    "Please check the data and the process below, describe what you observe and why they happened.   \n",
    "$Hint$ :  why `.isnull()` didn't work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>missing_example</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id missing_example\n",
       "0  A             NaN\n",
       "1  B             NaN\n",
       "2  C             NaN\n",
       "3  D            None\n",
       "4  E            None\n",
       "5  F                "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "NA_dict = [{ 'id': 'A', 'missing_example': np.nan },\n",
    "           { 'id': 'B'                    },\n",
    "           { 'id': 'C', 'missing_example': 'NaN'  },\n",
    "           { 'id': 'D', 'missing_example': 'None' },\n",
    "           { 'id': 'E', 'missing_example':  None  },\n",
    "           { 'id': 'F', 'missing_example': ''     }]\n",
    "\n",
    "NA_df = pd.DataFrame(NA_dict, columns = ['id','missing_example'])\n",
    "NA_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "Name: missing_example, dtype: bool"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NA_df['missing_example'].isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Dealing with Duplicate Data\n",
    "Dealing with duplicate data is just as painful as dealing with missing data. The only difference between the two is that now there is more of the same. The worst case is that you have duplicate data that has missing values. But let us not get carried away. Let us keep it to the basics. As we have learned in our Data Mining course, duplicated data can occur because of many reasons. The majority of the times it has to do with how we store data or how we collect and merge data. For instance, we may collected and stored a tweet, and retweet of that same tweet as two different records. The result is case of data duplication; the only difference being that one is the original tweet and the other the retweeted one. Here you will learn that dealing with duplicate data is not as challenging as missing values. But this also all depends on what you consider as duplicates; this all depends on your criteria for what is considered as a duplicate and also what type of data you are dealing with. For text it may not be so trivial as it is for numerical values. Anyhow let us look at some code on how to deal with duplicate records in our `X` dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let us check how many duplicates we have in our current dataset. Here is the line of code that checks for duplicates; it is very similar to the `isnull` operation that we used to check for missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "5       False\n",
       "6       False\n",
       "7       False\n",
       "8       False\n",
       "9       False\n",
       "10      False\n",
       "11      False\n",
       "12      False\n",
       "13      False\n",
       "14      False\n",
       "15      False\n",
       "16      False\n",
       "17      False\n",
       "18      False\n",
       "19      False\n",
       "20      False\n",
       "21      False\n",
       "22      False\n",
       "23      False\n",
       "24      False\n",
       "25      False\n",
       "26      False\n",
       "27      False\n",
       "28      False\n",
       "29      False\n",
       "        ...  \n",
       "2227    False\n",
       "2228    False\n",
       "2229    False\n",
       "2230    False\n",
       "2231    False\n",
       "2232    False\n",
       "2233    False\n",
       "2234    False\n",
       "2235    False\n",
       "2236    False\n",
       "2237    False\n",
       "2238    False\n",
       "2239    False\n",
       "2240    False\n",
       "2241    False\n",
       "2242    False\n",
       "2243    False\n",
       "2244    False\n",
       "2245    False\n",
       "2246    False\n",
       "2247    False\n",
       "2248    False\n",
       "2249    False\n",
       "2250    False\n",
       "2251    False\n",
       "2252    False\n",
       "2253    False\n",
       "2254    False\n",
       "2255    False\n",
       "2256    False\n",
       "Length: 2257, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check the sum of duplicated by simply doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(X.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on that output, you may be asking why did the `duplicated` operation only returned one single column that indicates whether there is a duplicate record or not. So yes, all the operation does is to check per records instead of per column. That is why the operation only returns on value instead of three values for each columns, for each record as before. It appears that we don't have any duplicates since none of our records resulted in `True`. If we want to check for duplicates as we did above for some particular columns instead of all columns we do something as below. As you may have noticed, in this case where we select some columns instead of checking by all columns, we are kind of lowering the criteria of what is considered as a duplicate record. So let us only check for duplicateS by only the text. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(X.duplicated('text'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us create some dummy duplicate data and append it to the main dataframe `X`. And subsequenlty, let us try to get rid of the duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_duplicate_dict = [{\n",
    "                             'text': 'dummy record',\n",
    "                             'category': 1, \n",
    "                             'category_name': \"dummy category\"\n",
    "                        },\n",
    "                        {\n",
    "                             'text': 'dummy record',\n",
    "                             'category': 1, \n",
    "                             'category_name': \"dummy category\"\n",
    "                        }]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.append(dummy_duplicate_dict, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2259"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(X.duplicated('text'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have added the dummy duplicate to `X`. Now we are faced with the decision as to what to do with the duplicate after we have found it. In our case we want to get rid of all the duplicated records without preserving a copy. So we simple do that with the following line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop_duplicates(keep=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the Pandas [documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html?highlight=duplicate#duplicate-data) for more information on dealing with duplicate data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.  Data Preprocessing\n",
    "In the Data Mining course we learned about the many ways of performing data preprocessing. In reality, the list is quiet general as the specifics of what data preprocessing involves is too much to cover in one course. This is especially true when you are dealing with unstructured data, as we are dealing with in this particular notebook. But let us look at some examples for each data preprocessing technique that we learned in the class. We will cover each item one by one, and provide example code for each category. You will learn how to peform each of the operations, using Pandas, that cover the essentials to Preprocessing in Data Mining. We are not going to follow any strict order, but the items we will cover in the preprocessing section of this notebook are as follows:\n",
    "\n",
    "- Aggregation\n",
    "- Sampling\n",
    "- Dimensionality Reduction\n",
    "- Feature Subset Selection\n",
    "- Feature Creation\n",
    "- Discretization and Binarization\n",
    "- Attribute Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Sampling\n",
    "The first concept that we are going to cover from the above list is sampling. Sampling refers to the technique used for selecting data. The functionalities that we use to  selected data through queries provided by Pandas are actually methods for sampling. The reasons for sampling are sometimes due to the size of data; we want a smaller subset of the data that still best represents the complete dataset. We don't have a problem of size in our dataset since our dataset is just a couple thousand records long. But if we pay attention to how much content is included in the `text` field of each of those records, you will realize that sampling may not be a bad idea. In fact, we have already done some sampling by just reducing the records we are using here in this notebook; we only used four categories from the all the 20 categories available. Let us get an idea of what sampling involves in terms of using pandas code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sample = X.sample(n=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>text</th>\n",
       "      <th>category</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>From: caf@omen.UUCP (Chuck Forsberg WA7KGX) Su...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>From: Nanci Ann Miller &lt;nm0w+@andrew.cmu.edu&gt; ...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>From: debbie@csd4.csd.uwm.edu (Debbie Forest) ...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>From: kempmp@phoenix.oulu.fi (Petri Pihko) Sub...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text  category  \\\n",
       "668  From: caf@omen.UUCP (Chuck Forsberg WA7KGX) Su...         2   \n",
       "60   From: Nanci Ann Miller <nm0w+@andrew.cmu.edu> ...         0   \n",
       "886  From: debbie@csd4.csd.uwm.edu (Debbie Forest) ...         2   \n",
       "927  From: kempmp@phoenix.oulu.fi (Petri Pihko) Sub...         3   \n",
       "\n",
       "              category_name  \n",
       "668                 sci.med  \n",
       "60              alt.atheism  \n",
       "886                 sci.med  \n",
       "927  soc.religion.christian  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sample[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do something cool here while we are in sampling! Let us look at the distribution of categories in both the sample and original dataset. Let us visually analyze the disparity between the two datasets. To generate some visualizations, we are going to use `matplotlib` python library. With matplotlib, things are faster and compatability-wise it may just be the best visualization library for visualizing content extracted from dataframes. Let's take a loot at the magic of `matplotlib` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "soc.religion.christian    599\n",
      "sci.med                   594\n",
      "comp.graphics             584\n",
      "alt.atheism               480\n",
      "Name: category_name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1140fac50>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140fa518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X.category_name.value_counts())\n",
    "\n",
    "# plot barchart for X_sample\n",
    "X.category_name.value_counts().plot(kind = 'bar',\n",
    "                                    title = 'Category distribution',\n",
    "                                    ylim = [0, 650],        \n",
    "                                    rot = 0, fontsize = 11, figsize = (8,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sci.med                   264\n",
      "comp.graphics             263\n",
      "soc.religion.christian    261\n",
      "alt.atheism               212\n",
      "Name: category_name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1141472b0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141425f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X_sample.category_name.value_counts())\n",
    "\n",
    "# plot barchart for X_sample\n",
    "X_sample.category_name.value_counts().plot(kind = 'bar',\n",
    "                                           title = 'Category distribution',\n",
    "                                           ylim = [0, 300], \n",
    "                                           rot = 0, fontsize = 12, figsize = (8,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use following command to see other available style to pretty your graph.\n",
    "```python\n",
    "print(plt.style.available)```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Exercise 3:** \n",
    "We can also do a side by side comparison of the distribution between the two datasets, but maybe you can try that as an excerise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>category_name</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alt.atheism</th>\n",
       "      <td>480</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp.graphics</th>\n",
       "      <td>584</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sci.med</th>\n",
       "      <td>594</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soc.religion.christian</th>\n",
       "      <td>599</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        category_name  category_name\n",
       "alt.atheism                       480            212\n",
       "comp.graphics                     584            263\n",
       "sci.med                           594            264\n",
       "soc.religion.christian            599            261"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example answer\n",
    "new_df = pd.concat([X.category_name.value_counts(), X_sample.category_name.value_counts()],\n",
    "              axis = 1)\n",
    "\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114186dd8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141e99b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.plot(kind = 'bar',\n",
    "            title = 'Category distribution',\n",
    "            ylim = [1, 650],\n",
    "            rot = 0, fontsize = 12, figsize = (8,5), width = 0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I must also make mention that there are other libraries for visualizing, but we are going to focus on that in the later sections. For now, we just show you what are the ways to sample and to verify the distributions of your samplings. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing that stood out from the both datasets, is that the distribution of the categories remain relatively the same, which is a good sign for us data scientist. For now that is. Actually there are many ways to conduct sampling on the dataset and still have a representative enough dataset. That is not the main focus in this notebook, but if you would like to know more about sampling and how the `sample` feature works, just reference the Pandas documentation and you will find interesting ways to conduct more advanced sampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Feature Creation\n",
    "The other operation from the list above that we are going to practise on is the so-called feature creation. As the name suggests, in feature creation we are looking at creating new interesting and useful features from the original dataset; a feature which captures the most important information from the raw information we already have access to. In our `X` table, we would like to create some features from the `text` field, but we are still not clear what kind of features we want to create. We can think of an interesting problem we want to solve, or something we want to analyze from the data, or some questions we want to answer. This is one process to come up with features -- this process is usually called `feature engineering` in the data science community. \n",
    "\n",
    "We know what feature creation is so let us get real involved with our dataset and make it more interesting by adding some special features or attributes if you will. First we are going to obtain the **unigrams** for each text. (Unigram is just a fancy word we use in Text Mining which stands for 'individual words'.) Yes, we want to extract all the words found in each text and append it as a new feature. The reason for extracting unigrams is not so clear yet, but we can start to think of obtaining some statistics about the articles we have: something like **word distribution** or **word frequency**.\n",
    "\n",
    "Before going into any further coding, we will also introduce some basic library called [NLTK](http://www.nltk.org/). The NLTK library is a natural language processing tool used for text mining tasks, so might as well we start to familiarize ourselves with it from now (It may come in handy for the final project!). In partcular, we are going to use the NLTK library to conduct tokenization because we are interested in splitting a sentence into its individual components, which can be words, emojis, emails, etc. So let us go for it! We can call the `nltk` library as follows:\n",
    "\n",
    "```python\n",
    "import nltk\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# takes a like a minute or two to process\n",
    "X['unigrams'] = X['text'].apply(lambda x: dmh.tokenize_text(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [From, :, sd345, @, city.ac.uk, (, Michael, Co...\n",
       "1    [From, :, ani, @, ms.uky.edu, (, Aniruddha, B....\n",
       "2    [From, :, djohnson, @, cs.ucsd.edu, (, Darin, ...\n",
       "3    [From, :, s0612596, @, let.rug.nl, (, M.M, ., ...\n",
       "Name: unigrams, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:4][\"unigrams\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you take at the `X` table now, you will see the new columns `unigrams` that we have added. You will notice that it contains an array of tokens, which were extracted from the original `text` field. On first impression, you will notice that the tokenizer is not doing a great job, let us take a closer at a single records and see what was the exact result of the tokenization using the `nltk` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['From',\n",
       "  ':',\n",
       "  'sd345',\n",
       "  '@',\n",
       "  'city.ac.uk',\n",
       "  '(',\n",
       "  'Michael',\n",
       "  'Collier',\n",
       "  ')',\n",
       "  'Subject',\n",
       "  ':',\n",
       "  'Converting',\n",
       "  'images',\n",
       "  'to',\n",
       "  'HP',\n",
       "  'LaserJet',\n",
       "  'III',\n",
       "  '?',\n",
       "  'Nntp-Posting-Host',\n",
       "  ':',\n",
       "  'hampton',\n",
       "  'Organization',\n",
       "  ':',\n",
       "  'The',\n",
       "  'City',\n",
       "  'University',\n",
       "  'Lines',\n",
       "  ':',\n",
       "  '14',\n",
       "  'Does',\n",
       "  'anyone',\n",
       "  'know',\n",
       "  'of',\n",
       "  'a',\n",
       "  'good',\n",
       "  'way',\n",
       "  '(',\n",
       "  'standard',\n",
       "  'PC',\n",
       "  'application/PD',\n",
       "  'utility',\n",
       "  ')',\n",
       "  'to',\n",
       "  'convert',\n",
       "  'tif/img/tga',\n",
       "  'files',\n",
       "  'into',\n",
       "  'LaserJet',\n",
       "  'III',\n",
       "  'format',\n",
       "  '.',\n",
       "  'We',\n",
       "  'would',\n",
       "  'also',\n",
       "  'like',\n",
       "  'to',\n",
       "  'do',\n",
       "  'the',\n",
       "  'same',\n",
       "  ',',\n",
       "  'converting',\n",
       "  'to',\n",
       "  'HPGL',\n",
       "  '(',\n",
       "  'HP',\n",
       "  'plotter',\n",
       "  ')',\n",
       "  'files',\n",
       "  '.',\n",
       "  'Please',\n",
       "  'email',\n",
       "  'any',\n",
       "  'response',\n",
       "  '.',\n",
       "  'Is',\n",
       "  'this',\n",
       "  'the',\n",
       "  'correct',\n",
       "  'group',\n",
       "  '?',\n",
       "  'Thanks',\n",
       "  'in',\n",
       "  'advance',\n",
       "  '.',\n",
       "  'Michael',\n",
       "  '.',\n",
       "  '--',\n",
       "  'Michael',\n",
       "  'Collier',\n",
       "  '(',\n",
       "  'Programmer',\n",
       "  ')',\n",
       "  'The',\n",
       "  'Computer',\n",
       "  'Unit',\n",
       "  ',',\n",
       "  'Email',\n",
       "  ':',\n",
       "  'M.P.Collier',\n",
       "  '@',\n",
       "  'uk.ac.city',\n",
       "  'The',\n",
       "  'City',\n",
       "  'University',\n",
       "  ',',\n",
       "  'Tel',\n",
       "  ':',\n",
       "  '071',\n",
       "  '477-8000',\n",
       "  'x3769',\n",
       "  'London',\n",
       "  ',',\n",
       "  'Fax',\n",
       "  ':',\n",
       "  '071',\n",
       "  '477-8565',\n",
       "  'EC1V',\n",
       "  '0HB',\n",
       "  '.']]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X[0:1]['unigrams'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `nltk` library does a pretty decent job of tokenizing our text. There are many other tokenizers online, such as [spacy](https://spacy.io/), and the built in libraries provided by [scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html). We are making use of the NLTK library because it is open source and because it simple does a good job of segmentating text-based data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Feature subset selection\n",
    "Okay, so we are making some headway here. Let us now make things a litte bit more interesting. We are going to do something different from what we have been doing thus far. We are going use a bit of everything that we have learned so far. Briefly speaking, we are going to move away from our main dataset (one form of feature subset selection), and we are going to generate a document-term matrix from the original dataset. In other words we are going to be creating something like this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt txt](https://docs.google.com/drawings/d/e/2PACX-1vS01RrtPHS3r1Lf8UjX4POgDol-lVF4JAbjXM3SAOU-dOe-MqUdaEMWwJEPk9TtiUvcoSqTeE--lNep/pub?w=748&h=366)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, it won't have the same shape as the table above, but we will get into that later. For now, let us use scikit learn built in functionalities to generate this document. You will see for yourself how easy it is to generate this table without much coding. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect = CountVectorizer()\n",
    "X_counts = count_vect.fit_transform(X.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we did with those two lines of code is that we transorfmed the articles into a term-document matrix. Those lines tokenize each article using a built-in, default tokenizer (often referred to as an `analzyer`) and then produces the word frequency vector for each document. We can create our own analyzers or even use the nltk analyzer that we previously built. To keep things tidy and minimal we are going to use the default analyzer provided by `CountVectorizer`. Let us look closely at this analyzer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['from',\n",
       " 'stanly',\n",
       " 'grok11',\n",
       " 'columbiasc',\n",
       " 'ncr',\n",
       " 'com',\n",
       " 'stanly',\n",
       " 'subject',\n",
       " 're',\n",
       " 'elder',\n",
       " 'brother',\n",
       " 'organization',\n",
       " 'ncr',\n",
       " 'corp',\n",
       " 'columbia',\n",
       " 'sc',\n",
       " 'lines',\n",
       " '15',\n",
       " 'in',\n",
       " 'article',\n",
       " 'apr',\n",
       " '00',\n",
       " '57',\n",
       " '41',\n",
       " '1993',\n",
       " '28246',\n",
       " 'athos',\n",
       " 'rutgers',\n",
       " 'edu',\n",
       " 'rexlex',\n",
       " 'fnal',\n",
       " 'gov',\n",
       " 'writes',\n",
       " 'in',\n",
       " 'article',\n",
       " 'apr',\n",
       " '01',\n",
       " '56',\n",
       " '56',\n",
       " '1993',\n",
       " '22824',\n",
       " 'athos',\n",
       " 'rutgers',\n",
       " 'edu',\n",
       " 'shrum',\n",
       " 'hpfcso',\n",
       " 'fc',\n",
       " 'hp',\n",
       " 'com',\n",
       " 'matt',\n",
       " '22',\n",
       " '14',\n",
       " 'go',\n",
       " 'therefore',\n",
       " 'to',\n",
       " 'the',\n",
       " 'main',\n",
       " 'highways',\n",
       " 'and',\n",
       " 'as',\n",
       " 'many',\n",
       " 'as',\n",
       " 'you',\n",
       " 'find',\n",
       " 'there',\n",
       " 'invite',\n",
       " 'to',\n",
       " 'the',\n",
       " 'wedding',\n",
       " 'feast',\n",
       " 'hmmmmmm',\n",
       " 'sounds',\n",
       " 'like',\n",
       " 'your',\n",
       " 'theology',\n",
       " 'and',\n",
       " 'christ',\n",
       " 'are',\n",
       " 'at',\n",
       " 'odds',\n",
       " 'which',\n",
       " 'one',\n",
       " 'am',\n",
       " 'to',\n",
       " 'believe',\n",
       " 'in',\n",
       " 'this',\n",
       " 'parable',\n",
       " 'jesus',\n",
       " 'tells',\n",
       " 'the',\n",
       " 'parable',\n",
       " 'of',\n",
       " 'the',\n",
       " 'wedding',\n",
       " 'feast',\n",
       " 'the',\n",
       " 'kingdom',\n",
       " 'of',\n",
       " 'heaven',\n",
       " 'is',\n",
       " 'like',\n",
       " 'unto',\n",
       " 'certain',\n",
       " 'king',\n",
       " 'which',\n",
       " 'made',\n",
       " 'marriage',\n",
       " 'for',\n",
       " 'his',\n",
       " 'son',\n",
       " 'so',\n",
       " 'the',\n",
       " 'wedding',\n",
       " 'clothes',\n",
       " 'were',\n",
       " 'customary',\n",
       " 'and',\n",
       " 'given',\n",
       " 'to',\n",
       " 'those',\n",
       " 'who',\n",
       " 'chose',\n",
       " 'to',\n",
       " 'attend',\n",
       " 'this',\n",
       " 'man',\n",
       " 'refused',\n",
       " 'to',\n",
       " 'wear',\n",
       " 'the',\n",
       " 'clothes',\n",
       " 'the',\n",
       " 'wedding',\n",
       " 'clothes',\n",
       " 'are',\n",
       " 'equalivant',\n",
       " 'to',\n",
       " 'the',\n",
       " 'clothes',\n",
       " 'of',\n",
       " 'righteousness',\n",
       " 'when',\n",
       " 'jesus',\n",
       " 'died',\n",
       " 'for',\n",
       " 'our',\n",
       " 'sins',\n",
       " 'those',\n",
       " 'clothes',\n",
       " 'were',\n",
       " 'then',\n",
       " 'provided',\n",
       " 'like',\n",
       " 'that',\n",
       " 'man',\n",
       " 'it',\n",
       " 'is',\n",
       " 'our',\n",
       " 'decision',\n",
       " 'to',\n",
       " 'put',\n",
       " 'the',\n",
       " 'clothes',\n",
       " 'on']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyze = count_vect.build_analyzer()\n",
    "analyze(\" \".join(list(X[4:5].text)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'From: stanly@grok11.columbiasc.ncr.com (stanly) Subject: Re: Elder Brother Organization: NCR Corp., Columbia SC Lines: 15  In article <Apr.8.00.57.41.1993.28246@athos.rutgers.edu> REXLEX@fnal.gov writes: >In article <Apr.7.01.56.56.1993.22824@athos.rutgers.edu> shrum@hpfcso.fc.hp.com >Matt. 22:9-14 \\'Go therefore to the main highways, and as many as you find >there, invite to the wedding feast.\\'...  >hmmmmmm.  Sounds like your theology and Christ\\'s are at odds. Which one am I  >to believe?  In this parable, Jesus tells the parable of the wedding feast. \"The kingdom of heaven is like unto a certain king which made a marriage for his son\". So the wedding clothes were customary,  and \"given\" to those who \"chose\" to attend.  This man \"refused\" to wear the clothes.  The wedding clothes are equalivant to the \"clothes of righteousness\".  When Jesus died for our sins, those \"clothes\" were then provided.  Like that man, it is our decision to put the clothes on. '"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(list(X[4:5].text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2257, 35788)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can check the shape of this matrix by:\n",
    "X_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00',\n",
       " '000',\n",
       " '0000',\n",
       " '0000001200',\n",
       " '000005102000',\n",
       " '0001',\n",
       " '000100255pixel',\n",
       " '00014',\n",
       " '000406',\n",
       " '0007']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can obtain the feature names of the vectorizer, i.e., the terms\n",
    "count_vect.get_feature_names()[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see the features found in the all the documents `X`, which are basically all the terms found in all the documents. As I said earlier, the transformation is not in the pretty format (table) we saw above -- the term-document matrix. We can do many things with the `count_vect` vectorizer and its transformation `X_counts`. You can find more information on other cool stuff you can do with the [CountVectorizer](http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction). \n",
    "\n",
    "Now let us try to obtain something that is as close to the pretty table I provided above. Before jumping into the code for doing just that, it is important to mention that the reason for choosing the `fit_transofrm` for the `CountVectorizer` is that it efficiently learns the vocabulary dictionary and returns a term-document matrix.\n",
    "\n",
    "In the next bit of code, we want to extract the first five articles and transform them into document-term matrix, or in this case a 2-dimensional array. Here it goes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>category</th>\n",
       "      <th>category_name</th>\n",
       "      <th>unigrams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: sd345@city.ac.uk (Michael Collier) Subje...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "      <td>[From, :, sd345, @, city.ac.uk, (, Michael, Co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "      <td>[From, :, ani, @, ms.uky.edu, (, Aniruddha, B....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, djohnson, @, cs.ucsd.edu, (, Darin, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>From: s0612596@let.rug.nl (M.M. Zwart) Subject...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, s0612596, @, let.rug.nl, (, M.M, ., ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>From: stanly@grok11.columbiasc.ncr.com (stanly...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, stanly, @, grok11.columbiasc.ncr.com...</td>\n",
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       "                                                text  category  \\\n",
       "0  From: sd345@city.ac.uk (Michael Collier) Subje...         1   \n",
       "1  From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...         1   \n",
       "2  From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...         3   \n",
       "3  From: s0612596@let.rug.nl (M.M. Zwart) Subject...         3   \n",
       "4  From: stanly@grok11.columbiasc.ncr.com (stanly...         3   \n",
       "\n",
       "            category_name                                           unigrams  \n",
       "0           comp.graphics  [From, :, sd345, @, city.ac.uk, (, Michael, Co...  \n",
       "1           comp.graphics  [From, :, ani, @, ms.uky.edu, (, Aniruddha, B....  \n",
       "2  soc.religion.christian  [From, :, djohnson, @, cs.ucsd.edu, (, Darin, ...  \n",
       "3  soc.religion.christian  [From, :, s0612596, @, let.rug.nl, (, M.M, ., ...  \n",
       "4  soc.religion.christian  [From, :, stanly, @, grok11.columbiasc.ncr.com...  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_counts[0:5,0:100].toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the result is just this huge sparse matrix, which is computationally intensive to generate and difficult to visualize. But we can see that the fifth record, specifically, contain a `1` in the beginning, which from our feature names we can deduce that this article contains exactly one `00` term.\n",
    "\n",
    "We can also use the vectorizer to generate word frequency vector for new documents or articles. Let us try that below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.transform(['Something completely new.']).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us put a `00` in the document to see if it is detected as we expect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.transform(['00 Something completely new.']).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impressive, huh!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get you started in thinking about how to better analyze your data or transformation, let us look at this nice little heat map of our term-document matrix. It may come to your suprise to things that you can see or the gems you can mine when you start to look at the data from a different perspective>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_x = [\"term_\"+str(i) for i in count_vect.get_feature_names()[0:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['term_00',\n",
       " 'term_000',\n",
       " 'term_0000',\n",
       " 'term_0000001200',\n",
       " 'term_000005102000',\n",
       " 'term_0001',\n",
       " 'term_000100255pixel',\n",
       " 'term_00014',\n",
       " 'term_000406',\n",
       " 'term_0007',\n",
       " 'term_000usd',\n",
       " 'term_0010',\n",
       " 'term_001004',\n",
       " 'term_0010580b',\n",
       " 'term_001125',\n",
       " 'term_001200201pixel',\n",
       " 'term_0014',\n",
       " 'term_001642',\n",
       " 'term_00196',\n",
       " 'term_002']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_y = [\"doc_\"+ str(i) for i in list(X.index)[0:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_z = X_counts[0:20, 0:20].toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are goind to use another visualize library called `seaborn`. It's built on top of matplotlib and closely integrated with pandas data structures. One of the biggest advantages of seaborn is that its default aesthetics are much more visually appealing than matplotlib. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt txt](https://i.imgur.com/Kg0v4Ee.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other big advantage of seaborn is that seaborn has some built-in plots that matplotlib does not. Most of these can eventually be replicated by hacking away at matplotlib, but they’re not built in and require much more code.\n",
    "\n",
    "So without further ado, let us try it now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11798fb70>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c1b5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df_todraw = pd.DataFrame(plot_z, columns = plot_x, index = plot_y)\n",
    "plt.subplots(figsize=(10, 7))\n",
    "sns.heatmap(df_todraw,\n",
    "            cmap=\"PuRd\",\n",
    "            vmin=0, vmax=1, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Exercise 4:** \n",
    "From the chart above, we can see how sparse the term-document matrix is; i.e., there is only one terms with frequency of `1` in the subselection of the matrix. By the way, you may have noticed that we only selected 20 articles and 20 terms to plot the histrogram. As an excersise you can try to modify the code above to plot the entire term-document matrix or just a sample of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The great thing about what we have done so far is that we now open doors to new problems. Let us be optimistic. Even though we have the problem of sparsity and a very high dimensional data, we are now closer to uncovering wonders from the data. You see, the price you pay for the hard work is worth it because now you are gaining a lot of knowledge from what was just a list of what appeared to be irrelevant articles. Just the fact that you can blow up the data and find out interesting characteristics about the dataset in just a couple lines of code, is something that truly inspires me to practise Data Science. That's the motivation right there!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Dimensionality Reduction\n",
    "Since we have just touched on the concept of sparsity most naturally the problem of \"curse of dimentionality\" comes up. I am not going to get into the full details of what dimensionality reduction is and what it is good for just the fact that is an excellent technique for visualizing data efficiently (please refer to notes for more information). All I can say is that we are going to deal with the issue of sparsity with a few lines of code. And we are going to try to visualize our data more efficiently with the results.\n",
    "\n",
    "We are going to make use of Principal Component Analysis to efficeintly reduce the dimensions of our data, with the main goal of \"finding a projection that captures the largest amounty of variation in the data.\" This concept is important as it is very useful for visualizing and observing the characteristics of our dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[PCA Algorithm](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n",
    "\n",
    "**Input:** Raw term-vector matrix\n",
    "\n",
    "**Output:** Projections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduced = PCA(n_components = 2).fit_transform(X_counts.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2257, 2)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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USp+AUulTe9gCBQAAAACAoWQBHAAAAACAoWQBnCL1er30er2mxwDYRZ+AEmkTUCp9AkqlT+1hD3CKtLGx0fQIAAPpE1AibQJKpU9AqfSpPSyAU6SpqammRwAYSJ+AEmkTUCp9AkqlT+1hAZwijY6ONj0CwED6BJRIm4BS6RNQKn1qD3uAAwAAAAAwlCyAU6SVlZWsrKw0PQbALvoElEibgFLpE1AqfWoPC+AUqd/vp9/vNz0GwC76BJRIm4BS6RNQKn1qD3uAU6TDhw83PQLAQPoElEibgFLpE1AqfWoPC+AUqdPpND0CwED6BJRIm4BS6RNQKn1qD1ugAAAAAAAwlCyAU6S1tbWsra01PQbALvoElEibgFLpE1AqfWoPC+AUaXl5OcvLy02PAbCLPgEl0iagVPoElEqf2sMe4BRpYmKi6REABtInoETaBJRKn4BS6VN7HJgrwKuq+ptVVb1YVdVvXHXsdFVVv11V1Se2/3nPVV/781VVfaaqquerqvrDzUzNO3XkyJEcOXKk6TEAdtEnoETaBJRKn4BS6VN7HJgF8CR/O8nXDjj+A3Vdf9n2P7+QJFVVfUmSP57kS7e/50erqhq9Y5MCAAAAANC4A7MAXtf1P0pyqxvzPJbk79R1famu699K8pkkX7FvwwEAAAAAUJxh2AP8T1ZV9U1Jzib5nrquX0nyhUn+2VWP+ez2sRtaX1/P4uJijh49euXY8vJy1tbWcuTIkSt7A/X7/aysrKTT6eTw4cNJko2NjfR6vYyOjmZqaurK9y8tLWV9fT2Tk5MZGxtLkqyurmZ1dTWHDh3KoUOHrrz20tJSxsbGMjk5eeX7e71eNjY2MjU1ldHRrYvYV1ZW0u/3c/jw4XQ6nSRbd65dXl7OxMTEjr++sbi4mCQH7pwWFxfz5ptv5u677x6acxrGn5Nzck5tPKelpaUkyaFDh4bmnIbx5+ScnFPbzmlpaSmTk5NXzmsYzmkYf07OyTm18Zw+9alP5a677sqXfumXDs05DePPyTk5pzae07lz57K6uprjx48PzTkN48/pRud0qw7MFeDX8VSS/yjJlyX5XJL/9XafoKqq2aqqzlZVdfbll1/e6/kAAAAAAGhIVdd10zPcsqqqppP8fF3Xv+tGX6uq6s8nSV3Xf2X7a/8gyem6rp+90fOfPHmyPnv27B5PDQAAAADAXqqq6rm6rk/e7HEH+grwqqrefdWnfyzJb2x//LNJ/nhVVeNVVX1Rki9O8s/v9HwAAAAAADTnwOwBXlXVx5I8muSBqqo+m+R/SvJoVVVflqROcj7JtyVJXdf/uqqqv5vkU0neSvIddV1vNDE3AAAAAADNOFBboOw3W6CUY3l5OUl2bI4PUAJ9AkqkTUCp9AkolT4dfLe6BcqBuQKcdllbW2t6BICB9AkokTYBpdInoFT61B4WwCmSd9+AUukTUCJtAkqlT0Cp9Kk9LIBTpImJiaZHABhIn4ASaRNQKn0CSqVP7THS9AAAAAAAALAfLIBTpH6/n36/3/QYALvoE1AibQJKpU9AqfSpPWyBQpFWVlaSJJ1Op+FJAHbSJ6BE2gSUSp+AUulTe1gAp0jiA5RKn4ASaRNQKn0CSqVP7WEBnCIdPny46REABtInoETaBJRKn4BS6VN72AMcAAAAAIChZAGcIm1sbGRjY6PpMQB20SegRNoElEqfgFLpU3tYAKdIvV4vvV6v6TEAdtEnoETaBJRKn4BS6VN72AOcIo2OjjY9AsBA+gSUSJuAUukTUCp9ag8L4BRpamqq6REABtInoETaBJRKn4BS6VN72AIFAAAAAIChZAEcAAAAAIChZAGcIi0tLWVpaanpMQB20SegRNoElEqfgFLpU3vYA5wira+vNz0CwED6BJRIm4BS6RNQKn1qDwvgFGlycrLpEQAG0iegRNoElEqfgFLpU3tYAKdIY2NjTY8AMJA+ASXSJqBU+gSUSp/awx7gAAAAAAAMJQvgFGl1dTWrq6tNjwGwiz4BJdImoFT6BJRKn9rDAjhFEiGgVPoElEibgFLpE1AqfWoPe4BTpEOHDjU9AsBA+gSUSJuAUukTUCp9ag8L4BRJhIBS6RNQIm0CSqVPQKn0qT1sgQIAAAAAwFCyAE6R1tfXs76+3vQYALvoE1AibQJKpU9AqfSpPSyAU6SlpaUsLS01PQbALvoElEibgFLpE1AqfWoPe4BTpLGxsaZHABhIn4ASaRNQKn0CSqVP7WEBnCJNTk42PQLAQPoElEibgFLpE1AqfWoPW6AAAAAAADCULIADAAAAADCULIBTpF6vl16v1/QYALvoE1AibQJKpU9AqfSpPewBTpE2NjaaHgFgIH0CSqRNQKn0CSiVPrWHBXCKNDU11fQIAAPpE1AibQJKpU9AqfSpPSyAU6TR0dGmRwAYSJ+AEmkTUCp9AkqlT+1hD3AAAAAAAIaSBXCKtLKykpWVlabHANhFn4ASaRNQKn0CSqVP7WEBnCL1+/30+/2mxwDYRZ+AEmkTUCp9AkqlT+1hD3CKdPjw4aZHABhIn4ASaRNQKn0CSqVP7WEBnCJ1Op2mRwAYSJ+AEmkTUCp9AkqlT+1hCxQAAAAAAIaSBXCKtLa2lrW1tabHANhFn4ASaRNQKn0CSqVP7WEBnCItLy9neXm56TEAdtEnoETaBJRKn4BS6VN72AOcIk1MTDQ9AsBA+gSUSJuAUukTUCp9ag8L4BTpyJEjTY8AMJA+ASXSJqBU+gSUSp/awxYoAAAAAAAMJQvgAAAAAAAMJQvgFGlxcTGLi4tNjwGwiz4BJdImoFT6BJRKn9rDAjgAAAAAAEPJTTAp0tGjR5seAWAgfQJKpE1AqfQJKJU+tYcrwAEAAAAAGEoWwAEAAAAAGEoWwCnS8vJylpeXmx4DYBd9AkqkTUCp9AkolT61hz3AKdLa2lrTIwAMpE9AibQJKJU+AaXSp/awAE6Rjhw50vQIAAPpE1AibQJKpU9AqfSpPSyAU6SJiYmmRwAYSJ+AEmkTUCp9AkqlT+1xYPYAr6rqb1ZV9WJVVb9x1bEjVVX9SlVVv7n96xdsH6+qqvrhqqo+U1XVJ6uq+vLmJgcAAAAAoAkHZgE8yd9O8rXXHPtQkqfruv7iJE9vf54kX5fki7f/mU3y1B2akT3S7/fT7/ebHgNgF30CSqRNQKn0CSiVPrXHgVkAr+v6HyW59tasjyX56PbHH03y3quO/0S95Z8lOVxV1bvvzKTshZWVlaysrDQ9BsAu+gSUSJuAUukTUCp9ao8DswB+HVN1XX9u++MXkkxtf/yFSf79VY/77PaxG1pfX8/i4uKOY8vLy1lcXNxxZ9h+v5/FxcUdv0k2NjayuLiYXq+34/uXlpayuLiY9fX1K8dWV1ezuLiY1dXVXa+9tLS04/t7vV4WFxezsbFx5djKykoWFxd3vEu1traWxcXFLC/vfI9gcXHxQJ5Tp9PJ6OjoUJ1TMnw/J+fknNp4Tp1OJ51OZ6jO6TLn5Jyc08E9p9XV1XQ6naE6p2H8OTkn59TGc1pdXd3xOsNwTsP4c3JOzqmN51RVVVZXV4fqnIbx53Sjc7pVQ3MTzLqu66qq6tv9vqqqZrO1TUq+8AtvukbOHXL48OGsra3t+s0B0LTDhw8nyY4/EAA07dChQ1f6BFCSQ4cOZWxsrOkxAHa57777Ute3vZTIAVQdpB90VVXTSX6+ruvftf3580kerev6c9tbnDxT1/XDVVX9je2PP3bt4270/CdPnqzPnj27r+cAAAAAAMDnp6qq5+q6Pnmzxx30LVB+NskHtz/+YJKfuer4N1Vb/kCSizdb/KYsGxsbO/4KBUAp9AkokTYBpdInoFT61B4HZgG8qqqPJXk2ycNVVX22qqpvSfLhJF9TVdVvJvnq7c+T5BeSnEvymST/e5LHGxiZz0Ov19u1NxFACfQJKJE2AaXSJ6BU+tQeB2YP8Lquv/E6Xzo14LF1ku/Y34nYT6Ojo02PADCQPgEl0iagVPoElEqf2uPALIDTLlNTU02PADCQPgEl0iagVPoElEqf2uPAbIECAAAAAAC3wwI4AAAAAABDyQI4RVpaWsrS0lLTYwDsok9AibQJKJU+AaXSp/awBzhFWl9fb3oEgIH0CSiRNgGl0iegVPrUHhbAKdLk5GTTIwAMpE9AibQJKJU+AaXSp/awAE6RxsbGmh4BYCB9AkqkTUCp9AkolT61hz3AAQAAAAAYShbAKdLq6mpWV1ebHgNgF30CSqRNQKn0CSiVPrWHBXCKJEJAqfQJKJE2AaXSJ6BU+tQe9gCnSIcOHWp6BICB9AkokTYBpdInoFT61B4WwCmSCAGl0iegRNoElEqfgFLpU3vYAgUAAAAAgKFkAZwira+vZ319vekxAHbRJ6BE2gSUSp+AUulTe1gAp0hLS0tZWlpqegyAXfQJKJE2AaXSJ6BU+tQe9gCnSGNjY02PADCQPgEl0iagVPoElEqf2sMCOEWanJxsegSAgfQJKJE2AaXSJ6BU+tQetkABAAAAAGAoWQAHAAAAAGAoWQCnSL1eL71er+kxAHbRJ6BE2gSUSp+AUulTe9gDnCJtbGw0PQLAQPoElEibgFLpE1AqfWoPC+AUaWpqqukRAAbSJ6BE2gSUSp+AUulTe1gAp0ijo6NNjwAwkD4BJdImoFT6BJRKn9rDHuAAAAAAAAwlC+AUaWVlJSsrK02PAbCLPgEl0iagVPoElEqf2sMCOEXq9/vp9/tNjwGwiz4BJdImoFT6BJRKn9rDHuAU6fDhw02PADCQPgEl0iagVPoElEqf2sMCOEXqdDpNjwAwkD4BJdImoFT6BJRKn9rDFigAAAAAAAwlC+AUaW1tLWtra02PAbCLPgEl0iagVPoElEqf2sMCOEVaXl7O8vJy02MA7KJPQIm0CSiVPgGl0qf2sAc4RZqYmGh6BICB9AkokTYBpdInoFT61B4WwCnSkSNHmh4BYCB9AkqkTUCp9AkolT61hy1QAAAAAAAYShbAAQAAAAAYShbAKdLi4mIWFxebHgNgF30CSqRNQKn0CSiVPrWHBXAAAAAAAIaSm2BSpKNHjzY9AsBA+gSUSJuAUukTUCp9ag9XgAMAAAAAMJQsgAMAAAAAMJQsgFOk5eXlLC8vNz0GwC76BJRIm4BS6RNQKn1qD3uAU6S1tbWmRwAYSJ+AEmkTUCp9AkqlT+1hAZwiHTlypOkRAAbSJ6BE2gSUSp+AUulTe1gAp0gTExNNjwAwkD4BJdImoFT6BJRKn9rDHuAAAAAAAAwlC+AUqd/vp9/vNz0GwC76BJRIm4BS6RNQKn1qD1ugUKSVlZUkSafTaXgSgJ30CSiRNgGl0iegVPrUHhbAKZL4AKXSJ6BE2gSUSp+AUulTe1gAp0iHDx9uegSAgfQJKJE2AaXSJ6BU+tQe9gAHAAAAAGAoWQCnSBsbG9nY2Gh6DIBd9AkokTYBpdInoFT61B4WwClSr9dLr9dregyAXfQJKJE2AaXSJ6BU+tQe9gCnSKOjo02PADCQPgEl0iagVPoElEqf2sMCOEWamppqegSAgfQJKJE2AaXSJ6BU+tQetkABAAAAAGAoWQAHAAAAAGAoWQCnSEtLS1laWmp6DIBd9AkokTYBpdInoFT61B5DsQd4VVXnk6wm2UjyVl3XJ6uqOpLk/0oyneR8kvfVdf1KUzNye9bX15seAWAgfQJKpE1AqfQJKJU+tcdQLIBv+6q6rl+66vMPJXm6rusPV1X1oe3P/1wzo3G7Jicnmx6Bluh1ezk3dy6XLlzK+LHxnJg/kakZN8Lg+vQJKJE2AaXSJ6BU+tQeo6dPn256hs/bE0888aeTnDl9+nT/qmM/kuS7T58+/doTTzzx75J8+PTp0z9yo+f5yEc+cvqxxx7LoUOHrhxbXl7OK6+8krGxsdx119b7Bf1+P0tLS9nY2MjExESSZGNjIy+88EL6/X7e9a53Xfn+paWlXLx4MRMTExkdHU2SrK6u5uWXX06SjI+PJ9l616nX62VtbS333nvvle/v9Xp59dVX0+l0MjKytWPNyspKlpeXMzo6mrGxsSTJ2tpaXnzxxayvr+eee+658v2Li4tZXV09cOc0Ojqa9fX1oTqnYfw5HfRz6nV7+ZfiBEgNAAAgAElEQVTf+y+zurmakY2R1C/VWf6l5VS/o8rrk68fyHMaxp9Taed0uVHDdE7D+HNyTs6pbefU7/dz+PDhoTqnYfw5OSfn1MZzWlpayvr6+lCd0zD+nJyTc2rjOV26dCnLy8tDdU7D+HO60Tk9+eSTnzt9+vSZ3MSw7AFeJ/nlqqqeq6pqdvvYVF3Xn9v++IUkAy/prKpqtqqqs1VVnb38Awba4dzcuWy+sbnj2GZ/M+f/0vlmBgIAAABgT1V1XTc9w+etqqovrOv6t6uqejDJryT5ziQ/W9f14ase80pd119wo+c5efJkffbs2X2elluxurqaJDveBYK99szIM1tvn12rSh7dfPQOT8NBoU9AibQJKJU+AaXSp4Ovqqrn6ro+ebPHDcUV4HVd//b2ry8m+ekkX5GkV1XVu5Nk+9cXm5uQ27W6unolRLBfxo+N39ZxSPQJKJM2AaXSJ6BU+tQeB34BvKqqe6uqOnT54yT/ZZLfSPKzST64/bAPJvmZZibknTh06JB34Nh3J+ZPZKSzM4MjnZGcmD/R0EQcBPoElEibgFLpE1AqfWqPu5oeYA9MJfnpqqqSrfP5P+u6/qWqqn49yd+tqupbkiwkeV+DM3KbBIg7YWpm69YA5+bO5dKFSxk/Np4T8yeuHIdB9AkokTYBpdInoFT61B5DsQf4XrEHOAAAAABA+W51D/A9uwK8qqojN/p6XdfLe/VaDL/19fUkydjYWMOTAOykT0CJtAkolT4BpdKn9tjLLVCeS1InqQZ8rU5iU11u2dLSUpLk6NGjDU8CsJM+ASXSJqBU+gSUSp/aY88WwOu6/qK9ei7w7htQKn0CSqRNQKn0CSiVPrXHyF4/YbXl/VVV/cXtz49VVfUVe/06DLfJyclMTk42PcY70u12Mz09nZGRkUxPT6fb7TY9ErCHDnKfgOGlTUCp9AkolT61x54vgCf50SSPJPlvtz9fTfKRfXgdKE63283s7GwWFhZS13UWFhYyOztrERz2WK/by7PTz+aZkWfy7PSz6XV7TY8EAAAAFGg/FsB/f13X35FkLUnqun4lyd378DpQnLm5ufT7/R3H+v1+5ubmGpoIhk+v28vzs8/n0sKlpE4uLVzK87PPWwQHAAAAdtmPBfD1qqpGs3Xjy1RVNZlkcx9ehyHW6/XS6x28xawLFy7c1nHg9p2bO5fN/s7/rGz2N3Nu7twdef2D2idguGkTUCp9AkqlT+2xHwvgP5zkp5NMVVU1n+QfJ/nL+/A6DLGNjY1sbGw0PcZtO3bs2G0dB27fpQuXbuv4XjuofQKGmzYBpdInoFT61B537fUT1nXdrarquSSntg+9t67rT+/16zDcpqammh7hHZmfn8/s7OyObVA6nU7m5+cbnAqGy/ix8a3tTwYcvxMOap+A4aZNQKn0CSiVPrXHflwBniSdJKPbz3/PPr0GQ2x0dDSjo6NNj3HbZmZmcubMmRw/fjxVVeX48eM5c+ZMZmZmmh4NhsaJ+RMZ6ez8z9dIZyQn5k/ckdc/qH0Chps2AaXSJ6BU+tQeVV3Xe/uEVfU/JvmGJH8/SZXkvUn+Xl3X//OevtA+OHnyZH327NmmxwDgJnrdXs7NnculC5cyfmw8J+ZPZGrGu/cAAADQFlVVPVfX9cmbPm4fFsCfT/J76rpe2/78niSfqOv64T19oX1gAbwcKysrSZLDhw83PAnATvoElEibgFLpE1AqfTr4bnUBfD+2QFlMMnHV5+NJfnsfXoch1u/3d+yjDVAKfQJKpE1AqfQJKJU+tcee3QSzqqr/LUmd5GKSf11V1a9sf/41Sf75Xr0O7eDdN6BU+gSUSJuAUukTUCp9ao89WwBPcnnvkOeS/PRVx5/Zw9egJTqdTtMjAAykT0CJtAkolT4BpdKn9tizBfC6rj+6V88FAAAAAACfr728AjxJUlXVFyf5K0m+JFftBV7X9Ym9fi2G19raWpJkYmLiJo8EuLP0CSiRNgGl0iegVPrUHvtxE8y/leSpJG8l+aokP5HkJ/fhdRhiy8vLWV5ebnoMgF30CSiRNgGl0iegVPrUHvuxAH5PXddPJ6nqul6o6/p0kj+yD6/DEJuYmPAOHBxgvW4vz04/m2dGnsmz08+m1+01PdKe0SegRNoElEqfgFLpU3vs+RYoSS5VVTWS5DerqvqTSX47ybv24XUYYkeOHGl6BOAd6nV7eX72+Wz2N5MklxYu5fnZ55MkUzNTTY62J/QJKJE2AaXSJ6BU+tQe+3EF+Hcl6ST5U0l+X5L3J/mmfXgdAAp0bu7clcXvyzb7mzk3d66hiQAAAIC22vMF8Lquf72u69fquv5sXdffXNf1f5PkG/b6dSjXMG99ANzcpQuXbus4AAAAwH7ZjyvAB3nfHXodGnZ564NLC5eS+u2tD253EXxxcTGLi4v7NCWwn8aPjd/W8YNGn+Cd8Qb5/tImoFT6BJRKn9rjTi2AV3fodWiYrQ8oTbfbzfT0dEZGRjI9PZ1ut9v0SEPvxPyJjHR2/udlpDOSE/MnGpoIaNpevUEOAABwu/bsJphVVV1v5/gqFsBbY6+2Pjh69OhejEPLdbvdzM7Opt/vJ0kWFhYyOzubJJmZmWlytKF2+UaX5+bO5dKFSxk/Np4T8yeG4gaYiT7BO3GjN8iHpQ1N0yagVPoElEqf2qOq63pvnqiqfitJncGL3XVd18Vf+nfy5Mn67NmzTY9xoD07/ezW1V3XGD8+nkfOP9LARLTZ9PR0FhYWdh0/fvx4zp8/f+cHAmipZ0ae2fpT4rWq5NHNR+/wNAAAwDCoquq5uq5P3uxxe3YFeF3XX7RXz8XBdWL+RJ6ffX7HVV62PqApFy5cuK3jAOyP8WPjg98gH5J7AwAAAOW6U3uA0xJTM1N5+MzDGT8+nlRbV34/fObh2/7rzcvLy1leXt6nKd8Ze0kfPMeOHbut43ArSuwTlM69AfafNgGl0iegVPrUHnt2BThcNjUz9Xnv57m2trZH0+wNe0kfTPPz8zt+bknS6XQyPz/f4FQcdKX1CQ6CYb83QAm0CSiVPgGl0qf22Ms9wH8hyeN1XZ/fkydsgD3Ay3E5QhMTEw1PssVe0gdXt9vN3NxcLly4kGPHjmV+ft6bFnxeSusTQKJNQLn0CSiVPh18t7oH+F4ugH9DkvkkH03yV+u6Xt+TJ76DLIBzPSMjIxn0e6Wqqmxubg74DvZbr9tzJSEAAABASzVxE8y/V1XVLyb5i0nOVlX1fyTZvOrrT+7Va8GdduzYsYFXgNtLuhm9bm/HzVYvLVzK87PPJ4lFcAAAAACu2OubYL6Z5PUk40kOXfMP3LJ+v79j3+amzc/Pp9Pp7DhmL+nmnJs7d2Xx+7LN/mbOzZ1raCLapLQ+ASTaBJRLn4BS6VN77NkV4FVVfW2SJ5P8bJIvr+vav0G8YysrK0mya9G5KZf3jLaXdBkuXbh0W8dhL5XWJ4BEm4By6RNQKn1qj728AnwuyTfUdf0hi998vjqdzq4AdbvdTE9PZ2RkJNPT0+l2u3d0ppmZmZw/fz6bm5s5f/68xe8GjR8bv63jsJcG9QmgadoElEqfgFLpU3vs5R7g//lePRccPnx4x+fdbjezs7NX/mrKwsJCZmdnk2TXQrSbIw6/E/MnduwBniQjnZGcmD/R4FS0xbV9AiiBNgGl0iegVPrUHnu9Bzjsi7m5uV37MvX7/czNze04dvnmiJcWLiX12zdH7HV7d3Jc9tnUzFQePvNwxo+PJ1Uyfnw8D5952BsdUJBet5dnp5/NMyPP5NnpZ3UYAACARlR1XTc9QzFOnjxZnz17tukxSLKxsZEkGR0dTZKMjIxk0L+rVVVlc/Ptq4CfnX52a/H7GuPHx/PI+Uf2aVqgTa7tE7tdfjPy2r+l4Y0q2D/aBJRKn4BS6dPBV1XVc3Vdn7zZ41wBTpF6vV56vbevFjx27NjAx1173M0Rgf12bZ/Y7dzcuR2L30my2d/MublzDU0Ew0+bgFLpE1AqfWoPC+AUaXR0dMc7cPPz87tuTNDpdDI/P7/jmJsjAvvt2j6xmzcj4c7TJqBU+gSUSp/aY89uggl7aWpq51+Rv3yjy7m5uVy4cCHHjh3L/Pz8rhtgFndzxE9+PHm6m1x8KbnvgeTUTPK7/2AzswB74to+sdv4sfHB21F5MxL2jTYBpdInoFT61B72AL+KPcCHQ6/by7m5c7l04VLGj43nxPyJZvac/eTHk597Klm/ahFobDz5+j9hERwYavYABwAAYL/ZA5zWmpqZyiPnH8mjm4/mkfOPNLfY8nR35+J3svX5090kSbebTE8nIyNbv3a7d3xCgH0xNTOVh888nPHj40m1dSNii98AAAA0wRYoFGlpaSlJMjk52fAkn4eLL133eLebzM4m/f7WoYWFrc+T5JpdXYDCDEWf7oCpmSkL3nAHaRNQKn0CSqVP7eEKcIq0vr6e9fX1PXmubreb6enpjIyM5IEHHsgDDzyQkZGRTE9Pp3vVZde9bi/PTj+bZ0aeybPTz6bX/TzvBHzfA9c9Pjf39uL39pTp96fz/vfvngsoy172CWCvaBNQKn0CSqVP7WEBnCJNTk5e9x24qxe0b7ZY3O12Mzs7m4WFhdR1nZdffjkvv/xy6rrOwsJCZmdn0+1281OP/1Q+8YFPbN20rU4uLVzK87PPp9ftvfOF8VMzW3t+X21sPDk1kwsXdkyZZDbJQpKdc72Tcwb21436BNAUbQJKpU9AqfSpPdwE8ypugrl/ut1u5ubmcuHChRw7dizz8/OZeQd7fVxe0O5fdfl0p9PJmTNnBj7f9PR0FhYWbvic999/fz6y/JFM1bv/qv7o/aOp36jf+Y3cPvnxrT2/L760dUX4qZnkd//BTE9vbXuyPWW2Fr93On78eM6fP3/b5wwAAAAAw+5Wb4JpAfwqFsD3x14u4F5vQfvyYvG1RkZGciv/jj+dpzNyG38hYvz4eB45/8gtP/5aO/cAH0mye8aqqrK5uXnb5wwAAAAAw+5WF8BtgcI7drNtOS5vHXL0/Ufz4/0fz6mcuvK1fr+fubm56z736upqVldXdx2/sHPvkJseP3bs2K2cSl7Mi7f0uMsuXbh0aw/85MeTH5hNTv/XW79+8uNJtm50eeZMcvx4kgye8fLst3vO0CZ7vnf/LbhenwCapE1AqfQJKJU+tYcFcN6Ra/fWXlhYyAc+8IFUVZW77rorX1199ZU9tatUeSgP5XvzvTsWwRcWFq4snj/++OOZnp6+8v0PP/xwHn300R2L6o8/3k1dD/5X9noL3e95z3tSVdV1z6PT6eT+++/Pj+XHspa1HV+7VF3KXfffNfD7xo+NDzy+wyc/nvzcU8nFpST11q8/99SORfDz55Of/Mn5dDqdXXPNz8/f8NxudXG/aU0sUNIOvW4vz88+P3Dv/v3kD0lAibQJKJU+AaXSp/awBcpVbIFyfZf38F5YWMjo6Gg2NjZu+PiP5WN5KA/tOv5W3spIRvJiXsyP5cfydJ4e+P2HDh1KkmxsbOTMmTP5J/8k+bdPdfOt+aY8mAd3fP/1tlMZtPVKktx7773p9/tX9iJPktnZ2TzSfyTfmm/Ng3kwS9VSxr59LF/5lV+Z52efv+09wHvdXs5959lceqWT8ftWc+LUP8nUf/Jvt75432Ty3Wd2zXq9PdIP8h7glxco3/Ee6nADz04/u7X4fY3Pd4uim7n8B6TLnQIogTYBpdInoFT6dPDZA/wdsAC+2+OPP56nnnrqtr/vVvbUfjNv5q28lXtyT5LkYi7mR/IjuxbFjx8/nv944cvyvfn2TGTiyvG1rOX78/35xP2fyA/90A9lZmZma+F57lwuXbiUl0Zeyl/f+OsDn2/r5pLJ3Fxy4UJy5Eg3yVyWl3cvQF/9nOPHxnNi/sSuxdurF7Dfd+R9mV2dzcibb5//yNh6Hv76X91eBK+S0z91W/9/vv0GxIWMjh7LxsZ8jh+fyfz81pXkJep1e/n0Bz+dDHivZL8XKGmHZ0aeGbR9flIlj24+eoenAQAAAO4kC+DvgAXwt3W73Xzbt31bXn/99R3HT+XUlaukX82rGctYOtnavuPyAnaSfCgfyl0ZvH3IjdSpdzzP5dfazObA59vIxpWF9npsIyO5K1l/++uXF8l3LoJXSTZTVcnV//pXVfLt35786I/e3szXXqF9vavfx+97NY/86b818ArwW3udq2+cuaXT2dpLvLRF8EFXfu9ggZI90NQV4AAAAEDz3ASTd+zxxx/P+9///iuL36dyKr+UX8qv5dcyl7k8lIcykpEczuHcm3tTbf/vcA5nbvt/72TxO8mV5/mese/J94x9z5XXut7zjWb0yuuPrO9c/E6SiUzkW/Ot13zX1t7Z1773U9fJU08lDzywtdh8q+bm5nZsT/JgHhz4uLWLh5Kx8XTXHrzhzUOv/zo7F7+Trc8H3Uv0Zjco3W/n5s5df/E7t7iHOtzEifkTGens/M/YSGckJ+ZP7Ovrrq+vZ319/eYPBLiDtAkolT4BpdKn9nhnq5QMrW63m6eeeip/NX81J/P2GyhVrn8jyavd6uNu5q0H3tr64HOf/3PtXJDuJJm/4eNffnnrSutk55XVg7ZCSZIPL3x4x77kL+bFgVeAv5gX8/i/XMtH/+8fuLJgvrCwkNntF7vZft4XLtza8WuvSB/4Gp/8ePJ0N7n4UnLfA8mpmeR3/8Ebvv7tuHRh91W5l92JBUra4fJWRDfbomivLS0tJUmOHj26r68DcDu0CSiVPgGl0qf2sAXKVWyBknxX9V15b96bZO8Ws9+J1x/Yuvr83pfu/byf6628lQ/nw3k6n0jyQ0lubb+Q48eT8+e3Ph64pcdYUlVV6jff/j20lrX8Yn4xX5evG7hf+T8ceSabm7s3xb68L/mNTE8nCws3nnPrcdNZGPDAK6/xyY8nP/dUsn7VIvXYePL1f2LPFsGvtzVFRpPf+dHfeXBvgLnPbxxwMFz+Q9Lk5GTDkwC8TZuAUukTUCp9OvhsgbKtqqqvrarq+aqqPlNV1Yeanqdk3139tbw3772ypUiT7n3p3j1Z/E6Su3JXvjffmz+VD+ZjmcrTeSYfy7M5ld72I7pJprP122F6+/NuFhaOZ2RkJA88MJ2Pf9O/2r2lx3p2LH4nW1uufFW+Kt+f788LeSGb2cwLeeHKPuSDFr+TZGHhQqank5GRrYXuQTuWzM9v7fl9tU5n6/jVLlznUvELFy5sLeD+9A/vXPxOtj5/eu+2Sfnce07kUrUzL9XdVUYPj+bTH/h0np1+Nr1u7zrfXajLbxxcXEpSb/36c09tHadVJicn/QEJKI42AaXSJ6BU+tQeQ70AXlXVaJKPJPm6JF+S5BurqvqSZqcq1x/J72184Xu/TGQi781/lYdyV0aSPJRLmcun82v5h/m1HM1P5wdzKn8oyUKS/z7JNye5kLqu8/LLC3lg89Z/q9yX+zI+dk++Md+YUzmVb8w3XnUTztGB31NVx7KwsLUP+cLC1hYs1y6Cz8xs3fDy+PGtG3YeP5588IPdzM3t3Ov72LFjA1/j2EMPbi3Y1tfZm/viS7sO9bq9PDv9bJ4ZeeaWF6273eQDH53KX6sfzgsZz2aSi7krGxt1Nl7eSOrk0sKlPD/7/MFaBH+6u+9vHAAAAACwt4Z6ATzJVyT5TF3X5+q6fjPJ30nyWMMzFWv0Oouzw+Laxf1q+9jlG2/+2fzZnMqpJG/m2rtpvppXb+t1Zte/K1v7jV+tk2R21/Gq6qSuty7jPpVePpZn83P9ZzL+wd0LzjMzW9udbG4m8/PdfPSjs1lYWEhd11f2+n7Pe96TzjWXinc6ncz/oS/ZvYB7tfse2PHp5W1fLi1cuq1F68s363w6U/nGPJJTeTRvZDQj11z8vtnfzLm5czd8rqIMeIPghscBAAAAaNzo6dOnm55h3zzxxBN/IMmDp0+f/tntz78oye88ffr0Lwx6/Ec+8pHTjz32WA4dOnTl2PLycl555ZWMjY3lrru27hna7/eztLSUjY2NTExs7fO8sbGRF154If1+P+9617uufP/S0lIuXryYiYmJjI5uLTCvrq7m5ZdfTpKMj48n2brzbK/Xy9raWu699+2tP3q9Xl599dV0Op2MjGy9X7GyspLl5eWMjo5mbGwsSbK2tpYXX3wx6+vrueeee658/+LiYlZXV2/pnD71tz6VjCZ3Xdo6tjmymdceei3r967n7tfvvvL9rz/wei7ddyl3XborI9tXRl86dClv3P9GUiV3vbn1/RtjG3l96vW8NfFW7u6//f2vTb2WS//BpYz1x1LVW4vSa/et5Y0jb2RkcySj66N5beq1vHHkjazfu57Nuzcz9sbYle9fffdq3jz0ZsZfG79y7I0jb2Tt8FpG10czsrE103pnPf0H+qlH61s6p7fueytfeulLc3HzYv5y/nK+5dC35Kvv/+q8Wb2Z3/fm78tEJm75nMYzkqX7TuW1I7+Vzc2LWV8/muQHMz7+3Zma+g8zNnYub7yxlPvvP55+/wfz7nd/VU4dWsifeu2zOZy3thbnv+C1/PY//e1MfMFE7vs99+36d+9973tfXnrppYyMjOShhx7Kvffem5WVlSwtLeUHf/AH85nPfCajo6OZnJzMk08+mZn8VlZzV17OeH7q078/7+3+hfyZf/DN+Ynf+C/yrvtfy+/4o/9Z7j32xVfO6Z9+8J/m9bde3/FzeqPzRnrP93L0A0ev++/en/kzW9//7ncv5tCh1bz22qE8nn+XasDPaePVjTz4PzzY3O+nT348Kz/5v2T5lz+W0ed+JWPvOpRMTQ/+/fT/PZ3FS3VWM5ZDeevt30+HHsor01/eikY4p61zeumll/L6669v/w2R4TinYfw5OSfn1LZzuvyG+OW5huGchvHn5JycUxvP6dOf/nSWl5fzwANvX3Bz0M9pGH9Ozsk5tfGczp8/n89+9rO5++67h+achvHndKNzevLJJz93+vTpM7mJYb8C/KaqqpqtqupsVVVnL/+A2+of5/nUKeOmqPVInXrkzs/yYB7M9+X78lAeykhGcn/uz3fmO3Nf7rut56mS/PE8mOTnkiwlOZ9TuSc/np/Jj9YP5AfyoZzKfJaXk+QDSb4+781nMpGd25Nsrm3m/F86P/A1brTX98zMTH71V381v/7rv55/82/+TWZmZq5c4f2Ln/m9+bO/8sEsXHwwdUby7199MPP/zzfk53/zP93xPG9+7s2Bz//mC4OPXzZoB5YXM777YJLxY4OP3xGX9/RefTlJnay+dOM9vU/NJHfdvfPY2HjyyB/d91Epy8bGRjY2Bu/nD9AUbQJKpU9AqTY3N/WpJaq6LmPBcz9UVfVIktN1Xf/h7c//fJLUdf1XBj3+5MmT9dmzZ+/ghOX5rup0Hst/cWW7kKb2BN8c2VoIHrmNvbf3U536tv+/2ExyKo8mSU7lp/K96WQiE1e+vpa1KzfHTJKn83RGBrwnVafOV9Vftev49PR0FhYWdh0/fvx4zp8/v3ug7QXf6f+/vfuPsvu86wP/fjQeazKJIkWWrEShkmIaDAFcWnRofLbUISqFpk3TZPsDdkKzXVyVpJwGTrMcQGf3mGWnP4AlJG3jomOSungo0Ky3iSHAgkK89ByXIhfW/Mi6pMYSyU0mY09GVpiMPJ757h/fGWl+3JFmZI3uM/e+Xhwxus+9c+fznXHevv7cZz7Pj74/5y7c3uXz2vEqSTv+5JPv+GTS5d8Du4/uzt1P373+jiUTE+0M89nZK2t/ZXgy7ylPZtfzVxr8u0Z35c7Td+bQ2KENn2tbvffk0oGWa+w9mHzvBm8ePvFoO/P7wjPtGwonxpK77tneOqnO8guk5XfIAWogm4BaySegVvJp5yulPN40zfFrPa6O7uL2+a0kry2lvKaUcmuSb0vy0R7XVLX3Nffls//k43ljviX/If8hL+SFNGv+72bYtbirmuZ30r4RsNUrX7nr+d4srmp+J+3BnPfm3hWP//wGz9Tk4b/9eI4dS3btSo4da5vMb3rTeErpMut7fLz709x1T/Lmd+b8he4nHC9vKF+e/d2t+b1rdFfuGL9jgzpb3Q7rHPvQoXz1B+/M7qO7k9I20W9q8/uJR9uG931vaz8+8ej1zfS+6562OX7fw+1Hze+BNDQ05AUSUB3ZBNRKPgG1kk+Do693gCdJKeVNSX4iyVCSDzbLpw12YQf4Gk88mokf+cOc+oW/nnNLTdMT+Xy+L7+XW1ccmHk9u6NrsV21X0rJ/cNH8pH51yRJPp5f7/p1mjQZz3jO5ExO5ER+MD/YdRf4ZHbn23Jl1/XwcNtcfv75iSSnkpxPKUfyXd81ng98YOyqO5WPHUu6bBy/vAP8sWOPtQdfrjWUfNWDX3WlaX2TdkNPTLQHa54/345XGR9vm+ybtjzqZOUBoMO723EmX7q4/vFX2wEOAAAAQBXsAF/SNM3Hmqb5iqZpvvxqzW+6uOuejD10b56euT1NU/LQQyWfOnooP5KvyeeyO4tp8rk8k7M5mxeycEN3iM/tncvc3rkb8lxXsx3N7ybJj+Yr80uLX5ZdKw5L3Ojrf1++Lydy4vIolG4OZnVDen4+ef75JBlL8nSSxTTN0/nYx8auNHwvTLXVXJhaNdt6fDwZXb1xPKOj7XqSXDrfpfmdJItZ3fy+ytfYjIffNZkP3/JYPl4+kQ/f8lgeftfkuscsj1M5dy5pmvbjyZPt+qadmVjd/E6u3B5eM4N8eHfbyN+BJicm89ixx/KJXZ/IY8cey+TE+u8nN8bMzExmZmZ6XQbAKrIJqJV8AmolnwbHLb0ugJ1jbGx55+2hpT+tV7866XSSpMmJTOa786nszXySZDazmc9i9uZl2Uq7eX60/fyRCyPXeOT2aJLrbo1PZnfO5FCykOwqi7nWewK35tZ8d747Z/Ib+Xym8sqsHwuycpzKiUzm3tHN8b0AACAASURBVDyV23Mpn8/uPJA72q+Xdpd0u2v//Tl/4UCO7H0m4yceytjX/kbyf70/STI21u7SXt5VvX9/+7zf8R3t2gP7d+eWZ9c3wVcdWLlRU/nMxKZ2gT/8rsmM3v/k5UM/Dyxcytz9T+bhJG/7wJXrP3Vq9SzxpL196tQWdoFvNNLkS19M3vbu7d3FfpN2yS+PrVmcbb+fl85dasfYJL2bsd7HZpf+ody3b1+PKwG4QjYBtZJPQK3k0+DQAOdF+8xnlv9WkieezMSP/GG+5xf+es5dONCuLbWSlxu3h3IpC2my6/K961vN29X4XrlDfaN2/AtpR5i89Dp2s89lVx7IlRnZi037NRZSrvo/tr3ZmzP55TyXxTyf+dya4a7PeSKTeU+uNI5fmUt5T9pG55kcyv6XXMzJn397Zufb79+5C7fn5CPvSpK2Cf7I/e3fx+7J2Nj6AyvPnUt+fPiOvOfW9QdWrpr9fT3zs1dYPP3U5WtYNpLFfPH0U8mKBvjyXPK1Nlrv2nDee2CDwy4PtM3o7ZrjvXb0yvIu+eSGf82nTj11ufm9bHF2MU+dekoDfBt4cQTUSDYBtZJPQK3k0+Do+xEo3GQrx6b8v7+Rh8Z+Ki8dnk3S5Exuz7fn7rwxb8g355tyIt+UyXRvdN8yO5xbZofTJOv+/EkW141a2czolcUsZjzjeWPemMl0Hw+xmOSf5avykk0+X5MmL+SFLGYxn8sL+bHceXk39kqP5PBVn7GkZFdK9mUoyXBmcksWk3wuu1c9573p3ji+N0+lpMmzsy+73PxeNjs/klNn3t7emL+UPPwTlw+CfOTdk/mp2cdyJp/Iv8tjOZHJ/NL8oZzec40DK/ce6H4hG62vsX+h+5iVtetHjnT//K7rG41lee3X92bUydV2yd9gG42t2XCcDS/K6OhoRtfOEQLoMdkE1Eo+AbWST4PDDnC2z133ZOyhezL2UHtz7WGGb3pT8rP/9o581588uaqpO5dd+bF8Rc7klRs+9Yk8nHuzmNuzP5/PdB7LSO7Obbk9l1bsOb9iMU0+kl+8PGf7gTyQ9+Q9GVnRgF9M8pEczpkcyr15Kq/M+uZhs/T/JzOZB/LAmrndR9PO416pJGny/nxFkuRvpHPN0Sq3JvlCduVE3nB51/wP5pN5ptyag83zXT/n9lxKc5VnPn9hTWP6wlQmf/hX8z8+W7ruJv+5Zw/lP73sUM4nOZJkPO208ctOjHU/WHKTTeXpod050KUJPj20ulE9Pr56h3qyel75Khs1nP/w8eTN77wpo0hWeZG75Ldi95HdXQ8uXTW2BgAAAGAAaYBz01yZIb7CBw7l4XclXzz9VPYvXMr00O7sOnlH/u7r9+bxd89lZqb7DvEzedu6IyPfv/SxnUP+h9m7dADlhQzlX+YrciadtE3q8zmTTyWZzb3Z23WW9gO5Y9WokWS5MX9nzuRV6T7Ye6O5HO0s8Pc3r8035fPZd42DMZPkYJ7PiXwu78l/vVzD7c3z2Wg6+coZ4d0c2bu+6frUr37DhrvJP14O5dy5dm354Mlkxc9vuXl8nU3lXSfvyNz967+/u07esepxy19v5Rsn4+MbzP++WsN5O0edbORqo1dusDvG71g1AzzpMraGG2Zurj2gd2SkN2cUAHQjm4BaySegVvJpcJSm2fqc4351/Pjx5uzZs70ugySdTie/9EvJD//w4csHNV66lHzxizevho0PmzyW5FyXz+i2AzxZ2bQ+k09sau7QTIYyl1s22IXerJpffqUx333W8+jwXE6/+QPtDPAVPvFD/yjdmumLaRvqa6/76NHk6RWXt3ZH/4aN6Q08/K7JLK5542PlAZhb9t6TGzScDybfe/r6n/d6rZ0BnrS75N/8zm07CPOpU0/l0vlL2X1kd+4Yv8P8723SaU/9zeHDh3tcCcAVsgmolXwCaiWfdr5SyuNN0xy/1uPsAKdKIyMjeetbk+/8ztXry03Xdndy993QN8qZHNqgqTye5GSSFXM5Mrq03s2VGj+f3V2b2t0+5/YNHtekZPbWxbz0+ZLdey/mXz//lTnzpW51tm9uvWR4vuvz7N57MZcuvLzrfcs1rhyL8vHzV75Gt8Mz1+0S38CVxvmhHDlyaMPG+ZYb7C9yLMsN9yJ3yW/VobFDGt43id0BQI1kE1Ar+QTUSj4NDjvAV7ADfGdZ3QxfqcltoxeTpsmzX9qTdJ0K/mI0SX4myalk4ynZXZ3I5LrRKt0s78Lu1iz/XHbn2/P6PPS292bsa38j7/rFe/Ovz75pzQzwtW8OtN+T933rAxn72t/IxO9+Y/79I2/Nd83/8apaNnpL4XPZne8/evflHeDHjnX7vmfdLvG11jbOk3am9+nTq5vbm33cOk88evNnfQMAAABw0212B7gG+Aoa4H1kRSP0wI8+mGdn91zHk6xvB4/cupCh5vn8yfzyu4Rbb6yvHK2ymO6/hvG5pdEjG88hP5ThXc9n9y0v5IvPv2QLdTR52a1fyuzzt2Yxt6wb83Jo6RDRtRaTfPaf3J4ced0Gbzq0SkkWr9Lb32zj/Hob7AAAAAAMBg3w66AB3p927Uqu5x/zl740OXDgygiON70pefDB1buSr89yMaXrjvCVTe6N55Bvj3+Xx7ruOv98Gc78f/9fcvIX3pXZuaENP/9aDeqNfhZrG+flKv18kQUAAADAZhvgmzmPD266Tqdz+TCCF+vIkY3vGx1N3vnOZHh49frwcPKTP9k2cxcX248f+1j35vfRo8lDDyVDG/eF13/O3vawxjM5lB/Lnflcdmcx7c7vtvl9++X7vz1350TekG/P3dva/E6SB3JH5tbEwlx25Zav/6859at/56rN79HRdk731Wz0s1i7vtH3civfY9guNzKfAG4U2QTUSj4BtZJPg0MDnL43Pt42Z9e67bZ2pvQHPpB86ENtI7uU9uOHPrR+1vT5892f//z59rEPPrj5Bu0Xnx/J8K7nk6xvcj82eijv/FufydFXPJsru8VvjrUN+WfKLZk9/t/ytr/60Zy/cGDDzzt6dBPzudP9Z9Gtcb6w0P3zN1oHAAAAgG6MQFnBCJT+tXxg5vI4k/Hxazdr19rMXOqNHtPNrUPz2bNvOM8+2zbOFxaufDx6tK3xO76jNyM/ju79fJ7+nn+wau3YT/xkzl24ff1jtziXezM/i6t9H5e/N1v9+QEAAADQP4xAgRXGxlaPM7me5ulmdi9vtEu8207u5xeG87KXtQ3uBx9sn2t5h/O5c8nJk8n+/d2frZTk1lu3fAmX3XL55M31dQ3vms/4vU8kw7tXrY9/889ldGT1FuzNjD1ZazM/i/Hxja9v+XszMbG1rwvAxiYnJvPYscfyiV2fyGPHHsvkxGSvSwIAALghNMBhk8bG2jEfK0elrB37cbV5490sN8xPnVo/X3z5drem+0//dPLBD16pZauzsa8cOLn+tMn5xVty6sN/KRO7/tdk78H2MXsPZux/uSunHxi66vVftyceTd57Mrnvbe3H839w1Z3vs7Pt92zbra3riUdvwhcFuLkmJybz5Mknc+ncpaRJLp27lCdPPqkJDgAA9AUNcKo0PT2d6enpXpexzrV2L2+0S/y229Y3mpMrDfONdo5PT2/cdF9Zy/IO8tW6d5BLWdkA7/qIdpf1//66TNx+Ornv4eR7Tyd33bPu+pN2XMmuXe3Ha+3KnphY8fjDc5l4+wPJfW9NHv6J5MJUW/OFqZz65wczP3/151r5PVv1vJuoY1OeeDR55P5VdeWR+9c3wTXJB06t+QTX66lTT2VxdvW/GBZnF/PUqad6VBHXQzYBtZJPQK3k0+DQAKdKc3NzmZub63UZW7bRLvH3ve/q41M22jl+5Ej3pvvahm+y/uu+851l3RiRW2/d/Ezxa+2ynphoR5GcO9c+57VGk6x7/GdHcvLn356J3/3GdY89f+G2a9a3/D3bah2bdmYimb+0em3+Uru+bLNNcvrKTs0n2Mil85e2tE6dZBNQK/kE1Eo+DQ4NcKq0f//+7N9oAHblujWsrzU+ZTPzxZdt1PBNVn/dD3xg9ZiUo0fb21ux8Uzzjce2bNQ07/r4+ZGcOvP2dY89sveZq9a18nuz1To27cIGNaxc30yTnL6zk/MJutl9ZPeW1qmTbAJqJZ+AWsmnwaEBTpVGRkYyMjLS6zJuqKuNT9nMfPFlW2n4dvuat117c/VlV5tpvlFzfMvrFw6sWxs/8VBGh1c3lsvSFJm135utfr21Nhyfsnd9XevWN9Mkp+/0Yz4x2O4YvyO7Rle/JNw1uit3jN/Ro4q4HrIJqJV8AmolnwaHBjhU4lrzxZe92Ibv+96XdaNRbrklGR5evbbRDvRlVxvbsmxlc3nXBmnTbbf32J/7zzn9Q/9t1RsCP/3T7Y73td+bzdSxkauOTzkxlgyv2f04vLtdX7aZJjlA5Q6NHcqdp+/M7qO7k5LsPro7d56+M4fGDvW6NAAAgBdNA5wqzc7OZnbtNmeSvLiGb9I2j9eORvk3/yb50Ic2twN92bXGtqxtLi8srH+O0eG5jJ94aPXi3oPJm9+ZsR943VXfEFhurp87d2V3eLc6ruaqu+nvuid58zvbelIu15W77rny4M00yek78ol+dGjsUO5++u68YfENufvpuzW/dyDZBNRKPgG1kk+DozSbPRFvABw/frw5e/Zsr8sgSafTSZIcPny4x5XUZ7mxvDKjR0ev3bDerlpOnWp3nx850jadl2tYbk6vNTTUNrWPvHIu4298KGN/+mPtjukTY6uby9f4umu/B6W0jfajR1fXcTW7dnU/FLSUtsZNeeLRdub3hWe2fB3sTPIJqJFsAmoln4Bayaedr5TyeNM0x6/5OA3wKzTA6zEzM5Mk2bdvX48rqdPVGs+1uCHN5Q1s1Fw/erTdLX6zn4fBIp+AGskmoFbyCaiVfNr5NMCvgwY43Djb2Vy+Uc31mnbTAwAAALB5m22AmwEObItrzQh/MTaad75//9aeZ2ysbXZvZfY5AAAAADuHBjhVWlhYyEK3UxPZMbazuTw+ntx66/r1555rd3Vvtc6rHbYJa8knoEayCaiVfAJqJZ8GhwY4VZqcnMzk5GSvy+BF2q7m8thYsmfP+vX5+eQd72hHpBw7tvVmeC9MTLS17qSaB518ol9NTkzmsWOP5RO7PpHHjj2WyQn/nO8ksgmolXwCaiWfBsctvS4AuhkaGup1CVRuerr7+vKbt+fOtfO9k3p3da+dQb4TakY+0Z8mJybz5MknszjbHqRw6dylPHnyySTJobFDvSyNTZJNQK3kE1Ar+TQ4HIK5gkMwYefY6JDNtW7EoZvbZTsPCr2qJx5NzkwkF55J9h5ITowld92zjV8QqN1jxx7LpXOX1q3vPro7dz99dw8qAgAAuDqHYAJ9rdshm92cP7/9tVyvjWrb1pqfeDR55P7kwlSSpv34yP3tOjCwLp1f3/y+2joAAMBOoQEO7EhrD9nc6DeXjhy5uXVtxUa1bWvNZyaS+TUNrflL7TowsHYf2b2ldQAAgJ1CA5wqTU1NZWpqqtdlULmVh2w++OD6HeGjo+1O8VqsPfDyTW/qQc0XntnaOuvIJ/rRHeN3ZNfo6peFu0Z35Y7xO3pUEVslm4BaySegVvJpcGiAU6X5+fnMz8/3ugx2kLU7wo8ebW/36jDJtc3ud72rPeDy3LmkadqPDz6YvOMdN7nmvQe2ts468ol+dGjsUO48fWd2H92dlHb2952n73QA5g4im4BaySegVvJpcDgEcwWHYNZjOYCGh4d7XAlszcRE8u53J88+u3q9lLbxvdZNP6RzeQb4yjEow7uTN7/TQZibJJ+AGskmoFbyCaiVfNr5NnsI5i03oxjYKuHDTjQx0e7ynp1df99G7zXe9EM6l5vcZybasSd7DyQnxjS/t0A+ATWSTUCt5BNQK/k0ODTAAW6QU6e6N7+vpieHdN51j4Y3AAAAMBDMAKdKFy9ezMWLF3tdBmzJtXZzl7L6dm2HdLI58gmokWwCaiWfgFrJp8FhBzhVWg6gPXv29LgS2LwjR9rDLbsZHW0PvPzYx9pG+ZEjbfO7V4d0suSJR7c8DkY+ATWSTUCt5BNQK/k0ODTAqZLwYScaH+8+A/y225L3vU+zuzprDwS9MNXeTq7aBJdPQI1kE1Ar+QTUSj4NDiNQqNKePXsEETvO2Fhy+nRy9Gg77uTo0eShh5JnntH8vpEmJpJjx5Jdu9qPExPX+URnJq40v5fNX2rXr0I+ATWSTUCt5BNQK/k0ODTAAW6gsbHk6aeTxcX2o8b3jTUx0e6yP3cuaZr248mT19kEv/DM1ta57Ia9CQEAAADbTAOcKs3Pz2d+fr7XZQCVOXVq/YiZ2dl2fcv2Htja+pJBz6cb+iYEcMMMejYB9ZJPQK3k0+DQAKdKU1NTmZqa6nUZQGXOn9/a+lWdGEuGd69eG97drl/FoOfTDX0TArhhBj2bgHrJJ6BW8mlwOASTKg0PD/e6BKBCR460O467rW/Z8kGXZybasSd7D7TN76scgJnIpxv6JgRwwwx6NgH1kk9AreTT4NAAp0oHDx7sdQlAhcbH23EbK3cgj46269flrnuu2fBea9Dz6Ya+CQHcMIOeTUC95BNQK/k0OIxAAWBDtR12ODaWnD6dHD2alNJ+PH3aYaM30/h4+6bDSi/qTQgAAADYRhrgAHS1nYcdvpjG+thY8vTTyeJi+7Fb87u2xn0/8SYEAAAAO0lpmqbXNVTj+PHjzdmzZ3tdBkkmJyeTJIcOHepxJTC4jh3rPuri6NG28Xy9lhvra8eY3Kgm6nY/v3wCaiSbgFrJJ6BW8mnnK6U83jTN8Ws9zg5wqrSwsJCFhYVelwEDbbsOOzx1anVzOmlvnzr14p73Zj2/fAJqJJuAWsknoFbyaXA4BJMqefcNem+7Djvcrsb6zXp++QTUSDYBtZJPQK3k0+CwA5wqDQ0NZWhoqNdlwEDbrsMON2qgv9jG+nY///Jc8eHhoXz5lw+ZKw5UxWsnoFbyCaiVfBocGuAAdLVdhx1uV2N9O59/Ow8EBQAAALaPQzBXcAhmPWZmZpIk+/bt63ElwHaYmGhncp8/3+7MHh+/MQdUbtfzrzwQdO/eNp8uXNj3og8EBbhRvHYCaiWfgFrJp51vs4dgaoCvoAFej06nkyQ5fPhwjysBSHbtand+J8mrXtXm02c/ezilJIuLPSwMYInXTkCt5BNQK/m08222Ae4QTKrk3TegJisPBL1wYd+qdYAaeO0E1Eo+AbWST4PDDHCqNDo6mtG1Q3wBemTlXPHZ2dHMzo7m1luTL36x3R1+7Jh54EBvee0E1Eo+AbWST4NjRzfASyn3lVI+U0r5naU/b1px3w+UUj5VSnmylPItvawTgJ1t7YGgt93WjkR59lmHYgIAAEDNdnQDfMl7m6b5uqU/H0uSUsrrknxbkq9O8q1JPlBKGeplkWzN3Nxc5ubmel0GwGVjY+2Bl7Ozc3nFK+YyP7/6/tnZ9uBNgF7w2gmolXwCaiWfBkc/NMC7eUuSn22a5lLTNH+U5FNJvqHHNbEF09PTmZ6e7nUZAOtMT09ndrZ7Pp0/f5OLAVjitRNQK/kE1Eo+DY5+aIB/dynliVLKB0spr1hae3WSP17xmE8vrV3V/Pz85RNgl01PT6fT6ax6R2h2djadTiczMzOX1xYWFtLpdDI5Obnq86emptLpdDK/YqvgxYsX0+l0cvHixXVfe2pqatXnT05OptPpZGFh4fLazMxMOp1OZmdnL6/Nzc2l0+ms+x9up9PZkdc0MjKSUkpfXVPSfz8n1+SaBvGaRkZGsn//SPbsuZhXvaqTPXuuXNNrXrMzr2lZP/2cXJNrGrRreu655zIyMtJX19SPPyfX5JoG8Zqee+65VXX2wzX148/JNbmmQbympmny3HPP9dU19ePP6WrXtFnVN8BLKb9WSvm9Ln/ekuT+JF+e5OuSfDbJ/3Edz3+ylHK2lHL22WefvcHVc73279+fV7ziFdd+IMBNtn///nz/9+/PS16yen10NLnvvp6UBJCXv/zl2b9/f6/LAFjn5S9/efbu3dvrMgDWecUrXpGXv/zlvS6Dm6A0TdPrGm6IUsqxJL/QNM3XlFJ+IEmapvmnS/f9SpL7mqZ57GrPcfz48ebs2bPbXSoAfWBiop35ff58cuRIMj7ezglXDwAAAGy/UsrjTdMcv9bjbrkZxWyXUsqrmqb57NLNtyb5vaW/fzTJz5RSfjzJ4SSvTfKfe1AiAH1qbKyeBvPERHLyZHsQZ5KcO9feTuqpEQAAAHqh+hEo1/AjpZTfLaU8keSbknxvkjRN8/tJfj7JHyT55ST/sGmahY2fhtp0mwMEUIMa8+nUqSvN72Wzs+06MBhqzCaARD4B9ZJPg2NH7wBvmuY7rnLfeJLxm1gOAPTE+fNbWwcAAIBBsaMb4PSvw4cP97oEgK5qzKcjR9qxJ93WgcFQYzYBJPIJqJd8Ghwa4ACww42Pr54BniSjo+060CNPPJqcmUguPJPsPZCcGEvuuqfXVQEAwMDZ6TPAAWDgjY0lp08nR48mpbQfT592ACb0zBOPJo/cn1yYStK0Hx+5v10HAABuKjvAqdL09HSSZP/+/T2uBGC1WvNpbEzDG6pxZiKZv7R6bf5Su75Nu8BrzSYA+QTUSj4NDg1wqjQ3N9frEgC6kk/ANV14ZmvrN4BsAmoln4BayafBoQFOlbz7BtRKPgHXtPfA0viTLuvbRDYBtZJPQK3k0+AwA5wqjYyMZGRkpNdlAKwjn4BrOjGWDO9evTa8u13fJrIJqJV8AmolnwaHHeAAAHAjLc/5PjPRjj3Ze6Btfm/T/G8AAGBjGuBUaXZ2NkkyOjra40oAVpNPwKbcdc9NbXjLJqBW8gmolXwaHBrgVGlmZiaJEALqI59gADzx6I7bvS2bgFrJJ6BW8mlwaIBTJeED1Eo+QZ974tHkkfuT+Uvt7QtT7e2k6ia4bAJqJZ+AWsmnwaEBTpX27dvX6xIAupJP0OfOTFxpfi+bv9SuV9wAl01AreQTUCv5NDh29boAAACoxoVntrYOAABUTQOcKi0sLGRhYaHXZQCsI5+gz+09sLX1SsgmoFbyCaiVfBocGuBUaXJyMpOTk70uA2Ad+QR97sRYMrx79drw7na9YrIJqJV8AmolnwaHGeBUaWhoqNclAHQln6DPLc/5PjPRjj3Ze6Btflc8/zuRTUC95BNQK/k0OErTNL2uoRrHjx9vzp492+syAAAAAAC4ilLK403THL/W44xAAQAAAACgL2mAAwAAAADQlzTAqdLU1FSmpqZ6XQbAOvIJqJFsAmoln4BayafB4RBMqjQ/P9/rEgC6kk9AjWQTUCv5BNRKPg0ODXCqdPDgwV6XANCVfAJqJJuAWsknoFbyaXBogFOl4eHhXpcA0JV8Amokm4BaySegVvJpcJgBDgAAAABAX7IDnCpdvHgxSbJnz54eVwKwmnwCanTxN//v5D8+nD0XP5/sPZCcGEvuuqfXZQF47QRUSz4NDg1wqiSEgFrJJ6A6Tzyai7/6M8kLz2dPmuTCVPLI/e19muBAj3ntBNRKPg0ODXCqJHyAWsknoDpnJrLnhT9ZvTZ/KTkzoQEO9JzXTkCt5NPg0ACnSkIIqJV8Aqpz4Zl253eXdYBe89oJqJV8GhwOwQQAgJ1s74GtrQMAwADRAKdK8/PzmZ+f73UZAOvIJ6A6J8Yyf8tI5lOurA3vbg/CBOgxr52AWsmnwaEBTpWmpqYyNTXV6zIA1pFPQHXuuidT3ziWqZe9KklJ9h5M3vxO87+BKnjtBNRKPg0OM8Cp0vDwcK9LAOhKPgE1Gn7dn09e9+eTgwd7XQrAKl47AbWST4NDA5wqHfQfb0Cl5BNQI9kE1Eo+AbWST4PDCBQAAAAAAPqSBjgAAAAAAH1JA5wqTU5OZnJystdlAKwjn4AaySagVvIJqJV8GhxmgFOlhYWFXpcA0JV8Amokm4BaySegVvJpcGiAU6VDhw71ugSAruQTUCPZBNRKPgG1kk+DQwOcKg0NDfW6BICu5BNQI9kE1Eo+AbWST4PDDHAAAAAAAPqSBjhVmpmZyczMTK/LAFhHPgE1kk1AreQTUCv5NDg0wKnS7OxsZmdne10GwDryCaiRbAJqJZ+AWsmnwWEGOFXat29fr0sA6Eo+ATWSTUCt5BNQK/k0ODTAqdLo6GivSwDoSj4BNZJNQK3kE1Ar+TQ4jEABAAAAAKAvaYBTpbm5uczNzfW6DIB15BNQI9kE1Eo+AbWST4NDA5wqTU9PZ3p6utdlAKwjn4AaySagVvIJqJV8GhxmgFOlkZGRXpcA0JV8Amokm4BaySegVvJpcGiAU6X9+/f3ugSAruQTUCPZBNRKPgG1kk+DwwgUAAAAAAD6kgY4AAAAAAB9SQOcKnU6nXQ6nV6XAbCOfAJqJJuAWsknoFbyaXBogAMAAAAA0JccgkmVDh8+3OsSALqST0CNZBNQK/kE1Eo+DQ47wAEAAAAA6Esa4AAAAAAA9KUd0QAvpfytUsrvl1IWSynH19z3A6WUT5VSniylfMuK9W9dWvtUKeX7b37VvBjT09OZnp7udRkA68gnoEayCaiVfAJqJZ8Gx06ZAf57Sd6W5CdXLpZSXpfk25J8dZLDSX6tlPIVS3f/qyTfnOTTSX6rlPLRpmn+4OaVzIsxNzfX6xIAupJPQI1kE1Ar+QTUSj4Njh3RAG+a5pNJUkpZe9dbkvxs0zSXkvxRKeVTSb5h6b5PNU3z1NLn/ezSYzXAd4j9+/f3ugSAruQTUCPZBNRKPgG1kk+DY0eMQLmKVyf54xW3P720ttH6Vc3Pz6fT6axam56eTqfTWfWu0OzsbDqdTmZmZi6vLSwspNPpZHJyctXnT01NpdPpZH5+/vLaxYsX0+l0cvHixXVfe2pqatXnT05OptPp/gg5fwAAE8BJREFUZGFh4fLazMxMOp1OZmdnL6/Nzc2l0+ms+9WNTqezI69pZGTkcv39ck1J//2cXJNrGsRrGhkZycjISF9d0zLX5Jpc0869punp6cuvn/rlmvrx5+SaXNMgXtP09PSqtX64pn78Obkm1zSI17S4uJjp6em+uqZ+/Dld7Zo2q5od4KWUX0vyyi53nWqa5iPb+HVPJjmZJK9+9TV75AAAAAAA7BClaZpe17BppZRPJHlP0zRnl27/QJI0TfNPl27/SpL7lh5+X9M039LtcRs5fvx4c/bs2W2pna1ZfqdodHS0x5UArCafgBrJJqBW8gmolXza+UopjzdNc/xaj6tmB/h1+miSnyml/HjaQzBfm+Q/JylJXltKeU2Sz6Q9KPN/6FmVbNnyrzUIIaA28gmokWwCaiWfgFrJp8GxIxrgpZS3JvkXSQ4m+cVSyu80TfMtTdP8finl59MebvlCkn/YNM3C0ud8d5JfSTKU5INN0/x+j8rnOggfoFbyCaiRbAJqJZ+AWsmnwbGjRqBsNyNQAAAAAADqt9kRKLtuRjEAAAAAAHCzaYBTpYWFhSwsLPS6DIB15BNQI9kE1Eo+AbWST4NDA5wqTU5OZnJystdlAKwjn4AaySagVvIJqJV8Ghw74hBMBs/Q0FCvSwDoSj4BNZJNQK3kE1Ar+TQ4NMCp0qFDh3pdAkBX8gmokWwCaiWfgFrJp8FhBAoAAAAAAH1JAxwAAAAAgL6kAU6VpqamMjU11esyANaRT0CNZBNQK/kE1Eo+DQ4zwKnS/Px8r0sA6Eo+ATWSTUCt5BNQK/k0ODTAqdLBgwd7XQJAV/IJqJFsAmoln4BayafBoQFOlYaHh3tdAkBX8gmokWwCaiWfgFrJp8FhBjgAAAAAAH1JA5wqXbx4MRcvXux1GQDryCegRrIJqJV8AmolnwaHBjhVEkJAreQTUCPZBNRKPgG1kk+DwwxwqrRnz55elwDQlXwCaiSbgFrJJ6BW8mlwaIBTJSEE1Eo+ATWSTUCt5BNQK/k0OIxAAQAAAACgL2mAU6X5+fnMz8/3ugyAdeQTUCPZBNRKPgG1kk+DQwOcKk1NTWVqaqrXZQCsI5+AGskmoFbyCaiVfBocZoBTpeHh4V6XANCVfAJqJJuAWsknoFbyaXBogFOlgwcP9roEgK7kE1Aj2QTUSj4BtZJPg8MIFAAAAAAA+pIGOAAAAAAAfUkDnCpNTk5mcnKy12UArCOfgBrJJqBW8gmolXwaHGaAU6WFhYVelwDQlXwCaiSbgFrJJ6BW8mlwaIBTpUOHDvW6BICu5BNQI9kE1Eo+AbWST4NDA5wqDQ0N9boEgK7kE1Aj2QTUSj4BtZJPg8MMcAAAAAAA+pIGOFWamZnJzMxMr8sAWEc+ATWSTUCt5BNQK/k0ODTAqdLs7GxmZ2d7XQbAOvIJqJFsAmoln4BayafBYQY4Vdq3b1+vSwDoSj4BNZJNQK3kE1Ar+TQ4NMCp0ujoaK9LAOhKPgE1kk1AreQTUCv5NDiMQAEAAAAAoC9pgFOlubm5zM3N9boMgHXkE1Aj2QTUSj4BtZJPg0MDnCpNT09nenq612UArCOfgBrJJqBW8gmolXwaHGaAU6WRkZFelwDQlXwCaiSbgFrJJ6BW8mlwaIBTpf379/e6BICu5BNQI9kE1Eo+AbWST4PDCBQAAAAAAPqSBjgAAAAAAH1JA5wqdTqddDqdXpcBsI58Amokm4BaySegVvJpcGiAAwAAAADQlxyCSZUOHz7c6xIAupJPQI1kE1Ar+QTUSj4NDjvAAQAAAADoSxrgAAAAAAD0JQ1wqjQ9PZ3p6elelwGwjnwCaiSbgFrJJ6BW8mlwmAFOlebm5npdAkBX8gmokWwCaiWfgFrJp8GhAU6V9u/f3+sSALqST0CNZBNQK/kE1Eo+DQ4NcKo0MjLS6xIAupJPQI1kE1Ar+QTUSj4NDjPAAQAAAADoSxrgVGl2djazs7O9LgNgHfkE1Eg2AbWST0Ct5NPgMAKFKs3MzCRJRkdHe1wJwGryCaiRbAJqJZ+AWsmnwaEBTpWED1Ar+QTUSDYBtZJPQK3k0+DQAKdK+/bt63UJAF3JJ6BGsgmolXwCaiWfBocZ4AAAAAAA9CUNcKq0sLCQhYWFXpcBsI58Amokm4BaySegVvJpcGiAU6XJyclMTk72ugyAdeQTUCPZBNRKPgG1kk+DY0c0wEspf6uU8vullMVSyvEV68dKKV8qpfzO0p9/veK+ry+l/G4p5VOllPeXUkpvqud6DA0NZWhoqNdlAKwjn4AaySagVvIJqJV8Ghw75RDM30vytiQ/2eW+/9Y0zdd1Wb8/yd9P8ptJPpbkW5P80rZVyA116NChXpcA0JV8Amokm4BaySegVvJpcOyIHeBN03yyaZonN/v4Usqrkry8aZr/1DRNk+TfJvkb21YgAAAAAADV2REN8Gt4TSnlt0spj5ZSvnFp7dVJPr3iMZ9eWruq+fn5dDqdVWvT09PpdDqZm5u7vDY7O5tOp5OZmZnLawsLC+l0OutmB01NTaXT6WR+fv7y2sWLF9PpdHLx4sV1X3tqamrV509OTqbT6awayj8zM5NOp5PZ2dnLa3Nzc+l0Opmenl71+Z1OxzW5JtfkmlyTa3JNrsk1uSbX5Jpck2tyTa7JNbkm1+Sa+uqaNquaESillF9L8soud51qmuYjG3zaZ5McaZrm2VLK1yf5D6WUr97i1z2Z5GSSvPrV1+yRc5NMTU3l0qVL2bWrH96jAfrJ8r/0R0ZGelwJwBVf+MIXMjw8nIMHD/a6FIBVvvCFL+SWW26RT0B1nnnmmVy8eDGjo6O9LoVtVtoJITtDKeUTSd7TNM3Zq92f5DNJfr1pmq9cWv/2JG9omuYfXO35jx8/3pw92/WpucmW38U5fPhwjysBWE0+ATWSTUCt5BNQK/m085VSHm+a5vi1HlfNDvDrUUo5mGS6aZqFUsodSV6b5KmmaaZLKc+VUl6f9hDMv5vkX/SyVrbG7gCgVvIJqJFsAmoln4BayafBsSMa4KWUt6ZtYB9M8oullN9pmuZbkvzFJP9bKWU+yWKS72qaZnmgzLuS/JskL0nyS0t/2CGGh4d7XQJAV/IJqJFsAmoln4BayafBsaNGoGw3I1AAAAAAAOq32REoThikShcvXlx14ixALeQTUCPZBNRKPgG1kk+DY0eMQGHwLAfQnj17elwJwGryCaiRbAJqJZ+AWu34fHri0eTMRHLhmWTvgeTEWHLXPb2uqkoa4FRpx4YP0PfkE1Aj2QTUSj4BtdrR+fTEo8kj9yfzl9rbF6ba24kmeBca4FRpR4cQ0NfkE1Aj2QTUSj4BtdrR+XRm4krze9n8pXZdA3wdM8ABAAAAAHaKC89sbX3AaYBTpfn5+czPz/e6DIB15BNQI9kE1Eo+AbXa0fm098DW1gecBjhVmpqaytTUVK/LAFhHPgE1kk1AreQTUKsdnU8nxpLh3avXhne366xjBjhVGh4e7nUJAF3JJ6BGsgmolXwCarWj82l5zveZiXbsyd4DbfPb/O+uStM0va6hGsePH2/Onj3b6zIAAAAAALiKUsrjTdMcv9bjjEABAAAAAKAvaYADAAAAANCXNMCp0uTkZCYnJ3tdBsA68gmokWwCaiWfgFrJp8HhEEyqtLCw0OsSALqST0CNZBNQK/kE1Eo+DQ4NcKp06NChXpcA0JV8Amokm4BaySegVvJpcGiAU6WhoaFelwDQlXwCaiSbgFrJJ6BW8mlwmAEOAAAAAEBf0gCnSjMzM5mZmel1GQDryCegRrIJqJV8AmolnwaHBjhVmp2dzezsbK/LAFhHPgE1kk1AreQTUCv5NDjMAKdK+/bt63UJAF3JJ6BGsgmolXwCaiWfBocGOFUaHR3tdQkAXcknoEayCaiVfAJqJZ8GhxEoAAAAAAD0JQ1wqjQ3N5e5ublelwGwjnwCaiSbgFrJJ6BW8mlwaIBTpenp6UxPT/e6DIB15BNQI9kE1Eo+AbWST4PDDHCqNDIy0usSALqST0CNZBNQK/kE1Eo+DQ4NcKq0f//+XpcA0JV8Amokm4BaySegVvJpcBiBAgAAAABAX9IABwAAAACgL2mAU6VOp5NOp9PrMgDWkU9AjWQTUCv5BNRKPg0ODXAAAAAAAPpSaZqm1zVUo5QyleRcr+vgsgNJnul1EQBdyCegRrIJqJV8Amoln3a2o03THLzWgzTAqVYp5WzTNMd7XQfAWvIJqJFsAmoln4BayafBYAQKAAAAAAB9SQMcAAAAAIC+pAFOzU73ugCADcgnoEayCaiVfAJqJZ8GgBngAAAAAAD0JTvAAQAAAADoSxrgVKeU8q2llCdLKZ8qpXx/r+sBBksp5U+VUn69lPIHpZTfL6W8e2l9fynlV0spf7j08RVL66WU8v6lzHqilPLnensFQD8rpQyVUn67lPILS7dfU0r5zaUM+rlSyq1L67uXbn9q6f5jvawb6G+llH2llA+XUv6/UsonSyl3e+0E1KCU8r1L/133e6WUf1dKGfH6afBogFOVUspQkn+V5K8keV2Sby+lvK63VQED5oUk/7hpmtcleX2Sf7iUQ9+f5EzTNK9NcmbpdtLm1WuX/pxMcv/NLxkYIO9O8skVt/95kvc2TfOnk3whyXcurX9nki8srb936XEA2+V9SX65aZqvTPJn0uaU105AT5VSXp3kHyU53jTN1yQZSvJt8fpp4GiAU5tvSPKppmmeaprm+SQ/m+QtPa4JGCBN03y2aZr/svT3i2n/A+7VabPowaWHPZjkbyz9/S1J/m3T+k9J9pVSXnWTywYGQCnly5L81SQPLN0uSd6Y5MNLD1mbTcuZ9eEkJ5YeD3BDlVL2JvmLSX4qSZqmeb5pmpl47QTU4ZYkLyml3JJkNMln4/XTwNEApzavTvLHK25/emkN4KZb+pW3P5vkN5Mcaprms0t3fS7JoaW/yy3gZvmJJN+XZHHp9m1JZpqmeWHp9sr8uZxNS/dfWHo8wI32miRTST60NKLpgVLKS+O1E9BjTdN8JsmPJTmftvF9Icnj8fpp4GiAA0AXpZSXJfk/k3xP0zTPrbyvaZomSdOTwoCBVEr5a0k+3zTN472uBWCNW5L8uST3N03zZ5P8Sa6MO0nitRPQG0tnD7wl7Rt1h5O8NMm39rQoekIDnNp8JsmfWnH7y5bWAG6aUspw2ub3RNM0Dy8tTy7/eu7Sx88vrcst4Gb475L89VLK02lHxL0x7czdfUu/0puszp/L2bR0/94kz97MgoGB8ekkn26a5jeXbn84bUPcayeg1/5Skj9qmmaqaZr5JA+nfU3l9dOA0QCnNr+V5LVLJ/LemvZwgo/2uCZggCzNePupJJ9smubHV9z10STvWPr7O5J8ZMX63y2t1ye5sOLXfQFuiKZpfqBpmi9rmuZY2tdHH2+aZizJryf5m0sPW5tNy5n1N5ceb/clcMM1TfO5JH9cSrlzaelEkj+I105A751P8vpSyujSf+ct55PXTwOm+DlSm1LKm9LOuBxK8sGmacZ7XBIwQEopfyHJbyT53VyZs/uDaeeA/3ySI0nOJfnbTdNML72Q+pdpf5VuNsnfa5rm7E0vHBgYpZQ3JHlP0zR/rZRyR9od4fuT/HaStzdNc6mUMpLkp9OeYzCd5NuapnmqVzUD/a2U8nVpD+i9NclTSf5e2g13XjsBPVVK+aEkfyfJC2lfK92bdta3108DRAMcAAAAAIC+ZAQKAAAAAAB9SQMcAAAAAIC+pAEOAAAAAEBf0gAHAAAAAKAvaYADAAAAANCXNMABAAAAAOhLGuAAAAAAAPQlDXAAAAAAAPqSBjgAAAAAAH1JAxwAAAAAgL6kAQ4AAAAAQF/SAAcAAAAAoC9pgAMAAAAA0Jc0wAEAAAAA6Esa4AAAAAAA9CUNcAAAAAAA+pIGOAAAAAAAfUkDHAAAKlRK+VOllD8qpexfuv2KpdvHujz2i1t43vtKKe/ZYi2bfn4AAKiJBjgAAFSoaZo/TnJ/kn+2tPTPkpxumubpnhUFAAA7jAY4AADU671JXl9K+Z4kfyHJj232E0spby6l/GYp5bdLKb9WSjm04u4/U0p5rJTyh6WUv7/ic/7nUspvlVKeKKX80I27DAAA6I1bel0AAADQXdM086WU/znJLyf5y03TzG/h0/9jktc3TdOUUu5N8n1J/vHSfXcleX2Slyb57VLKLyb5miSvTfINSUqSj5ZS/mLTNP/PDbocAAC46TTAAQCgbn8lyWfTNqh/dQuf92VJfq6U8qoktyb5oxX3faRpmi8l+VIp5dfTNr3/QpK/nOS3lx7zsrQNcQ1wAAB2LCNQAACgUqWUr0vyzWl3a3/vUjN7s/5Fkn/ZNM3XJvkHSUZW3NeseWyTdtf3P22a5uuW/vzppml+6kWUDwAAPacBDgAAFSqllLSHYH5P0zTnk/xotjADPMneJJ9Z+vs71tz3llLKSCnltiRvSPJbSX4lyf9USnnZ0td/dSnl9hdxCQAA0HNGoAAAQJ3+fpLzTdMsjz35QJK/V0q5p2maR9c8drSU8ukVt388yX1J/n0p5QtJPp7kNSvufyLJryc5kOSHm6bpJOmUUr4qyWNt7z1fTPL2JJ+/sZcFAAA3T2matb/9CAAAAAAAO58RKAAAAAAA9CUNcAAAAAAA+pIGOAAAAAAAfUkDHAAAAACAvqQBDgAAAABAX9IABwAAAACgL2mAAwAAAADQlzTAAQAAAADoS/8/VUK30WCEqYQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb169b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = ['coral', 'blue', 'black', 'm']\n",
    "\n",
    "# plot\n",
    "fig = plt.figure(figsize = (25,10))\n",
    "ax = fig.subplots()\n",
    "\n",
    "for c, category in zip(col, categories):\n",
    "    xs = X_reduced[X['category_name'] == category].T[0]\n",
    "    ys = X_reduced[X['category_name'] == category].T[1]\n",
    "   \n",
    "    ax.scatter(xs, ys, c = c, marker='o')\n",
    "\n",
    "ax.grid(color='gray', linestyle=':', linewidth=2, alpha=0.2)\n",
    "ax.set_xlabel('\\nX Label')\n",
    "ax.set_ylabel('\\nY Label')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the 2D visualization above, we see slight \"hint of separation in the data\"; i.e., they might have some special grouping by category, but it is not immediately clear. The PCA was applied to the raw frequencies and this is considered a very naive approach as some words are not really unique to a document. Only categorizing by word frequency is considered a \"bag of word\" approach. Later on in the course you will learn about different approaches on how to create better features from the term-vector matrix, such as term-frequency inverse document frequency so-called [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Exercise 6:** \n",
    "Please try to reduce the dimention to 3, and plot the result use 3-D plot.\n",
    "Use at least 3 different angle (camera position) to check your result and describe what you foun.  \n",
    "$Hint$: you can refer to `Axes3D`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb0f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer example\n",
    "X_reduced = PCA(n_components = 3).fit_transform(X_counts.toarray())\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# plot\n",
    "fig = plt.figure(figsize = (25,10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "col = ['coral', 'blue', 'black', 'm']\n",
    "shape = ['o', '*', 'D', '^']\n",
    "for c, m, category in zip(col, shape, categories):\n",
    "    xs = X_reduced[X['category_name'] == category].T[0]\n",
    "    ys = X_reduced[X['category_name'] == category].T[1]\n",
    "    zs = X_reduced[X['category_name'] == category].T[2]\n",
    "    \n",
    "    ax.scatter(xs, ys, zs, c = c, marker = m, alpha=0.6)\n",
    "\n",
    "ax.set_xlim(0,750)\n",
    "ax.set_xlabel('\\nX Label')\n",
    "ax.set_ylabel('\\nY Label')\n",
    "ax.set_zlabel('\\nZ Label')\n",
    "\n",
    "# adjust angel\n",
    "# ax.view_init(elev = 10, azim = 20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Atrribute Transformation / Aggregation\n",
    "We can do other things with the term-vector matrix besides applying dimensionalaity reduction technique to deal with sparsity problem. Here we are going to generate a simple distribution of the words found in all the entire set of articles. Intuitively, this may not make any sense, but in data science sometimes we take some things for granted, as we just have to explore the data first to make any premature conclusions. On the topic of attribute transformation, we will take the distribution and put the distribution in a scale that makes the it easy to analyze patterns in the distrubution of words. Let us get into it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to compute these frequencies for each term in all documents. Visually speaking, we are seeking to add values of the 2D matrix, vertically; i.e., sum of columns. You can also refer to this process as aggregation, which we won't explore further in this notebook because of the type of data we are dealing with. But I believe you get the idea of what that includes.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt txt](https://docs.google.com/drawings/d/e/2PACX-1vTMfs0zWsbeAl-wrpvyCcZqeEUf7ggoGkDubrxX5XtwC5iysHFukD6c-dtyybuHnYigiRWRlRk2S7gp/pub?w=750&h=412)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# note this takes time to compute. You may want to reduce the amount of terms you want to compute frequencies for\n",
    "# term_frequencies = []\n",
    "# for j in range(0,X_counts.shape[1]):\n",
    "#     term_frequencies.append(X_counts[:,j].sum)\n",
    "\n",
    "term_frequencies = np.asarray(X_counts.sum(axis=0))[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red>\n",
    " I change to code in previous cell. use `.sum` is faster\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb1ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(100, 10))\n",
    "g = sns.barplot(x=count_vect.get_feature_names()[:300], \n",
    "            y=term_frequencies[:300])\n",
    "g.set_xticklabels(count_vect.get_feature_names()[:300], rotation = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red>\n",
    "Without `Plotly`, it's hard to show all the value in one graph. Thus, I am not ure if you still want to use `Plotly` here or not.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# py.iplot(ta.plot_word_frequency([count_vect.get_feature_names(), term_frequencies], \"Term Frequency Distribution\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Exercise 6:** \n",
    "The chart above contains all the vocabulary, and it's computationally intensive to both compute and visualize. You can try to reduce the number of terms you want to visualize as an exercise. \n",
    "\n",
    "### **Exercise 7:** \n",
    "Additionally, you can attempt to sort the terms on the `x-axis` by frequency instead of in alphabetical order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have those term frequencies, we can also transform the values in that vector into the log distribution. All we need is to import the `math` library provided by python and apply it to the array of values of the term frequency vector. This is a typical example of attribute transformation. Let's go for it. The log distribution is a technique to visualize the term frequency into a scale that makes you easily visualize the distribution in a more readable format. In other words, the discrepancies between the term frequencies are now better observable. Let us try it out. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "term_frequencies_log = [math.log(i) for i in term_frequencies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110191dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(100, 10))\n",
    "g = sns.barplot(x=count_vect.get_feature_names()[:300],\n",
    "                y=term_frequencies_log[:300])\n",
    "g.set_xticklabels(count_vect.get_feature_names()[:300], rotation = 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# py.iplot(ta.plot_word_frequency([count_vect.get_feature_names(), term_frequencies_log], \"Term Frequency Distribution\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides observing a complete transformation on the disrtibution, notice the scale on the y-axis. The log distribution in our unsorted example has no meaning, but try to properly sort the terms by their frequency, and you will see an interesting effect. Go for it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.6 Discretization and Binarization\n",
    "In this section we are going to discuss a very important pre-preprocessing technique used to transform the data, specifically categorical values, into a format that satisfies certain criteria required by particular algorithms. Given our current original dataset, we would like to transform one of the attributes, `category_name`, into four binary attributes. In other words, we are taking the category name and replacing it with a `n` asymmetric binary attributes. The logic behind this transformation is dicussed in detail in the required text book (please refer to it on page 58). People from the Deep Learning community also refer to this transformation as one-hot encoding, but as you may become aware later in the course, these concepts are all the same, we just have different prefrence on how we refer to the concetps. Let us take a look at what we want to achieve in code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing, metrics, decomposition, pipeline, dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlb = preprocessing.LabelBinarizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlb.fit(X.category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlb.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['bin_category'] = mlb.transform(X['category']).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>category</th>\n",
       "      <th>category_name</th>\n",
       "      <th>unigrams</th>\n",
       "      <th>bin_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: sd345@city.ac.uk (Michael Collier) Subje...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "      <td>[From, :, sd345, @, city.ac.uk, (, Michael, Co...</td>\n",
       "      <td>[0, 1, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "      <td>[From, :, ani, @, ms.uky.edu, (, Aniruddha, B....</td>\n",
       "      <td>[0, 1, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, djohnson, @, cs.ucsd.edu, (, Darin, ...</td>\n",
       "      <td>[0, 0, 0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>From: s0612596@let.rug.nl (M.M. Zwart) Subject...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, s0612596, @, let.rug.nl, (, M.M, ., ...</td>\n",
       "      <td>[0, 0, 0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>From: stanly@grok11.columbiasc.ncr.com (stanly...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, stanly, @, grok11.columbiasc.ncr.com...</td>\n",
       "      <td>[0, 0, 0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, vbv, @, lor.eeap.cwru.edu, (, Virgil...</td>\n",
       "      <td>[0, 0, 0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>From: jodfishe@silver.ucs.indiana.edu (joseph ...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "      <td>[From, :, jodfishe, @, silver.ucs.indiana.edu,...</td>\n",
       "      <td>[0, 0, 0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>From: aldridge@netcom.com (Jacquelin Aldridge)...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "      <td>[From, :, aldridge, @, netcom.com, (, Jacqueli...</td>\n",
       "      <td>[0, 0, 1, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>From: geb@cs.pitt.edu (Gordon Banks) Subject: ...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "      <td>[From, :, geb, @, cs.pitt.edu, (, Gordon, Bank...</td>\n",
       "      <td>[0, 0, 1, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  category  \\\n",
       "0  From: sd345@city.ac.uk (Michael Collier) Subje...         1   \n",
       "1  From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...         1   \n",
       "2  From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...         3   \n",
       "3  From: s0612596@let.rug.nl (M.M. Zwart) Subject...         3   \n",
       "4  From: stanly@grok11.columbiasc.ncr.com (stanly...         3   \n",
       "5  From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...         3   \n",
       "6  From: jodfishe@silver.ucs.indiana.edu (joseph ...         3   \n",
       "7  From: aldridge@netcom.com (Jacquelin Aldridge)...         2   \n",
       "8  From: geb@cs.pitt.edu (Gordon Banks) Subject: ...         2   \n",
       "\n",
       "            category_name                                           unigrams  \\\n",
       "0           comp.graphics  [From, :, sd345, @, city.ac.uk, (, Michael, Co...   \n",
       "1           comp.graphics  [From, :, ani, @, ms.uky.edu, (, Aniruddha, B....   \n",
       "2  soc.religion.christian  [From, :, djohnson, @, cs.ucsd.edu, (, Darin, ...   \n",
       "3  soc.religion.christian  [From, :, s0612596, @, let.rug.nl, (, M.M, ., ...   \n",
       "4  soc.religion.christian  [From, :, stanly, @, grok11.columbiasc.ncr.com...   \n",
       "5  soc.religion.christian  [From, :, vbv, @, lor.eeap.cwru.edu, (, Virgil...   \n",
       "6  soc.religion.christian  [From, :, jodfishe, @, silver.ucs.indiana.edu,...   \n",
       "7                 sci.med  [From, :, aldridge, @, netcom.com, (, Jacqueli...   \n",
       "8                 sci.med  [From, :, geb, @, cs.pitt.edu, (, Gordon, Bank...   \n",
       "\n",
       "   bin_category  \n",
       "0  [0, 1, 0, 0]  \n",
       "1  [0, 1, 0, 0]  \n",
       "2  [0, 0, 0, 1]  \n",
       "3  [0, 0, 0, 1]  \n",
       "4  [0, 0, 0, 1]  \n",
       "5  [0, 0, 0, 1]  \n",
       "6  [0, 0, 0, 1]  \n",
       "7  [0, 0, 1, 0]  \n",
       "8  [0, 0, 1, 0]  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the new attribute we have added to the `X` table. You can see that the new attribute, which is called `bin_category`, contains an array of 0's and 1's. The `1` is basically to indicate the position of the label or category we binarized. If you look at the first two records, the one is places in slot 2 in the array; this helps to indicate to any of the algorithms which we are feeding this data to, that the record belong to that specific category. \n",
    "\n",
    "Attributes with continuous values also have strategies to tranform the data; this is usually called **Discretization** (please refer to the text book for more inforamation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Concluding Remarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! We have come a long way! We can now call ourselves experts of Data Preprocessing. You should feel excited and proud because the process of Data Mining usually involves 70% preprocessing and 30% training algorithms. You will learn this as you progress in the Data Mining course. I really feel that if you go through the exercise and challenge yourself, you have just become an expert. \n",
    "\n",
    "From here the possibilities for you are endless. You know how to use almost every technique for preprocessing with state-of-the-art tools, such as as Pandas, which makes even more an expert. You are now with the trend! \n",
    "\n",
    "After completing this notebook you can do a lot with the results we have generated. You can train algorithms and models that are able to classify articles into certain categories and much more. You can also try to experiment with different datasets, or venture further into text analytics by using new deep learning techniques such as word2vec. All of this will be presented in the next lab session. Until then, go teach machines how to be intelligent to make the world a better place. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pandas cook book ([Recommended for starters](http://pandas.pydata.org/pandas-docs/stable/cookbook.html))"
   ]
  }
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