DM2022-Lab1-Master.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Mining Lab 1\n",
    "In this lab session we will focus on the use of scientific computing libraries to efficiently process, transform, and manage data. Furthermore, we will provide best practices and introduce visualization tools for effectively conducting big data analysis and visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1. Data Source\n",
    "2. Data Preparation\n",
    "3. Data Transformation\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. Data Exploration\n",
    "7. Conclusion\n",
    "8. 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. 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 proficiency of students and motivate students to explore beyond the techniques covered in the notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "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: 8 GB\n",
    "- Disk space: Mininium 8 GB\n",
    "\n",
    "#### Software Requirements\n",
    "Here is a list of the required programs and libraries necessary for this lab session:\n",
    "\n",
    "##### Language:\n",
    "- [Python 3+](https://www.python.org/download/releases/3.0/) (Note: coding will be done strictly on Python 3)\n",
    "    - We are using Python 3.9.6.\n",
    "    - You can use newer version, but use at your own risk.\n",
    "    \n",
    "##### Environment:\n",
    "Using an environment is to avoid some library conflict problems. You can refer this [Setup Instructions](http://cs231n.github.io/setup-instructions/) to install and setup.\n",
    "\n",
    "- [Anaconda](https://www.anaconda.com/download/) (recommended but not required)\n",
    "    - Install anaconda environment\n",
    "    \n",
    "- [Python virtualenv](https://virtualenv.pypa.io/en/stable/userguide/) (recommended to Linux/MacOS user)\n",
    "    - Install virtual environment\n",
    "\n",
    "- [Kaggle Kernel](https://www.kaggle.com/kernels/)\n",
    "    - Run on the cloud  (with some limitations)\n",
    "    - Reference: [Kaggle Kernels Instructions](https://github.com/omarsar/data_mining_lab/blob/master/kagglekernel.md)\n",
    "    \n",
    "##### Necessary Libraries:\n",
    "- [Jupyter](http://jupyter.org/) (Strongly recommended but not required)\n",
    "    - Install `jupyter` and Use `$jupyter notebook` in terminal to run\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",
    "- [Seaborn](https://seaborn.pydata.org/)\n",
    "    - Install and signup for `seaborn`\n",
    "- [NLTK](http://www.nltk.org/)\n",
    "    - Install `nltk` library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# TEST necessary for when working with external scripts\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. The Data\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. "
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pic1.png](attachment:pic1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Preparation\n",
    "In the following we will use the built-in dataset loader for 20 newsgroups from scikit-learn. Alternatively, it is possible to download the dataset manually from the website and use the sklearn.datasets.load_files function by pointing it to the 20news-bydate-train sub-folder of the uncompressed archive folder.\n",
    "\n",
    "In order to get faster execution times for this first example we will work on a partial dataset with only 4 categories out of the 20 available in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categories\n",
    "categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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": 119,
   "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": 119,
     "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": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'comp.graphics', 'sci.med', 'soc.religion.christian']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(twenty_train.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 122,
     "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": 123,
   "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": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comp.graphics\n"
     ]
    }
   ],
   "source": [
    "print(twenty_train.target_names[twenty_train.target[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 3, 3, 3, 3, 3, 2, 2, 2])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# category of first 10 documents.\n",
    "twenty_train.target[0:10]"
   ]
  },
  {
   "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": 127,
   "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"
     ]
    }
   ],
   "source": [
    "for t in twenty_train.target[:10]:\n",
    "    print(twenty_train.target_names[t])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** >>> Exercise 1 (5 min): **  \n",
    "In this exercise, please print out the *text* data for the first three samples in the dataset. (See the above code for help)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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",
      "From: ani@ms.uky.edu (Aniruddha B. Deglurkar)\n",
      "Subject: help: Splitting a trimming region along a mesh \n",
      "Organization: University Of Kentucky, Dept. of Math Sciences\n",
      "Lines: 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",
      "-- \n",
      "To get irritated is human, to stay cool, divine.\n",
      "\n",
      "From: djohnson@cs.ucsd.edu (Darin Johnson)\n",
      "Subject: Re: harrassed at work, could use some prayers\n",
      "Organization: =CSE Dept., U.C. San Diego\n",
      "Lines: 63\n",
      "\n",
      "(Well, I'll email also, but this may apply to other people, so\n",
      "I'll post also.)\n",
      "\n",
      ">I've been working at this company for eight years in various\n",
      ">engineering jobs.  I'm female.  Yesterday I counted and realized that\n",
      ">on seven different occasions I've been sexually harrassed at this\n",
      ">company.\n",
      "\n",
      ">I dreaded coming back to work today.  What if my boss comes in to ask\n",
      ">me some kind of question...\n",
      "\n",
      "Your boss should be the person bring these problems to.  If he/she\n",
      "does not seem to take any action, keep going up higher and higher.\n",
      "Sexual harrassment does not need to be tolerated, and it can be an\n",
      "enormous emotional support to discuss this with someone and know that\n",
      "they are trying to do something about it.  If you feel you can not\n",
      "discuss this with your boss, perhaps your company has a personnel\n",
      "department that can work for you while preserving your privacy.  Most\n",
      "companies will want to deal with this problem because constant anxiety\n",
      "does seriously affect how effectively employees do their jobs.\n",
      "\n",
      "It is unclear from your letter if you have done this or not.  It is\n",
      "not inconceivable that management remains ignorant of employee\n",
      "problems/strife even after eight years (it's a miracle if they do\n",
      "notice).  Perhaps your manager did not bring to the attention of\n",
      "higher ups?  If the company indeed does seem to want to ignore the\n",
      "entire problem, there may be a state agency willing to fight with\n",
      "you.  (check with a lawyer, a women's resource center, etc to find out)\n",
      "\n",
      "You may also want to discuss this with your paster, priest, husband,\n",
      "etc.  That is, someone you know will not be judgemental and that is\n",
      "supportive, comforting, etc.  This will bring a lot of healing.\n",
      "\n",
      ">So I returned at 11:25, only to find that ever single\n",
      ">person had already left for lunch.  They left at 11:15 or so.  No one\n",
      ">could be bothered to call me at the other building, even though my\n",
      ">number was posted.\n",
      "\n",
      "This happens to a lot of people.  Honest.  I believe it may seem\n",
      "to be due to gross insensitivity because of the feelings you are\n",
      "going through.  People in offices tend to be more insensitive while\n",
      "working than they normally are (maybe it's the hustle or stress or...)\n",
      "I've had this happen to me a lot, often because they didn't realize\n",
      "my car was broken, etc.  Then they will come back and wonder why I\n",
      "didn't want to go (this would tend to make me stop being angry at\n",
      "being ignored and make me laugh).  Once, we went off without our\n",
      "boss, who was paying for the lunch :-)\n",
      "\n",
      ">For this\n",
      ">reason I hope good Mr. Moderator allows me this latest indulgence.\n",
      "\n",
      "Well, if you can't turn to the computer for support, what would\n",
      "we do?  (signs of the computer age :-)\n",
      "\n",
      "In closing, please don't let the hateful actions of a single person\n",
      "harm you.  They are doing it because they are still the playground\n",
      "bully and enjoy seeing the hurt they cause.  And you should not\n",
      "accept the opinions of an imbecile that you are worthless - much\n",
      "wiser people hold you in great esteem.\n",
      "-- \n",
      "Darin Johnson\n",
      "djohnson@ucsd.edu\n",
      "  - Luxury!  In MY day, we had to make do with 5 bytes of swap...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Answer here\n",
    "for t in twenty_train.data[:3]:\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Transformation\n",
    "So we want to explore and understand our data a little bit better. Before we do that we definitely need to apply some transformations just so we can have our dataset in a nice format to be able to explore it freely and more efficient. Lucky for us, there are powerful 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 format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Converting Dictionary into Pandas Dataframe\n",
    "Here we will show you how to convert dictionary objects into a pandas dataframe. And by the way, a pandas dataframe is nothing more than a table magically stored for efficient information retrieval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# my functions\n",
    "import helpers.data_mining_helpers as dmh\n",
    "\n",
    "# construct dataframe from a list\n",
    "X = pd.DataFrame.from_records(dmh.format_rows(twenty_train), columns= ['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: sd345@city.ac.uk (Michael Collier) Subject: Converting images to HP LaserJet III? Nntp-Posting-Host: hampton Organization: The City University Lines: 14  Does anyone know of a good way (standard PC application/PD utility) to convert tif/img/tga files into LaserJet III format.  We would also like to do the same, converting to HPGL (HP plotter) files.  Please email any response.  Is this the correct group?  Thanks in advance.  Michael. --  Michael Collier (Programmer)                 The Computer Unit, Email: M.P.Collier@uk.ac.city                The City University, Tel: 071 477-8000 x3769                      London, Fax: 071 477-8565                            EC1V 0HB. \n",
      "From: ani@ms.uky.edu (Aniruddha B. Deglurkar) Subject: help: Splitting a trimming region along a mesh  Organization: University Of Kentucky, Dept. of Math Sciences Lines: 28    \tHi,  \tI have a problem, I hope some of the 'gurus' can help me solve.  \tBackground of the problem: \tI have a rectangular mesh in the uv domain, i.e  the mesh is a  \tmapping of a 3d Bezier patch into 2d. The area in this domain \twhich is inside a trimming loop had to be rendered. The trimming \tloop is a set of 2d Bezier curve segments. \tFor the sake of notation: the mesh is made up of cells.  \tMy problem is this : \tThe trimming area has to be split up into individual smaller \tcells bounded by the trimming curve segments. If a cell \tis wholly inside the area...then it is output as a whole , \telse it is trivially rejected.   \tDoes any body know how thiss can be done, or is there any algo.  \tsomewhere for doing this.  \tAny help would be appreciated.  \tThanks,  \tAni. --  To get irritated is human, to stay cool, divine. \n",
      "From: djohnson@cs.ucsd.edu (Darin Johnson) Subject: Re: harrassed at work, could use some prayers Organization: =CSE Dept., U.C. San Diego Lines: 63  (Well, I'll email also, but this may apply to other people, so I'll post also.)  >I've been working at this company for eight years in various >engineering jobs.  I'm female.  Yesterday I counted and realized that >on seven different occasions I've been sexually harrassed at this >company.  >I dreaded coming back to work today.  What if my boss comes in to ask >me some kind of question...  Your boss should be the person bring these problems to.  If he/she does not seem to take any action, keep going up higher and higher. Sexual harrassment does not need to be tolerated, and it can be an enormous emotional support to discuss this with someone and know that they are trying to do something about it.  If you feel you can not discuss this with your boss, perhaps your company has a personnel department that can work for you while preserving your privacy.  Most companies will want to deal with this problem because constant anxiety does seriously affect how effectively employees do their jobs.  It is unclear from your letter if you have done this or not.  It is not inconceivable that management remains ignorant of employee problems/strife even after eight years (it's a miracle if they do notice).  Perhaps your manager did not bring to the attention of higher ups?  If the company indeed does seem to want to ignore the entire problem, there may be a state agency willing to fight with you.  (check with a lawyer, a women's resource center, etc to find out)  You may also want to discuss this with your paster, priest, husband, etc.  That is, someone you know will not be judgemental and that is supportive, comforting, etc.  This will bring a lot of healing.  >So I returned at 11:25, only to find that ever single >person had already left for lunch.  They left at 11:15 or so.  No one >could be bothered to call me at the other building, even though my >number was posted.  This happens to a lot of people.  Honest.  I believe it may seem to be due to gross insensitivity because of the feelings you are going through.  People in offices tend to be more insensitive while working than they normally are (maybe it's the hustle or stress or...) I've had this happen to me a lot, often because they didn't realize my car was broken, etc.  Then they will come back and wonder why I didn't want to go (this would tend to make me stop being angry at being ignored and make me laugh).  Once, we went off without our boss, who was paying for the lunch :-)  >For this >reason I hope good Mr. Moderator allows me this latest indulgence.  Well, if you can't turn to the computer for support, what would we do?  (signs of the computer age :-)  In closing, please don't let the hateful actions of a single person harm you.  They are doing it because they are still the playground bully and enjoy seeing the hurt they cause.  And you should not accept the opinions of an imbecile that you are worthless - much wiser people hold you in great esteem. --  Darin Johnson djohnson@ucsd.edu   - Luxury!  In MY day, we had to make do with 5 bytes of swap... \n"
     ]
    }
   ],
   "source": [
    "for t in X[\"text\"][:3]:\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the great advantages of a pandas dataframe is its flexibility. We can add columns to the current dataset programmatically with very little effort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add category to the dataframe\n",
    "X['category'] = twenty_train.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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 and see what our table looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>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": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice! Isn't it? With this format we can conduct many operations easily and efficiently since Pandas dataframes provide us with a wide range of built-in features/functionalities. 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 table (hereinafter referred to as a dataframe), which is desirable in almost every data mining project. 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 awesomeness 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 (documents), and we only want to keep the `text` and `category_name` attributes or fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a simple query\n",
    "X[: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": [
    "#### Let's query the last 10 records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>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",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>From: dyer@spdcc.com (Steve Dyer) Subject: Re:...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  category  \\\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",
       "2256  From: dyer@spdcc.com (Steve Dyer) Subject: Re:...         2   \n",
       "\n",
       "               category_name  \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  \n",
       "2256                 sci.med  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ready for some sourcery? Brace yourselves! Let us see if we can query the first 10th record in our dataframe. For this we will use the build-in function called `loc`. This allows us to explicity define the columns you want to query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     From: sd345@city.ac.uk (Michael Collier) Subje...\n",
       "1     From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...\n",
       "2     From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...\n",
       "3     From: s0612596@let.rug.nl (M.M. Zwart) Subject...\n",
       "4     From: stanly@grok11.columbiasc.ncr.com (stanly...\n",
       "5     From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...\n",
       "6     From: jodfishe@silver.ucs.indiana.edu (joseph ...\n",
       "7     From: aldridge@netcom.com (Jacquelin Aldridge)...\n",
       "8     From: geb@cs.pitt.edu (Gordon Banks) Subject: ...\n",
       "9     From: libman@hsc.usc.edu (Marlena Libman) Subj...\n",
       "10    From: anasaz!karl@anasazi.com (Karl Dussik) Su...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using loc (by label)\n",
    "X.loc[:10, 'text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the `iloc` function to query a selection of our dataset by position. Take a look at this [great discussion](https://stackoverflow.com/questions/28757389/pandas-loc-vs-iloc-vs-ix-vs-at-vs-iat/43968774) on the differences between the `iloc` and `loc` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    From: sd345@city.ac.uk (Michael Collier) Subje...\n",
       "1    From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...\n",
       "2    From: djohnson@cs.ucsd.edu (Darin Johnson) Sub...\n",
       "3    From: s0612596@let.rug.nl (M.M. Zwart) Subject...\n",
       "4    From: stanly@grok11.columbiasc.ncr.com (stanly...\n",
       "5    From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B...\n",
       "6    From: jodfishe@silver.ucs.indiana.edu (joseph ...\n",
       "7    From: aldridge@netcom.com (Jacquelin Aldridge)...\n",
       "8    From: geb@cs.pitt.edu (Gordon Banks) Subject: ...\n",
       "9    From: libman@hsc.usc.edu (Marlena Libman) Subj...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using iloc (by position)\n",
    "X.iloc[:10, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** >>> Exercise 2 (take home):** \n",
    "Experiment with other querying techniques using pandas dataframes. Refer to their [documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html) for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Answer here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** >>> Exercise 3 (5 min): **  \n",
    "Try to fetch records belonging to the ```sci.med``` category, and query every 10th record. Only show the first 5 records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>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>49</th>\n",
       "      <td>From: jimj@contractor.EBay.Sun.COM (Jim Jones)...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>From: jason@ab20.larc.nasa.gov (Jason Austin) ...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>From: rogers@calamari.hi.com (Andrew Rogers) S...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>From: lady@uhunix.uhcc.Hawaii.Edu (Lee Lady) S...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text  category category_name\n",
       "7    From: aldridge@netcom.com (Jacquelin Aldridge)...         2       sci.med\n",
       "49   From: jimj@contractor.EBay.Sun.COM (Jim Jones)...         2       sci.med\n",
       "82   From: jason@ab20.larc.nasa.gov (Jason Austin) ...         2       sci.med\n",
       "118  From: rogers@calamari.hi.com (Andrew Rogers) S...         2       sci.med\n",
       "142  From: lady@uhunix.uhcc.Hawaii.Edu (Lee Lady) S...         2       sci.med"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer here\n",
    "X[X['category_name']=='sci.med'].iloc[::10][0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Mining using Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do some serious work now. Let's learn to program some of the ideas and concepts learned so far in the data mining course. This is the only way we can be convince ourselves of the true power of Pandas dataframes. "
   ]
  },
  {
   "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 some code to deal with these objects that contain missing values. You will see for yourself how easy it is to deal with missing values once you have your data transformed into a Pandas dataframe.\n",
    "\n",
    "Before we jump into coding, let us do a quick review of what we have learned in the Data Mining course. Specifically, let's review the methods used to deal with missing values.\n",
    "\n",
    "The most common reasons for having missing values in datasets has to do with how the data was initially collected. A good example of this is when a patient 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 is 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 learning 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 lose some information, and in some cases too much of it. 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 ignore missing values, especially when in this day and age it is difficult to obtain high quality datasets? Again, there are some tradeoffs, which we 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 approach 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. 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": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "2     False     False          False\n",
       "3     False     False          False\n",
       "4     False     False          False\n",
       "...     ...       ...            ...\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": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values\n",
    "X.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `isnull` function looks through the entire dataset for null values and returns `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` returns **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 found. This excerice will also encourage us to explore other capabilities of pandas dataframes. In most cases, the build-in functions are good enough, but as you saw above when the entire table was printed, it is impossible to tell if there are missing records just by looking at preview of records manually, especially in cases where the dataset is huge. We want a more reliable way to achieve this. Let's get to it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</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>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 text                            category  \\\n",
       "0  The amoung of missing records is:   The amoung of missing records is:    \n",
       "1                                   0                                   0   \n",
       "\n",
       "                        category_name  \n",
       "0  The amoung of missing records is:   \n",
       "1                                   0  "
      ]
     },
     "execution_count": 143,
     "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 row that essentially counts to see if there are missing values in each record and print out how much missing values we found. In other words the `check_missing_values` function looks through each field (attribute or column) in the dataset and counts how many missing values were found. \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": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> **Exercise 4 (5 min):** \n",
    "Let's try something different. Instead of calculating missing values by column let's try to calculate the missing values in every record instead of every column.  \n",
    "$Hint$ : `axis` parameter. Check the documentation for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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",
       "                         ...                   \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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer here\n",
    "X.isnull().apply(lambda x: dmh.check_missing_values(x), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have our function to check for missing records, now let us do something mischievous and insert some dummy data into the dataframe and test the reliability of our function. This dummy data is intended to corrupt the dataset. I mean this happens a lot today, especially when hackers want to hijack or corrupt a database.\n",
    "\n",
    "We will insert a `Series`, which is basically a \"one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index.\", into our current dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_series = pd.Series([\"dummy_record\", 1], index=[\"text\", \"category\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text        dummy_record\n",
       "category               1\n",
       "dtype: object"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dummy_record</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           text category\n",
       "0  dummy_record        1"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_series.to_frame().T\n",
    "# .to_frame() -> Convert Series to DataFrame\n",
    "# .T          -> Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_with_series = pd.concat([X, dummy_series.to_frame().T], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2258"
      ]
     },
     "execution_count": 149,
     "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 with some missing values. Let try our function and see if it can detect that there is a missing value on the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>0</th>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 text                            category  \\\n",
       "0  The amoung of missing records is:   The amoung of missing records is:    \n",
       "1                                   0                                   0   \n",
       "\n",
       "                        category_name  \n",
       "0  The amoung of missing records is:   \n",
       "1                                   1  "
      ]
     },
     "execution_count": 150,
     "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. Specifically, 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 that above we are not 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 modification will create a need to remove this dummy record 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": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummy record as dictionary format\n",
    "dummy_dict = [{'text': 'dummy_record',\n",
    "               'category': 1\n",
    "              }]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X, pd.DataFrame(dummy_dict)], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2258"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                                 text                            category  \\\n",
       "0  The amoung of missing records is:   The amoung of missing records is:    \n",
       "1                                   0                                   0   \n",
       "\n",
       "                        category_name  \n",
       "0  The amoung of missing records is:   \n",
       "1                                   1  "
      ]
     },
     "execution_count": 154,
     "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": 155,
   "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": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "      <td>The amoung of missing records is:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 text                            category  \\\n",
       "0  The amoung of missing records is:   The amoung of missing records is:    \n",
       "1                                   0                                   0   \n",
       "\n",
       "                        category_name  \n",
       "0  The amoung of missing records is:   \n",
       "1                                   0  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().apply(lambda x: dmh.check_missing_values(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(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, refer to 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 5 (take home)** \n",
    "There is an old saying that goes, \"The devil is in the details.\" When we are working with extremely large data, it's difficult to check records one by one (as we have been doing so far). And also, we don't even know what kind of missing values we are facing. Thus, \"debugging\" skills get sharper as we spend more time solving bugs. Let's focus on a different method to check for missing values and the kinds of missing values you may encounter. It's not easy to check for missing values as you will find out in a minute.\n",
    "\n",
    "Please check the data and the process below, describe what you observe and why it happened.   \n",
    "$Hint$ :  why `.isnull()` didn't work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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": 158,
     "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": 159,
   "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": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NA_df['missing_example'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "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 worst case is that you have duplicate data that has missing values. But let us not get carried away. Let us stick with the basics. As we have learned in our Data Mining course, duplicate 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 have collected and stored a tweet, and a retweet of that same tweet as two different records; this results in a 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 duplicate data, i.e., this all depends on your criteria for what is considered as a duplicate record and also what type of data you are dealing with. For textual data, it may not be so trivial as it is for numerical values or images. 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` function that we used to check for missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "2252    False\n",
       "2253    False\n",
       "2254    False\n",
       "2255    False\n",
       "2256    False\n",
       "Length: 2257, dtype: bool"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check the sum of duplicate records by simply doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 162,
     "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 `duplicated()` operation does is to check per records instead of per column. That is why the operation only returns one value instead of three values for each column. 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 column, instead of all columns, we do something as shown below. As you may have noticed, in the 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 checking the `text` attribute. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(X.duplicated('text'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us create some duplicated dummy records and append it to the main dataframe `X`. Subsequenlty, let us try to get rid of the duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X, pd.DataFrame(dummy_duplicate_dict)], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2259"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(X.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have added the dummy duplicates to `X`. Now we are faced with the decision as to what to do with the duplicated records after we have found it. In our case, we want to get rid of all the duplicated records without preserving a copy. We can simply do that with the following line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop_duplicates(keep=False, inplace=True) # inplace applies changes directly on our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2257"
      ]
     },
     "execution_count": 169,
     "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 perform 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 basic methods for sampling. The reasons for sampling are sometimes due to the size of data -- we want a smaller subset of the data that is still representatitive enough as compared to the original dataset. \n",
    "\n",
    "We don't have a problem of size in our current dataset since it 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 after all. In fact, we have already done some sampling by just reducing the records we are using here in this notebook; remember that we are only using four categories from the all the 20 categories available. Let us get an idea on how to sample using pandas operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sample = X.sample(n=1000) #random state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>68</th>\n",
       "      <td>From: revdak@netcom.com (D. Andrew Kille) Subj...</td>\n",
       "      <td>3</td>\n",
       "      <td>soc.religion.christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>From: harvey@oasys.dt.navy.mil (Betty Harvey) ...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>From: dyer@spdcc.com (Steve Dyer) Subject: Re:...</td>\n",
       "      <td>2</td>\n",
       "      <td>sci.med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>From: mathew &lt;mathew@mantis.co.uk&gt; Subject: Al...</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  category  \\\n",
       "68    From: revdak@netcom.com (D. Andrew Kille) Subj...         3   \n",
       "770   From: harvey@oasys.dt.navy.mil (Betty Harvey) ...         2   \n",
       "482   From: dyer@spdcc.com (Steve Dyer) Subject: Re:...         2   \n",
       "1596  From: mathew <mathew@mantis.co.uk> Subject: Al...         0   \n",
       "\n",
       "               category_name  \n",
       "68    soc.religion.christian  \n",
       "770                  sci.med  \n",
       "482                  sci.med  \n",
       "1596             alt.atheism  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sample[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> Exercise 6 (take home):\n",
    "Notice any changes from the `X` dataframe to the `X_sample` dataframe? What are they? Report every change you noticed as compared to the previous state of `X`. Feel free to query and look more closely at the dataframe for these changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do something cool here while we are working with sampling! Let us look at the distribution of categories in both the sample and original dataset. Let us visualize and 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 and when using Jupyter notebooks. Let's take a loot at the magic of `matplotlib` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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": [
       "<AxesSubplot: title={'center': 'Category distribution'}>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X.category_name.value_counts())\n",
    "\n",
    "# plot barchart for X\n",
    "X.category_name.value_counts().plot(kind = 'bar',\n",
    "                                    title = 'Category distribution',\n",
    "                                    ylim = [0, 700],        \n",
    "                                    rot = 0, fontsize = 11, figsize = (8,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "soc.religion.christian    284\n",
      "comp.graphics             258\n",
      "sci.med                   257\n",
      "alt.atheism               201\n",
      "Name: category_name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Category distribution'}>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "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 styles to prettify your charts.\n",
    "```python\n",
    "print(plt.style.available)```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> **Exercise 7 (5 min):**\n",
    "Notice that for the `ylim` parameters we hardcoded the maximum value for y. Is it possible to automate this instead of hard-coding it? How would you go about doing that? (Hint: look at code above for clues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "soc.religion.christian    284\n",
      "comp.graphics             258\n",
      "sci.med                   257\n",
      "alt.atheism               201\n",
      "Name: category_name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Category distribution'}>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer here\n",
    "\n",
    "# plot barchart for X_sample\n",
    "upper_bound = max(X_sample.category_name.value_counts() + 50) #276 + 50\n",
    "print(X_sample.category_name.value_counts())\n",
    "\n",
    "X_sample.category_name.value_counts().plot(kind = 'bar',\n",
    "                                           title = 'Category distribution',\n",
    "                                           ylim = [0, upper_bound], \n",
    "                                           rot = 0, fontsize = 12, figsize = (8,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> **Exercise 8 (take home):** \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. Below we show you an snapshot of the type of chart we are looking for. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt txt](https://i.imgur.com/9eO431H.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "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. There are many ways to conduct sampling on the dataset and still obtain 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 sure 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 'tokens' or 'individual words'.) Yes, we want to extract all the words found in each text and append it as a new feature to the pandas dataframe. 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 a useful text mining 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 we refer to as 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": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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": 182,
   "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": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:4][\"unigrams\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you take a closer look 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. At first glance, you will notice that the tokenizer is not doing a great job, let us take a closer at a single record and see what was the exact result of the tokenization using the `nltk` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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",
       "      <th>unigrams</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",
       "    </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",
       "  </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",
       "\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, ., ...  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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": 184,
     "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 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 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": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 14887)\t1\n",
      "  (0, 29022)\t1\n",
      "  (0, 8696)\t4\n",
      "  (0, 4017)\t2\n",
      "  (0, 33256)\t2\n",
      "  (0, 21661)\t3\n",
      "  (0, 9031)\t3\n",
      "  (0, 31077)\t1\n",
      "  (0, 9805)\t2\n",
      "  (0, 17366)\t1\n",
      "  (0, 32493)\t4\n",
      "  (0, 16916)\t2\n",
      "  (0, 19780)\t2\n",
      "  (0, 17302)\t2\n",
      "  (0, 23122)\t1\n",
      "  (0, 25663)\t1\n",
      "  (0, 16881)\t1\n",
      "  (0, 16082)\t1\n",
      "  (0, 23915)\t1\n",
      "  (0, 32142)\t5\n",
      "  (0, 33597)\t2\n",
      "  (0, 20253)\t1\n",
      "  (0, 587)\t1\n",
      "  (0, 12051)\t1\n",
      "  (0, 5201)\t1\n",
      "  :\t:\n",
      "  (0, 25361)\t1\n",
      "  (0, 25337)\t1\n",
      "  (0, 12833)\t2\n",
      "  (0, 5195)\t1\n",
      "  (0, 27836)\t1\n",
      "  (0, 18474)\t1\n",
      "  (0, 32270)\t1\n",
      "  (0, 9932)\t1\n",
      "  (0, 15837)\t1\n",
      "  (0, 32135)\t1\n",
      "  (0, 17556)\t1\n",
      "  (0, 4378)\t1\n",
      "  (0, 26175)\t1\n",
      "  (0, 9338)\t1\n",
      "  (0, 33572)\t1\n",
      "  (0, 31915)\t1\n",
      "  (0, 177)\t2\n",
      "  (0, 2326)\t2\n",
      "  (0, 3062)\t1\n",
      "  (0, 35416)\t1\n",
      "  (0, 20459)\t1\n",
      "  (0, 14085)\t1\n",
      "  (0, 3166)\t1\n",
      "  (0, 12541)\t1\n",
      "  (0, 230)\t1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect = CountVectorizer()\n",
    "X_counts = count_vect.fit_transform(X.text) #learn the vocabulary and return document-term matrix\n",
    "print(X_counts[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from'"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.get_feature_names_out()[14887]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sd345'"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.get_feature_names_out()[29022]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'city'"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.get_feature_names_out()[8696]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ac'"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.get_feature_names_out()[4017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['from',\n",
       " 'sd345',\n",
       " 'city',\n",
       " 'ac',\n",
       " 'uk',\n",
       " 'michael',\n",
       " 'collier',\n",
       " 'subject',\n",
       " 'converting',\n",
       " 'images',\n",
       " 'to',\n",
       " 'hp',\n",
       " 'laserjet',\n",
       " 'iii',\n",
       " 'nntp',\n",
       " 'posting',\n",
       " 'host',\n",
       " 'hampton',\n",
       " 'organization',\n",
       " 'the',\n",
       " 'city',\n",
       " 'university',\n",
       " 'lines',\n",
       " '14',\n",
       " 'does',\n",
       " 'anyone',\n",
       " 'know',\n",
       " 'of',\n",
       " 'good',\n",
       " 'way',\n",
       " 'standard',\n",
       " 'pc',\n",
       " 'application',\n",
       " 'pd',\n",
       " 'utility',\n",
       " 'to',\n",
       " 'convert',\n",
       " 'tif',\n",
       " 'img',\n",
       " 'tga',\n",
       " 'files',\n",
       " 'into',\n",
       " 'laserjet',\n",
       " 'iii',\n",
       " 'format',\n",
       " 'we',\n",
       " 'would',\n",
       " 'also',\n",
       " 'like',\n",
       " 'to',\n",
       " 'do',\n",
       " 'the',\n",
       " 'same',\n",
       " 'converting',\n",
       " 'to',\n",
       " 'hpgl',\n",
       " 'hp',\n",
       " 'plotter',\n",
       " 'files',\n",
       " 'please',\n",
       " 'email',\n",
       " 'any',\n",
       " 'response',\n",
       " 'is',\n",
       " 'this',\n",
       " 'the',\n",
       " 'correct',\n",
       " 'group',\n",
       " 'thanks',\n",
       " 'in',\n",
       " 'advance',\n",
       " 'michael',\n",
       " 'michael',\n",
       " 'collier',\n",
       " 'programmer',\n",
       " 'the',\n",
       " 'computer',\n",
       " 'unit',\n",
       " 'email',\n",
       " 'collier',\n",
       " 'uk',\n",
       " 'ac',\n",
       " 'city',\n",
       " 'the',\n",
       " 'city',\n",
       " 'university',\n",
       " 'tel',\n",
       " '071',\n",
       " '477',\n",
       " '8000',\n",
       " 'x3769',\n",
       " 'london',\n",
       " 'fax',\n",
       " '071',\n",
       " '477',\n",
       " '8565',\n",
       " 'ec1v',\n",
       " '0hb']"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyze(X.text[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we did with those two lines of code is that we transformed the articles into a **term-document matrix**. Those lines of code 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": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['am', 'craving', 'for', 'hawaiian', 'pizza', 'right', 'now']"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyze = count_vect.build_analyzer()\n",
    "analyze(\"I am craving for a hawaiian pizza right now\")\n",
    "\n",
    "# tokenization, remove stop words (e.g i, a, the), create n-gram (or unigram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **>>> Exercise 9 (5 min):**\n",
    "Let's analyze the first record of our X dataframe with the new analyzer we have just built. Go ahead try it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['from',\n",
       " 'sd345',\n",
       " 'city',\n",
       " 'ac',\n",
       " 'uk',\n",
       " 'michael',\n",
       " 'collier',\n",
       " 'subject',\n",
       " 'converting',\n",
       " 'images',\n",
       " 'to',\n",
       " 'hp',\n",
       " 'laserjet',\n",
       " 'iii',\n",
       " 'nntp',\n",
       " 'posting',\n",
       " 'host',\n",
       " 'hampton',\n",
       " 'organization',\n",
       " 'the',\n",
       " 'city',\n",
       " 'university',\n",
       " 'lines',\n",
       " '14',\n",
       " 'does',\n",
       " 'anyone',\n",
       " 'know',\n",
       " 'of',\n",
       " 'good',\n",
       " 'way',\n",
       " 'standard',\n",
       " 'pc',\n",
       " 'application',\n",
       " 'pd',\n",
       " 'utility',\n",
       " 'to',\n",
       " 'convert',\n",
       " 'tif',\n",
       " 'img',\n",
       " 'tga',\n",
       " 'files',\n",
       " 'into',\n",
       " 'laserjet',\n",
       " 'iii',\n",
       " 'format',\n",
       " 'we',\n",
       " 'would',\n",
       " 'also',\n",
       " 'like',\n",
       " 'to',\n",
       " 'do',\n",
       " 'the',\n",
       " 'same',\n",
       " 'converting',\n",
       " 'to',\n",
       " 'hpgl',\n",
       " 'hp',\n",
       " 'plotter',\n",
       " 'files',\n",
       " 'please',\n",
       " 'email',\n",
       " 'any',\n",
       " 'response',\n",
       " 'is',\n",
       " 'this',\n",
       " 'the',\n",
       " 'correct',\n",
       " 'group',\n",
       " 'thanks',\n",
       " 'in',\n",
       " 'advance',\n",
       " 'michael',\n",
       " 'michael',\n",
       " 'collier',\n",
       " 'programmer',\n",
       " 'the',\n",
       " 'computer',\n",
       " 'unit',\n",
       " 'email',\n",
       " 'collier',\n",
       " 'uk',\n",
       " 'ac',\n",
       " 'city',\n",
       " 'the',\n",
       " 'city',\n",
       " 'university',\n",
       " 'tel',\n",
       " '071',\n",
       " '477',\n",
       " '8000',\n",
       " 'x3769',\n",
       " 'london',\n",
       " 'fax',\n",
       " '071',\n",
       " '477',\n",
       " '8565',\n",
       " 'ec1v',\n",
       " '0hb']"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer here\n",
    "# How do we turn our array[0] text document into a tokenized text using the build_analyzer()?\n",
    "analyze(X.text[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['from',\n",
       " 'sd345',\n",
       " 'city',\n",
       " 'ac',\n",
       " 'uk',\n",
       " 'michael',\n",
       " 'collier',\n",
       " 'subject',\n",
       " 'converting',\n",
       " 'images',\n",
       " 'to',\n",
       " 'hp',\n",
       " 'laserjet',\n",
       " 'iii',\n",
       " 'nntp',\n",
       " 'posting',\n",
       " 'host',\n",
       " 'hampton',\n",
       " 'organization',\n",
       " 'the',\n",
       " 'city',\n",
       " 'university',\n",
       " 'lines',\n",
       " '14',\n",
       " 'does',\n",
       " 'anyone',\n",
       " 'know',\n",
       " 'of',\n",
       " 'good',\n",
       " 'way',\n",
       " 'standard',\n",
       " 'pc',\n",
       " 'application',\n",
       " 'pd',\n",
       " 'utility',\n",
       " 'to',\n",
       " 'convert',\n",
       " 'tif',\n",
       " 'img',\n",
       " 'tga',\n",
       " 'files',\n",
       " 'into',\n",
       " 'laserjet',\n",
       " 'iii',\n",
       " 'format',\n",
       " 'we',\n",
       " 'would',\n",
       " 'also',\n",
       " 'like',\n",
       " 'to',\n",
       " 'do',\n",
       " 'the',\n",
       " 'same',\n",
       " 'converting',\n",
       " 'to',\n",
       " 'hpgl',\n",
       " 'hp',\n",
       " 'plotter',\n",
       " 'files',\n",
       " 'please',\n",
       " 'email',\n",
       " 'any',\n",
       " 'response',\n",
       " 'is',\n",
       " 'this',\n",
       " 'the',\n",
       " 'correct',\n",
       " 'group',\n",
       " 'thanks',\n",
       " 'in',\n",
       " 'advance',\n",
       " 'michael',\n",
       " 'michael',\n",
       " 'collier',\n",
       " 'programmer',\n",
       " 'the',\n",
       " 'computer',\n",
       " 'unit',\n",
       " 'email',\n",
       " 'collier',\n",
       " 'uk',\n",
       " 'ac',\n",
       " 'city',\n",
       " 'the',\n",
       " 'city',\n",
       " 'university',\n",
       " 'tel',\n",
       " '071',\n",
       " '477',\n",
       " '8000',\n",
       " 'x3769',\n",
       " 'london',\n",
       " 'fax',\n",
       " '071',\n",
       " '477',\n",
       " '8565',\n",
       " 'ec1v',\n",
       " '0hb']"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyze(\" \".join(list(X[:1].text)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us look at the term-document matrix we built above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2257, 35788)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can check the shape of this matrix by:\n",
    "X_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00', '000', '0000', '0000001200', '000005102000', '0001',\n",
       "       '000100255pixel', '00014', '000406', '0007'], dtype=object)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can obtain the feature names of the vectorizer, i.e., the terms\n",
    "# usually on the horizontal axis\n",
    "count_vect.get_feature_names_out()[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt txt](https://i.imgur.com/57gA1sd.png)"
   ]
  },
  {
   "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_transform` 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": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2257, 35788)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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, 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],\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, 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],\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, 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],\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, 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],\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": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we convert from sparse array to normal array\n",
    "X_counts[0:5, 0:100].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00'], dtype=object)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.get_feature_names_out()[0:1]"
   ]
  },
  {
   "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, contains a `1` in the beginning, which from our feature names we can deduce that this article contains exactly one `00` term."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **>>> Exercise 10 (take home):**\n",
    "We said that the `1` at the beginning of the fifth record represents the `00` term. Notice that there is another 1 in the same record. Can you provide code that can verify what word this 1 represents from the vocabulary. Try to do this as efficient as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "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 as a surpise to see the gems you can mine when you start to look at the data from a different perspective. Visualization are good for this reason."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first twenty features only\n",
    "plot_x = [\"term_\"+str(i) for i in count_vect.get_feature_names_out()[0:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain document index\n",
    "plot_y = [\"doc_\"+ str(i) for i in list(X.index)[0:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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],\n",
       "       [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],\n",
       "       [1, 0, 0, 0, 0, 0, 0, 0, 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],\n",
       "       [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],\n",
       "       [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],\n",
       "       [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],\n",
       "       [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],\n",
       "       [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],\n",
       "       [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],\n",
       "       [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]])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_z = X_counts[0:20, 0:20].toarray() #X_counts[how many documents, how many terms]\n",
    "plot_z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the heat map, we are going to use another visualization 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. See comparison below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt txt](https://i.imgur.com/1isxmIV.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other big advantage of seaborn is that seaborn has some built-in plots that matplotlib does not support. Most of these can eventually be replicated by hacking away at matplotlib, but they’re not built in and require much more effort to build.\n",
    "\n",
    "So without further ado, let us try it now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "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=(9, 7))\n",
    "ax = sns.heatmap(df_todraw,\n",
    "                 cmap=\"PuRd\",\n",
    "                 vmin=0, vmax=1, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out more beautiful color palettes here: https://python-graph-gallery.com/197-available-color-palettes-with-matplotlib/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **>>> Exercise 11 (take home):** \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. How would you do this efficiently? Remember there is a lot of words in the vocab. Report below what methods you would use to get a nice and useful visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "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 efficiently reduce the dimensions of our data, with the main goal of \"finding a projection that captures the largest amount 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": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduced = PCA(n_components = 2).fit_transform(X_counts.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2257, 2)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = ['coral', 'blue', 'black', 'orange']\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 can see a 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 words\" 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> Exercise 12 (take home):\n",
    "Please try to reduce the dimension to 3, and plot the result use 3-D plot. Use at least 3 different angle (camera position) to check your result and describe what you found.\n",
    "\n",
    "$Hint$: you can refer to Axes3D in the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Attribute Transformation / Aggregation\n",
    "We can do other things with the term-vector matrix besides applying dimensionality 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, and we just have to explore the data first before making any premature conclusions. On the topic of attribute transformation, we will take the word distribution and put the distribution in a scale that makes 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 each column. 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": 206,
   "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(sum(X_counts[:,j].toarray()))\n",
    "\n",
    "#[3, 8, 5, 2, 5, 8, 2, 5, 3, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_frequencies = np.asarray(X_counts.sum(axis=0))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term_frequencies[0] #sum of first term: 00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 10000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(100, 10))\n",
    "g = sns.barplot(x=count_vect.get_feature_names_out()[:300], \n",
    "            y=term_frequencies[:300])\n",
    "g.set_xticklabels(count_vect.get_feature_names_out()[:300], rotation = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> **Exercise 13 (take home):**\n",
    "If you want a nicer interactive visualization here, I would encourage you try to install and use plotly to achieve this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> **Exercise 14 (take home):** \n",
    "The chart above only contains 300 vocabulary in the documents, and it's already computationally intensive to both compute and visualize. Can you efficiently reduce the number of terms you want to visualize as an exercise. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### >>> **Exercise 15 (take home):** \n",
    "Additionally, you can attempt to sort the terms on the `x-axis` by frequency instead of in alphabetical order. This way the visualization is more meaninfgul and you will be able to observe the so called [long tail](https://en.wikipedia.org/wiki/Long_tail) (get familiar with this term since it will appear a lot in data mining and other statistics courses). see picture below\n",
    "\n",
    "![alt txt](https://upload.wikimedia.org/wikipedia/commons/thumb/8/8a/Long_tail.svg/1000px-Long_tail.svg.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we already 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 variations between the term frequencies are now easy to observe. Let us try it out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "term_frequencies_log = [math.log(i) for i in term_frequencies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 10000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(100, 10))\n",
    "g = sns.barplot(x=count_vect.get_feature_names_out()[:300],\n",
    "                y=term_frequencies_log[:300])\n",
    "g.set_xticklabels(count_vect.get_feature_names_out()[:300], rotation = 90);"
   ]
  },
  {
   "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 discussed in detail in the recommended Data Mining text book (please refer to it on page 58). People from the machine 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 concepts. Let us take a look at what we want to achieve in code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing, metrics, decomposition, pipeline, dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlb = preprocessing.LabelBinarizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LabelBinarizer()"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlb.fit(X.category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['bin_category'] = mlb.transform(X['category']).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <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": 218,
     "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": [
    "### >>> **Exercise 16 (take home):**\n",
    "Try to generate the binarization using the `category_name` column instead. Does it work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you need to take a peek at your data to understand the relationships in your dataset. Here, we will focus in a similarity example. Let's take 3 documents and compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We retrieve 3 sentences for a random record\n",
    "document_to_transform_1 = []\n",
    "random_record_1 = X.iloc[50]\n",
    "random_record_1 = random_record_1['text']\n",
    "document_to_transform_1.append(random_record_1)\n",
    "\n",
    "document_to_transform_2 = []\n",
    "random_record_2 = X.iloc[100]\n",
    "random_record_2 = random_record_2['text']\n",
    "document_to_transform_2.append(random_record_2)\n",
    "\n",
    "document_to_transform_3 = []\n",
    "random_record_3 = X.iloc[150]\n",
    "random_record_3 = random_record_3['text']\n",
    "document_to_transform_3.append(random_record_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at our emails."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['From: ab@nova.cc.purdue.edu (Allen B) Subject: Re: TIFF: philosophical significance of 42 Organization: Purdue University Lines: 39  In article <prestonm.735400848@cs.man.ac.uk> prestonm@cs.man.ac.uk (Martin   Preston) writes: > Why not use the PD C library for reading/writing TIFF files? It took me a > good 20 minutes to start using them in your own app.  I certainly do use it whenever I have to do TIFF, and it usually works very well.  That\\'s not my point.  I\\'m >philosophically< opposed to it because of its complexity.  This complexity has led to some programs\\' poor TIFF writers making some very bizarre files, other programs\\' inability to load TIFF images (though they\\'ll save them, of course), and a general inability to interchange images between different environments despite the fact they all think they understand TIFF.  As the saying goes, \"It\\'s not me I\\'m worried about- it\\'s all the >other<  assholes out there!\"  I\\'ve had big trouble with misuse and abuse of TIFF over the years, and I chalk it all up to the immense (and unnecessary) complexity of the format.  In the words of the TIFF 5.0 spec, Appendix G, page G-1 (capitalized emphasis mine):  \"The only problem with this sort of success is that TIFF was designed to be powerful and flexible, at the expense of simplicity.  It takes a fair amount of effort to handle all the options currently defined in this specification (PROBABLY NO APPLICATION DOES A COMPLETE JOB), and that is currently the only way you can be >sure< that you will be able to import any TIFF image, since there are so many image-generating applications out there now.\"   If a program (or worse all applications) can\\'t read >every< TIFF image, that means there are some it won\\'t- some that I might have to deal with.  Why would I want my images to be trapped in that format?  I don\\'t and neither should anyone who agrees with my reasoning- not that anyone does, of course! :-)  ab ']\n",
      "['From: mathew <mathew@mantis.co.uk> Subject: Re: university violating separation of church/state? Organization: Mantis Consultants, Cambridge. UK. X-Newsreader: rusnews v1.01 Lines: 29  dmn@kepler.unh.edu (...until kings become philosophers or philosophers become kings) writes: >      Recently, RAs have been ordered (and none have resisted or cared about > it apparently) to post a religious flyer entitled _The Soul Scroll: Thoughts > on religion, spirituality, and matters of the soul_ on the inside of bathroom > stall doors. (at my school, the University of New Hampshire) It is some sort > of newsletter assembled by a Hall Director somewhere on campus. It poses a > question about \\'spirituality\\' each issue, and solicits responses to be  > included in the next \\'issue.\\' It\\'s all pretty vague. I assume it\\'s put out > by a Christian, but they\\'re very careful not to mention Jesus or the bible. > I\\'ve heard someone defend it, saying \"Well it doesn\\'t support any one religion. > \" So what??? This is a STATE university, and as a strong supporter of the > separation of church and state, I was enraged. >  >      What can I do about this?  It sounds to me like it\\'s just SCREAMING OUT for parody.  Give a copy to your friendly neighbourhood SubGenius preacher; with luck, he\\'ll run it through the mental mincer and hand you back an outrageously offensive and gut-bustingly funny parody you can paste over the originals.  I can see it now:                                 The Stool Scroll          Thoughts on Religion, Spirituality, and Matters of the Colon                         (You can use this text to wipe)   mathew ']\n",
      "['From: lfoard@hopper.virginia.edu (Lawrence C. Foard) Subject: Re: Assurance of Hell Organization: ITC/UVA Community Access UNIX/Internet Project Lines: 43  In article <Apr.20.03.01.19.1993.3755@geneva.rutgers.edu> REXLEX@fnal.fnal.gov writes: > >I dreamed that the great judgment morning had dawned, >     and the trumpet had blown. >I dreamed that the sinners had gathered for judgment >     before the white throne. >Oh what weeping and wailing as the lost were told of their fate. >They cried for the rock and the mountains. >They prayed, but their prayers were too late. >The soul that had put off salvation,  >\"Not tonight I\\'ll get saved by and by. > No time now to think of ....... religion,\"  >Alas, he had found time to die. >And I saw a Great White Throne.  If I believed in the God of the bible I would be very fearful of making this statement. Doesn\\'t it say those who judge will be judged by the same measure?   >Now, some have protest by saying that the fear of hell is not good for >motivation, yet Jesus thought it was.  Paul thought it was.  Paul said,  >\"Knowing therefore, the terror of the Lord, we persuade men.\"  A God who must motivate through fear is not a God worthy of worship. If the God Jesus spoke of did indeed exist he would not need hell to convince people to worship him.  >Today, too much of our evangelism is nothing but soft soap and some of >it is nothing but evangelical salesmanship.  We don\\'t tell people anymore, that >there\\'s such a thing as sin or that there\\'s such a place as hell.    It was the myth of hell that made me finally realize that the whole thing was untrue. If it hadn\\'t been for hell I would still be a believer today. The myth of hell made me realize that if there was a God that he was not the all knowing and all good God he claimed to be. Why should I take such a being at his word, even if there was evidence for his existance?  --  ------          Join the Pythagorean Reform Church!               . \\\\    /        Repent of your evil irrational numbers             . .  \\\\  /   and bean eating ways. Accept 10 into your heart!        . . .   \\\\/   Call the Pythagorean Reform Church BBS at 508-793-9568  . . . .      ']\n"
     ]
    }
   ],
   "source": [
    "print(document_to_transform_1)\n",
    "print(document_to_transform_2)\n",
    "print(document_to_transform_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's take a look at the count vectors:\n",
      "[[0 0 0 ... 0 0 0]]\n",
      "[[0 0 0 ... 0 0 0]]\n",
      "[[0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import binarize\n",
    "\n",
    "# Transform sentence with Vectorizers\n",
    "document_vector_count_1 = count_vect.transform(document_to_transform_1)\n",
    "document_vector_count_2 = count_vect.transform(document_to_transform_2)\n",
    "document_vector_count_3 = count_vect.transform(document_to_transform_3)\n",
    "\n",
    "# Binarize vectors to simplify: 0 for abscence, 1 for prescence\n",
    "document_vector_count_1_bin = binarize(document_vector_count_1)\n",
    "document_vector_count_2_bin = binarize(document_vector_count_2)\n",
    "document_vector_count_3_bin = binarize(document_vector_count_3)\n",
    "\n",
    "# print vectors\n",
    "print(\"Let's take a look at the count vectors:\")\n",
    "print(document_vector_count_1.todense())\n",
    "print(document_vector_count_2.todense())\n",
    "print(document_vector_count_3.todense())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine Similarity using count bw 1 and 2: 0.608862\n",
      "Cosine Similarity using count bw 1 and 3: 0.622050\n",
      "Cosine Similarity using count bw 2 and 3: 0.565566\n",
      "Cosine Similarity using count bw 1 and 1: 1.000000\n",
      "Cosine Similarity using count bw 2 and 2: 1.000000\n",
      "Cosine Similarity using count bw 3 and 3: 1.000000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "# Calculate Cosine Similarity\n",
    "cos_sim_count_1_2 = cosine_similarity(document_vector_count_1, document_vector_count_2, dense_output=True)\n",
    "cos_sim_count_1_3 = cosine_similarity(document_vector_count_1, document_vector_count_3, dense_output=True)\n",
    "cos_sim_count_2_3 = cosine_similarity(document_vector_count_2, document_vector_count_3, dense_output=True)\n",
    "\n",
    "cos_sim_count_1_1 = cosine_similarity(document_vector_count_1, document_vector_count_1, dense_output=True)\n",
    "cos_sim_count_2_2 = cosine_similarity(document_vector_count_2, document_vector_count_2, dense_output=True)\n",
    "cos_sim_count_3_3 = cosine_similarity(document_vector_count_3, document_vector_count_3, dense_output=True)\n",
    "\n",
    "# Print \n",
    "print(\"Cosine Similarity using count bw 1 and 2: %(x)f\" %{\"x\":cos_sim_count_1_2})\n",
    "print(\"Cosine Similarity using count bw 1 and 3: %(x)f\" %{\"x\":cos_sim_count_1_3})\n",
    "print(\"Cosine Similarity using count bw 2 and 3: %(x)f\" %{\"x\":cos_sim_count_2_3})\n",
    "\n",
    "print(\"Cosine Similarity using count bw 1 and 1: %(x)f\" %{\"x\":cos_sim_count_1_1})\n",
    "print(\"Cosine Similarity using count bw 2 and 2: %(x)f\" %{\"x\":cos_sim_count_2_2})\n",
    "print(\"Cosine Similarity using count bw 3 and 3: %(x)f\" %{\"x\":cos_sim_count_3_3})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, cosine similarity between a sentence and itself is 1. Between 2 entirely different sentences, it will be 0. \n",
    "\n",
    "We can assume that we have the more common features in the documents 1 and 3 than in documents 1 and 2. This reflects indeed in a higher similarity than that of sentences 1 and 3. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 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 learning models. You will learn this as you progress in the Data Mining course. I really feel that if you go through the exercises and challenge yourself, you are on your way to becoming a super Data Scientist. \n",
    "\n",
    "From here the possibilities for you are endless. You now know how to use almost every common technique for preprocessing with state-of-the-art tools, such as as Pandas and Scikit-learn. 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": [
    "## . References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pandas cook book ([Recommended for starters](http://pandas.pydata.org/pandas-docs/stable/cookbook.html))\n",
    "- [Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley](https://dl.acm.org/citation.cfm?id=1095618)"
   ]
  }
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