brock-university-tutorial-textmining-with-python.ipynb

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    "![](https://www.python.org/static/img/python-logo@2x.png)   ![](https://brocku.ca/goodman/wp-content/uploads/primary-site/sites/6/centre-for-business-analytics-logo.png?x59852) \n",
    "\n",
    "# Introduction\n",
    "This is an entry-level tutorial of TextMining With Python Created for Brock University [Goodman business school](https://brocku.ca/goodman/) Business analysis students. This tutorial teaches you the basic idea about python (Anocanda), how to run python code, data operation with python, and how to use python for Text Mining. ( I assume the reader of the audience have 0 knowledge about any programming language)\n",
    "\n",
    "Thanks, professor [Anteneh Ayanso](https://www.linkedin.com/in/aayanso/) for giving me this chance to create this tutorial.\n",
    "\n",
    "If you have any concern about this tutorial, you can reach me at my LinkedIn.([click here](https://www.linkedin.com/in/zhengang-dai/))\n",
    "\n",
    "# About Python\n",
    "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales.[26] Van Rossum led the language community until stepping down as the leader in July 2018. --wikipedia.\n",
    "\n",
    "# Why Python\n",
    "<img src=\"https://cdn-images-1.medium.com/max/1000/1*CExT2OJfdOpfI72dEYX6Mg.jpeg\" height=400 width=400>\n",
    "\n",
    "## Rank of program languages \n",
    "[tiobe.com](https://www.tiobe.com/tiobe-index/) \n",
    "\n",
    "### 1.  Beginner Friendliness\n",
    "Python was designed to be easy to understand and fun to use (its name came from Monty Python so a lot of its beginner tutorials reference it). Fun is a great motivator, and since you'll be able to build prototypes and tools quickly with Python, many find coding in Python a satisfying experience. Thus, Python has gained popularity for being a beginner-friendly language, and it has replaced Java as the most popular introductory language at Top U.S. Universities.\n",
    "\n",
    "### 2.  Easy to Understand\n",
    "Being a very high-level language, Python reads like English, which takes a lot of syntax-learning stress off coding beginners. Python handles a lot of complexity for you, so it is very beginner-friendly in that it allows beginners to focus on learning programming concepts and not have to worry about too many details.\n",
    "\n",
    "### 3. Very Flexible\n",
    "As a dynamically typed language, Python is really flexible. This means there are no hard rules on how to build features, and you'll have more flexibility solving problems using different methods (though the Python philosophy encourages using the obvious way to solve things). Furthermore, Python is also more forgiving of errors, so you'll still be able to compile and run your program until you hit the problematic part.\n",
    "\n",
    "### 4. Community\n",
    "As you step into the programming world, you'll soon understand how vital support is, as the developer community is all about giving and receiving help. The larger a community, the more likely you'd get help and the more people will be building useful tools to ease the process of development.\n",
    "\n",
    "### 5. Multifunction\n",
    "With Python, you can do almost anything you want. Web design, database operation(all databases), game design, commercial applications, information system, machine learning, text mining, and deep learning......\n",
    "\n",
    "If you only want to learn one programming language, no doubt, you should choose python!\n",
    "\n",
    "# Python 2 vs Python 3\n",
    "Python 3.x is the future, and with Python 2.x support dwindling, you should put your time into learning the version that will help you into the future. So python 3 please, I am not offering you an option, just let you know, avoid python 2. (Though they are all named python, the syntax is a little different.)\n",
    "\n",
    "# Install python\n",
    "<img src=\"https://www.anaconda.com/wp-content/uploads/2018/06/cropped-Anaconda_horizontal_RGB-1-600x102.png\" height=200>\n",
    "In Brock University's labs, they have python(anaconda both 2 and 3) installed. I will skip this process in the class. Just introduce some software here.\n",
    "### If you want to install Python on your machine, my recommendation is to install anaconda 3. GO to https://www.anaconda.com/distribution/ [Anaconda official website](https://www.anaconda.com/distribution/) download python 3.x **distribution** version choose your machine type. (Suggest you select the environment option.)\n",
    "<img src=\"https://cdn-images-1.medium.com/max/1250/1*7a9zVyGP3iMXu9aB4e_Vhw.png\" height=500 width=500>\n",
    "\n",
    "## Additional options\n",
    "### Jupyter Notebook\n",
    "<img src=\"https://jupyter.org/assets/main-logo.svg\" height=200, width=200>\n",
    "Most popular IDE for Python. If you are using anaconda, this package is already included.\n",
    "\n",
    "### Pycharm\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a1/PyCharm_Logo.svg/192px-PyCharm_Logo.svg.png)\n",
    "Install Pycharm form [Pycharm website](https://www.jetbrains.com/pycharm/download/#section=windows) dowload community version (Free!) Install on your machine\n",
    "### PS:how to fix Interpreter field is empty in pycharm \n",
    "[Youtobe Vedio](https://www.youtube.com/watch?v=ypSSGgKAjhc)\n",
    "\n",
    "### Kaggle\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/thumb/7/7c/Kaggle_logo.png/200px-Kaggle_logo.png)\n",
    "You can run your script from the kaggle website by creating a kernel (Jupyter Notebook environment). [Kaggle Website](https://www.kaggle.com/)\n",
    "\n",
    "### PyPI\n",
    "![](https://pypi.org/static/images/logo-large.72ad8bf1.svg)\n",
    "The official place to find python libraries. [Pypi website](https://pypi.org/)\n",
    "\n",
    "### Github\n",
    "![](https://avatars1.githubusercontent.com/u/9919?s=200&v=4)\n",
    "The world's leading software development platform. [Github website](https://github.com/)\n",
    "\n",
    "### Stack Overflow\n",
    "<img src=\"https://i0.wp.com/wptavern.com/wp-content/uploads/2016/07/stack-overflow.png?resize=768%2C301&ssl=1\"  height=200 width=500>\n",
    "Stack Overflow is a question and answer site for professional and enthusiast programmers.The biggest community. [Stack Overflow website](https://stackoverflow.com/) \n",
    "\n",
    "### Spyder\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/7/7e/Spyder_logo.svg/1024px-Spyder_logo.svg.png\"  width=\"200\" height=\"200\">\n",
    "Another IDE inside anaconda package."
   ]
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "30a5b1415cb938dee37f1fb6f28669c27a0cd293"
   },
   "source": [
    "# First Code \"Hello Python!\"\n",
    "Jupyter notebook or Syder provide \"console\" to run your python code, witch means you can run your code line py line or model by model (You do not have to run you whole script one time)\n",
    "Once you run part of your code, your defined variables are stored in the console (memory) and can be refered in the later codes.\n",
    "\n",
    "Lets run our first code:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "df0e80c278ff6b824e53350ade12651059ab2ffe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Python!\n"
     ]
    }
   ],
   "source": [
    "print('Hello Python!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "59e7929d5ebbd72301930f58143f5eca82268c8f"
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   "source": [
    "<h3 style=\"color:red;\">Notice that python is case sensitive, which means upercase and lowercase are different!<br>\"print\" is different from \"Print\"<p>\n",
    "   Try the following code, you will get an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "ff9294b47e76d3177c92a2c7ab463530d7042cb0"
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   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Print' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-3630f070b056>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPrint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Hello Python!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Print' is not defined"
     ]
    }
   ],
   "source": [
    "Print('Hello Python!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f885354d1b24a0b49360c7ac11eb2b62e6890e3e"
   },
   "source": [
    "# Variable\n",
    "You can temporarily store you data in variables and use them later.\n",
    "###    Variable Names\n",
    "*     A variable can have a short name (like x and y) or a more descriptive name (age, carname, total_volume). Rules for Python variables:\n",
    "*     A variable name must start with a letter or the underscore character\n",
    "*     A variable name cannot start with a number\n",
    "*     A variable name can only contain alpha-numeric characters and underscores (A-z, 0-9, and _ )\n",
    "*     Variable names are case-sensitive (age, Age and AGE are three different variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "e86778981ee094c01d5e3f1cdeaab04b7b2ee41d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello python\n"
     ]
    }
   ],
   "source": [
    "p = 'hello python'\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "09c0eba495bddf8a93ae5195530b98b6bb7292ae"
   },
   "source": [
    "# Operator\n",
    "Different data types have different meanings on operators.\n",
    "More infomation about operators can be found [here](https://www.w3schools.com/python/python_operators.asp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "bac2c92935378feb39fed6bec9fcffcd3353b3ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "a = 1 + 1\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "ef233ca0477a84ddab86d48ea72385330acfdc22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "b = '1' + '1'\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "d6f0f1c6b56af255196b7d871c6503bad8bb4541"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111\n"
     ]
    }
   ],
   "source": [
    "c = b*2  # equal \"11\" * 2\n",
    "print(c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "3ee0c8f3fce2f37036a212ed71d34f0f37804030"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcabc\n"
     ]
    }
   ],
   "source": [
    "print('abc'*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "b1c2b6d1390785dc40b64a42a1e9cf562ca6e740"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hellopython\n",
      "hellopython\n",
      "\n"
     ]
    }
   ],
   "source": [
    "d = 'hello'\n",
    "e = 'python'\n",
    "print((d+e+'\\n')*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b3b59e0835a7413fe41d169353344c55c0aaca8c"
   },
   "source": [
    "# Open data\n",
    "With python you can easily read a file as a variable.\n",
    "\n",
    "As I am using kaggle server, the file 'WeatherAnimalsSports.csv' is stored at '../input/' folder\n",
    "\n",
    "### On your computer you can use a path like \"c:/files/data.csv\" to open your local data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": false,
    "_kg_hide-output": false,
    "_uuid": "98c2ee59e4b143dd3beb6ca9635d30af61b6d221"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target_Subject,TextField\n",
      "A,Bob has two dogs and one cat.  The cat is bigger than either of the dogs.\n",
      "S,Carmelo Anthony scored 42 points to lead the NY Knicks basketball team to a win over the Florida Pelicans.\n",
      "S,Come play baseball with us.\n",
      "S,\"Derek Jeter, the captain of the New York Yankees baseball team, said 2014 will be his last season playing.\"\n",
      "S,Do you have a baseball or a football that we could play with?  You can be on my team.\n",
      "A,Do you like big dogs or little dogs?    Dogs are such wonderful animals.\n",
      "W,\"During the winter, the sun is lower in the sky than it is during the summer.  That's why winter days are colder than summer days.\"\n",
      "A,\"House cats behave very much like their big cousins, lions, tigers and leopards.  They are all all efficient predators.\"\n",
      "A,I have a friend  who had 5 cats in her hourse.  She's a true animal lover.\n",
      "W,I like the springtime when the weather is not too hot nor too cold.\n",
      "A,\"I think animals with spots and stripes, like tigers, leopards and zebras, are especially beautiful.\"\n",
      "W,I think I prefer very hot weather to very cold weather.  I like to go to the beach when it is hot and sunny.\n",
      "S,I used to play Little League baseball and basketball when I was a kid.\n",
      "W,\"If it rains tomorrow, let's not go outside.  It is also supposed to be pretty cold.\"\n",
      "W,\"If there is rain or snow, I am still going out.  I will not let the weather stop me.\"\n",
      "A,\"If we only have 30 minutes, should we visit the monkeys, or look at the elephants?  My preference is the monkeys.\"\n",
      "S,\"In the National Basketball Association, three All-Stars are among several sons of former players.\"\n",
      "W,Jack and Mary could not go to the picnic because of bad weather.  They rescheduled next Sunday when it should be a warm day.\n",
      "W,Jack likes the snow and ice of winter.  He does not like the hot weather of summer.\n",
      "A,\"John went to the zoo and saw a lion, a tiger, elephants and zebras.\"\n",
      "A,\"Lions are usually a little smaller than tigers.  Cheetahs, jaguars and leopards are big cats but are all smaller than lions and tigers.\"\n",
      "A,Mary likes to watch animal documentaries on television.  She is especially fond of watching shows about big cats.\n",
      "W,More snow is predicted for the Northeast.\n",
      "S,My favorite baseball player of all times is Willie Mays.\n",
      "A,My favorite zoo is the Bronx Zoo.  I usually go see the polar bears first and then I go to the lions and tigers.\n",
      "A,My favorite zoo is the San Diego Zoo.  I  love to watch the monkeys and gorillas.\n",
      "A,\"Orca whales prey on seals and lions prey on zebras.  Bears prey on deer, antelope and other ungulates, but they are omnivorous animals.\"\n",
      "W,\"Phoenix, Arizona, had it's fourth hottest day on record in June, 2013, when the temperature reached 119 degrees.  Summers are brutal there.\"\n",
      "S,Second-half goals gave the Bayern Munich soccer team a victory in the first game of their Champion League series.\n",
      "S,Ted Williams is the last person to have a batting average higher than .400 among all major league baseball players.\n",
      "A,The biggest animal in the world is the blue whale.\n",
      "A,The honey badger is a small but ferocious animal that can defend itself against bears and cougars.\n",
      "S,\"The Japanese pitcher, Masahiro Tanaka, has signed a big contract to play baseball with the New York Yankees team.\"\n",
      "A,The lions are near the zebras in the Bronx Zoo.\n",
      "S,The number one rated  Syracuse University basketball team lost to Boston College by a score of 62 to 59.\n",
      "W,The snow fell heavily but then the rain came and washed it away.\n",
      "S,The U.S. soccer team beat the Russian team in hockey at Sochi by a score of 4-3.\n",
      "W,\"The weather in NYC is hot in the summer and cold in the winter, but we do not get as much snow as in Chicago.\"\n",
      "W,\"There was so much rain, we could not go out.  It was also very cold.\"\n",
      "W,\"This has been a very difficult winter, much colder than usual with lots of snow, ice and rain.\"\n",
      "S,UCLA has won the NCAA basketball championship many times.\n",
      "W,\"We have had snow, snow and more snow for 10 days in a row.\"\n",
      "A,\"We went to the zoo and saw a lion, a tiger, elephants and zebras, but we did not get a chance to see the monkeys.\"\n",
      "A,\" If you like hot weather, the Arizona desert is very interesting to visit.  There are lots of unusual animals living there, including wildcats and boars.\"\n",
      "S,\"Which sport do you like more:  soccer, baseball, football or basketball?\"\n",
      "W,\"Winter days are often so pretty, even if it is cold.   \"\n",
      "W,Winter is my favorite season.  I love the cold and the snow.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../input/WeatherAnimalsSports.csv') as file:\n",
    "    f = file.read()\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "de2b725be165116f364d10156daedae64e18a678"
   },
   "source": [
    "# Data Type\n",
    "Here variable \"f\" is a \"string\", we can check its data type and length (how many letters in the variable) \n",
    "\n",
    "Python have many differnt data types, I will show some common ones below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "e308a920ec52251751aabc6ec74bff0813fd0ccb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of f is:  <class 'str'>\n",
      "The length of f is:  4428\n"
     ]
    }
   ],
   "source": [
    "print(\"The type of f is: \",type(f))\n",
    "print(\"The length of f is: \", len(f) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "ddf2bb0fec65149b0259aa3bac8e8d20de4eab16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'str'>\n",
      "<class 'list'>\n",
      "<class 'dict'>\n",
      "<class 'builtin_function_or_method'>\n"
     ]
    }
   ],
   "source": [
    "print(type(1))\n",
    "print(type(1.1))\n",
    "print(type('abc'))\n",
    "print(type([1,2,3]))\n",
    "print(type({\"name\":\"Jack\"}))\n",
    "print(type(print))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0f8e783eb668e407fe80e81cf8795040595c3362"
   },
   "source": [
    "# Python Library\n",
    "Python’s standard library is very extensive, offering a wide range of facilities as indicated by the long table of contents listed below. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming. Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c3c728b89670040a5577925f7208157a06838496"
   },
   "source": [
    "## Pandas\n",
    "<p style=\"color:red\">The most important data management library in python!<p>\n",
    "    see: Pandas official [tutorial](https://pandas.pydata.org/pandas-docs/stable/getting_started/tutorials.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "2117e8945d8d02ce65598e41f07c191826e5033d"
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Target_Subject</th>\n",
       "      <th>TextField</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Bob has two dogs and one cat.  The cat is bigg...</td>\n",
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       "      <th>1</th>\n",
       "      <td>S</td>\n",
       "      <td>Carmelo Anthony scored 42 points to lead the N...</td>\n",
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       "      <td>S</td>\n",
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      ],
      "text/plain": [
       "  Target_Subject                                          TextField\n",
       "0              A  Bob has two dogs and one cat.  The cat is bigg...\n",
       "1              S  Carmelo Anthony scored 42 points to lead the N...\n",
       "2              S                        Come play baseball with us.\n",
       "3              S  Derek Jeter, the captain of the New York Yanke...\n",
       "4              S  Do you have a baseball or a football that we c..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv('../input/WeatherAnimalsSports.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "052eb7c1a67d35a41651e7b4b7e20eb15ff35f3f"
   },
   "source": [
    "When using pandas read files, we get a DataDrame variable, it manages data by columns and rows, quite similiar as excel sheet and SQL table.\n",
    "We can select columns or rows from pandas DataFrame.\n",
    "<p style=\"color:red\"> In python, index starts from 0, so first row is row 0, second row is row 1....<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "92e44a0abc296697bdba00f984cd36b8dc3832a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     A\n",
       "1     S\n",
       "2     S\n",
       "3     S\n",
       "4     S\n",
       "5     A\n",
       "6     W\n",
       "7     A\n",
       "8     A\n",
       "9     W\n",
       "10    A\n",
       "11    W\n",
       "12    S\n",
       "13    W\n",
       "14    W\n",
       "15    A\n",
       "16    S\n",
       "17    W\n",
       "18    W\n",
       "19    A\n",
       "20    A\n",
       "21    A\n",
       "22    W\n",
       "23    S\n",
       "24    A\n",
       "25    A\n",
       "26    A\n",
       "27    W\n",
       "28    S\n",
       "29    S\n",
       "30    A\n",
       "31    A\n",
       "32    S\n",
       "33    A\n",
       "34    S\n",
       "35    W\n",
       "36    S\n",
       "37    W\n",
       "38    W\n",
       "39    W\n",
       "40    S\n",
       "41    W\n",
       "42    A\n",
       "43    A\n",
       "44    S\n",
       "45    W\n",
       "46    W\n",
       "Name: Target_Subject, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Target_Subject']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "70bbea2bf6cdd918c370e1b11bd7912ff71f1846"
   },
   "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>Target_Subject</th>\n",
       "      <th>TextField</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Bob has two dogs and one cat.  The cat is bigg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S</td>\n",
       "      <td>Carmelo Anthony scored 42 points to lead the N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>Come play baseball with us.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S</td>\n",
       "      <td>Derek Jeter, the captain of the New York Yanke...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S</td>\n",
       "      <td>Do you have a baseball or a football that we c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target_Subject                                          TextField\n",
       "0              A  Bob has two dogs and one cat.  The cat is bigg...\n",
       "1              S  Carmelo Anthony scored 42 points to lead the N...\n",
       "2              S                        Come play baseball with us.\n",
       "3              S  Derek Jeter, the captain of the New York Yanke...\n",
       "4              S  Do you have a baseball or a football that we c..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "1b4962bc6f89739f031f0a2312c6d327ca93c1e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bob has two dogs and one cat.  The cat is bigger than either of the dogs.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[0,'TextField']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "2a9be8e4f332ad29dd70811d6e7617822767fbd8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bob has two dogs and one cat.  The cat is bigger than either of the dogs.'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "77d74b69a623395e2d225033554f434f8ae8d2c2"
   },
   "source": [
    "# For loop\n",
    "A for loop is used for iterating over a sequence (that is either a list, a tuple, a dictionary, a set, or a string).\n",
    "\n",
    "This is less like the for keyword in other programming language, and works more like an iterator method as found in other object-orientated programming languages.\n",
    "\n",
    "With the for loop we can execute a set of statements, once for each item in a list, tuple, set etc.\n",
    "\n",
    "<p style=\"color:red\">Automatic do same thing many times on different target<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "51fff56b0cf98e280427d3c5bd6b5cbff4ea1d4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "for i in range(3):\n",
    "    print(j)\n",
    "    j += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "291bf20e5d0a0a42cc0b45fe751b4b348d0115e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bob has two dogs and one cat.  The cat is bigger than either of the dogs.\n",
      "Carmelo Anthony scored 42 points to lead the NY Knicks basketball team to a win over the Florida Pelicans.\n",
      "Come play baseball with us.\n"
     ]
    }
   ],
   "source": [
    "for i in data['TextField'][:3]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e9cbe79406f36a16538ca0141993de6bf5a8169d"
   },
   "source": [
    "# List Comprehensions\n",
    "Advanced loop syntax with a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "25ec4e06f368fb570a3bf44f3d5d26bc5823236c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bob has two dogs and one cat.  The cat is bigger than either of the dogs.', 'Carmelo Anthony scored 42 points to lead the NY Knicks basketball team to a win over the Florida Pelicans.', 'Come play baseball with us.']\n"
     ]
    }
   ],
   "source": [
    "subtext = [i for i in data['TextField'][:3]]\n",
    "print(subtext)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6b833ae49058f70462f8a6dc94c087b0cd202e55"
   },
   "source": [
    "# Text Parsing\n",
    "Breaking texts into small component, eg. words, sentences.\n",
    "Here I only show you one word parsing, not cover sentence parsing or big-gram parsing (multipe-words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "aa733fbea332d38781ca373fca36fac6ade893cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bob has two dogs and one cat.  The cat is bigger than either of the dogs.\n"
     ]
    }
   ],
   "source": [
    "text1 = data.loc[0,'TextField']\n",
    "print(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "71cb2c8b171a8c6a618549740c69bb64da66d7cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bob', 'has', 'two', 'dogs', 'and', 'one', 'cat.', '', 'The', 'cat', 'is', 'bigger', 'than', 'either', 'of', 'the', 'dogs.']\n"
     ]
    }
   ],
   "source": [
    "token1 = text1.split(' ')  # split text by spaces\n",
    "print(token1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "645676f8511f344d8dbc68cc1255db2c175789f9"
   },
   "source": [
    "Now we get words inside the first record.\n",
    "We need do the same thing for all the records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "3ebb03c9a2262043d8607ad9067d115afd5e71a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Bob', 'has', 'two', 'dogs', 'and', 'one', 'cat.', '', 'The', 'cat', 'is', 'bigger', 'than', 'either', 'of', 'the', 'dogs.'], ['Carmelo', 'Anthony', 'scored', '42', 'points', 'to', 'lead', 'the', 'NY', 'Knicks', 'basketball', 'team', 'to', 'a', 'win', 'over', 'the', 'Florida', 'Pelicans.'], ['Come', 'play', 'baseball', 'with', 'us.'], ['Derek', 'Jeter,', 'the', 'captain', 'of', 'the', 'New', 'York', 'Yankees', 'baseball', 'team,', 'said', '2014', 'will', 'be', 'his', 'last', 'season', 'playing.'], ['Do', 'you', 'have', 'a', 'baseball', 'or', 'a', 'football', 'that', 'we', 'could', 'play', 'with?', '', 'You', 'can', 'be', 'on', 'my', 'team.']]\n"
     ]
    }
   ],
   "source": [
    "tokens = [tx.split(' ') for tx in data.loc[:, 'TextField']]  # use for-loop to tokenize every text in the file\n",
    "print(tokens[:5])  # print the first 5 tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1b6c42c31daf40c3f79d4d7974fba265bd500607"
   },
   "source": [
    "# Data cleaning\n",
    "We need to clean the text data before the text mining.\n",
    "1. Unify the cases (Turn Capitals into lowercase)\n",
    "2. Delete stopwords\n",
    "3. Delete punctuations and numbers\n",
    "4. Stemming and lemmatization (not for here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "8d6581579d24ea351342ff2e8b9ba1b4704d4d39"
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords  # use stop words from nltk library\n",
    "stopword = stopwords.words(['english'])  # define stopword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "cdb51be70b32908373ce624f713476a477502bb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['bob', 'two', 'dogs', 'one', 'cat', 'bigger', 'either'], ['carmelo', 'anthony', 'scored', 'points', 'lead', 'ny', 'knicks', 'basketball', 'team', 'win', 'florida'], ['come', 'play', 'baseball'], ['derek', 'captain', 'new', 'york', 'yankees', 'baseball', 'said', 'last', 'season'], ['baseball', 'football', 'could', 'play']]\n"
     ]
    }
   ],
   "source": [
    "cleaned_tokens = []  # create a new list to store result\n",
    "for token in tokens:  # look through all the element in tokens\n",
    "    cleaned_token = [word.lower() for word in token]  # lowercase\n",
    "    cleaned_token = [word for word in cleaned_token if word not in stopword]  # delete stopword in each token\n",
    "    cleaned_token = [word for word in cleaned_token if word.isalpha()]  # delte non alphabet word\n",
    "    cleaned_tokens.append(cleaned_token)  # put each result into new list\n",
    "print(cleaned_tokens[:5]) # check first 5 result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "66d083512ec0125470efa782af3696603403660d"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2f51d1286ce6db13ae5c487762e3d1e8c0383e47"
   },
   "source": [
    "# Vectorization (Manually way)\n",
    "This step is to convert the tokens into numeric numbers.\n",
    "\n",
    "first, we need create a list contains every word in every document.\n",
    "\n",
    "then, we count the frequency of the appearance of each in document. (Or use binary exist or not)\n",
    "\n",
    "last, we use TF-IDF technique to convert the matrix. (See the notes from your 5P12 class)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5bf53b1dbed5183c7d7c051701f44ad1ab1025f6"
   },
   "source": [
    "## Create wordlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "2e807256f72725ab04d1727360ceba0855eeb242"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bob', 'two', 'dogs', 'one', 'cat', 'bigger', 'either', 'carmelo', 'anthony', 'scored', 'points', 'lead', 'ny', 'knicks', 'basketball', 'team', 'win', 'florida', 'come', 'play', 'baseball', 'derek', 'captain', 'new', 'york', 'yankees', 'baseball', 'said', 'last', 'season', 'baseball', 'football', 'could', 'play', 'like', 'big', 'dogs', 'little', 'dogs', 'wonderful', 'sun', 'lower', 'sky', 'winter', 'days', 'colder', 'summer', 'house', 'cats', 'behave', 'much', 'like', 'big', 'tigers', 'efficient', 'friend', 'cats', 'true', 'animal', 'like', 'springtime', 'weather', 'hot', 'think', 'animals', 'spots', 'like', 'leopards', 'especially', 'think', 'prefer', 'hot', 'weather', 'cold', 'like', 'go', 'beach', 'hot', 'used', 'play', 'little', 'league', 'baseball', 'basketball', 'rains', 'go', 'also', 'supposed', 'pretty', 'rain', 'still', 'going', 'let', 'weather', 'stop', 'visit', 'look', 'preference', 'national', 'basketball', 'three', 'among', 'several', 'sons', 'former', 'jack', 'mary', 'could', 'go', 'picnic', 'bad', 'rescheduled', 'next', 'sunday', 'warm', 'jack', 'likes', 'snow', 'ice', 'like', 'hot', 'weather', 'john', 'went', 'zoo', 'saw', 'elephants', 'lions', 'usually', 'little', 'smaller', 'jaguars', 'leopards', 'big', 'cats', 'smaller', 'lions', 'mary', 'likes', 'watch', 'animal', 'documentaries', 'especially', 'fond', 'watching', 'shows', 'big', 'snow', 'predicted', 'favorite', 'baseball', 'player', 'times', 'willie', 'favorite', 'zoo', 'bronx', 'usually', 'go', 'see', 'polar', 'bears', 'first', 'go', 'lions', 'favorite', 'zoo', 'san', 'diego', 'love', 'watch', 'monkeys', 'orca', 'whales', 'prey', 'seals', 'lions', 'prey', 'bears', 'prey', 'antelope', 'omnivorous', 'fourth', 'hottest', 'day', 'record', 'temperature', 'reached', 'summers', 'brutal', 'goals', 'gave', 'bayern', 'munich', 'soccer', 'team', 'victory', 'first', 'game', 'champion', 'league', 'ted', 'williams', 'last', 'person', 'batting', 'average', 'higher', 'among', 'major', 'league', 'baseball', 'biggest', 'animal', 'world', 'blue', 'honey', 'badger', 'small', 'ferocious', 'animal', 'defend', 'bears', 'japanese', 'masahiro', 'signed', 'big', 'contract', 'play', 'baseball', 'new', 'york', 'yankees', 'lions', 'near', 'zebras', 'bronx', 'number', 'one', 'rated', 'syracuse', 'university', 'basketball', 'team', 'lost', 'boston', 'college', 'score', 'snow', 'fell', 'heavily', 'rain', 'came', 'washed', 'soccer', 'team', 'beat', 'russian', 'team', 'hockey', 'sochi', 'score', 'weather', 'nyc', 'hot', 'summer', 'cold', 'get', 'much', 'snow', 'much', 'could', 'go', 'also', 'difficult', 'much', 'colder', 'usual', 'lots', 'ice', 'ucla', 'ncaa', 'basketball', 'championship', 'many', 'snow', 'snow', 'days', 'went', 'zoo', 'saw', 'elephants', 'get', 'chance', 'see', 'like', 'hot', 'arizona', 'desert', 'interesting', 'lots', 'unusual', 'animals', 'living', 'including', 'wildcats', 'sport', 'like', 'football', 'winter', 'days', 'often', 'even', 'winter', 'favorite', 'love', 'cold']\n"
     ]
    }
   ],
   "source": [
    "wordlists = []  # creat a empty list for storing the result\n",
    "for t in cleaned_tokens:  # look through all the element in tokens\n",
    "    wordlists += t  # add every token into list\n",
    "print(wordlists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "db53264945b8eb5b26de80f9020d7b98f936877d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "317\n"
     ]
    }
   ],
   "source": [
    "print(len(wordlists))  # check how many words in total, we have duplicates in the list which need to be deleted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "5127bd4d673ac717a0715108d58e7e1ade1fc8d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "201\n"
     ]
    }
   ],
   "source": [
    "wordlist = list(set(wordlists))  # remove duplicate words from wordlist\n",
    "print(len(wordlist))  # check words number after removing duplicate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "55740d925d961056b0c95638b8d0dcbccbbf135b"
   },
   "source": [
    "## Count frequency of each word in each document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "6c34702fd53317688164259f75cabd34d4b1bf43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47\n",
      "201\n",
      "47\n",
      "0\n",
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      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "['winter', 'favorite', 'love', 'cold']\n"
     ]
    }
   ],
   "source": [
    "wordcounts = []  # creat a empty list for storing the whole result\n",
    "for token in cleaned_tokens:  # look through all the element in tokens\n",
    "    wordcount = []  # creat a empty list for storing each result temparorily, notice every loop, this list will be emptified\n",
    "    for word in wordlist: # look through all the element in wordlist\n",
    "        count = token.count(word)\n",
    "        wordcount.append(count)\n",
    "    wordcounts.append(wordcount)\n",
    "print(len(wordcounts))\n",
    "print(len(wordcounts[0]))\n",
    "print(len(cleaned_tokens))\n",
    "print(count)\n",
    "print(wordlist)\n",
    "print(wordcount)\n",
    "print(cleaned_tokens[-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "e5ac55e101a6ea42e89f5d6c589b2ea04d7e2d75"
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  go  honey  come  john   ...    like  ice  masahiro  bronx  colder\n",
       "0    1   0      0     0     0   ...       0    0         0      0       0\n",
       "1    0   0      0     0     0   ...       0    0         0      0       0\n",
       "2    0   0      0     1     0   ...       0    0         0      0       0\n",
       "3    0   0      0     0     0   ...       0    0         0      0       0\n",
       "4    0   0      0     0     0   ...       0    0         0      0       0\n",
       "\n",
       "[5 rows x 201 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordmatrix = pd.DataFrame(data=wordcounts, columns=wordlist)  # creat a dataframe to help you look the result\n",
    "wordmatrix.head()  # show first 5 documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9681f481bf23f8551b05869e38904488abd85194"
   },
   "source": [
    "## Calculae TF-IDF\n",
    "\n",
    "Detailed explaination see [wikipeida](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)\n",
    "\n",
    "![](https://www.researchgate.net/profile/Heloisa_Rocha/publication/221228354/figure/fig2/AS:650816818003985@1532178229971/TF-IDF-formula-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "8553c74d9389d4582edc92ff2a6221e19a68f428"
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  go  honey  come    ...      masahiro  bronx  colder  row_total\n",
       "0    1   0      0     0    ...             0      0       0          7\n",
       "1    0   0      0     0    ...             0      0       0         11\n",
       "2    0   0      0     1    ...             0      0       0          3\n",
       "3    0   0      0     0    ...             0      0       0          9\n",
       "4    0   0      0     0    ...             0      0       0          4\n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordmatrix['row_total'] = wordmatrix.aggregate('sum',axis=1) # add a sum column (total number of words in each document)\n",
    "wordmatrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "9e6de1b8ce57c12e01dd14ca4981fb842863781c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one             2\n",
      "go              5\n",
      "honey           1\n",
      "come            1\n",
      "john            1\n",
      "football        2\n",
      "summers         1\n",
      "dogs            2\n",
      "sky             1\n",
      "mary            2\n",
      "sun             1\n",
      "soccer          2\n",
      "temperature     1\n",
      "play            4\n",
      "fourth          1\n",
      "points          1\n",
      "champion        1\n",
      "new             2\n",
      "came            1\n",
      "hot             5\n",
      "anthony         1\n",
      "national        1\n",
      "york            2\n",
      "often           1\n",
      "beach           1\n",
      "prey            1\n",
      "lots            2\n",
      "williams        1\n",
      "japanese        1\n",
      "bayern          1\n",
      "               ..\n",
      "desert          1\n",
      "jack            2\n",
      "little          3\n",
      "seals           1\n",
      "captain         1\n",
      "whales          1\n",
      "cat             1\n",
      "knicks          1\n",
      "lost            1\n",
      "three           1\n",
      "munich          1\n",
      "weather         5\n",
      "rains           1\n",
      "ferocious       1\n",
      "day             1\n",
      "first           2\n",
      "beat            1\n",
      "let             1\n",
      "true            1\n",
      "hockey          1\n",
      "big             5\n",
      "polar           1\n",
      "arizona         1\n",
      "smaller         1\n",
      "like            8\n",
      "ice             2\n",
      "masahiro        1\n",
      "bronx           2\n",
      "colder          2\n",
      "row_total      47\n",
      "Length: 202, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "N = len(wordmatrix)\n",
    "n = wordmatrix.astype('bool').sum()  \n",
    "print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "665c6b8dec2442fea2b1f96a110193c0b5535c2a"
   },
   "outputs": [
    {
     "data": {
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       "      <th>one</th>\n",
       "      <th>go</th>\n",
       "      <th>honey</th>\n",
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       "      <th>dogs</th>\n",
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       "      <th>mary</th>\n",
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       "      <th>soccer</th>\n",
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       "      <th>play</th>\n",
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       "      <th>points</th>\n",
       "      <th>champion</th>\n",
       "      <th>new</th>\n",
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       "      <th>hot</th>\n",
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       "      <th>japanese</th>\n",
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       "      <th>contract</th>\n",
       "      <th>many</th>\n",
       "      <th>going</th>\n",
       "      <th>love</th>\n",
       "      <th>bears</th>\n",
       "      <th>orca</th>\n",
       "      <th>visit</th>\n",
       "      <th>badger</th>\n",
       "      <th>...</th>\n",
       "      <th>shows</th>\n",
       "      <th>victory</th>\n",
       "      <th>sochi</th>\n",
       "      <th>league</th>\n",
       "      <th>reached</th>\n",
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       "      <th>knicks</th>\n",
       "      <th>lost</th>\n",
       "      <th>three</th>\n",
       "      <th>munich</th>\n",
       "      <th>weather</th>\n",
       "      <th>rains</th>\n",
       "      <th>ferocious</th>\n",
       "      <th>day</th>\n",
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       "      <th>beat</th>\n",
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       "      <th>true</th>\n",
       "      <th>hockey</th>\n",
       "      <th>big</th>\n",
       "      <th>polar</th>\n",
       "      <th>arizona</th>\n",
       "      <th>smaller</th>\n",
       "      <th>like</th>\n",
       "      <th>ice</th>\n",
       "      <th>masahiro</th>\n",
       "      <th>bronx</th>\n",
       "      <th>colder</th>\n",
       "      <th>row_total</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.342767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.267509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.298744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one   go  honey      come    ...      masahiro  bronx  colder  row_total\n",
       "0  0.195867  0.0    0.0  0.000000    ...           0.0    0.0     0.0          7\n",
       "1  0.000000  0.0    0.0  0.000000    ...           0.0    0.0     0.0         11\n",
       "2  0.000000  0.0    0.0  0.557366    ...           0.0    0.0     0.0          3\n",
       "3  0.000000  0.0    0.0  0.000000    ...           0.0    0.0     0.0          9\n",
       "4  0.000000  0.0    0.0  0.000000    ...           0.0    0.0     0.0          4\n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "for row in range(len(wordmatrix)):  # go through every row\n",
    "    for col in wordmatrix.columns[:-1]:  # go through every column exclude 'row_total'\n",
    "        wordmatrix.loc[row,col] = wordmatrix.loc[row,col]/wordmatrix.loc[row,'row_total']*math.log10(N/n[col])\n",
    "        \n",
    "wordmatrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a9c0e9b2fbc106989d150b9b95c8f9c36cbe436e"
   },
   "source": [
    "# SVD decomposition\n",
    "![](https://intoli.com/blog/pca-and-svd/img/svd-matrices.png)\n",
    "\n",
    "See details [here](https://intoli.com/blog/pca-and-svd/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "e9320ed0f6dcf82f3cee38751893de9b6a15e21b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.79734445e-04  4.19718318e-17  2.25537917e-02 -6.35396284e-04\n",
      "   6.67179918e-03 -3.14859129e-02  3.31740791e-03  2.49238897e-02\n",
      "   5.52374086e-04  1.32667892e-01 -6.10798188e-02  3.40478534e-02\n",
      "   6.90012430e-03  3.90459107e-01  7.63479822e-02]\n",
      " [ 1.23071776e-05 -6.45510807e-17  6.27306555e-03 -1.07304571e-03\n",
      "  -5.16765618e-04  9.92244253e-03  1.77652577e-04  2.29876942e-03\n",
      "  -4.06045579e-04  4.23227228e-02  4.20798427e-02 -2.93015268e-04\n",
      "   2.76891379e-03  1.64908620e-03  1.74574722e-03]\n",
      " [ 1.11170821e-03 -2.22183200e-18  4.09446438e-01 -6.23984802e-02\n",
      "  -4.15143837e-02  4.68492157e-01 -3.10438782e-02  2.95796547e-02\n",
      "  -7.72699665e-03  3.22160500e-02 -1.03672890e-01 -3.47733310e-02\n",
      "  -1.24971296e-01 -6.83408578e-03 -5.44631707e-02]\n",
      " [ 1.87371786e-04  4.44273841e-18  6.10497093e-02 -7.33084300e-03\n",
      "  -5.79224381e-03  7.13838454e-02 -2.82280330e-03  5.82949980e-03\n",
      "   1.22134889e-03  1.08355540e-02 -2.03278591e-02 -4.53271197e-03\n",
      "  -3.29125689e-03  4.24911671e-03 -1.44324067e-03]\n",
      " [ 3.39538555e-03  9.82897390e-17  4.21252441e-01 -5.54303822e-02\n",
      "  -2.01675899e-02  8.20761882e-02 -1.97373246e-02 -1.81705505e-02\n",
      "  -1.99620614e-02 -8.68637451e-02  4.64243990e-02  2.00011915e-02\n",
      "  -3.59486588e-02 -2.59901558e-02  1.31090103e-02]\n",
      " [ 4.33288559e-03 -2.33115532e-17  9.56751234e-02 -1.81855913e-03\n",
      "   2.17746398e-02 -9.00107325e-02  8.14354768e-03  5.46757526e-02\n",
      "   1.20680687e-03  1.93048192e-01 -1.00053801e-01  4.46485574e-02\n",
      "   7.70877488e-03  4.10973570e-01  4.30640137e-02]\n",
      " [ 6.50521963e-02 -1.32792350e-17  2.11267258e-02  2.61948930e-01\n",
      "  -4.44314095e-02  9.44903631e-04 -2.93710479e-02 -2.96450104e-03\n",
      "  -3.10775559e-02 -7.28361928e-03  1.25058528e-02  1.03713585e-02\n",
      "  -3.20719737e-02  1.03526045e-02 -4.69694474e-02]\n",
      " [ 5.66161933e-03  2.07236325e-17  5.94845876e-02  1.45159918e-02\n",
      "   5.43701218e-02 -6.28140051e-02  3.45673591e-02 -1.90641445e-02\n",
      "  -9.83576036e-03  7.30583073e-02 -5.68972454e-02  9.02227527e-02\n",
      "  -4.37736754e-03  1.81974048e-02 -3.63949391e-02]\n",
      " [ 6.39782309e-04 -7.65710956e-19  2.45619304e-02  6.56620464e-02\n",
      "   4.13248053e-01  1.29615085e-02  4.03213652e-03  1.31520960e-02\n",
      "  -1.39623905e-02  1.25964193e-01 -1.28414444e-01  4.95251674e-01\n",
      "  -2.37792497e-02 -1.71796966e-01 -2.43499294e-02]\n",
      " [ 2.58921427e-02  4.59616194e-17  1.12892492e-01  2.43979599e-02\n",
      "   1.06510534e-02 -2.01213166e-01  6.50024727e-03 -8.99787015e-03\n",
      "   3.06557708e-02  2.06491687e-01 -1.75894171e-01 -1.44686042e-01\n",
      "  -5.00537360e-03 -6.50627033e-02 -1.10259868e-01]\n",
      " [ 4.80637501e-03  8.80034870e-17  6.50249321e-02  6.06410801e-03\n",
      "   1.75003515e-02 -1.02512078e-01  6.64518738e-03  2.08165477e-02\n",
      "   7.17964482e-03  7.96597964e-02 -6.17472691e-02 -1.70367683e-02\n",
      "   2.00061728e-02  2.34172674e-02 -5.59116649e-02]\n",
      " [ 1.89380939e-02  8.49878291e-17  7.57203502e-02  4.36133565e-02\n",
      "   3.95908798e-03 -1.14187104e-01  5.00165204e-02 -4.94474917e-02\n",
      "   1.70882884e-02  1.16653274e-01 -1.02895305e-01 -8.33319701e-02\n",
      "   4.16598802e-02 -3.83964456e-02 -6.49465485e-02]\n",
      " [ 6.81950036e-04 -1.64078111e-17  1.71080949e-01 -2.29857022e-02\n",
      "  -1.09651748e-02  1.64285091e-01 -2.27630533e-03  2.44499574e-02\n",
      "  -4.90975826e-03  9.52605618e-02  1.62127419e-02  1.17631782e-03\n",
      "  -2.79945251e-02  5.48503399e-02 -2.50436219e-03]\n",
      " [ 2.28846076e-03  2.75472236e-16  8.26856678e-02  2.69811104e-02\n",
      "   7.74061714e-03 -4.89979581e-02  3.25475884e-01 -4.22586400e-01\n",
      "  -1.18044652e-01 -6.42084582e-02  7.87866761e-02  2.52684858e-02\n",
      "   3.92045694e-03  4.88569704e-02  5.37758950e-02]\n",
      " [ 1.98154089e-02  3.39091984e-17  2.24635577e-02  1.59537549e-02\n",
      "   2.01932333e-03 -6.55504661e-02  1.07563355e-02 -1.65208969e-02\n",
      "   1.41240247e-02  1.72328069e-01 -1.60849559e-01 -1.71973675e-01\n",
      "  -1.07090272e-01 -1.74325463e-01  1.26391785e-01]\n",
      " [ 1.50883679e-18  9.65386148e-01 -1.90906504e-16 -2.14482908e-17\n",
      "  -9.56328021e-18  1.03311002e-16 -2.81217342e-16  1.31966764e-16\n",
      "   6.59744017e-17  1.76247594e-17  4.97939138e-18  6.07041853e-18\n",
      "  -1.19805913e-17  2.90961643e-18 -4.69708318e-18]\n",
      " [ 2.43453534e-05 -1.41562841e-17  1.41747778e-02 -2.54853288e-03\n",
      "  -1.38704714e-03  2.43540008e-02 -4.45614215e-04  5.03025312e-03\n",
      "  -9.43301541e-04  9.83609379e-02  9.94251589e-02 -1.77340729e-03\n",
      "   6.58321590e-03 -4.69576581e-03  6.23702813e-04]\n",
      " [ 4.61566074e-03  1.01985743e-16  5.55138592e-02  4.79338808e-03\n",
      "   4.26355607e-03 -1.20067341e-02  6.90508559e-02 -8.60624028e-02\n",
      "  -2.18931236e-02 -6.30514989e-03  4.70648638e-03 -1.46946407e-03\n",
      "  -6.22854592e-03 -2.98463990e-03 -4.59695851e-03]\n",
      " [ 1.27138111e-01  4.26794087e-17  6.31101389e-02  2.30145296e-02\n",
      "   8.61182648e-03 -1.08363399e-01  1.06945316e-02 -1.84503109e-02\n",
      "   8.52880786e-03  1.16490987e-01 -9.73746245e-02 -7.86936006e-02\n",
      "  -3.58523809e-02 -4.09475906e-02 -5.60018381e-02]\n",
      " [ 1.37477733e-03  1.15052982e-18  1.02993594e-02  4.12131716e-02\n",
      "   6.68844246e-04 -2.30789854e-03  1.44315055e-01  5.49440323e-03\n",
      "   5.22108399e-01 -4.80109142e-02  6.70591385e-02  3.77655150e-02\n",
      "  -1.59468099e-01  3.11728829e-02 -1.07398300e-02]\n",
      " [ 7.28056372e-04  3.44092554e-17  3.35860442e-02  1.54189363e-02\n",
      "   5.53105851e-02 -1.32288632e-02  1.22806262e-01  1.08736036e-01\n",
      "  -3.47591985e-02  6.29145529e-02 -4.16246629e-02  7.90085729e-02\n",
      "  -6.14392737e-03  5.50622756e-02  1.48788067e-02]\n",
      " [ 4.07133276e-03  3.26250252e-17  1.90517789e-02  1.97741632e-02\n",
      "   7.84027285e-02 -1.15300493e-02  7.47143841e-03 -1.00804231e-04\n",
      "   1.15779468e-02  3.71396465e-02 -3.21780259e-02  1.83017322e-02\n",
      "   2.00852215e-02  1.40654713e-02 -5.93581156e-03]\n",
      " [ 8.78802458e-01 -2.85138037e-17 -4.37832846e-02 -2.53432194e-01\n",
      "   3.89806140e-02  4.13699305e-02  3.10917341e-02  5.56434533e-03\n",
      "   3.49348540e-02 -5.61061789e-02  3.56835412e-02  5.01282858e-02\n",
      "   1.71882059e-01  4.55619171e-02 -4.95116153e-02]\n",
      " [ 2.35741894e-03  1.31619439e-17  1.30645313e-01  6.14893009e-02\n",
      "  -2.34083770e-02  1.64888277e-01  2.81073969e-02  1.73740128e-02\n",
      "   7.45512320e-02  2.43107064e-02 -9.28690000e-02 -4.31289087e-02\n",
      "   3.21914795e-01 -6.54799207e-02  8.77039323e-02]\n",
      " [ 1.91111880e-03  1.00614500e-16  3.43738854e-02  4.12821174e-02\n",
      "   1.40892005e-02 -5.19973092e-03  1.79874980e-01  1.99984408e-02\n",
      "   3.50400733e-02 -1.37836883e-02  6.90042793e-03 -4.68809335e-03\n",
      "   3.95394448e-02 -4.87109435e-03  2.93799542e-02]\n",
      " [ 3.57279059e-03  3.56531982e-17  2.66313841e-02  1.03352491e-01\n",
      "  -9.08708565e-03  2.50555062e-02  5.83212007e-02  8.00165445e-03\n",
      "   1.60828228e-01  4.98084944e-03 -3.48377955e-02 -1.29758535e-02\n",
      "   2.37401638e-01 -3.27854931e-02  3.90591953e-02]\n",
      " [ 9.64770678e-05  4.21069308e-17  6.97889166e-03  1.14727754e-02\n",
      "   2.42895809e-02 -3.57338091e-03  1.27475779e-01  1.11106474e-01\n",
      "  -3.84181320e-02 -7.91693558e-03  2.72391640e-03  7.66554663e-03\n",
      "  -2.98983674e-03 -7.70430438e-03  4.92796797e-01]\n",
      " [-4.55398219e-17 -4.55540511e-17  1.18614785e-15 -6.33915994e-14\n",
      "  -3.42625006e-14  9.24508662e-15 -1.04873261e-12 -6.08922839e-12\n",
      "  -3.15862151e-12  1.52676962e-10 -3.18901955e-10  3.95876986e-10\n",
      "  -2.40383434e-09  5.81940232e-09  1.33688218e-07]\n",
      " [ 4.87289331e-05 -1.20364334e-17  9.35113172e-03  1.10335332e-04\n",
      "  -1.88357642e-04  1.12004406e-02  7.53211684e-03  2.94992145e-03\n",
      "   1.36918517e-03  1.38604846e-02  7.05076168e-03 -1.54967539e-04\n",
      "   5.15106831e-04  9.13733566e-03  4.17644328e-03]\n",
      " [ 1.41424822e-04 -4.50080293e-18  4.60403090e-02 -5.47406182e-03\n",
      "  -4.58553910e-03  5.35570123e-02 -1.82291777e-03  4.84835937e-03\n",
      "   9.35970848e-04  1.93923335e-02 -9.65518612e-04 -3.90373142e-03\n",
      "  -3.35980085e-04  2.65904958e-03  3.61021918e-04]\n",
      " [ 2.93941281e-04 -2.00125284e-17  1.67786657e-02  9.82434102e-02\n",
      "   6.52292468e-01  6.30281521e-02 -8.76881135e-02 -3.69953930e-02\n",
      "   1.25527509e-02 -1.09594788e-01  1.03060645e-01 -3.31709366e-01\n",
      "   8.32871762e-03  9.45490495e-02 -1.52217151e-02]\n",
      " [ 1.70499409e-04  9.40300127e-18  6.75700616e-03  2.49274913e-02\n",
      "   1.44422761e-01  1.00352991e-02  1.06661382e-02  7.39925120e-03\n",
      "  -1.05353086e-05  5.23971524e-03 -8.82162589e-03  4.91720685e-02\n",
      "   2.71271318e-03 -2.62823678e-02  1.13728050e-01]\n",
      " [ 4.82651923e-04  2.54078873e-18  1.05707224e-01 -1.35211992e-02\n",
      "  -5.00266786e-03  1.02277932e-01 -2.35970253e-03  1.10880971e-02\n",
      "  -2.13218560e-03  2.27841807e-02 -3.13866713e-02  2.07471188e-03\n",
      "  -2.06732652e-02  2.60511830e-02 -6.19679344e-03]\n",
      " [ 2.77671412e-04  1.59557995e-16  2.12604727e-02  3.34024841e-02\n",
      "   4.19714480e-02 -1.66164068e-02  4.95042824e-01  4.54590193e-01\n",
      "  -1.69132459e-01 -6.62376155e-02  4.12784532e-02 -7.72388813e-02\n",
      "  -3.52471449e-02 -3.25089392e-02 -1.38444903e-01]\n",
      " [ 2.41758900e-05 -3.16647950e-18  7.17319219e-03 -1.08100326e-03\n",
      "  -1.50238235e-04  7.92268071e-03  4.51719681e-04  3.93841973e-03\n",
      "  -3.40086384e-04  5.22146823e-02  3.61960326e-02  2.65008043e-03\n",
      "   3.33378576e-03  3.86191282e-02  1.06269680e-02]\n",
      " [ 1.72492851e-01 -2.76688167e-17  1.12589322e-03  1.39650558e-02\n",
      "  -1.66411632e-03 -1.85819605e-02 -2.78396150e-03 -5.59873633e-03\n",
      "  -6.03207163e-03  9.12453595e-02 -8.25101785e-02 -1.06530862e-01\n",
      "  -1.66658880e-01 -1.33140420e-01  2.12521193e-01]\n",
      " [ 4.35537320e-06 -2.51126219e-17  2.12888144e-03 -2.08387607e-04\n",
      "  -8.46057156e-05  3.79589543e-03  1.36740367e-03  1.50159958e-03\n",
      "   1.60817899e-04  2.15010141e-02  1.66236924e-02  6.27508868e-04\n",
      "   1.50746230e-03  1.50109985e-02  6.73739251e-03]\n",
      " [ 1.15537182e-01  4.62361720e-17  4.20969365e-02  6.79795357e-02\n",
      "  -3.19155522e-03 -6.26779126e-02  4.45002191e-02 -4.70794585e-02\n",
      "   3.95276832e-02  9.31977853e-02 -8.32485353e-02 -6.77576718e-02\n",
      "  -4.43001612e-03 -5.04808760e-02 -4.90188439e-02]\n",
      " [ 7.80507357e-03  2.61134403e-16  1.48966223e-01  2.31193165e-02\n",
      "   6.63598775e-03 -3.67004433e-02  2.53828247e-01 -3.25840266e-01\n",
      "  -8.66403865e-02 -3.73820877e-02  3.84320487e-02  1.95974706e-02\n",
      "  -1.43009915e-02  1.07866786e-02 -9.30255953e-03]\n",
      " [ 1.45455798e-02  1.17724666e-17  3.55234505e-02  5.08049223e-02\n",
      "   1.84724380e-04 -4.26797013e-02  5.49387737e-02 -8.32614105e-02\n",
      "  -2.14320512e-02  5.51743312e-02 -4.42648700e-02 -1.80396968e-02\n",
      "  -2.95235951e-02 -1.52347151e-02 -1.20052216e-01]\n",
      " [ 3.32302382e-05 -2.62913554e-17  2.27598479e-02 -4.76468450e-03\n",
      "  -2.34642305e-03  4.86091607e-02 -7.31719485e-04  1.31805175e-02\n",
      "  -3.37201785e-03  4.37818887e-01  5.07414077e-01 -7.59553645e-03\n",
      "   5.26934495e-02 -8.64865179e-02 -2.17631521e-03]\n",
      " [ 5.65063653e-01  2.69891681e-17  4.12850726e-03  2.00391235e-01\n",
      "  -3.56669485e-02 -2.83645551e-03 -4.44758701e-02  7.59732786e-03\n",
      "  -5.12428388e-02  1.18624156e-02  7.18997690e-03 -1.59627040e-02\n",
      "  -2.01330957e-01 -1.73341364e-02  4.95951230e-02]\n",
      " [ 4.78425874e-03  8.37943590e-18  1.12768605e-02  3.69212002e-02\n",
      "   9.53501473e-04 -6.39144813e-03  1.21136224e-01  1.57665653e-03\n",
      "   3.98299131e-01 -2.87536750e-02  4.35303096e-02  2.18049568e-02\n",
      "  -1.18857287e-01  1.81886696e-02 -1.15582426e-02]\n",
      " [ 6.24014613e-03  2.37868399e-17  3.90765137e-02  8.00323882e-03\n",
      "   4.32029879e-03 -6.62163676e-02  3.41837513e-03 -3.81463034e-03\n",
      "   6.11360740e-03  5.80347113e-02 -4.73851515e-02 -3.13801462e-02\n",
      "   3.27010349e-03 -4.39536954e-03 -5.50643919e-02]\n",
      " [ 1.08345216e-02  7.75348174e-17  5.01772624e-01 -7.02707496e-02\n",
      "   5.88236903e-03 -4.39562166e-01 -1.67229040e-01  1.28382704e-01\n",
      "   2.01351238e-02 -1.70423455e-01  1.53317458e-01  5.26379841e-02\n",
      "   4.59042372e-02 -4.24190171e-02  5.12941282e-02]\n",
      " [ 1.18265588e-01 -4.01551765e-17  3.32228875e-02  5.82798258e-01\n",
      "  -1.03156608e-01  2.90763664e-02 -1.02920870e-01  3.59853540e-02\n",
      "  -8.63318292e-02 -8.55234634e-02  9.50055103e-02  7.30506478e-02\n",
      "  -8.12885487e-02  7.30693373e-02 -2.29889975e-02]\n",
      " [ 2.53048747e-02  2.14786803e-17  5.28183900e-02  2.93948057e-01\n",
      "  -4.68856897e-02  3.00035921e-02  2.70133241e-02  7.07549500e-03\n",
      "   8.97378869e-02  1.75141930e-02 -5.98529841e-02 -2.87731531e-02\n",
      "   3.54420870e-01 -4.54786132e-02  1.64249224e-02]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "svd = TruncatedSVD(n_components=15, n_iter=30, random_state=0)\n",
    "X = svd.fit_transform(wordmatrix.drop('row_total',axis=1))\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9a25fb11e63b4f6cbdedea03e492d4cbc2005ce9"
   },
   "source": [
    "# Clustering\n",
    "Because everyone learned clustering from 5P11 and 5P2, I won't explain clustering here.\n",
    "\n",
    "Comparing different algorism of clustering, [link](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html)\n",
    "\n",
    "## You can also watch\n",
    "\n",
    "<a style='color:blue'  href='http://www.rel8ed.to/2019/coldest_capital_hottest_women_owned_business/'>My Tabluea Clustering Tutorial<a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "dbc81ddcf2e32f084860e793181a7040b2b6ee4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 0 1 1 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "gmm = GaussianMixture(n_components=3)\n",
    "gmm.fit(X)\n",
    "result = gmm.predict(X)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "e4b464434b72fd02403ee7514e2ac835ccb7e216"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 2 0 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 1 0 0 2 0 0]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "clustering = SpectralClustering(n_clusters=3, random_state=0)\n",
    "clustering.fit(X)\n",
    "result0 = clustering.labels_\n",
    "print(result0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_kg_hide-output": false,
    "_uuid": "9254c2f6829cfff94be163dc3b44ffc462a08d1a"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\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>Target_Subject</th>\n",
       "      <th>TextField</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Bob has two dogs and one cat.  The cat is bigg...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S</td>\n",
       "      <td>Carmelo Anthony scored 42 points to lead the N...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>Come play baseball with us.</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S</td>\n",
       "      <td>Derek Jeter, the captain of the New York Yanke...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S</td>\n",
       "      <td>Do you have a baseball or a football that we c...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>Do you like big dogs or little dogs?    Dogs a...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>W</td>\n",
       "      <td>During the winter, the sun is lower in the sky...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A</td>\n",
       "      <td>House cats behave very much like their big cou...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A</td>\n",
       "      <td>I have a friend  who had 5 cats in her hourse....</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>W</td>\n",
       "      <td>I like the springtime when the weather is not ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A</td>\n",
       "      <td>I think animals with spots and stripes, like t...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>W</td>\n",
       "      <td>I think I prefer very hot weather to very cold...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>S</td>\n",
       "      <td>I used to play Little League baseball and bask...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>W</td>\n",
       "      <td>If it rains tomorrow, let's not go outside.  I...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>W</td>\n",
       "      <td>If there is rain or snow, I am still going out...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A</td>\n",
       "      <td>If we only have 30 minutes, should we visit th...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>S</td>\n",
       "      <td>In the National Basketball Association, three ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>W</td>\n",
       "      <td>Jack and Mary could not go to the picnic becau...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>W</td>\n",
       "      <td>Jack likes the snow and ice of winter.  He doe...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>A</td>\n",
       "      <td>John went to the zoo and saw a lion, a tiger, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A</td>\n",
       "      <td>Lions are usually a little smaller than tigers...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>A</td>\n",
       "      <td>Mary likes to watch animal documentaries on te...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>W</td>\n",
       "      <td>More snow is predicted for the Northeast.</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>S</td>\n",
       "      <td>My favorite baseball player of all times is Wi...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A</td>\n",
       "      <td>My favorite zoo is the Bronx Zoo.  I usually g...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>A</td>\n",
       "      <td>My favorite zoo is the San Diego Zoo.  I  love...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>A</td>\n",
       "      <td>Orca whales prey on seals and lions prey on ze...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>W</td>\n",
       "      <td>Phoenix, Arizona, had it's fourth hottest day ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>S</td>\n",
       "      <td>Second-half goals gave the Bayern Munich socce...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>S</td>\n",
       "      <td>Ted Williams is the last person to have a batt...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>A</td>\n",
       "      <td>The biggest animal in the world is the blue wh...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>A</td>\n",
       "      <td>The honey badger is a small but ferocious anim...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>S</td>\n",
       "      <td>The Japanese pitcher, Masahiro Tanaka, has sig...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>A</td>\n",
       "      <td>The lions are near the zebras in the Bronx Zoo.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>S</td>\n",
       "      <td>The number one rated  Syracuse University bask...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>W</td>\n",
       "      <td>The snow fell heavily but then the rain came a...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>S</td>\n",
       "      <td>The U.S. soccer team beat the Russian team in ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>W</td>\n",
       "      <td>The weather in NYC is hot in the summer and co...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>W</td>\n",
       "      <td>There was so much rain, we could not go out.  ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>W</td>\n",
       "      <td>This has been a very difficult winter, much co...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>S</td>\n",
       "      <td>UCLA has won the NCAA basketball championship ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>W</td>\n",
       "      <td>We have had snow, snow and more snow for 10 da...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>A</td>\n",
       "      <td>We went to the zoo and saw a lion, a tiger, el...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>A</td>\n",
       "      <td>If you like hot weather, the Arizona desert i...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>S</td>\n",
       "      <td>Which sport do you like more:  soccer, basebal...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>W</td>\n",
       "      <td>Winter days are often so pretty, even if it is...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>W</td>\n",
       "      <td>Winter is my favorite season.  I love the cold...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Target_Subject   ...    cluster0\n",
       "0               A   ...           0\n",
       "1               S   ...           0\n",
       "2               S   ...           2\n",
       "3               S   ...           0\n",
       "4               S   ...           2\n",
       "5               A   ...           0\n",
       "6               W   ...           0\n",
       "7               A   ...           0\n",
       "8               A   ...           0\n",
       "9               W   ...           0\n",
       "10              A   ...           0\n",
       "11              W   ...           0\n",
       "12              S   ...           2\n",
       "13              W   ...           0\n",
       "14              W   ...           0\n",
       "15              A   ...           0\n",
       "16              S   ...           0\n",
       "17              W   ...           0\n",
       "18              W   ...           0\n",
       "19              A   ...           0\n",
       "20              A   ...           0\n",
       "21              A   ...           0\n",
       "22              W   ...           1\n",
       "23              S   ...           0\n",
       "24              A   ...           0\n",
       "25              A   ...           0\n",
       "26              A   ...           0\n",
       "27              W   ...           0\n",
       "28              S   ...           0\n",
       "29              S   ...           0\n",
       "30              A   ...           0\n",
       "31              A   ...           0\n",
       "32              S   ...           0\n",
       "33              A   ...           0\n",
       "34              S   ...           0\n",
       "35              W   ...           0\n",
       "36              S   ...           0\n",
       "37              W   ...           0\n",
       "38              W   ...           0\n",
       "39              W   ...           0\n",
       "40              S   ...           0\n",
       "41              W   ...           1\n",
       "42              A   ...           0\n",
       "43              A   ...           0\n",
       "44              S   ...           2\n",
       "45              W   ...           0\n",
       "46              W   ...           0\n",
       "\n",
       "[47 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cluster'] = result\n",
    "data['cluster0'] = result0\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "50791e37ae813d3a1595827cab5e8a99737a9523"
   },
   "source": [
    "# Word Topics\n",
    "Simply speaking, word topic is to tanspose your text matrix, and generate cluster for keywords (orignally clusering works on docments)\n",
    "Here we use Transposed SVD to make word topics.\n",
    "1. Transpose the SVD data\n",
    "2. Clustering on transposed matrix\n",
    "3. Refer the keywords\n",
    "Let's do it step by step:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8f7ca4b5ca98136380a3a1d6580dceeb5fe66db2"
   },
   "source": [
    "## Transpose SVDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "d85774d4bf9cf8e1c54138dccf11fb8375b6c970"
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>dogs</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sky</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.238871</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mary</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.238871</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soccer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.124643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.171383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.209012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>play</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.356679</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.267509</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.17834</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.107004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fourth</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.209012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>points</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.152009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>champion</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.152009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.152341</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.137107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>came</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.278683</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hot</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.243282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.216251</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.139018</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.121641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2  ...         44   45   46\n",
       "one          0.195867  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "go           0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "honey        0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "come         0.000000  0.000000  0.557366 ...   0.000000  0.0  0.0\n",
       "john         0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "football     0.000000  0.000000  0.000000 ...   0.457023  0.0  0.0\n",
       "summers      0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "dogs         0.195867  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "sky          0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "mary         0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "sun          0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "soccer       0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "temperature  0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "play         0.000000  0.000000  0.356679 ...   0.000000  0.0  0.0\n",
       "fourth       0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "points       0.000000  0.152009  0.000000 ...   0.000000  0.0  0.0\n",
       "champion     0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "new          0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "came         0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "hot          0.000000  0.000000  0.000000 ...   0.000000  0.0  0.0\n",
       "\n",
       "[20 rows x 47 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docmatrix = wordmatrix.drop('row_total',axis=1).transpose()\n",
    "docmatrix.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "b0faf9f72dba6c6d8818a138ad63afa855d0500b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.95888464e-05  2.76529704e-17  6.13529096e-03 ...  2.56707478e-03\n",
      "   1.20033102e-01  2.52412431e-02]\n",
      " [ 4.78461873e-03  3.11884218e-16  8.31705855e-02 ...  1.18805761e-02\n",
      "   1.00836739e-02  9.19754643e-03]\n",
      " [ 3.76244258e-05  3.66114798e-18  1.86434271e-03 ...  9.41357344e-04\n",
      "  -9.27008638e-03  4.21242977e-02]\n",
      " ...\n",
      " [ 7.45553963e-05 -4.97149341e-19  2.04161638e-02 ... -5.02181538e-03\n",
      "   6.43200159e-03 -1.60633851e-03]\n",
      " [ 3.07983294e-04  1.07233950e-16  1.33662644e-02 ... -1.03918531e-02\n",
      "  -1.73499623e-02 -6.79046585e-02]\n",
      " [ 1.48413985e-02 -1.62528810e-17  1.41559840e-02 ... -1.89267967e-02\n",
      "  -2.14631483e-03 -5.68034601e-02]]\n"
     ]
    }
   ],
   "source": [
    "svdT = TruncatedSVD(n_components=5, n_iter=30, random_state=0)\n",
    "XT = svd.fit_transform(docmatrix)\n",
    "print(XT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "6d4bed234080a54b8367c7aa7fa75bea18bf1fa7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 15)\n",
      "(201, 15)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(XT.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "546398237a264979de6b51d6353427885bffbdb7"
   },
   "source": [
    "## Topic Creating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "0b7661ba2d1c134f168c8bae6b2f519476ab2f51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 5 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 2 0 0 0 0\n",
      " 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 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 0 0 4 0 0 0 0 0 4 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 4 0 0 4 0 0 0 0 0 0 0 3 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 3 0]\n"
     ]
    }
   ],
   "source": [
    "topic = GaussianMixture(n_components=6)\n",
    "topic.fit(XT)\n",
    "topic_label = topic.predict(XT)\n",
    "print(topic_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1748472c5745a3c046ccd085abca21001de2b07e"
   },
   "source": [
    "## Mark keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "a40c23a1930c2b481c620614afa638cc4cbd6e8b"
   },
   "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>one</th>\n",
       "      <th>stop</th>\n",
       "      <th>willie</th>\n",
       "      <th>likes</th>\n",
       "      <th>watching</th>\n",
       "      <th>score</th>\n",
       "      <th>record</th>\n",
       "      <th>wonderful</th>\n",
       "      <th>russian</th>\n",
       "      <th>derek</th>\n",
       "      <th>see</th>\n",
       "      <th>next</th>\n",
       "      <th>diego</th>\n",
       "      <th>think</th>\n",
       "      <th>san</th>\n",
       "      <th>yankees</th>\n",
       "      <th>university</th>\n",
       "      <th>efficient</th>\n",
       "      <th>behave</th>\n",
       "      <th>warm</th>\n",
       "      <th>wildcats</th>\n",
       "      <th>defend</th>\n",
       "      <th>bob</th>\n",
       "      <th>higher</th>\n",
       "      <th>lower</th>\n",
       "      <th>fell</th>\n",
       "      <th>tigers</th>\n",
       "      <th>documentaries</th>\n",
       "      <th>said</th>\n",
       "      <th>rain</th>\n",
       "      <th>bigger</th>\n",
       "      <th>pretty</th>\n",
       "      <th>two</th>\n",
       "      <th>basketball</th>\n",
       "      <th>signed</th>\n",
       "      <th>unusual</th>\n",
       "      <th>omnivorous</th>\n",
       "      <th>nyc</th>\n",
       "      <th>team</th>\n",
       "      <th>college</th>\n",
       "      <th>...</th>\n",
       "      <th>player</th>\n",
       "      <th>sons</th>\n",
       "      <th>animals</th>\n",
       "      <th>prefer</th>\n",
       "      <th>even</th>\n",
       "      <th>ucla</th>\n",
       "      <th>washed</th>\n",
       "      <th>spots</th>\n",
       "      <th>antelope</th>\n",
       "      <th>friend</th>\n",
       "      <th>gave</th>\n",
       "      <th>small</th>\n",
       "      <th>game</th>\n",
       "      <th>watch</th>\n",
       "      <th>brutal</th>\n",
       "      <th>ny</th>\n",
       "      <th>batting</th>\n",
       "      <th>colder</th>\n",
       "      <th>hottest</th>\n",
       "      <th>cold</th>\n",
       "      <th>syracuse</th>\n",
       "      <th>used</th>\n",
       "      <th>cats</th>\n",
       "      <th>bad</th>\n",
       "      <th>visit</th>\n",
       "      <th>preference</th>\n",
       "      <th>look</th>\n",
       "      <th>snow</th>\n",
       "      <th>predicted</th>\n",
       "      <th>lions</th>\n",
       "      <th>near</th>\n",
       "      <th>zebras</th>\n",
       "      <th>bronx</th>\n",
       "      <th>biggest</th>\n",
       "      <th>blue</th>\n",
       "      <th>animal</th>\n",
       "      <th>world</th>\n",
       "      <th>play</th>\n",
       "      <th>come</th>\n",
       "      <th>baseball</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>topic</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       one  stop  willie  likes    ...     world  play  come  baseball\n",
       "topic    0     0       0      0    ...         4     5     5         5\n",
       "\n",
       "[1 rows x 201 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_table = pd.DataFrame([topic_label], columns=docmatrix.index)\n",
    "topic_table.index = ['topic']\n",
    "topic_table = topic_table.sort_values(by='topic',axis=1)\n",
    "topic_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "18e591344de20f356b0cbe988f1bff6a3f469607"
   },
   "source": [
    "\n",
    "# Visualization T-SNE\n",
    "We can transform the topicmatrix into a two dimension matrix(by T-SNE), so we can use visualization to see the distribution of the words\n",
    "\n",
    "### For more about visualization check [here](https://seaborn.pydata.org/examples/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "c6d58353327ecedf4f4aa7ae48e4295d737d0344"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-6.76369238e+00 -5.23719358e+00]\n",
      " [-1.67009659e+01  4.06276655e+00]\n",
      " [ 9.69947052e+00  4.90797329e+00]\n",
      " [ 2.69338369e+00  9.73977661e+00]\n",
      " [-1.46976109e+01  8.20248276e-02]\n",
      " [-1.10127535e+01 -1.03571663e+01]\n",
      " [ 4.10937220e-01 -8.36442280e+00]\n",
      " [-1.14654980e+01 -3.54592824e+00]\n",
      " [-7.25076723e+00  9.18633556e+00]\n",
      " [-2.76946354e+00 -4.80147314e+00]\n",
      " [-7.31669664e+00  9.18250370e+00]\n",
      " [ 7.31961203e+00  1.11965094e+01]\n",
      " [-3.75270277e-01 -8.73570251e+00]\n",
      " [ 2.20890927e+00  1.02454376e+01]\n",
      " [ 9.14071202e-01 -8.89315796e+00]\n",
      " [ 3.30642247e+00 -1.17555773e+00]\n",
      " [ 5.34020901e-01  8.16595495e-01]\n",
      " [ 1.36187887e+00  2.85489774e+00]\n",
      " [-8.84150887e+00 -1.39988155e+01]\n",
      " [-2.80860472e+00 -1.74235344e+01]\n",
      " [ 3.32997537e+00 -1.70275831e+00]\n",
      " [-7.76730490e+00  1.57236481e+01]\n",
      " [ 1.62287664e+00  2.76791954e+00]\n",
      " [-5.68997955e+00  7.00568151e+00]\n",
      " [-2.16177678e+00  2.78121781e+00]\n",
      " [ 1.22636814e+01  1.26761093e+01]\n",
      " [ 5.72865868e+00 -1.46843576e+01]\n",
      " [-5.23921251e+00  2.72362804e+00]\n",
      " [ 3.38269901e+00  2.69616437e+00]\n",
      " [ 9.26695883e-01  8.32612932e-01]\n",
      " [-7.21816492e+00 -7.85041714e+00]\n",
      " [ 6.32113743e+00 -2.22701859e+00]\n",
      " [ 3.42581129e+00  2.32126689e+00]\n",
      " [-9.15271664e+00  2.77823186e+00]\n",
      " [-3.17294097e+00 -1.31882982e+01]\n",
      " [ 1.21698685e+01 -4.63682604e+00]\n",
      " [-3.77047801e+00  8.55199051e+00]\n",
      " [-1.94692171e+00 -1.09491110e+00]\n",
      " [-8.01215363e+00 -1.04986782e+01]\n",
      " [ 9.99329090e+00  5.59132481e+00]\n",
      " [-1.06247787e+01  6.81042671e+00]\n",
      " [-5.65561485e+00  2.74425292e+00]\n",
      " [ 6.06679678e+00 -1.50778990e+01]\n",
      " [ 6.78572035e+00  2.09338164e+00]\n",
      " [-1.38848057e+01  3.25072408e-01]\n",
      " [-9.11798763e+00  3.37251043e+00]\n",
      " [-2.69640899e+00  1.06007576e+01]\n",
      " [-3.00288391e+00 -1.32994747e+01]\n",
      " [ 2.83526564e+00 -2.72858381e+00]\n",
      " [-4.88417768e+00  2.37430021e-01]\n",
      " [-3.82611084e+00 -4.47712755e+00]\n",
      " [-6.38326645e+00  3.52848172e+00]\n",
      " [-1.69757423e+01 -5.32381916e+00]\n",
      " [ 9.17365456e+00 -4.94543123e+00]\n",
      " [ 1.13256431e+00  3.73961896e-01]\n",
      " [ 9.24816132e+00  5.58956575e+00]\n",
      " [ 1.97273344e-02  6.55266166e-01]\n",
      " [ 1.04740744e+01  9.35600185e+00]\n",
      " [-3.58551323e-01 -9.41965485e+00]\n",
      " [ 2.42659235e+00  1.04883881e+01]\n",
      " [ 2.27327394e+00  1.71218929e+01]\n",
      " [ 2.45221210e+00 -2.02946544e+00]\n",
      " [-6.47739697e+00  2.62399864e+00]\n",
      " [-4.28759718e+00 -4.16109228e+00]\n",
      " [ 1.22859869e+01 -4.79285908e+00]\n",
      " [ 1.15511978e+00 -9.38829708e+00]\n",
      " [ 2.53705001e+00  1.73369102e+01]\n",
      " [ 5.03878784e+00 -6.29178858e+00]\n",
      " [ 1.39305186e+00  5.33637142e+00]\n",
      " [-1.00780745e+01  6.80247593e+00]\n",
      " [-2.22521687e+00  1.04381666e+01]\n",
      " [-1.17351627e+01 -6.49035025e+00]\n",
      " [-6.94979286e+00  1.56634007e+01]\n",
      " [-2.53020406e+00 -9.01576281e-01]\n",
      " [-6.27615070e+00  2.73640800e+00]\n",
      " [ 3.42472291e+00 -9.45588684e+00]\n",
      " [-1.69640770e+01 -5.27505159e+00]\n",
      " [ 3.01991868e+00 -2.34384823e+00]\n",
      " [-1.67197151e+01  4.05641174e+00]\n",
      " [-1.63477957e-02 -4.69600248e+00]\n",
      " [ 3.35871100e-01 -3.59765244e+00]\n",
      " [ 3.54437184e+00 -2.71062398e+00]\n",
      " [ 1.34254837e+01 -2.71529317e-01]\n",
      " [ 2.24320388e+00  5.54694223e+00]\n",
      " [-8.70687389e+00 -1.42778625e+01]\n",
      " [ 6.62417603e+00 -2.22884536e+00]\n",
      " [ 7.20978451e+00 -2.45873466e-01]\n",
      " [ 1.59380598e+01  5.25363731e+00]\n",
      " [ 3.34709239e+00 -9.42370605e+00]\n",
      " [-7.66405535e+00  1.51872244e+01]\n",
      " [-2.07075810e+00  1.08273439e+01]\n",
      " [-9.59236908e+00 -7.88743305e+00]\n",
      " [-2.13446355e+00  2.86787629e+00]\n",
      " [-5.17639494e+00  6.98901367e+00]\n",
      " [ 9.73122978e+00  1.52654290e-01]\n",
      " [-1.39989414e+01  1.05925426e-01]\n",
      " [-3.79200292e+00 -1.74596214e+01]\n",
      " [ 9.70897579e+00 -6.85149479e+00]\n",
      " [-7.31533861e+00  1.58900242e+01]\n",
      " [-8.90886974e+00 -1.44815464e+01]\n",
      " [ 6.75021744e+00 -1.33337049e+01]\n",
      " [ 6.63654280e+00  2.23209786e+00]\n",
      " [-1.12692368e+00  5.21087646e+00]\n",
      " [-4.64081907e+00  5.83946928e-02]\n",
      " [-6.85378361e+00 -1.40862694e+01]\n",
      " [-9.68304920e+00 -7.97464609e+00]\n",
      " [-5.72583771e+00 -5.54998112e+00]\n",
      " [ 1.58530302e+01  5.28470325e+00]\n",
      " [ 5.36959124e+00  5.46039200e+00]\n",
      " [ 1.02024851e+01 -8.94726336e-01]\n",
      " [ 9.61873055e-01 -4.21893406e+00]\n",
      " [-5.86169481e+00  9.82300043e-01]\n",
      " [-1.91152930e+00 -1.19391167e+00]\n",
      " [ 9.34802532e+00 -5.74689388e+00]\n",
      " [ 6.37762737e+00  1.20903530e+01]\n",
      " [ 1.43955934e+00  6.23657703e+00]\n",
      " [-9.13607121e+00 -1.41936016e+01]\n",
      " [-6.14869022e+00 -6.10750198e+00]\n",
      " [ 1.39690580e+01 -3.96857810e+00]\n",
      " [ 7.17538238e-01 -6.91330731e-02]\n",
      " [ 1.18261445e+00  5.87704372e+00]\n",
      " [-7.33197117e+00  9.18039131e+00]\n",
      " [ 3.46225572e+00  2.36977673e+00]\n",
      " [-5.54567194e+00  3.21173453e+00]\n",
      " [ 9.96276855e+00 -8.01652241e+00]\n",
      " [-2.75596762e+00 -1.31205482e+01]\n",
      " [-6.67024469e+00 -1.96669734e+00]\n",
      " [-1.36015749e+00  6.75465727e+00]\n",
      " [-1.23532498e+00  5.22279024e+00]\n",
      " [ 5.47984505e+00  1.00690508e+01]\n",
      " [ 3.35373461e-01 -9.37738132e+00]\n",
      " [ 5.45475674e+00 -4.43892908e+00]\n",
      " [ 6.57593298e+00  1.04888430e+01]\n",
      " [-4.42287874e+00  4.51249748e-01]\n",
      " [-3.69386196e+00 -4.16496897e+00]\n",
      " [-7.91220546e-01  1.50092058e+01]\n",
      " [ 9.48171902e+00  5.92798805e+00]\n",
      " [-1.17670832e+01 -6.63310385e+00]\n",
      " [-1.50458169e+00  1.02150860e+01]\n",
      " [ 1.11499968e+01  8.60981178e+00]\n",
      " [-2.88192415e+00  1.37214017e+00]\n",
      " [-7.81617343e-01  1.49781218e+01]\n",
      " [ 2.24863076e+00  6.13130569e+00]\n",
      " [ 7.00972223e+00  2.09101796e+00]\n",
      " [ 7.14752102e+00  2.32446480e+00]\n",
      " [-4.24760294e+00 -3.85383940e+00]\n",
      " [-8.04824382e-03 -4.15565968e+00]\n",
      " [ 4.87340355e+00  5.16068983e+00]\n",
      " [-8.76339531e+00  6.96477699e+00]\n",
      " [-1.19139969e+00 -3.51160240e+00]\n",
      " [-1.47762418e+00  4.88696432e+00]\n",
      " [-1.40584879e+01  2.26603776e-01]\n",
      " [ 1.83750582e+00  5.31320381e+00]\n",
      " [ 4.57757652e-01 -4.28394890e+00]\n",
      " [ 1.00681086e+01 -7.80259657e+00]\n",
      " [ 2.65676689e+00 -1.51940906e+00]\n",
      " [ 9.75273418e+00  5.72063267e-01]\n",
      " [-3.88228893e+00 -3.41816449e+00]\n",
      " [ 5.54670274e-01  1.60484390e+01]\n",
      " [-1.31385164e+01  2.94271380e-01]\n",
      " [-5.81082010e+00  3.61163592e+00]\n",
      " [-7.63309777e-01  1.49923639e+01]\n",
      " [-1.37693286e+00  5.18780947e+00]\n",
      " [ 3.45786065e-01 -8.22964460e-02]\n",
      " [ 6.44907856e+00  1.03619080e+01]\n",
      " [ 4.87351036e+00 -6.43435383e+00]\n",
      " [ 1.96394086e-01 -8.54265690e+00]\n",
      " [ 3.90752959e+00 -2.28260159e+00]\n",
      " [-3.47586489e+00 -3.71008039e+00]\n",
      " [-1.75634441e+01 -4.30430508e+00]\n",
      " [ 5.87243748e+00 -1.51246157e+01]\n",
      " [-2.30654788e+00  9.84119701e+00]\n",
      " [ 2.17870817e-01 -5.07623053e+00]\n",
      " [-4.77789927e+00 -2.72243571e+00]\n",
      " [ 5.54232025e+00 -5.10954571e+00]\n",
      " [-2.36396241e+00 -1.20529199e+00]\n",
      " [-4.30674887e+00  5.71991861e-01]\n",
      " [-2.10557055e+00 -6.36856437e-01]\n",
      " [-5.99617624e+00 -6.30832195e+00]\n",
      " [ 3.88176203e+00 -1.58213949e+00]\n",
      " [ 1.82775033e+00  6.39122868e+00]\n",
      " [-7.12198830e+00  1.51440859e+01]\n",
      " [-9.64028388e-03  2.95027375e-01]\n",
      " [-3.12942529e+00 -1.73486118e+01]\n",
      " [ 1.59521770e+01  5.06241131e+00]\n",
      " [ 9.14473438e+00  4.85175562e+00]\n",
      " [ 4.14039969e-01 -9.83350754e+00]\n",
      " [ 1.21542998e-03  1.77156246e+00]\n",
      " [ 6.14672375e+00  1.09232817e+01]\n",
      " [-3.20450282e+00 -1.34724445e+01]\n",
      " [ 2.21267533e+00  1.71683025e+01]\n",
      " [ 6.31269455e+00  1.09341211e+01]\n",
      " [ 5.48121405e+00  6.88574433e-01]\n",
      " [-2.13179946e-01  2.49058890e+00]\n",
      " [ 7.74996638e-01 -4.80834055e+00]\n",
      " [ 7.38340425e+00 -2.62660599e+00]\n",
      " [-1.68176174e+00 -1.74774418e+01]\n",
      " [ 6.60064697e+00 -1.57822809e+01]\n",
      " [ 3.70529890e+00  2.63849926e+00]\n",
      " [-1.71577606e+01 -5.00703430e+00]\n",
      " [ 4.92427826e+00 -1.56783533e+01]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2, n_iter=300)\n",
    "coordinates = tsne.fit_transform(docmatrix)\n",
    "print(coordinates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "667092285f17e31037791d778c155e6ba8a473c3"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.763692</td>\n",
       "      <td>-5.237194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-16.700966</td>\n",
       "      <td>4.062767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.699471</td>\n",
       "      <td>4.907973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.693384</td>\n",
       "      <td>9.739777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-14.697611</td>\n",
       "      <td>0.082025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y\n",
       "0  -6.763692 -5.237194\n",
       "1 -16.700966  4.062767\n",
       "2   9.699471  4.907973\n",
       "3   2.693384  9.739777\n",
       "4 -14.697611  0.082025"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordmap = pd.DataFrame(coordinates, columns=['x','y'])\n",
    "wordmap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "d7f6f16a36ca101005e916498923c58f034c7302"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "word_label = KMeans(n_clusters=3).fit(wordmap).labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "18e33b44d245b5832bc92daa7b1312859ed712d3"
   },
   "outputs": [
    {
     "data": {
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       "      <th>word</th>\n",
       "      <th>cluster</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-6.763692</td>\n",
       "      <td>-5.237194</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-16.700966</td>\n",
       "      <td>4.062767</td>\n",
       "      <td>go</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.699471</td>\n",
       "      <td>4.907973</td>\n",
       "      <td>honey</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.693384</td>\n",
       "      <td>9.739777</td>\n",
       "      <td>come</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-14.697611</td>\n",
       "      <td>0.082025</td>\n",
       "      <td>john</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y   word  cluster\n",
       "0  -6.763692 -5.237194    one        0\n",
       "1 -16.700966  4.062767     go        2\n",
       "2   9.699471  4.907973  honey        1\n",
       "3   2.693384  9.739777   come        2\n",
       "4 -14.697611  0.082025   john        2"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordmap['word'] = docmatrix.index\n",
    "wordmap['cluster'] = word_label\n",
    "wordmap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b464280fefa9549ab36e407f21283d990925fec5"
   },
   "source": [
    "## Draw map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "4db228f5cbbc67f8dd61dabb08fe004606619412"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(20, 20))\n",
    "sns.scatterplot('x','y',hue='cluster',palette=\"Set1\",s=150, data=wordmap)\n",
    "for n in range(len(wordmap)):\n",
    "        plt.annotate(wordmap['word'][n],\n",
    "                     xy=(wordmap['x'][n],wordmap['y'][n]),\n",
    "                     xytext=(2,5), textcoords='offset points', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "64093ae3429ee46e59e2f0b02c913d6e3f71799f"
   },
   "source": [
    "# Auto Text Mining with TextBlob\n",
    "With professional textmining Python library, we don't have to create each step manually.\n",
    "\n",
    "The reason why I create detailed steps is to help you get better understanding of the machanism behind the Textmining Technology.\n",
    "\n",
    "In the production environment, it is better to use the tools to simplify you analysis\n",
    "\n",
    "### For your convinience, I restart from scrach.\n",
    "\n",
    "See more on textblob official [website](https://textblob.readthedocs.io/en/dev/classifiers.html)\n",
    "\n",
    "<p style='color:red'> To use textblob you need to install it first, in command line, run \"pip install textblob\", or check the ofiicial website for help <p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "04174f08f58a4d4ddd2a17fc51fa7684e2546aaf"
   },
   "source": [
    "## Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "980e0231ba3b00ae8bb2ae9fd4e68de2ab1f8654"
   },
   "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>Target_Subject</th>\n",
       "      <th>TextField</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Bob has two dogs and one cat.  The cat is bigg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S</td>\n",
       "      <td>Carmelo Anthony scored 42 points to lead the N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>Come play baseball with us.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S</td>\n",
       "      <td>Derek Jeter, the captain of the New York Yanke...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S</td>\n",
       "      <td>Do you have a baseball or a football that we c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Target_Subject                                          TextField\n",
       "0              A  Bob has two dogs and one cat.  The cat is bigg...\n",
       "1              S  Carmelo Anthony scored 42 points to lead the N...\n",
       "2              S                        Come play baseball with us.\n",
       "3              S  Derek Jeter, the captain of the New York Yanke...\n",
       "4              S  Do you have a baseball or a football that we c..."
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train_data = pd.read_csv('../input/WeatherAnimalsSports.csv')\n",
    "score_data = pd.read_csv('../input/Score_WeatherAnimalSports.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "7415c4336cf9ea7cd8f133ce238081240e5c1d78"
   },
   "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>TextField</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>We have a dog in our house.  His name is Princ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I spend a lot of time on the weekend watching ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The World Cup in soccer is held every four yea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The 2013 World Series was won by the Boston Re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The winter weather has been very harsh in many...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           TextField\n",
       "0  We have a dog in our house.  His name is Princ...\n",
       "1  I spend a lot of time on the weekend watching ...\n",
       "2  The World Cup in soccer is held every four yea...\n",
       "3  The 2013 World Series was won by the Boston Re...\n",
       "4  The winter weather has been very harsh in many..."
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3aed8027605284cf8c9825d83292204e5c7b9a90"
   },
   "source": [
    "## Transform data into textblob required format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "c482ec88bb7a366a6337295d7bed6c5b77ff3344"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Bob has two dogs and one cat.  The cat is bigger than either of the dogs.',\n",
       "  'A'),\n",
       " ('Carmelo Anthony scored 42 points to lead the NY Knicks basketball team to a win over the Florida Pelicans.',\n",
       "  'S'),\n",
       " ('Come play baseball with us.', 'S'),\n",
       " ('Derek Jeter, the captain of the New York Yankees baseball team, said 2014 will be his last season playing.',\n",
       "  'S'),\n",
       " ('Do you have a baseball or a football that we could play with?  You can be on my team.',\n",
       "  'S')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_blob = list(zip(train_data['TextField'],train_data['Target_Subject']))  # transform format\n",
    "train_blob[:5]  # check format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "462354c6c94055625bfd4d07f0f6adf1b90b6b6c"
   },
   "outputs": [],
   "source": [
    "from textblob.classifiers import NaiveBayesClassifier\n",
    "cl = NaiveBayesClassifier(train_blob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7c80e302222af71d62dcfeafac8f84e3d36cbfe5"
   },
   "source": [
    "## Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "2a762df11ed7ec5c4503f807b816a589fb9d97e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have a dog in our house.  His name is Princey and he is a part of our family.\n",
      "A\n"
     ]
    }
   ],
   "source": [
    "score0 = cl.classify(score_data['TextField'][0])\n",
    "print(score_data['TextField'][0])\n",
    "print(score0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "151c497e1484494fa45c956c5badaa833aedc203"
   },
   "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>TextField</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>We have a dog in our house.  His name is Princ...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I spend a lot of time on the weekend watching ...</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The World Cup in soccer is held every four yea...</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The 2013 World Series was won by the Boston Re...</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The winter weather has been very harsh in many...</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yesterday, I watched a documentary about the b...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>In our neighborhood, one man has 5 small dogs ...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>We have a problem with feral cats.</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Professional basketball players are paid enorm...</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>We have friends who live in a rural area and t...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>We have had 5 inches of snow since this morning.</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rainy weather in the summer can be very pleasa...</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>I could watch elephants all day.  They are suc...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>I would like to spend summers in Maine and win...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Leopards are nocturnal hunters.</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>I watched a nature movie about bears hibernati...</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            TextField prediction\n",
       "0   We have a dog in our house.  His name is Princ...          A\n",
       "1   I spend a lot of time on the weekend watching ...          S\n",
       "2   The World Cup in soccer is held every four yea...          S\n",
       "3   The 2013 World Series was won by the Boston Re...          S\n",
       "4   The winter weather has been very harsh in many...          W\n",
       "5   Yesterday, I watched a documentary about the b...          A\n",
       "6   In our neighborhood, one man has 5 small dogs ...          A\n",
       "7                  We have a problem with feral cats.          A\n",
       "8   Professional basketball players are paid enorm...          S\n",
       "9   We have friends who live in a rural area and t...          A\n",
       "10   We have had 5 inches of snow since this morning.          W\n",
       "11  Rainy weather in the summer can be very pleasa...          W\n",
       "12  I could watch elephants all day.  They are suc...          A\n",
       "13  I would like to spend summers in Maine and win...          A\n",
       "14                    Leopards are nocturnal hunters.          A\n",
       "15  I watched a nature movie about bears hibernati...          W"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = [cl.classify(sentence) for sentence in score_data['TextField']]\n",
    "score_data['prediction'] = scores\n",
    "score_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "62966739641ac58668e1669fd2cdf04979361f39"
   },
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "63f8230baefbf841ca16e77e032ce79150207f3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I spend a lot of time on the weekend watching sports shows:  football, baseball, basketball and soccer are all fun for me.\n"
     ]
    }
   ],
   "source": [
    "text0 = score_data['TextField'][1]\n",
    "print(text0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "28fd7b5ba9c8f9cd0f4b73615170d6739d595e9a"
   },
   "source": [
    "Sentiment analysis for the whole score data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "7e94e749f32ba47a0f7d6539b82a73d37753fb6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from textblob import TextBlob\n",
    "blob0 = TextBlob(text0)\n",
    "blob0.sentiment.polarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e174015fe38fb843ff76c4477ae0085be59be70e"
   },
   "source": [
    "sentiments = []\n",
    "for statement in score_data['TextField']:\n",
    "    blob = TextBlob(statement)\n",
    "    sentiment = blob.sentiment.polarity\n",
    "    sentiments.append(sentiment)\n",
    "score_data['sentiment'] = sentiments\n",
    "score_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "23cb991da752a87aa75253c6664d2bbcc94f2a83"
   },
   "source": [
    "Good lubck on your study!\n",
    "\n",
    "![](https://www.calliopegifts.co.uk/img/product/new-job-good-luck-in-your-new-job-flittered-3004553-0.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8cca9e3bbb0b3a05b5c6799307a80da4446d6048"
   },
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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