nlp/101/word2vec_experiments_plotting_and_data_visualization.ipynb

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  "metadata": {
    "colab": {
      "name": "word2vec experiments plotting and data visualization",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manishiitg/ML_Experments/blob/master/nlp/101/word2vec_experiments_plotting_and_data_visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9D0Ty5LAF0j",
        "colab_type": "text"
      },
      "source": [
        "**Visualizing word2vec embeddings**\n",
        "\n",
        "Further extension on word2vec and to be able to see more data and understand our embeddings. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2sA9tK2sbUu",
        "colab_type": "code",
        "outputId": "1b671497-a603-4f59-dfe4-3d0006296bbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "!pip install wordcloud"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.6/dist-packages (1.5.0)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from wordcloud) (1.17.3)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from wordcloud) (4.3.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->wordcloud) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKbhJulyaOmp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://medium.com/ml2vec/using-word2vec-to-analyze-reddit-comments-28945d8cee57\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import spacy \n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "from gensim.models import KeyedVectors\n",
        "\n",
        "from sklearn.cluster import KMeans;\n",
        "from sklearn.neighbors import KDTree;\n",
        "\n",
        "from wordcloud import WordCloud, ImageColorGenerator\n",
        "import matplotlib.pyplot as plt;\n",
        "from itertools import cycle;\n",
        "import matplotlib.pyplot as plt;\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "\n",
        "from collections import defaultdict\n",
        "\n",
        "import gensim, logging\n",
        "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6Yh1lo85o0F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip text_cv.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMGaI50aPbK_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def normalize(comment, lowercase=True, remove_stopwords=True):\n",
        "    if lowercase:\n",
        "        comment = comment.lower()\n",
        "    lines = comment.splitlines()\n",
        "    lines = [x.strip(' ') for x in lines]\n",
        "    lines = [x.replace('\"', '') for x in lines]\n",
        "    lines = [x.replace('\\\\\"', '') for x in lines]\n",
        "    lines = [x.replace(u'\\xa0', u'') for x in lines]\n",
        "    comment = \" \".join(lines)\n",
        "    doc = nlp(comment)\n",
        "\n",
        "    # for token in doc:\n",
        "    #   print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,\n",
        "    #     token.shape_, token.is_alpha, token.is_stop)\n",
        "\n",
        "    words = [token for token in doc if token.is_stop !=\n",
        "             True and token.is_punct != True]\n",
        "    # return \" \".join(words)\n",
        "    lemmatized = list()\n",
        "    for word in words:\n",
        "        lemma = word.lemma_.strip()\n",
        "        if lemma:\n",
        "            lemmatized.append(lemma)\n",
        "    return lemmatized"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyEbwGXaPgCy",
        "colab_type": "code",
        "outputId": "f9cde44f-3812-466d-a1d4-b48b182714ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import os\n",
        "\n",
        "if os.path.isdir(\"text_cv\"):\n",
        "  class MySentences(object):\n",
        "    def __init__(self, dirname):\n",
        "        self.dirname = dirname\n",
        " \n",
        "    def __iter__(self):\n",
        "        for fname in os.listdir(self.dirname):\n",
        "            for line in open(os.path.join(self.dirname, fname)):\n",
        "                yield gensim.utils.simple_preprocess(line)\n",
        " \n",
        "  sentences = MySentences('text_cv') # a memory-friendly iterator\n",
        "  model = gensim.models.Word2Vec(sentences)\n",
        "\n",
        "else:\n",
        "  from sklearn.datasets import fetch_20newsgroups\n",
        "  news = fetch_20newsgroups(subset=\"train\")\n",
        "\n",
        "  def writetofile(dir, filename, data):\n",
        "      if not os.path.exists(dir):\n",
        "          os.makedirs(dir)\n",
        "      f = os.path.join(dir,str(filename))\n",
        "\n",
        "      with open(f, 'wb') as the_file:\n",
        "        the_file.write(data)\n",
        "\n",
        "  news = fetch_20newsgroups(subset=\"train\", categories=['alt.atheism', 'comp.graphics'])\n",
        "\n",
        "\n",
        "  clean_data  = []\n",
        "\n",
        "  # print(news.keys())\n",
        "\n",
        "  # print(news[\"filenames\"][:10])\n",
        "\n",
        "  # print(len(news[\"data\"][:1000]))\n",
        "\n",
        "  # print(news[\"target_names\"][:50])\n",
        "  # print(news[\"target\"][:10])\n",
        "\n",
        "\n",
        "  max_limit = 1000\n",
        "\n",
        "  targets = news[\"target\"][:max_limit]\n",
        "  filenames = news[\"filenames\"][:max_limit]\n",
        "  news = news[\"data\"][:max_limit]\n",
        "\n",
        "  dir = \"news_group_cleaned\"\n",
        "\n",
        "  print(\"cleaning data\")\n",
        "  for i, row in enumerate(news):\n",
        "    filename = filenames[i]\n",
        "    filename = filename[(filename.rfind('/'))+1:]\n",
        "    if os.path.exists(filename):\n",
        "      with open(os.path.join(dir, filename), 'r') as content_file:\n",
        "        data = content_file.read()\n",
        "        cleaned = data.split(\" \")\n",
        "    else:\n",
        "      cleaned = normalize(row)\n",
        "      writetofile(dir, filename, \" \".join(cleaned).encode(\"utf-8\"))\n",
        "      \n",
        "    \n",
        "    clean_data.append(cleaned)\n",
        "\n",
        "  print(\"data cleaned\")\n",
        "  model = gensim.models.Word2Vec(clean_data)\n",
        "\n",
        "\n",
        "# print(clean_data[10])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-11-07 13:15:31,484 : INFO : collecting all words and their counts\n",
            "2019-11-07 13:15:31,488 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
            "2019-11-07 13:15:31,557 : INFO : PROGRESS: at sentence #10000, processed 20188 words, keeping 3640 word types\n",
            "2019-11-07 13:15:31,621 : INFO : PROGRESS: at sentence #20000, processed 41072 words, keeping 5888 word types\n",
            "2019-11-07 13:15:31,685 : INFO : PROGRESS: at sentence #30000, processed 61802 words, keeping 7358 word types\n",
            "2019-11-07 13:15:31,750 : INFO : PROGRESS: at sentence #40000, processed 82035 words, keeping 8672 word types\n",
            "2019-11-07 13:15:31,818 : INFO : PROGRESS: at sentence #50000, processed 102623 words, keeping 9972 word types\n",
            "2019-11-07 13:15:31,884 : INFO : PROGRESS: at sentence #60000, processed 123860 words, keeping 11051 word types\n",
            "2019-11-07 13:15:31,945 : INFO : PROGRESS: at sentence #70000, processed 143333 words, keeping 11934 word types\n",
            "2019-11-07 13:15:32,018 : INFO : PROGRESS: at sentence #80000, processed 163773 words, keeping 12774 word types\n",
            "2019-11-07 13:15:32,087 : INFO : PROGRESS: at sentence #90000, processed 185833 words, keeping 13540 word types\n",
            "2019-11-07 13:15:32,157 : INFO : PROGRESS: at sentence #100000, processed 207840 words, keeping 14255 word types\n",
            "2019-11-07 13:15:32,226 : INFO : PROGRESS: at sentence #110000, processed 229179 words, keeping 14987 word types\n",
            "2019-11-07 13:15:32,290 : INFO : PROGRESS: at sentence #120000, processed 249742 words, keeping 15699 word types\n",
            "2019-11-07 13:15:32,354 : INFO : PROGRESS: at sentence #130000, processed 271547 words, keeping 16292 word types\n",
            "2019-11-07 13:15:32,416 : INFO : PROGRESS: at sentence #140000, processed 291709 words, keeping 16863 word types\n",
            "2019-11-07 13:15:32,486 : INFO : PROGRESS: at sentence #150000, processed 312696 words, keeping 17311 word types\n",
            "2019-11-07 13:15:32,555 : INFO : PROGRESS: at sentence #160000, processed 333922 words, keeping 17970 word types\n",
            "2019-11-07 13:15:32,623 : INFO : PROGRESS: at sentence #170000, processed 353809 words, keeping 18453 word types\n",
            "2019-11-07 13:15:32,693 : INFO : PROGRESS: at sentence #180000, processed 374087 words, keeping 19096 word types\n",
            "2019-11-07 13:15:32,756 : INFO : PROGRESS: at sentence #190000, processed 394133 words, keeping 19519 word types\n",
            "2019-11-07 13:15:32,823 : INFO : PROGRESS: at sentence #200000, processed 415054 words, keeping 20082 word types\n",
            "2019-11-07 13:15:32,892 : INFO : PROGRESS: at sentence #210000, processed 435526 words, keeping 20528 word types\n",
            "2019-11-07 13:15:32,965 : INFO : PROGRESS: at sentence #220000, processed 455557 words, keeping 21240 word types\n",
            "2019-11-07 13:15:33,033 : INFO : PROGRESS: at sentence #230000, processed 477259 words, keeping 21741 word types\n",
            "2019-11-07 13:15:33,097 : INFO : PROGRESS: at sentence #240000, processed 496775 words, keeping 22204 word types\n",
            "2019-11-07 13:15:33,163 : INFO : PROGRESS: at sentence #250000, processed 518097 words, keeping 22717 word types\n",
            "2019-11-07 13:15:33,226 : INFO : PROGRESS: at sentence #260000, processed 536802 words, keeping 23208 word types\n",
            "2019-11-07 13:15:33,288 : INFO : PROGRESS: at sentence #270000, processed 555884 words, keeping 23593 word types\n",
            "2019-11-07 13:15:33,353 : INFO : PROGRESS: at sentence #280000, processed 577333 words, keeping 23965 word types\n",
            "2019-11-07 13:15:33,419 : INFO : PROGRESS: at sentence #290000, processed 599282 words, keeping 24517 word types\n",
            "2019-11-07 13:15:33,484 : INFO : PROGRESS: at sentence #300000, processed 619180 words, keeping 24831 word types\n",
            "2019-11-07 13:15:33,550 : INFO : PROGRESS: at sentence #310000, processed 640211 words, keeping 25263 word types\n",
            "2019-11-07 13:15:33,611 : INFO : PROGRESS: at sentence #320000, processed 660178 words, keeping 25589 word types\n",
            "2019-11-07 13:15:33,651 : INFO : collected 25804 word types from a corpus of 673072 raw words and 325680 sentences\n",
            "2019-11-07 13:15:33,652 : INFO : Loading a fresh vocabulary\n",
            "2019-11-07 13:15:33,674 : INFO : effective_min_count=5 retains 7566 unique words (29% of original 25804, drops 18238)\n",
            "2019-11-07 13:15:33,675 : INFO : effective_min_count=5 leaves 641366 word corpus (95% of original 673072, drops 31706)\n",
            "2019-11-07 13:15:33,704 : INFO : deleting the raw counts dictionary of 25804 items\n",
            "2019-11-07 13:15:33,705 : INFO : sample=0.001 downsamples 59 most-common words\n",
            "2019-11-07 13:15:33,706 : INFO : downsampling leaves estimated 544364 word corpus (84.9% of prior 641366)\n",
            "2019-11-07 13:15:33,734 : INFO : estimated required memory for 7566 words and 100 dimensions: 9835800 bytes\n",
            "2019-11-07 13:15:33,735 : INFO : resetting layer weights\n",
            "2019-11-07 13:15:35,113 : INFO : training model with 3 workers on 7566 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
            "2019-11-07 13:15:36,164 : INFO : EPOCH 1 - PROGRESS: at 29.55% examples, 154918 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:37,185 : INFO : EPOCH 1 - PROGRESS: at 59.26% examples, 156682 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:38,207 : INFO : EPOCH 1 - PROGRESS: at 89.12% examples, 157104 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:38,531 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
            "2019-11-07 13:15:38,539 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
            "2019-11-07 13:15:38,543 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
            "2019-11-07 13:15:38,545 : INFO : EPOCH - 1 : training on 673072 raw words (544256 effective words) took 3.4s, 158898 effective words/s\n",
            "2019-11-07 13:15:39,563 : INFO : EPOCH 2 - PROGRESS: at 28.12% examples, 151757 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:40,580 : INFO : EPOCH 2 - PROGRESS: at 54.72% examples, 147602 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:41,616 : INFO : EPOCH 2 - PROGRESS: at 83.42% examples, 147745 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:42,141 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
            "2019-11-07 13:15:42,152 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
            "2019-11-07 13:15:42,153 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
            "2019-11-07 13:15:42,154 : INFO : EPOCH - 2 : training on 673072 raw words (544566 effective words) took 3.6s, 151054 effective words/s\n",
            "2019-11-07 13:15:43,190 : INFO : EPOCH 3 - PROGRESS: at 28.12% examples, 149674 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:44,207 : INFO : EPOCH 3 - PROGRESS: at 56.24% examples, 150508 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:45,248 : INFO : EPOCH 3 - PROGRESS: at 84.98% examples, 149479 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:45,841 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
            "2019-11-07 13:15:45,850 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
            "2019-11-07 13:15:45,858 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
            "2019-11-07 13:15:45,859 : INFO : EPOCH - 3 : training on 673072 raw words (544509 effective words) took 3.7s, 147320 effective words/s\n",
            "2019-11-07 13:15:46,900 : INFO : EPOCH 4 - PROGRESS: at 28.12% examples, 148867 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:47,932 : INFO : EPOCH 4 - PROGRESS: at 56.24% examples, 148965 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:49,018 : INFO : EPOCH 4 - PROGRESS: at 86.32% examples, 148863 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:49,460 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
            "2019-11-07 13:15:49,463 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
            "2019-11-07 13:15:49,476 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
            "2019-11-07 13:15:49,477 : INFO : EPOCH - 4 : training on 673072 raw words (544230 effective words) took 3.6s, 150782 effective words/s\n",
            "2019-11-07 13:15:50,522 : INFO : EPOCH 5 - PROGRESS: at 28.12% examples, 148131 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:51,556 : INFO : EPOCH 5 - PROGRESS: at 57.70% examples, 152413 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:52,587 : INFO : EPOCH 5 - PROGRESS: at 87.72% examples, 153803 words/s, in_qsize 0, out_qsize 0\n",
            "2019-11-07 13:15:52,955 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
            "2019-11-07 13:15:52,959 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
            "2019-11-07 13:15:52,968 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
            "2019-11-07 13:15:52,970 : INFO : EPOCH - 5 : training on 673072 raw words (544366 effective words) took 3.5s, 156177 effective words/s\n",
            "2019-11-07 13:15:52,971 : INFO : training on a 3365360 raw words (2721927 effective words) took 17.9s, 152431 effective words/s\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7Xi78slPzhY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKDNl41BOdwn",
        "colab_type": "code",
        "outputId": "07156502-2ac1-40c4-b4a8-e930444fb540",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "Z = model.wv.vectors\n",
        "print(len(model.wv.vectors))\n",
        "print(Z[0].shape)\n",
        "model.wv.index2word[10]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7566\n",
            "(100,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'project'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xosbseCaSXY",
        "colab_type": "code",
        "outputId": "36a01d00-7305-4560-ba1f-d92604ca99cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "words = []\n",
        "\n",
        "for sents in clean_data:\n",
        "   for word in sents:\n",
        "     words.append(word)\n",
        "\n",
        "\n",
        "# Filter the list of vectors to include only those that Word2Vec has a vector for\n",
        "vector_list = [model[word] for word in words if word in model.wv.vocab]\n",
        "\n",
        "# Create a list of the words corresponding to these vectors\n",
        "words_filtered = [word for word in words if word in model.wv.vocab]\n",
        "\n",
        "# Zip the words together with their vector representations\n",
        "word_vec_zip = zip(words_filtered, vector_list)\n",
        "\n",
        "# Cast to a dict so we can turn it into a dataframe\n",
        "word_vec_dict = dict(word_vec_zip)\n",
        "df = pd.DataFrame.from_dict(word_vec_dict, orient='index')\n",
        "# df.shape\n",
        "df\n",
        "\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "      <th>64</th>\n",
              "      <th>65</th>\n",
              "      <th>66</th>\n",
              "      <th>67</th>\n",
              "      <th>68</th>\n",
              "      <th>69</th>\n",
              "      <th>70</th>\n",
              "      <th>71</th>\n",
              "      <th>72</th>\n",
              "      <th>73</th>\n",
              "      <th>74</th>\n",
              "      <th>75</th>\n",
              "      <th>76</th>\n",
              "      <th>77</th>\n",
              "      <th>78</th>\n",
              "      <th>79</th>\n",
              "      <th>80</th>\n",
              "      <th>81</th>\n",
              "      <th>82</th>\n",
              "      <th>83</th>\n",
              "      <th>84</th>\n",
              "      <th>85</th>\n",
              "      <th>86</th>\n",
              "      <th>87</th>\n",
              "      <th>88</th>\n",
              "      <th>89</th>\n",
              "      <th>90</th>\n",
              "      <th>91</th>\n",
              "      <th>92</th>\n",
              "      <th>93</th>\n",
              "      <th>94</th>\n",
              "      <th>95</th>\n",
              "      <th>96</th>\n",
              "      <th>97</th>\n",
              "      <th>98</th>\n",
              "      <th>99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>subject</th>\n",
              "      <td>0.018902</td>\n",
              "      <td>0.175763</td>\n",
              "      <td>0.020582</td>\n",
              "      <td>0.025837</td>\n",
              "      <td>0.016935</td>\n",
              "      <td>0.110208</td>\n",
              "      <td>-0.006789</td>\n",
              "      <td>-0.134234</td>\n",
              "      <td>-0.015744</td>\n",
              "      <td>-0.062082</td>\n",
              "      <td>-0.135255</td>\n",
              "      <td>0.173783</td>\n",
              "      <td>-0.041299</td>\n",
              "      <td>-0.110796</td>\n",
              "      <td>-0.037544</td>\n",
              "      <td>0.156492</td>\n",
              "      <td>0.056489</td>\n",
              "      <td>0.057597</td>\n",
              "      <td>0.206049</td>\n",
              "      <td>-0.075278</td>\n",
              "      <td>-0.189485</td>\n",
              "      <td>0.008507</td>\n",
              "      <td>-0.240688</td>\n",
              "      <td>-0.279055</td>\n",
              "      <td>-0.031032</td>\n",
              "      <td>0.090496</td>\n",
              "      <td>0.065691</td>\n",
              "      <td>0.168547</td>\n",
              "      <td>-0.075461</td>\n",
              "      <td>-0.159278</td>\n",
              "      <td>0.087560</td>\n",
              "      <td>0.102793</td>\n",
              "      <td>-0.153960</td>\n",
              "      <td>-0.057759</td>\n",
              "      <td>-0.056516</td>\n",
              "      <td>-0.128146</td>\n",
              "      <td>0.129116</td>\n",
              "      <td>-0.114532</td>\n",
              "      <td>0.185339</td>\n",
              "      <td>-0.192231</td>\n",
              "      <td>...</td>\n",
              "      <td>0.319617</td>\n",
              "      <td>0.008084</td>\n",
              "      <td>-0.052019</td>\n",
              "      <td>-0.011661</td>\n",
              "      <td>-0.073523</td>\n",
              "      <td>0.016426</td>\n",
              "      <td>0.159067</td>\n",
              "      <td>0.158635</td>\n",
              "      <td>-0.012877</td>\n",
              "      <td>0.008431</td>\n",
              "      <td>-0.069452</td>\n",
              "      <td>-0.062633</td>\n",
              "      <td>-0.204479</td>\n",
              "      <td>0.105823</td>\n",
              "      <td>-0.139511</td>\n",
              "      <td>-0.094031</td>\n",
              "      <td>0.180359</td>\n",
              "      <td>-0.312082</td>\n",
              "      <td>0.194157</td>\n",
              "      <td>-0.369300</td>\n",
              "      <td>0.217053</td>\n",
              "      <td>-0.167448</td>\n",
              "      <td>0.045732</td>\n",
              "      <td>-0.149071</td>\n",
              "      <td>-0.125327</td>\n",
              "      <td>0.089530</td>\n",
              "      <td>-0.079319</td>\n",
              "      <td>0.014414</td>\n",
              "      <td>0.195441</td>\n",
              "      <td>-0.071948</td>\n",
              "      <td>0.040897</td>\n",
              "      <td>-0.046685</td>\n",
              "      <td>-0.193336</td>\n",
              "      <td>0.220855</td>\n",
              "      <td>0.123455</td>\n",
              "      <td>-0.089717</td>\n",
              "      <td>0.371324</td>\n",
              "      <td>-0.218745</td>\n",
              "      <td>0.122599</td>\n",
              "      <td>0.213728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0.016582</td>\n",
              "      <td>0.425534</td>\n",
              "      <td>1.151692</td>\n",
              "      <td>0.777857</td>\n",
              "      <td>-0.179315</td>\n",
              "      <td>-1.349917</td>\n",
              "      <td>1.718300</td>\n",
              "      <td>-0.508238</td>\n",
              "      <td>-0.946690</td>\n",
              "      <td>0.015122</td>\n",
              "      <td>0.816912</td>\n",
              "      <td>0.211912</td>\n",
              "      <td>0.805383</td>\n",
              "      <td>-0.755502</td>\n",
              "      <td>0.021392</td>\n",
              "      <td>0.911381</td>\n",
              "      <td>0.467985</td>\n",
              "      <td>0.396467</td>\n",
              "      <td>1.008427</td>\n",
              "      <td>-1.023817</td>\n",
              "      <td>-1.006437</td>\n",
              "      <td>0.856717</td>\n",
              "      <td>-0.300229</td>\n",
              "      <td>-0.120634</td>\n",
              "      <td>-0.862997</td>\n",
              "      <td>1.467703</td>\n",
              "      <td>-0.579403</td>\n",
              "      <td>0.056983</td>\n",
              "      <td>0.355985</td>\n",
              "      <td>0.267889</td>\n",
              "      <td>0.393269</td>\n",
              "      <td>-0.341725</td>\n",
              "      <td>0.057648</td>\n",
              "      <td>0.273202</td>\n",
              "      <td>0.329356</td>\n",
              "      <td>-0.275487</td>\n",
              "      <td>0.741108</td>\n",
              "      <td>1.819581</td>\n",
              "      <td>-1.060666</td>\n",
              "      <td>-0.983120</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.327060</td>\n",
              "      <td>0.768796</td>\n",
              "      <td>0.205810</td>\n",
              "      <td>-0.008732</td>\n",
              "      <td>0.893480</td>\n",
              "      <td>0.876683</td>\n",
              "      <td>1.286490</td>\n",
              "      <td>-1.102538</td>\n",
              "      <td>-0.748430</td>\n",
              "      <td>-0.974908</td>\n",
              "      <td>-0.893916</td>\n",
              "      <td>0.453657</td>\n",
              "      <td>-2.587845</td>\n",
              "      <td>0.557025</td>\n",
              "      <td>-0.781019</td>\n",
              "      <td>-1.305490</td>\n",
              "      <td>0.395720</td>\n",
              "      <td>0.261360</td>\n",
              "      <td>-0.110628</td>\n",
              "      <td>-0.918220</td>\n",
              "      <td>1.656486</td>\n",
              "      <td>-1.234587</td>\n",
              "      <td>1.421094</td>\n",
              "      <td>-0.301590</td>\n",
              "      <td>0.717889</td>\n",
              "      <td>-0.184622</td>\n",
              "      <td>0.950744</td>\n",
              "      <td>1.163931</td>\n",
              "      <td>0.207486</td>\n",
              "      <td>1.249818</td>\n",
              "      <td>-0.173488</td>\n",
              "      <td>-0.734040</td>\n",
              "      <td>-0.695581</td>\n",
              "      <td>0.368864</td>\n",
              "      <td>0.417673</td>\n",
              "      <td>-0.735078</td>\n",
              "      <td>1.762899</td>\n",
              "      <td>-0.981371</td>\n",
              "      <td>-0.432451</td>\n",
              "      <td>0.805379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>christian</th>\n",
              "      <td>-0.085130</td>\n",
              "      <td>0.101119</td>\n",
              "      <td>-0.004949</td>\n",
              "      <td>0.005882</td>\n",
              "      <td>0.022155</td>\n",
              "      <td>-0.035101</td>\n",
              "      <td>-0.075631</td>\n",
              "      <td>-0.086292</td>\n",
              "      <td>-0.038992</td>\n",
              "      <td>0.009063</td>\n",
              "      <td>0.006328</td>\n",
              "      <td>-0.030193</td>\n",
              "      <td>0.001464</td>\n",
              "      <td>0.064636</td>\n",
              "      <td>0.037437</td>\n",
              "      <td>0.009156</td>\n",
              "      <td>0.033061</td>\n",
              "      <td>-0.080012</td>\n",
              "      <td>-0.041368</td>\n",
              "      <td>-0.073995</td>\n",
              "      <td>-0.061117</td>\n",
              "      <td>0.048267</td>\n",
              "      <td>-0.065361</td>\n",
              "      <td>-0.165866</td>\n",
              "      <td>0.041347</td>\n",
              "      <td>0.019503</td>\n",
              "      <td>-0.031021</td>\n",
              "      <td>0.058710</td>\n",
              "      <td>0.029416</td>\n",
              "      <td>-0.110064</td>\n",
              "      <td>0.012020</td>\n",
              "      <td>-0.066374</td>\n",
              "      <td>-0.033765</td>\n",
              "      <td>-0.072088</td>\n",
              "      <td>0.023237</td>\n",
              "      <td>-0.051210</td>\n",
              "      <td>0.090615</td>\n",
              "      <td>0.051369</td>\n",
              "      <td>0.052572</td>\n",
              "      <td>-0.036253</td>\n",
              "      <td>...</td>\n",
              "      <td>0.015692</td>\n",
              "      <td>0.036539</td>\n",
              "      <td>-0.016595</td>\n",
              "      <td>-0.044936</td>\n",
              "      <td>-0.081552</td>\n",
              "      <td>0.018034</td>\n",
              "      <td>0.047044</td>\n",
              "      <td>0.065145</td>\n",
              "      <td>-0.088536</td>\n",
              "      <td>0.030935</td>\n",
              "      <td>0.056309</td>\n",
              "      <td>-0.018715</td>\n",
              "      <td>0.001572</td>\n",
              "      <td>0.027526</td>\n",
              "      <td>0.052427</td>\n",
              "      <td>-0.023416</td>\n",
              "      <td>0.170821</td>\n",
              "      <td>-0.006678</td>\n",
              "      <td>0.080852</td>\n",
              "      <td>-0.070758</td>\n",
              "      <td>-0.040359</td>\n",
              "      <td>0.040921</td>\n",
              "      <td>0.035346</td>\n",
              "      <td>-0.012665</td>\n",
              "      <td>0.002724</td>\n",
              "      <td>-0.047666</td>\n",
              "      <td>-0.040384</td>\n",
              "      <td>0.009236</td>\n",
              "      <td>0.056661</td>\n",
              "      <td>0.015876</td>\n",
              "      <td>0.015118</td>\n",
              "      <td>-0.014739</td>\n",
              "      <td>-0.043972</td>\n",
              "      <td>-0.008158</td>\n",
              "      <td>0.028552</td>\n",
              "      <td>-0.022445</td>\n",
              "      <td>0.224948</td>\n",
              "      <td>-0.032221</td>\n",
              "      <td>0.091849</td>\n",
              "      <td>0.012291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>organization</th>\n",
              "      <td>-1.916205</td>\n",
              "      <td>-0.133026</td>\n",
              "      <td>2.059584</td>\n",
              "      <td>1.101935</td>\n",
              "      <td>0.027972</td>\n",
              "      <td>1.937142</td>\n",
              "      <td>1.853328</td>\n",
              "      <td>0.825325</td>\n",
              "      <td>0.388729</td>\n",
              "      <td>-0.669307</td>\n",
              "      <td>0.922748</td>\n",
              "      <td>0.752448</td>\n",
              "      <td>1.194707</td>\n",
              "      <td>1.457253</td>\n",
              "      <td>-1.402825</td>\n",
              "      <td>1.726748</td>\n",
              "      <td>-0.903366</td>\n",
              "      <td>-0.099318</td>\n",
              "      <td>2.172728</td>\n",
              "      <td>-0.265533</td>\n",
              "      <td>-0.914802</td>\n",
              "      <td>2.780946</td>\n",
              "      <td>-1.350355</td>\n",
              "      <td>-1.890603</td>\n",
              "      <td>-3.257090</td>\n",
              "      <td>-0.101679</td>\n",
              "      <td>0.476947</td>\n",
              "      <td>-1.838962</td>\n",
              "      <td>-0.953342</td>\n",
              "      <td>-0.608274</td>\n",
              "      <td>2.019664</td>\n",
              "      <td>1.249502</td>\n",
              "      <td>0.755380</td>\n",
              "      <td>2.602626</td>\n",
              "      <td>0.386986</td>\n",
              "      <td>-0.781800</td>\n",
              "      <td>-2.557409</td>\n",
              "      <td>1.926392</td>\n",
              "      <td>1.577278</td>\n",
              "      <td>-0.609596</td>\n",
              "      <td>...</td>\n",
              "      <td>2.560690</td>\n",
              "      <td>1.799921</td>\n",
              "      <td>0.405504</td>\n",
              "      <td>-0.598762</td>\n",
              "      <td>-1.180972</td>\n",
              "      <td>2.050941</td>\n",
              "      <td>-1.046969</td>\n",
              "      <td>-0.183881</td>\n",
              "      <td>1.203544</td>\n",
              "      <td>-1.743312</td>\n",
              "      <td>-1.756753</td>\n",
              "      <td>2.157980</td>\n",
              "      <td>-1.491130</td>\n",
              "      <td>2.127576</td>\n",
              "      <td>0.299913</td>\n",
              "      <td>-2.354048</td>\n",
              "      <td>-0.622779</td>\n",
              "      <td>1.233232</td>\n",
              "      <td>0.434578</td>\n",
              "      <td>-2.260237</td>\n",
              "      <td>2.982369</td>\n",
              "      <td>-4.203658</td>\n",
              "      <td>-1.580527</td>\n",
              "      <td>0.868196</td>\n",
              "      <td>2.539811</td>\n",
              "      <td>-0.158476</td>\n",
              "      <td>1.163413</td>\n",
              "      <td>1.198240</td>\n",
              "      <td>0.274984</td>\n",
              "      <td>0.511274</td>\n",
              "      <td>-0.039327</td>\n",
              "      <td>1.991786</td>\n",
              "      <td>-1.093178</td>\n",
              "      <td>1.171994</td>\n",
              "      <td>-1.293754</td>\n",
              "      <td>-0.174364</td>\n",
              "      <td>1.766000</td>\n",
              "      <td>0.553204</td>\n",
              "      <td>-0.963694</td>\n",
              "      <td>1.460151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ag</th>\n",
              "      <td>-0.069068</td>\n",
              "      <td>0.113276</td>\n",
              "      <td>-0.008672</td>\n",
              "      <td>-0.029886</td>\n",
              "      <td>-0.003315</td>\n",
              "      <td>-0.031499</td>\n",
              "      <td>-0.020032</td>\n",
              "      <td>-0.094187</td>\n",
              "      <td>-0.043009</td>\n",
              "      <td>0.029891</td>\n",
              "      <td>0.070765</td>\n",
              "      <td>-0.136886</td>\n",
              "      <td>-0.010313</td>\n",
              "      <td>0.120596</td>\n",
              "      <td>0.108179</td>\n",
              "      <td>0.057013</td>\n",
              "      <td>0.033341</td>\n",
              "      <td>-0.119248</td>\n",
              "      <td>-0.126033</td>\n",
              "      <td>-0.057270</td>\n",
              "      <td>-0.028950</td>\n",
              "      <td>-0.047343</td>\n",
              "      <td>-0.130952</td>\n",
              "      <td>-0.226551</td>\n",
              "      <td>0.047513</td>\n",
              "      <td>0.051383</td>\n",
              "      <td>0.080945</td>\n",
              "      <td>0.038219</td>\n",
              "      <td>0.007218</td>\n",
              "      <td>-0.177205</td>\n",
              "      <td>0.017517</td>\n",
              "      <td>-0.088004</td>\n",
              "      <td>-0.045157</td>\n",
              "      <td>-0.021094</td>\n",
              "      <td>-0.019335</td>\n",
              "      <td>-0.070846</td>\n",
              "      <td>0.115493</td>\n",
              "      <td>0.084904</td>\n",
              "      <td>0.094106</td>\n",
              "      <td>-0.020806</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.012464</td>\n",
              "      <td>0.105826</td>\n",
              "      <td>-0.018741</td>\n",
              "      <td>-0.033580</td>\n",
              "      <td>-0.153877</td>\n",
              "      <td>-0.004753</td>\n",
              "      <td>-0.019566</td>\n",
              "      <td>0.043845</td>\n",
              "      <td>-0.124733</td>\n",
              "      <td>-0.024526</td>\n",
              "      <td>0.031393</td>\n",
              "      <td>-0.076312</td>\n",
              "      <td>0.024039</td>\n",
              "      <td>-0.019088</td>\n",
              "      <td>0.121990</td>\n",
              "      <td>-0.052861</td>\n",
              "      <td>0.281949</td>\n",
              "      <td>-0.031459</td>\n",
              "      <td>0.087482</td>\n",
              "      <td>-0.096897</td>\n",
              "      <td>-0.077676</td>\n",
              "      <td>0.080625</td>\n",
              "      <td>0.062576</td>\n",
              "      <td>-0.050295</td>\n",
              "      <td>0.011008</td>\n",
              "      <td>-0.092540</td>\n",
              "      <td>-0.112763</td>\n",
              "      <td>0.048491</td>\n",
              "      <td>0.006644</td>\n",
              "      <td>-0.052973</td>\n",
              "      <td>-0.008835</td>\n",
              "      <td>-0.005725</td>\n",
              "      <td>-0.032000</td>\n",
              "      <td>0.022669</td>\n",
              "      <td>0.031640</td>\n",
              "      <td>0.005426</td>\n",
              "      <td>0.347087</td>\n",
              "      <td>0.033306</td>\n",
              "      <td>0.114728</td>\n",
              "      <td>0.023623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lady</th>\n",
              "      <td>-0.002883</td>\n",
              "      <td>0.041840</td>\n",
              "      <td>0.002387</td>\n",
              "      <td>-0.021054</td>\n",
              "      <td>-0.009671</td>\n",
              "      <td>0.000882</td>\n",
              "      <td>-0.030573</td>\n",
              "      <td>-0.041238</td>\n",
              "      <td>-0.012845</td>\n",
              "      <td>0.002865</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>-0.036025</td>\n",
              "      <td>-0.001930</td>\n",
              "      <td>0.036931</td>\n",
              "      <td>0.018863</td>\n",
              "      <td>0.022241</td>\n",
              "      <td>0.021884</td>\n",
              "      <td>-0.021365</td>\n",
              "      <td>-0.021769</td>\n",
              "      <td>-0.038334</td>\n",
              "      <td>-0.034472</td>\n",
              "      <td>-0.016676</td>\n",
              "      <td>-0.048126</td>\n",
              "      <td>-0.077156</td>\n",
              "      <td>0.005957</td>\n",
              "      <td>0.021574</td>\n",
              "      <td>0.029104</td>\n",
              "      <td>0.021552</td>\n",
              "      <td>0.009023</td>\n",
              "      <td>-0.083930</td>\n",
              "      <td>0.024269</td>\n",
              "      <td>-0.028901</td>\n",
              "      <td>-0.035500</td>\n",
              "      <td>0.001352</td>\n",
              "      <td>0.001537</td>\n",
              "      <td>-0.012605</td>\n",
              "      <td>0.035660</td>\n",
              "      <td>0.041581</td>\n",
              "      <td>0.027919</td>\n",
              "      <td>-0.014880</td>\n",
              "      <td>...</td>\n",
              "      <td>0.010066</td>\n",
              "      <td>0.052004</td>\n",
              "      <td>-0.002109</td>\n",
              "      <td>-0.033274</td>\n",
              "      <td>-0.070608</td>\n",
              "      <td>0.020923</td>\n",
              "      <td>-0.001771</td>\n",
              "      <td>0.035372</td>\n",
              "      <td>-0.039375</td>\n",
              "      <td>-0.001416</td>\n",
              "      <td>0.000855</td>\n",
              "      <td>-0.016928</td>\n",
              "      <td>0.022606</td>\n",
              "      <td>-0.008248</td>\n",
              "      <td>0.041093</td>\n",
              "      <td>-0.014419</td>\n",
              "      <td>0.106178</td>\n",
              "      <td>-0.024628</td>\n",
              "      <td>0.009690</td>\n",
              "      <td>-0.048029</td>\n",
              "      <td>-0.017634</td>\n",
              "      <td>0.018183</td>\n",
              "      <td>0.016749</td>\n",
              "      <td>-0.015954</td>\n",
              "      <td>-0.005990</td>\n",
              "      <td>-0.025512</td>\n",
              "      <td>-0.034019</td>\n",
              "      <td>0.021029</td>\n",
              "      <td>-0.002580</td>\n",
              "      <td>-0.014191</td>\n",
              "      <td>0.003710</td>\n",
              "      <td>0.002430</td>\n",
              "      <td>-0.009012</td>\n",
              "      <td>-0.011228</td>\n",
              "      <td>0.024563</td>\n",
              "      <td>0.001247</td>\n",
              "      <td>0.112656</td>\n",
              "      <td>-0.000227</td>\n",
              "      <td>0.016410</td>\n",
              "      <td>0.012693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sc</th>\n",
              "      <td>0.048143</td>\n",
              "      <td>0.327290</td>\n",
              "      <td>-0.200101</td>\n",
              "      <td>0.499103</td>\n",
              "      <td>0.122642</td>\n",
              "      <td>-0.529012</td>\n",
              "      <td>0.231763</td>\n",
              "      <td>-0.756511</td>\n",
              "      <td>-0.457399</td>\n",
              "      <td>0.041554</td>\n",
              "      <td>-0.283980</td>\n",
              "      <td>-0.016323</td>\n",
              "      <td>0.098044</td>\n",
              "      <td>-0.494574</td>\n",
              "      <td>0.124207</td>\n",
              "      <td>0.040398</td>\n",
              "      <td>-0.287661</td>\n",
              "      <td>-0.329056</td>\n",
              "      <td>-0.408709</td>\n",
              "      <td>-0.462554</td>\n",
              "      <td>-0.401028</td>\n",
              "      <td>0.174703</td>\n",
              "      <td>-0.421697</td>\n",
              "      <td>-0.625858</td>\n",
              "      <td>0.554897</td>\n",
              "      <td>0.230033</td>\n",
              "      <td>-0.393799</td>\n",
              "      <td>0.154756</td>\n",
              "      <td>-0.111132</td>\n",
              "      <td>0.021438</td>\n",
              "      <td>-0.457098</td>\n",
              "      <td>-0.288178</td>\n",
              "      <td>-0.098787</td>\n",
              "      <td>-0.423371</td>\n",
              "      <td>0.222305</td>\n",
              "      <td>-0.676763</td>\n",
              "      <td>0.854194</td>\n",
              "      <td>0.054946</td>\n",
              "      <td>-0.149916</td>\n",
              "      <td>-0.384108</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.263154</td>\n",
              "      <td>0.009024</td>\n",
              "      <td>0.444260</td>\n",
              "      <td>0.402248</td>\n",
              "      <td>-0.246927</td>\n",
              "      <td>-0.147124</td>\n",
              "      <td>0.423255</td>\n",
              "      <td>-0.203440</td>\n",
              "      <td>-0.603206</td>\n",
              "      <td>-0.517804</td>\n",
              "      <td>0.524423</td>\n",
              "      <td>-0.066413</td>\n",
              "      <td>-0.856378</td>\n",
              "      <td>-0.066599</td>\n",
              "      <td>-0.141636</td>\n",
              "      <td>-0.004116</td>\n",
              "      <td>0.464529</td>\n",
              "      <td>0.049305</td>\n",
              "      <td>0.901115</td>\n",
              "      <td>-0.892913</td>\n",
              "      <td>-0.198555</td>\n",
              "      <td>0.151677</td>\n",
              "      <td>0.063285</td>\n",
              "      <td>-0.009742</td>\n",
              "      <td>-0.179484</td>\n",
              "      <td>-0.352983</td>\n",
              "      <td>-0.006359</td>\n",
              "      <td>0.149680</td>\n",
              "      <td>0.513415</td>\n",
              "      <td>0.364200</td>\n",
              "      <td>0.178091</td>\n",
              "      <td>-0.612462</td>\n",
              "      <td>-0.050514</td>\n",
              "      <td>0.670617</td>\n",
              "      <td>-0.517928</td>\n",
              "      <td>-0.473886</td>\n",
              "      <td>0.848877</td>\n",
              "      <td>-0.115648</td>\n",
              "      <td>0.445100</td>\n",
              "      <td>0.156961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colony</th>\n",
              "      <td>-0.835331</td>\n",
              "      <td>1.228851</td>\n",
              "      <td>-0.820097</td>\n",
              "      <td>0.399936</td>\n",
              "      <td>0.630292</td>\n",
              "      <td>-1.075655</td>\n",
              "      <td>-0.035660</td>\n",
              "      <td>-0.944401</td>\n",
              "      <td>-0.314473</td>\n",
              "      <td>0.697655</td>\n",
              "      <td>-0.046175</td>\n",
              "      <td>-0.601287</td>\n",
              "      <td>-0.159900</td>\n",
              "      <td>0.414505</td>\n",
              "      <td>0.207797</td>\n",
              "      <td>-0.597301</td>\n",
              "      <td>0.432622</td>\n",
              "      <td>-1.467759</td>\n",
              "      <td>-0.938436</td>\n",
              "      <td>-0.138965</td>\n",
              "      <td>-0.196632</td>\n",
              "      <td>1.244073</td>\n",
              "      <td>-0.419541</td>\n",
              "      <td>-0.977127</td>\n",
              "      <td>1.562086</td>\n",
              "      <td>-0.711110</td>\n",
              "      <td>-0.351279</td>\n",
              "      <td>0.628509</td>\n",
              "      <td>0.684431</td>\n",
              "      <td>-0.622102</td>\n",
              "      <td>-1.062006</td>\n",
              "      <td>-1.127516</td>\n",
              "      <td>-0.035392</td>\n",
              "      <td>-0.760597</td>\n",
              "      <td>0.510272</td>\n",
              "      <td>-0.501478</td>\n",
              "      <td>1.000150</td>\n",
              "      <td>-0.421808</td>\n",
              "      <td>0.313001</td>\n",
              "      <td>0.397729</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007783</td>\n",
              "      <td>-0.170260</td>\n",
              "      <td>-0.834917</td>\n",
              "      <td>0.025570</td>\n",
              "      <td>-0.576363</td>\n",
              "      <td>-0.147095</td>\n",
              "      <td>-0.754056</td>\n",
              "      <td>0.739212</td>\n",
              "      <td>-0.630706</td>\n",
              "      <td>0.087445</td>\n",
              "      <td>0.531102</td>\n",
              "      <td>-0.506225</td>\n",
              "      <td>0.351053</td>\n",
              "      <td>-0.021815</td>\n",
              "      <td>0.289360</td>\n",
              "      <td>0.356713</td>\n",
              "      <td>1.429331</td>\n",
              "      <td>0.553370</td>\n",
              "      <td>0.544496</td>\n",
              "      <td>-1.251086</td>\n",
              "      <td>-0.846381</td>\n",
              "      <td>0.721575</td>\n",
              "      <td>0.198736</td>\n",
              "      <td>-0.332891</td>\n",
              "      <td>-0.033316</td>\n",
              "      <td>-0.524629</td>\n",
              "      <td>-0.508404</td>\n",
              "      <td>0.078893</td>\n",
              "      <td>-0.180300</td>\n",
              "      <td>0.004678</td>\n",
              "      <td>-0.559677</td>\n",
              "      <td>-0.942348</td>\n",
              "      <td>0.393637</td>\n",
              "      <td>0.563248</td>\n",
              "      <td>-0.359479</td>\n",
              "      <td>-0.384613</td>\n",
              "      <td>1.062215</td>\n",
              "      <td>0.165736</td>\n",
              "      <td>0.707428</td>\n",
              "      <td>-0.190040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jee</th>\n",
              "      <td>-0.032176</td>\n",
              "      <td>0.015548</td>\n",
              "      <td>-0.006201</td>\n",
              "      <td>0.022667</td>\n",
              "      <td>0.030840</td>\n",
              "      <td>0.017910</td>\n",
              "      <td>-0.022516</td>\n",
              "      <td>-0.056130</td>\n",
              "      <td>-0.033611</td>\n",
              "      <td>0.083182</td>\n",
              "      <td>0.091070</td>\n",
              "      <td>-0.070001</td>\n",
              "      <td>0.096580</td>\n",
              "      <td>0.072322</td>\n",
              "      <td>0.024156</td>\n",
              "      <td>0.017555</td>\n",
              "      <td>-0.019078</td>\n",
              "      <td>-0.020361</td>\n",
              "      <td>-0.043662</td>\n",
              "      <td>-0.062836</td>\n",
              "      <td>-0.056388</td>\n",
              "      <td>0.015558</td>\n",
              "      <td>-0.071086</td>\n",
              "      <td>-0.014717</td>\n",
              "      <td>-0.050087</td>\n",
              "      <td>-0.012230</td>\n",
              "      <td>0.013113</td>\n",
              "      <td>0.031359</td>\n",
              "      <td>-0.001079</td>\n",
              "      <td>-0.032333</td>\n",
              "      <td>0.088693</td>\n",
              "      <td>-0.068664</td>\n",
              "      <td>0.015512</td>\n",
              "      <td>-0.030936</td>\n",
              "      <td>0.067545</td>\n",
              "      <td>0.051411</td>\n",
              "      <td>0.085833</td>\n",
              "      <td>0.003954</td>\n",
              "      <td>-0.038029</td>\n",
              "      <td>0.017574</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.067590</td>\n",
              "      <td>0.053193</td>\n",
              "      <td>0.013617</td>\n",
              "      <td>-0.033822</td>\n",
              "      <td>-0.004979</td>\n",
              "      <td>0.034918</td>\n",
              "      <td>-0.041099</td>\n",
              "      <td>0.099717</td>\n",
              "      <td>-0.067766</td>\n",
              "      <td>-0.039955</td>\n",
              "      <td>0.002232</td>\n",
              "      <td>-0.012895</td>\n",
              "      <td>-0.008339</td>\n",
              "      <td>-0.019294</td>\n",
              "      <td>0.087133</td>\n",
              "      <td>0.050033</td>\n",
              "      <td>0.158275</td>\n",
              "      <td>0.015152</td>\n",
              "      <td>-0.036366</td>\n",
              "      <td>0.043647</td>\n",
              "      <td>-0.135486</td>\n",
              "      <td>0.012992</td>\n",
              "      <td>-0.012458</td>\n",
              "      <td>0.034662</td>\n",
              "      <td>-0.055172</td>\n",
              "      <td>-0.001351</td>\n",
              "      <td>-0.039300</td>\n",
              "      <td>-0.008226</td>\n",
              "      <td>0.007533</td>\n",
              "      <td>0.056203</td>\n",
              "      <td>-0.013222</td>\n",
              "      <td>0.035523</td>\n",
              "      <td>-0.045397</td>\n",
              "      <td>-0.046444</td>\n",
              "      <td>0.136733</td>\n",
              "      <td>0.013452</td>\n",
              "      <td>0.156695</td>\n",
              "      <td>0.025001</td>\n",
              "      <td>-0.019532</td>\n",
              "      <td>-0.141740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ov</th>\n",
              "      <td>-0.020388</td>\n",
              "      <td>0.040175</td>\n",
              "      <td>-0.005961</td>\n",
              "      <td>-0.010260</td>\n",
              "      <td>-0.008979</td>\n",
              "      <td>-0.010407</td>\n",
              "      <td>-0.017940</td>\n",
              "      <td>-0.028919</td>\n",
              "      <td>-0.000041</td>\n",
              "      <td>0.018151</td>\n",
              "      <td>0.002513</td>\n",
              "      <td>-0.031418</td>\n",
              "      <td>0.008937</td>\n",
              "      <td>0.027439</td>\n",
              "      <td>0.023749</td>\n",
              "      <td>0.027375</td>\n",
              "      <td>-0.000549</td>\n",
              "      <td>-0.043575</td>\n",
              "      <td>-0.017384</td>\n",
              "      <td>-0.012439</td>\n",
              "      <td>-0.021750</td>\n",
              "      <td>-0.002207</td>\n",
              "      <td>-0.052451</td>\n",
              "      <td>-0.064087</td>\n",
              "      <td>0.016365</td>\n",
              "      <td>-0.001278</td>\n",
              "      <td>0.012740</td>\n",
              "      <td>0.000718</td>\n",
              "      <td>0.010495</td>\n",
              "      <td>-0.048151</td>\n",
              "      <td>-0.000508</td>\n",
              "      <td>-0.025176</td>\n",
              "      <td>-0.035252</td>\n",
              "      <td>-0.010514</td>\n",
              "      <td>-0.007019</td>\n",
              "      <td>-0.022794</td>\n",
              "      <td>0.012843</td>\n",
              "      <td>0.034444</td>\n",
              "      <td>0.038064</td>\n",
              "      <td>-0.008184</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005966</td>\n",
              "      <td>0.031636</td>\n",
              "      <td>0.010381</td>\n",
              "      <td>-0.026216</td>\n",
              "      <td>-0.059635</td>\n",
              "      <td>0.020203</td>\n",
              "      <td>0.000906</td>\n",
              "      <td>0.021180</td>\n",
              "      <td>-0.018249</td>\n",
              "      <td>0.001251</td>\n",
              "      <td>-0.005949</td>\n",
              "      <td>-0.028035</td>\n",
              "      <td>0.010461</td>\n",
              "      <td>-0.014292</td>\n",
              "      <td>0.015808</td>\n",
              "      <td>-0.019037</td>\n",
              "      <td>0.099886</td>\n",
              "      <td>-0.000469</td>\n",
              "      <td>0.042346</td>\n",
              "      <td>-0.035810</td>\n",
              "      <td>-0.031699</td>\n",
              "      <td>0.008687</td>\n",
              "      <td>0.020838</td>\n",
              "      <td>-0.024028</td>\n",
              "      <td>0.004539</td>\n",
              "      <td>-0.030970</td>\n",
              "      <td>-0.023796</td>\n",
              "      <td>0.031754</td>\n",
              "      <td>0.004088</td>\n",
              "      <td>-0.004701</td>\n",
              "      <td>0.002001</td>\n",
              "      <td>-0.005333</td>\n",
              "      <td>-0.017018</td>\n",
              "      <td>0.010795</td>\n",
              "      <td>-0.002224</td>\n",
              "      <td>0.011243</td>\n",
              "      <td>0.102943</td>\n",
              "      <td>0.004189</td>\n",
              "      <td>0.021399</td>\n",
              "      <td>0.022489</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2669 rows × 100 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    0         1         2   ...        97        98        99\n",
              "subject       0.018902  0.175763  0.020582  ... -0.218745  0.122599  0.213728\n",
              "year          0.016582  0.425534  1.151692  ... -0.981371 -0.432451  0.805379\n",
              "christian    -0.085130  0.101119 -0.004949  ... -0.032221  0.091849  0.012291\n",
              "organization -1.916205 -0.133026  2.059584  ...  0.553204 -0.963694  1.460151\n",
              "ag           -0.069068  0.113276 -0.008672  ...  0.033306  0.114728  0.023623\n",
              "...                ...       ...       ...  ...       ...       ...       ...\n",
              "lady         -0.002883  0.041840  0.002387  ... -0.000227  0.016410  0.012693\n",
              "sc            0.048143  0.327290 -0.200101  ... -0.115648  0.445100  0.156961\n",
              "colony       -0.835331  1.228851 -0.820097  ...  0.165736  0.707428 -0.190040\n",
              "jee          -0.032176  0.015548 -0.006201  ...  0.025001 -0.019532 -0.141740\n",
              "ov           -0.020388  0.040175 -0.005961  ...  0.004189  0.021399  0.022489\n",
              "\n",
              "[2669 rows x 100 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvCTHOCfW9Vz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clustering_on_wordvecs(word_vectors, num_clusters):\n",
        "    # Initalize a k-means object and use it to extract centroids\n",
        "    kmeans_clustering = KMeans(n_clusters = num_clusters, init='k-means++');\n",
        "    idx = kmeans_clustering.fit_predict(word_vectors);\n",
        "    \n",
        "    return kmeans_clustering.cluster_centers_, idx;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlloTw5mXB8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "centers, clusters = clustering_on_wordvecs(Z, 50);\n",
        "centroid_map = dict(zip(model.wv.index2word, clusters));"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KKzLO4pXmLT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_top_words(index2word, k, centers, wordvecs):\n",
        "    tree = KDTree(wordvecs);\n",
        "\n",
        "    #Closest points for each Cluster center is used to query the closest 20 points to it.\n",
        "    closest_points = [tree.query(np.reshape(x, (1, -1)), k=k) for x in centers];\n",
        "    closest_words_idxs = [x[1] for x in closest_points];\n",
        "\n",
        "    #Word Index is queried for each position in the above array, and added to a Dictionary.\n",
        "    closest_words = {};\n",
        "    for i in range(0, len(closest_words_idxs)):\n",
        "        closest_words['Cluster #' + str(i+1).zfill(2)] = [index2word[j] for j in closest_words_idxs[i][0]]\n",
        "\n",
        "    #A DataFrame is generated from the dictionary.\n",
        "    df = pd.DataFrame(closest_words);\n",
        "    df.index = df.index+1\n",
        "\n",
        "    return df;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zp5uE9PEX9bW",
        "colab_type": "code",
        "outputId": "11104e70-7d34-4152-8f36-b28ee5f159f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        }
      },
      "source": [
        "top_words = get_top_words(model.wv.index2word, 5, centers, Z);\n",
        "\n",
        "top_words\n",
        "\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Cluster #01</th>\n",
              "      <th>Cluster #02</th>\n",
              "      <th>Cluster #03</th>\n",
              "      <th>Cluster #04</th>\n",
              "      <th>Cluster #05</th>\n",
              "      <th>Cluster #06</th>\n",
              "      <th>Cluster #07</th>\n",
              "      <th>Cluster #08</th>\n",
              "      <th>Cluster #09</th>\n",
              "      <th>Cluster #10</th>\n",
              "      <th>Cluster #11</th>\n",
              "      <th>Cluster #12</th>\n",
              "      <th>Cluster #13</th>\n",
              "      <th>Cluster #14</th>\n",
              "      <th>Cluster #15</th>\n",
              "      <th>Cluster #16</th>\n",
              "      <th>Cluster #17</th>\n",
              "      <th>Cluster #18</th>\n",
              "      <th>Cluster #19</th>\n",
              "      <th>Cluster #20</th>\n",
              "      <th>Cluster #21</th>\n",
              "      <th>Cluster #22</th>\n",
              "      <th>Cluster #23</th>\n",
              "      <th>Cluster #24</th>\n",
              "      <th>Cluster #25</th>\n",
              "      <th>Cluster #26</th>\n",
              "      <th>Cluster #27</th>\n",
              "      <th>Cluster #28</th>\n",
              "      <th>Cluster #29</th>\n",
              "      <th>Cluster #30</th>\n",
              "      <th>Cluster #31</th>\n",
              "      <th>Cluster #32</th>\n",
              "      <th>Cluster #33</th>\n",
              "      <th>Cluster #34</th>\n",
              "      <th>Cluster #35</th>\n",
              "      <th>Cluster #36</th>\n",
              "      <th>Cluster #37</th>\n",
              "      <th>Cluster #38</th>\n",
              "      <th>Cluster #39</th>\n",
              "      <th>Cluster #40</th>\n",
              "      <th>Cluster #41</th>\n",
              "      <th>Cluster #42</th>\n",
              "      <th>Cluster #43</th>\n",
              "      <th>Cluster #44</th>\n",
              "      <th>Cluster #45</th>\n",
              "      <th>Cluster #46</th>\n",
              "      <th>Cluster #47</th>\n",
              "      <th>Cluster #48</th>\n",
              "      <th>Cluster #49</th>\n",
              "      <th>Cluster #50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ci</td>\n",
              "      <td>invoices</td>\n",
              "      <td>successful</td>\n",
              "      <td>polytechnic</td>\n",
              "      <td>hibernate</td>\n",
              "      <td>particulars</td>\n",
              "      <td>tag</td>\n",
              "      <td>functionality</td>\n",
              "      <td>associate</td>\n",
              "      <td>movies</td>\n",
              "      <td>utilize</td>\n",
              "      <td>rachna</td>\n",
              "      <td>then</td>\n",
              "      <td>executing</td>\n",
              "      <td>will</td>\n",
              "      <td>sekhar</td>\n",
              "      <td>leadership</td>\n",
              "      <td>github</td>\n",
              "      <td>mr</td>\n",
              "      <td>true</td>\n",
              "      <td>ahmad</td>\n",
              "      <td>toward</td>\n",
              "      <td>cultural</td>\n",
              "      <td>nagar</td>\n",
              "      <td>looking</td>\n",
              "      <td>back</td>\n",
              "      <td>akhpur</td>\n",
              "      <td>developing</td>\n",
              "      <td>adobe</td>\n",
              "      <td>portal</td>\n",
              "      <td>electronics</td>\n",
              "      <td>limited</td>\n",
              "      <td>declare</td>\n",
              "      <td>myself</td>\n",
              "      <td>customers</td>\n",
              "      <td>willing</td>\n",
              "      <td>ui</td>\n",
              "      <td>certification</td>\n",
              "      <td>dr</td>\n",
              "      <td>distt</td>\n",
              "      <td>framework</td>\n",
              "      <td>organization</td>\n",
              "      <td>pvt</td>\n",
              "      <td>which</td>\n",
              "      <td>april</td>\n",
              "      <td>passed</td>\n",
              "      <td>window</td>\n",
              "      <td>seo</td>\n",
              "      <td>december</td>\n",
              "      <td>week</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>volley</td>\n",
              "      <td>balance</td>\n",
              "      <td>organisation</td>\n",
              "      <td>aligarh</td>\n",
              "      <td>jdbc</td>\n",
              "      <td>furnished</td>\n",
              "      <td>youtube</td>\n",
              "      <td>modules</td>\n",
              "      <td>intern</td>\n",
              "      <td>watching</td>\n",
              "      <td>contribute</td>\n",
              "      <td>srinagar</td>\n",
              "      <td>when</td>\n",
              "      <td>review</td>\n",
              "      <td>would</td>\n",
              "      <td>kapoorchand</td>\n",
              "      <td>interpersonal</td>\n",
              "      <td>linkedin</td>\n",
              "      <td>mrs</td>\n",
              "      <td>correct</td>\n",
              "      <td>parvesh</td>\n",
              "      <td>teamwork</td>\n",
              "      <td>annual</td>\n",
              "      <td>colony</td>\n",
              "      <td>seeking</td>\n",
              "      <td>backend</td>\n",
              "      <td>educo</td>\n",
              "      <td>design</td>\n",
              "      <td>ide</td>\n",
              "      <td>designed</td>\n",
              "      <td>tech</td>\n",
              "      <td>india</td>\n",
              "      <td>hereby</td>\n",
              "      <td>potential</td>\n",
              "      <td>get</td>\n",
              "      <td>flexible</td>\n",
              "      <td>frontend</td>\n",
              "      <td>internship</td>\n",
              "      <td>abdul</td>\n",
              "      <td>road</td>\n",
              "      <td>ajax</td>\n",
              "      <td>opportunity</td>\n",
              "      <td>private</td>\n",
              "      <td>it</td>\n",
              "      <td>jan</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>linux</td>\n",
              "      <td>media</td>\n",
              "      <td>september</td>\n",
              "      <td>two</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>parsing</td>\n",
              "      <td>regularly</td>\n",
              "      <td>become</td>\n",
              "      <td>pcm</td>\n",
              "      <td>servlet</td>\n",
              "      <td>given</td>\n",
              "      <td>image</td>\n",
              "      <td>into</td>\n",
              "      <td>trainee</td>\n",
              "      <td>badminton</td>\n",
              "      <td>enhance</td>\n",
              "      <td>manav</td>\n",
              "      <td>purchase</td>\n",
              "      <td>recommendations</td>\n",
              "      <td>be</td>\n",
              "      <td>purbia</td>\n",
              "      <td>excellent</td>\n",
              "      <td>url</td>\n",
              "      <td>kumar</td>\n",
              "      <td>above</td>\n",
              "      <td>harsh</td>\n",
              "      <td>determination</td>\n",
              "      <td>sports</td>\n",
              "      <td>vihar</td>\n",
              "      <td>opportunity</td>\n",
              "      <td>front</td>\n",
              "      <td>pratapgarh</td>\n",
              "      <td>designing</td>\n",
              "      <td>sublime</td>\n",
              "      <td>developed</td>\n",
              "      <td>cse</td>\n",
              "      <td>private</td>\n",
              "      <td>mentioned</td>\n",
              "      <td>while</td>\n",
              "      <td>they</td>\n",
              "      <td>adapt</td>\n",
              "      <td>backend</td>\n",
              "      <td>certified</td>\n",
              "      <td>kalam</td>\n",
              "      <td>near</td>\n",
              "      <td>nodejs</td>\n",
              "      <td>career</td>\n",
              "      <td>noida</td>\n",
              "      <td>that</td>\n",
              "      <td>feb</td>\n",
              "      <td>xii</td>\n",
              "      <td>ubuntu</td>\n",
              "      <td>campaigns</td>\n",
              "      <td>february</td>\n",
              "      <td>successfully</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>retrofit</td>\n",
              "      <td>payments</td>\n",
              "      <td>being</td>\n",
              "      <td>bseb</td>\n",
              "      <td>mvc</td>\n",
              "      <td>correctness</td>\n",
              "      <td>article</td>\n",
              "      <td>products</td>\n",
              "      <td>designation</td>\n",
              "      <td>singing</td>\n",
              "      <td>abilities</td>\n",
              "      <td>kerala</td>\n",
              "      <td>easy</td>\n",
              "      <td>preparing</td>\n",
              "      <td>get</td>\n",
              "      <td>supernetting</td>\n",
              "      <td>analytical</td>\n",
              "      <td>id</td>\n",
              "      <td>mother</td>\n",
              "      <td>best</td>\n",
              "      <td>saini</td>\n",
              "      <td>reliable</td>\n",
              "      <td>participation</td>\n",
              "      <td>near</td>\n",
              "      <td>job</td>\n",
              "      <td>frontend</td>\n",
              "      <td>cor</td>\n",
              "      <td>applications</td>\n",
              "      <td>dreamweaver</td>\n",
              "      <td>made</td>\n",
              "      <td>mca</td>\n",
              "      <td>infotech</td>\n",
              "      <td>furnished</td>\n",
              "      <td>effectively</td>\n",
              "      <td>users</td>\n",
              "      <td>deadlines</td>\n",
              "      <td>designer</td>\n",
              "      <td>days</td>\n",
              "      <td>rajasthan</td>\n",
              "      <td>po</td>\n",
              "      <td>angular</td>\n",
              "      <td>company</td>\n",
              "      <td>trainee</td>\n",
              "      <td>also</td>\n",
              "      <td>aug</td>\n",
              "      <td>cbse</td>\n",
              "      <td>os</td>\n",
              "      <td>social</td>\n",
              "      <td>november</td>\n",
              "      <td>three</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>softwares</td>\n",
              "      <td>buying</td>\n",
              "      <td>result</td>\n",
              "      <td>bareilly</td>\n",
              "      <td>frameworks</td>\n",
              "      <td>provided</td>\n",
              "      <td>instagram</td>\n",
              "      <td>features</td>\n",
              "      <td>analyst</td>\n",
              "      <td>cooking</td>\n",
              "      <td>skills</td>\n",
              "      <td>iet</td>\n",
              "      <td>each</td>\n",
              "      <td>fixing</td>\n",
              "      <td>any</td>\n",
              "      <td>reader</td>\n",
              "      <td>strong</td>\n",
              "      <td>mail</td>\n",
              "      <td>yadav</td>\n",
              "      <td>mentioned</td>\n",
              "      <td>mayank</td>\n",
              "      <td>continuously</td>\n",
              "      <td>quiz</td>\n",
              "      <td>road</td>\n",
              "      <td>myself</td>\n",
              "      <td>end</td>\n",
              "      <td>ansible</td>\n",
              "      <td>implementation</td>\n",
              "      <td>netbeans</td>\n",
              "      <td>module</td>\n",
              "      <td>bachelor</td>\n",
              "      <td>noida</td>\n",
              "      <td>particulars</td>\n",
              "      <td>grow</td>\n",
              "      <td>them</td>\n",
              "      <td>ready</td>\n",
              "      <td>intern</td>\n",
              "      <td>weeks</td>\n",
              "      <td>uttar</td>\n",
              "      <td>block</td>\n",
              "      <td>jquery</td>\n",
              "      <td>professional</td>\n",
              "      <td>june</td>\n",
              "      <td>provide</td>\n",
              "      <td>august</td>\n",
              "      <td>class</td>\n",
              "      <td>win</td>\n",
              "      <td>strategies</td>\n",
              "      <td>sep</td>\n",
              "      <td>seminar</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Cluster #01 Cluster #02   Cluster #03  ... Cluster #48 Cluster #49   Cluster #50\n",
              "1          ci    invoices    successful  ...         seo    december          week\n",
              "2      volley     balance  organisation  ...       media   september           two\n",
              "3     parsing   regularly        become  ...   campaigns    february  successfully\n",
              "4    retrofit    payments         being  ...      social    november         three\n",
              "5   softwares      buying        result  ...  strategies         sep       seminar\n",
              "\n",
              "[5 rows x 50 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nLv5lBaYkge",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def display_cloud(cluster_num, cmap):\n",
        "    wc = WordCloud(background_color=\"black\", max_words=2000, max_font_size=80, colormap=cmap);\n",
        "    wordcloud = wc.generate(' '.join([word for word in top_words['Cluster #' + str(cluster_num).zfill(2)]]))\n",
        "    plt.imshow(wordcloud, interpolation='bilinear')\n",
        "    plt.axis(\"off\")\n",
        "    plt.savefig('cluster_' + str(cluster_num), bbox_inches='tight')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oOxKRAbYrtF",
        "colab_type": "code",
        "outputId": "5592d048-92b3-4322-d215-84471d68b6e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "cmaps = cycle([\n",
        "            'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',\n",
        "            'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',\n",
        "            'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])\n",
        "i = 0\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D8UEeQ4a7gyQPbx0kQpoyhUSBDE3Ns/UxU0JiXjFFtxkRsDpEIHsR4etD/NWm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\nrkKhUJSR/wcW0w+OZW2NAQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3NdoBoA3mEN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "8fde2efa-228a-4cf4-9b75-1a9d2ca4cc91"
      },
      "source": [
        "i = 1\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ePB/uvgsqwqozbHkPPvM1IVEAVYXSYrj0fLjhaphRCVmZwXyrRvkeJEzU54M+\nN/Q6ob4RHn4c/vGY/D9xSsBYXLgOvvZ5mFAaefzQEfjkHTKZ0olJE+Gvv+oPBgih5jj84H/h9Y1D\nv6bZLNe97EK46hKYOgkyM8FqkX4zBBOtBwKSr9btkUHf1AxPvQAPPCJBCekM/55YBl/7nPjgDhU7\n98BXvgs1telrT7KwZ8Ckcrj4XPFhnzZV5pPVIrlKjAYZh6H+DARkvHm9EszhcotAsGcfvPgavPaW\njKGhjMl4MBrA4ZC5cdN1MHeWtNVqAYs58h37/f3vuKVV0mQ+9JgQcFeSEYNDgaJIW+bPgcsvgovO\nkQXKZhXhKTR3Q/M31G9en/SZ2yPz+EgNbHoXXngFqvZDr2t0wsHTQroGg0g40RmckkFLm5BcumG1\nyqo3LazQqtMlx3bslgF95kr4zlckv29oEMUrIqmq8nubTSSPCaWwaB782/Xw3bvhlTehO8kw4D37\nJfR0+tT+QQHSzoXzJCR1ANfpIUFR4KxVsGZl5HMFArLi70mh/NOEUrj2cvjsrTC5IthvxO83g0E+\nVitkO2RizJ0F//YB+N1fZdGqqR0+QYBMtLKS1MZgZ1cwsf4owmiUIKGPfBCuv0rmT4hcExUyDfWn\nySQEQ5YcLy8TArr+KhnbN3wcjg5jAcnJhnVrJHp09XIhMjWBIjLUJrNZBJaiApg1HT54LTzyBNz7\nW9hdJcScDhiNsGQ+fOxmGYd5ebILgMT9Fmq72SxkDTLHpk6S/Mpfuk0Est/8BZ54VoSrkcRppdPN\nccCUChm0F50L990Da1fL36EBnwwURc5ZOBd+9n0hEUdWcueeqIe3NsVKeQaDhKca01iS3Ri8ZvRz\nOV2yULS0Jn8tRRHC/K+vwz13yaJhMkryn6H0m9EoC9+37oD/+Z4sCMbTTMnlyISrL4Hf/gy+8ClZ\nyIzGoY3BcCiKEI+qyiJ2vC71tk0sgy9+Cn55N5x3tggaiQg3YVsMQsAfuQnu/wlcsFak4+FCUUSq\nvf8n8KmPQVGhjPGBFqqBrqWq0u+Zdli1XHbHpND/Q0VaSDcQ1Ht2dsm2J12SWrqR7RDp7Mwz4Dtf\nFnVCvJelaeDxylZjoGdRFLneFz4F554lJJQMnloPHV2x1z57VawaYDiYVA7LF0ce0zRR6Ty9fmjX\nWjxfCPfm6wbemQQCwW1v38DSjc0KV1wEP/w2rFo2tLbEg88v46+rW+47XsdgXi589Ga4+7uiI00n\nPG547JnUdw6F+fCNL8DnPhGr/gqHpvVv1Qcqc2RQYcVi+MG3RHUy3N3EiiVw11dh0fyBf6dpMndd\nruQl7NoTomobjYyHaZExXGtjIMYAACAASURBVC742yOweatsRbLssg3PyRaiC33OXg252amt5ulA\nVpa8/PPOFinVECZVNrfAu9th4zsiKYTUBZl22S6tXS0v3RBHEp0+FT71UdENHqkZvB279ooOrrQ4\n8nhJkWzrHvxnyo8YgQvPkfcQjW27xJCQLGZUSpmli8+LT7hOJ7y1Gd5+R4wU3T0y8G1W0f0uXiAS\nbUlRpNSkqnDGMvju1+C2Lw+tTdFoboVf/BYefzaozsiSZ3dk9Y+/0mJRt6RD6koFmXZZtL7yGSG1\nRPPA7YbDNbD/oOyMOrrEpmCzyjNNLJMxN6Uicpdw7AS88XZqbVNV+Own4JYbpZ3R6OwSHe2WbWJ/\n6OgSwrVZoaQYli6UOVJWEnmewQAL5sLXPy+657c2p7YgFuTDp/9d5m205B0IQHWNPPu+Q2Ig63UK\n4ZrNkJkBpSVinJwzS8qGhWea8/th737Y9M7Q25UK0kK6Pj9UHZQPyApnNInkZwx+TEZ4/IFYyWs0\nYTTA2lXyf7NZXn57Jzz9AvzhQRlM7R2i0wmt4KoqeqDf/xUuvUD0XFMn9+uRQr9Zt0Ym9LETgxfa\n9HjgX0+LdBxO4vYMMUw98oRIEsOBPUPaGz64QJ750SeTLwZamA+3fQyuvDg2JaLHA29uErLbthPa\nOmUBDvWdoojhxeGAigmiA//w9UIcIcJRlH7d+h3fTt3y3dcni8m2XfK30RA7BqdOgif/NjYJy00m\nuPxCMfbFI1x/UFJ/7FnRdVfXQI8T3H0yFjQtqM81yqJiz5Ck7GtWyjiaO0sWnKGojEJQVbjxGvj3\nf+vXeYbgdIoq6v4/ibDQ2dVPaCDPYTHLO55SAf/xYfjAFbLIhZ7RYBDvli98SgSa6sTl4hLi/LX9\nqsAQNE12Nvf9Hv72qBhqe51iKAuXwA0GaWOGTQy+pUWySJy3FlYuld88vV64YFQwWjXSAO2Np2Nr\npDUfQFuxJP01ilYtRzv4TuS9oj91e9A+/0k0ewaaogx+TbMZ7ZrL0Ha/heZvir3e+kfRcrKTa9+U\nCrTanbH1uvZuRFu5dPjPf8FatINbYvv7yFa0ionJXcNoRLvpWrTO6th2dhxB+/kP5DlUdfBrKQqa\nzYr27x9Ca6iKvV7bIbRvfgnNah25ulWTK+SdR7+3zevRpk8dufsC2pyZaDveiH0fWgtazzG0Fx5B\nu2AdWqY9ubEY6lOzGS0rE23pIrSykuTPDf8snIu26YXYMd1bi/az76Pl5yX/jvNz0e64Ha1ud+yz\neurRvngbmiVB3cFEH5sV7d4fovkaI6/nrEW784toOY6hXU8BzWREy8iQunm33IhWmJ/e9z0uaqSN\nN7g98MvfwX2/k9UxmS2PxyNS8V8fga443gorloiPYDJoaYvVqyqKSEHnrx2a8SIaJiOcc5Zs9aIl\nqseflW1eMigqEC+FaCNhn1sqOP/oXpFaBtLrhaBpouf926Nw98+hLaxquaKI9Hv9VenR7443mIzi\npbBgTuz76OmVncdnvy4uXz29yY1F4ORuo7sHtm5PzfMl0w63BNsWPua8Xnjsafje/0je62TfcWs7\n/PYBePBfYrANh8kEt35Y1BFDQVGheGhEq/Z27xN3r44h+n5ryO7B6RQ/57/8Q9RTo4XTlnQ3bpHO\nHqrRwesT1UDt8dgBbrPCnJnJXcfphPWvxrqaZWWKCiZa3zsUTCqHJQvF8hyOtg7ZKkZPhkRYuzrW\naKFp4oz/f7+H+hSqWPf1wSNPwrMvxk7kWdNFD53tGPp1xzMmlcM1l8Ue9/lEx3nPL4anzx4OFs0X\n1Vj0WNl7QFQKyS7Q4ejqhj89JG6J0XOkcooYjIcCe4YEO0SjrkGMwqcaTkvS7e2FP/8dGptSO/9w\ntSjsoy2jiirEkQz8AYls274r6hoKzJ0ZdF9JEYsXCPlHS1XvbpOVPRmpRVXhA1fGWpw9XnjuZYnM\nSxV1DfDEc2IxDofFLEbOysmpX3s84pLzYxfRkD3hl78TI85YwGKGM5ZKRGE4PF6p6LJr79Al5xCq\nDkjgQbTtwGiQoJqhIBTcEA2bTWwzpxpOS9LduVcMP6kaq/wB2LUndkApDM1IU3sCXn4zlrwrykVV\nEW0ESwbZDjm3tCjyuNcLr22A2iR9OGdUisN9tJrD6RRDTzLEnQiBgHi6vLczdlLPnyOf94vvrsUs\nvqW2OF4fGzbDy6+PfptCKCsR/9RoKbf2hBBmKHIzFfj98Mam2GKgiiKqjML85K/V09vvEROO+bNl\nnA5HFTcWOMWaO3xomhTCHE7EDsDx+jiSrjK0rXGvU9ysDlVHHjebxBNi6uShtyu0fYsmrX0H4e13\nk/dDXLNSfEqjpeXdVenZCh+vE9LtdUYet1rk2bPibCdPRcydJcEg0cTg84t+e6SjnwZC+QSx4kfj\nwCFRLwwX23fJYh+O0BxJVg0H4tFy5GiskFRcCF/9rHggWCzDbu6o4bQjXVefkNxwVnGQ82OSZSji\nkjIU7NgtIYjRkuPShSIRxPMLTgSTUcKTF86NPO7zyz127ol/XjwsWRCf+N5+N3Ep86FA02T72tQS\n+93KJUPvx/GKBXMgLyd28WpsFmlyrGA0yGJQPiHyeCAgeTGiVT+p4Njx+LtJq0WEg2Th9Yru+9jx\nyOOqCqtXwB/uFXe3WdNHJqVAunHakW5zS+q63HB4vYgZNAwKkf67yaCpRbba4dZ8EIPa2jVDk5yz\ns8XzIXrVb22FTVuTN4rYM8QB3xQngmjfQfCnqRhnTW18Q8iUCnn+9wOmTJKgnGjs2C2qmrGCxRKb\n/wNkQW1qTU/iF59f/LajYTINTb0Asjt9fUP8is0zp8GPvwu/+BF87lbx+472Nx5PeJ9ozpJHe2cs\nwY0lNE1W8eqaWHez88+Gn+YnT5alxXDOmtjrVx+TIIZkUZAvbmLxIqZqakWnnQ40NMVPkWm3S/Ra\n1YHUDTnjAarSn/0qGvsPgdsbe3y0YDGLV0U0DKrkSkjW9XEgmIwiCMTcwzD0RbWtHe7/o3jlLJoX\nOTYVBTIyROA4Y5n07Zb34KXXZdw3x9lNjSVOO9J19cVffVNCmsKZ9x0Qb4CFcyOtsZPKYfkSOFg9\neAy5wSDZ0oqjDGhuD7yzTTwukkVudnwjntcrEXvpIsL2DtExa1oswZcWy7FTmXTtGbJ4xTP0nKgH\n3xiSrskkOtFomM1CXGeMoL+0oqSmg922C+78b5Fq587qv1Y4Mu39qrlLzoeDhyWnwhPPyS4t2UjM\nkcRpp17wuNOjkwRi1Aupwu2RQRFNaAaDBAxYkxigVou4eIUPQk2D9vZgEpQhDLZMe/xJ0d0r7kTp\nQigHazxizc0ZlYRPIwq7PdYzIITm5vSlO0wFBoMYSscCoaxoQ0UgIAEkH7xV0jDWNyZOsGUyweRy\ncUH8xhfghUfgwV/DZRdAfu7YesecdqTr9cVaVMcDXnkDDh+NPb56uejeBsOCOfGzVu07JKkkhwKz\nOX6KSY9neK5i8eB2x580Niu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/Fzp3woQPQclV0PqaqAiiYZsMJVeCqxacNUJqICHBfXXi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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLlH17s73nsc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "1a2d3ca2-da19-4bc9-aae8-dd4cc62c0201"
      },
      "source": [
        "i = 2\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vZFBU46oBxQan3pdxTxpFPmjnxM4Y2e4pCpIopRSNYIzjeBCALGKn0xzHI+J3CVQoPpuE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\nQVcQBCGKRNAVBEGIIhF0BUEQouj/ALn1WZGep/rKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjvkNk163opQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "b22d8c50-f813-4973-907b-aa28a74952d8"
      },
      "source": [
        "i = 3\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3rM0VOPPmFdbmCjz97SPE2yx6DrfRM9aG70uEIupZaEHPMdf2mL64zAffv8za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\nVv3HAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQvw1cpZ3p1i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "1c9a5bd3-dbad-4246-ea30-fbae8e1e8abd"
      },
      "source": [
        "i = 4\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dG3Lys4eEXtFOoOVTuMJrsc0smdFDJC+9hrXC7gCXkfUgFWs+h6/h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\njNhG18bGxmYZsY2ujY2NzTLy/wH5cuJFYfDL5wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFQ-_gp83sVV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "337b5487-0cda-47ff-ef62-8577931756ce"
      },
      "source": [
        "i = 5\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5GO67z55RmPh8DNu321BaKkBVOY4cic3JzrqYDAzoaG9XwRjDxo0Ktm6dvY9u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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLFhxePh3xar",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "12dc1ccf-c6ec-42a6-e990-8a9c67b951d2"
      },
      "source": [
        "i = 6\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EoKDcj16Bn7sIrtkkN8fWduhrlStiLkIWfGCjWueNe0nvYU9wdzUVc0NBd8sf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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t68rhI0j3yUy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "6c1c306d-d362-4b86-e3ba-7acacd217f8e"
      },
      "source": [
        "i = 7\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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crG8ZJboWTfD2dQt5ePPSqZ34VJgFQ6wyy8fDm5fwptVVLMxJG7XE1W7RyXI7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\n/gdharbtC4i5fgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxT2tPke3zNq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "6cde8b06-c4b2-4f71-e4e1-841cd9b4d460"
      },
      "source": [
        "i = 8\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gv8CMc5bGZ/bQ2uhzE39BoFxuHvpRTGmHpoYxxlIHOTb77QXvNRDbwr7h92FLbWv0VYmX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\nlzUk8isAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAsfGlFA30UF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "91276bff-5dba-4ccb-ae40-8a3c5b447f68"
      },
      "source": [
        "i = 9\n",
        "col = next(cmaps);\n",
        "display_cloud(i+1, col)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EtEYrSegliUA0SmNv/0S7GRdZUXj+w8PUFDi4cuGCtG9OEAT0Gg3XLV3I6uoK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\nyYxuRkZGxhmSGd2MjIyMM+T/A5ccBO06dzaPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_UHalqRZ_vy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def print_word_table(table, key):\n",
        "    return pd.DataFrame(table, columns=[key, 'similarity'])"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}