practice7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "practice7.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHZj_1TOCyN6",
        "outputId": "9cdadffd-983a-4dab-eba7-c8f7ffca786d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "['I', 'will', 'walk', '500', 'miles', 'and', 'I', 'would', 'walk', '500', 'more', ',', 'just', 'to', 'be', 'the', 'man', 'who', 'walks', 'a', 'thousand', 'miles', 'to', 'fall', 'down', 'at', 'your', 'door', '!']\n",
            "['I will walk 500 miles and I would walk 500 more, just to be the man who walks a thousand miles to fall down at your door!']\n"
          ]
        }
      ],
      "source": [
        "#Tokenizer\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk import word_tokenize, sent_tokenize\n",
        "sent = \"I will walk 500 miles and I would walk 500 more, just to be the man who walks a thousand miles to fall down at your door! \"\n",
        "print(word_tokenize(sent))\n",
        "print(sent_tokenize(sent))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Stop words\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "stop_words = stopwords.words('english')\n",
        "token = word_tokenize(sent)\n",
        "cleaned_token = []\n",
        "for word in token:\n",
        "  if word not in stop_words:\n",
        "    cleaned_token.append(word)\n",
        "\n",
        "print(\"This is the unclean version:\",token)\n",
        "print(\"This is the cleaned version:\",cleaned_token)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yQRUiOBD_ns",
        "outputId": "75656c22-dd2f-4a53-8adc-15145be57058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "This is the unclean version: ['I', 'will', 'walk', '500', 'miles', 'and', 'I', 'would', 'walk', '500', 'more', ',', 'just', 'to', 'be', 'the', 'man', 'who', 'walks', 'a', 'thousand', 'miles', 'to', 'fall', 'down', 'at', 'your', 'door', '!']\n",
            "This is the cleaned version: ['I', 'walk', '500', 'miles', 'I', 'would', 'walk', '500', ',', 'man', 'walks', 'thousand', 'miles', 'fall', 'door', '!']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Stemming \n",
        "\n",
        "from nltk.stem import PorterStemmer\n",
        "stemmer = PorterStemmer()\n",
        "words = ['play','playing','plays','played','playfullness','playful']\n",
        "stemmed = [stemmer.stem(word) for word in words]\n",
        "print(stemmed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrEppMPMFYR0",
        "outputId": "a404649f-2398-40f4-f2b6-aec896defcfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['play', 'play', 'play', 'play', 'playful', 'play']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Tagging Parts of Speech(pos)\n",
        "import nltk\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "from nltk import pos_tag\n",
        "token = word_tokenize(sent) + word_tokenize(sent)\n",
        "tagged = pos_tag(cleaned_token)\n",
        "print(tagged)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JauCxoiGGnA",
        "outputId": "16351c86-1149-4014-b9c3-b3c2f6cdc69b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[('I', 'PRP'), ('walk', 'VBP'), ('500', 'CD'), ('miles', 'NNS'), ('I', 'PRP'), ('would', 'MD'), ('walk', 'VB'), ('500', 'CD'), (',', ','), ('man', 'NN'), ('walks', 'NNS'), ('thousand', 'VBP'), ('miles', 'NNS'), ('fall', 'VB'), ('door', 'NN'), ('!', '.')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lemmatization\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "words = ['play','playing','plays','played','playfullness','playful']\n",
        "lemmatized = [lemmatizer.lemmatize(word) for word in words]\n",
        "print(lemmatized)\n",
        "print('\\n')\n",
        "list1 = ['kites', 'babies', 'dogs', 'flying', 'smiling','driving', 'died', 'tried', 'feet']\n",
        "l = [lemmatizer.lemmatize(li) for li in list1]\n",
        "print(l)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGHXFTduHKiC",
        "outputId": "f4c56a9b-4b6c-4c0a-fee0-a1128ed88324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "['play', 'playing', 'play', 'played', 'playfullness', 'playful']\n",
            "\n",
            "\n",
            "['kite', 'baby', 'dog', 'flying', 'smiling', 'driving', 'died', 'tried', 'foot']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#part 2\n",
        "#tf and idf\n",
        "\n",
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "import math\n",
        "\n",
        "first_sentence = \"Data Science is the sexiest job of the 21st century\"\n",
        "second_sentence = \"machine learning is the key for data science\"\n",
        "\n",
        "first_sentence = first_sentence.split(\" \")\n",
        "second_sentence = second_sentence.split(\" \")\n",
        "total= set(first_sentence).union(set(second_sentence))\n",
        "print(total)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaYO4ZWBJILn",
        "outputId": "6fd33de2-9489-4dac-badc-c2944f19c3f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'of', 'science', 'Science', 'machine', 'job', 'learning', 'the', 'for', 'key', 'data', '21st', 'is', 'sexiest', 'century', 'Data'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wordDictA = dict.fromkeys(total, 0) \n",
        "wordDictB = dict.fromkeys(total, 0)\n",
        "for word in first_sentence:\n",
        "    wordDictA[word]+=1\n",
        "    \n",
        "for word in second_sentence:\n",
        "    wordDictB[word]+=1"
      ],
      "metadata": {
        "id": "u6osPfs3J96B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame([wordDictA, wordDictB])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "AMp3oR_jKE7_",
        "outputId": "b33a2940-8c4d-4baa-a10b-699ba0e110c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   of  science  Science  machine  job  learning  the  for  key  data  21st  \\\n",
              "0   1        0        1        0    1         0    2    0    0     0     1   \n",
              "1   0        1        0        1    0         1    1    1    1     1     0   \n",
              "\n",
              "   is  sexiest  century  Data  \n",
              "0   1        1        1     1  \n",
              "1   1        0        0     0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-240197fd-3b8d-4577-80d7-4bbeb190b9b5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>of</th>\n",
              "      <th>science</th>\n",
              "      <th>Science</th>\n",
              "      <th>machine</th>\n",
              "      <th>job</th>\n",
              "      <th>learning</th>\n",
              "      <th>the</th>\n",
              "      <th>for</th>\n",
              "      <th>key</th>\n",
              "      <th>data</th>\n",
              "      <th>21st</th>\n",
              "      <th>is</th>\n",
              "      <th>sexiest</th>\n",
              "      <th>century</th>\n",
              "      <th>Data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-240197fd-3b8d-4577-80d7-4bbeb190b9b5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-240197fd-3b8d-4577-80d7-4bbeb190b9b5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-240197fd-3b8d-4577-80d7-4bbeb190b9b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#tf\n",
        "def computeTF(wordDict, doc):\n",
        "    tfDict = {}\n",
        "    corpusCount = len(doc)\n",
        "    for word, count in wordDict.items():\n",
        "        tfDict[word] = count/float(corpusCount)\n",
        "    return(tfDict)\n",
        "\n",
        "tfFirst = computeTF(wordDictA, first_sentence)\n",
        "tfSecond = computeTF(wordDictB, second_sentence)\n",
        "\n",
        "tf = pd.DataFrame([tfFirst, tfSecond])\n",
        "\n",
        "tf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "sS_fo7O9KJM4",
        "outputId": "d10a45c3-3afe-4a18-ddbb-2e1aad8c5125"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    of  science  Science  machine  job  learning    the    for    key   data  \\\n",
              "0  0.1    0.000      0.1    0.000  0.1     0.000  0.200  0.000  0.000  0.000   \n",
              "1  0.0    0.125      0.0    0.125  0.0     0.125  0.125  0.125  0.125  0.125   \n",
              "\n",
              "   21st     is  sexiest  century  Data  \n",
              "0   0.1  0.100      0.1      0.1   0.1  \n",
              "1   0.0  0.125      0.0      0.0   0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3c8a79cd-9e00-4f29-971c-08ef89b4b6eb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>of</th>\n",
              "      <th>science</th>\n",
              "      <th>Science</th>\n",
              "      <th>machine</th>\n",
              "      <th>job</th>\n",
              "      <th>learning</th>\n",
              "      <th>the</th>\n",
              "      <th>for</th>\n",
              "      <th>key</th>\n",
              "      <th>data</th>\n",
              "      <th>21st</th>\n",
              "      <th>is</th>\n",
              "      <th>sexiest</th>\n",
              "      <th>century</th>\n",
              "      <th>Data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c8a79cd-9e00-4f29-971c-08ef89b4b6eb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c8a79cd-9e00-4f29-971c-08ef89b4b6eb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c8a79cd-9e00-4f29-971c-08ef89b4b6eb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import stopwords\n",
        "stop_words = set(stopwords.words('english'))\n",
        "filtered_sentence = [w for w in wordDictA if not w in stop_words]\n",
        "print(filtered_sentence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1n2GadJLT48",
        "outputId": "f8285efd-a978-4eb7-f0c8-e3ec52359b09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['science', 'Science', 'machine', 'job', 'learning', 'key', 'data', '21st', 'sexiest', 'century', 'Data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#idf\n",
        "def computeIDF(docList):\n",
        "    idfDict = {}\n",
        "    N = len(docList)\n",
        "    \n",
        "    idfDict = dict.fromkeys(docList[0].keys(), 0)\n",
        "    for word, val in idfDict.items():\n",
        "        idfDict[word] = math.log10(N / (float(val) + 1))\n",
        "        \n",
        "    return(idfDict)\n",
        "\n",
        "idfs = computeIDF([wordDictA, wordDictB])\n",
        "idfs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmBmf9BZLWJt",
        "outputId": "40d6b539-236b-43f0-a701-d9a0e2a94cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'21st': 0.3010299956639812,\n",
              " 'Data': 0.3010299956639812,\n",
              " 'Science': 0.3010299956639812,\n",
              " 'century': 0.3010299956639812,\n",
              " 'data': 0.3010299956639812,\n",
              " 'for': 0.3010299956639812,\n",
              " 'is': 0.3010299956639812,\n",
              " 'job': 0.3010299956639812,\n",
              " 'key': 0.3010299956639812,\n",
              " 'learning': 0.3010299956639812,\n",
              " 'machine': 0.3010299956639812,\n",
              " 'of': 0.3010299956639812,\n",
              " 'science': 0.3010299956639812,\n",
              " 'sexiest': 0.3010299956639812,\n",
              " 'the': 0.3010299956639812}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def computeTFIDF(tfBow, idfs):\n",
        "    tfidf = {}\n",
        "    for word, val in tfBow.items():\n",
        "        tfidf[word] = val*idfs[word]\n",
        "    return(tfidf)\n",
        "\n",
        "idfFirst = computeTFIDF(tfFirst, idfs)\n",
        "idfSecond = computeTFIDF(tfSecond, idfs)\n",
        "\n",
        "idf= pd.DataFrame([idfFirst, idfSecond])\n",
        "print(idf.to_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uwbCOUhVLeKF",
        "outputId": "55034313-714c-416f-ac69-42b979e88245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         of   science   Science   machine       job  learning       the       for       key      data      21st        is   sexiest   century      Data\n",
            "0  0.030103  0.000000  0.030103  0.000000  0.030103  0.000000  0.060206  0.000000  0.000000  0.000000  0.030103  0.030103  0.030103  0.030103  0.030103\n",
            "1  0.000000  0.037629  0.000000  0.037629  0.000000  0.037629  0.037629  0.037629  0.037629  0.037629  0.000000  0.037629  0.000000  0.000000  0.000000\n"
          ]
        }
      ]
    }
  ]
}