Edureka_NLP_Assignment_1_Part_C.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Edureka NLP Assignment 1 - Part C.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPEJd5Gx6mP/qOe+JdJTJkK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nandinib1999/edureka-assignments/blob/main/Edureka_NLP_Assignment_1_Part_C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78XZBW7Wuuci"
      },
      "source": [
        "import nltk"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWw4d-CLvIns",
        "outputId": "f8cdc968-a786-4f3d-ddc6-22ac1aff4e20"
      },
      "source": [
        "dir(nltk.corpus)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['AlignedCorpusReader',\n",
              " 'AlpinoCorpusReader',\n",
              " 'BNCCorpusReader',\n",
              " 'BracketParseCorpusReader',\n",
              " 'CHILDESCorpusReader',\n",
              " 'CMUDictCorpusReader',\n",
              " 'CategorizedBracketParseCorpusReader',\n",
              " 'CategorizedCorpusReader',\n",
              " 'CategorizedPlaintextCorpusReader',\n",
              " 'CategorizedSentencesCorpusReader',\n",
              " 'CategorizedTaggedCorpusReader',\n",
              " 'ChasenCorpusReader',\n",
              " 'ChunkedCorpusReader',\n",
              " 'ComparativeSentencesCorpusReader',\n",
              " 'ConllChunkCorpusReader',\n",
              " 'ConllCorpusReader',\n",
              " 'CorpusReader',\n",
              " 'CrubadanCorpusReader',\n",
              " 'DependencyCorpusReader',\n",
              " 'EuroparlCorpusReader',\n",
              " 'FramenetCorpusReader',\n",
              " 'IEERCorpusReader',\n",
              " 'IPIPANCorpusReader',\n",
              " 'IndianCorpusReader',\n",
              " 'KNBCorpusReader',\n",
              " 'LazyCorpusLoader',\n",
              " 'LinThesaurusCorpusReader',\n",
              " 'MTECorpusReader',\n",
              " 'MWAPPDBCorpusReader',\n",
              " 'MacMorphoCorpusReader',\n",
              " 'NKJPCorpusReader',\n",
              " 'NPSChatCorpusReader',\n",
              " 'NombankCorpusReader',\n",
              " 'NonbreakingPrefixesCorpusReader',\n",
              " 'OpinionLexiconCorpusReader',\n",
              " 'PPAttachmentCorpusReader',\n",
              " 'PanLexLiteCorpusReader',\n",
              " 'Pl196xCorpusReader',\n",
              " 'PlaintextCorpusReader',\n",
              " 'PortugueseCategorizedPlaintextCorpusReader',\n",
              " 'PropbankCorpusReader',\n",
              " 'ProsConsCorpusReader',\n",
              " 'RTECorpusReader',\n",
              " 'RegexpTokenizer',\n",
              " 'ReviewsCorpusReader',\n",
              " 'SemcorCorpusReader',\n",
              " 'SensevalCorpusReader',\n",
              " 'SentiSynset',\n",
              " 'SentiWordNetCorpusReader',\n",
              " 'SinicaTreebankCorpusReader',\n",
              " 'StringCategoryCorpusReader',\n",
              " 'SwadeshCorpusReader',\n",
              " 'SwitchboardCorpusReader',\n",
              " 'SyntaxCorpusReader',\n",
              " 'TEICorpusView',\n",
              " 'TaggedCorpusReader',\n",
              " 'TimitCorpusReader',\n",
              " 'TimitTaggedCorpusReader',\n",
              " 'ToolboxCorpusReader',\n",
              " 'TwitterCorpusReader',\n",
              " 'UdhrCorpusReader',\n",
              " 'UnicharsCorpusReader',\n",
              " 'VerbnetCorpusReader',\n",
              " 'WordListCorpusReader',\n",
              " 'WordNetCorpusReader',\n",
              " 'WordNetICCorpusReader',\n",
              " 'XMLCorpusReader',\n",
              " 'YCOECorpusReader',\n",
              " '_LazyModule__lazymodule_globals',\n",
              " '_LazyModule__lazymodule_import',\n",
              " '_LazyModule__lazymodule_init',\n",
              " '_LazyModule__lazymodule_loaded',\n",
              " '_LazyModule__lazymodule_locals',\n",
              " '_LazyModule__lazymodule_name',\n",
              " '__builtins__',\n",
              " '__cached__',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__file__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattr__',\n",
              " '__getattribute__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__lazymodule_loaded',\n",
              " '__le__',\n",
              " '__loader__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__name__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__package__',\n",
              " '__path__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__spec__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " 'abc',\n",
              " 'alpino',\n",
              " 'brown',\n",
              " 'cess_cat',\n",
              " 'cess_esp',\n",
              " 'cmudict',\n",
              " 'comparative_sentences',\n",
              " 'comtrans',\n",
              " 'conll2000',\n",
              " 'conll2002',\n",
              " 'conll2007',\n",
              " 'crubadan',\n",
              " 'demo',\n",
              " 'dependency_treebank',\n",
              " 'find_corpus_fileids',\n",
              " 'floresta',\n",
              " 'framenet',\n",
              " 'framenet15',\n",
              " 'gazetteers',\n",
              " 'genesis',\n",
              " 'gutenberg',\n",
              " 'ieer',\n",
              " 'inaugural',\n",
              " 'indian',\n",
              " 'jeita',\n",
              " 'knbc',\n",
              " 'lin_thesaurus',\n",
              " 'mac_morpho',\n",
              " 'machado',\n",
              " 'masc_tagged',\n",
              " 'movie_reviews',\n",
              " 'multext_east',\n",
              " 'names',\n",
              " 'nombank',\n",
              " 'nombank_ptb',\n",
              " 'nonbreaking_prefixes',\n",
              " 'nps_chat',\n",
              " 'opinion_lexicon',\n",
              " 'perluniprops',\n",
              " 'ppattach',\n",
              " 'product_reviews_1',\n",
              " 'product_reviews_2',\n",
              " 'propbank',\n",
              " 'propbank_ptb',\n",
              " 'pros_cons',\n",
              " 'ptb',\n",
              " 'qc',\n",
              " 're',\n",
              " 'reader',\n",
              " 'reuters',\n",
              " 'rte',\n",
              " 'semcor',\n",
              " 'senseval',\n",
              " 'sentence_polarity',\n",
              " 'sentiwordnet',\n",
              " 'shakespeare',\n",
              " 'sinica_treebank',\n",
              " 'state_union',\n",
              " 'stopwords',\n",
              " 'subjectivity',\n",
              " 'swadesh',\n",
              " 'swadesh110',\n",
              " 'swadesh207',\n",
              " 'switchboard',\n",
              " 'tagged_treebank_para_block_reader',\n",
              " 'teardown_module',\n",
              " 'timit',\n",
              " 'timit_tagged',\n",
              " 'toolbox',\n",
              " 'treebank',\n",
              " 'treebank_chunk',\n",
              " 'treebank_raw',\n",
              " 'twitter_samples',\n",
              " 'udhr',\n",
              " 'udhr2',\n",
              " 'universal_treebanks',\n",
              " 'util',\n",
              " 'verbnet',\n",
              " 'webtext',\n",
              " 'wordnet',\n",
              " 'wordnet_ic',\n",
              " 'words']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELPXPCeFvZx-",
        "outputId": "25d4c948-d0d5-46ac-b326-a0114e4abb16"
      },
      "source": [
        "nltk.download('twitter_samples')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package twitter_samples to /root/nltk_data...\n",
            "[nltk_data]   Package twitter_samples is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zq4k5fa2wFA0",
        "outputId": "f80c90f5-e37e-4135-d8a1-bd0493a0fffd"
      },
      "source": [
        "nltk.corpus.twitter_samples.fileids()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['negative_tweets.json', 'positive_tweets.json', 'tweets.20150430-223406.json']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUSIIpTDv5WP",
        "outputId": "fe45f132-abdc-4e87-9cef-7a8518d20996"
      },
      "source": [
        "nltk.download('swadesh')\n",
        "nltk.download('gutenberg')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package swadesh to /root/nltk_data...\n",
            "[nltk_data]   Package swadesh is already up-to-date!\n",
            "[nltk_data] Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/gutenberg.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qH-Y2scv_tm",
        "outputId": "0d0d5bb5-c97c-4853-8a31-9186a6a9c768"
      },
      "source": [
        "nltk.corpus.gutenberg.fileids()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['austen-emma.txt',\n",
              " 'austen-persuasion.txt',\n",
              " 'austen-sense.txt',\n",
              " 'bible-kjv.txt',\n",
              " 'blake-poems.txt',\n",
              " 'bryant-stories.txt',\n",
              " 'burgess-busterbrown.txt',\n",
              " 'carroll-alice.txt',\n",
              " 'chesterton-ball.txt',\n",
              " 'chesterton-brown.txt',\n",
              " 'chesterton-thursday.txt',\n",
              " 'edgeworth-parents.txt',\n",
              " 'melville-moby_dick.txt',\n",
              " 'milton-paradise.txt',\n",
              " 'shakespeare-caesar.txt',\n",
              " 'shakespeare-hamlet.txt',\n",
              " 'shakespeare-macbeth.txt',\n",
              " 'whitman-leaves.txt']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FyzufPhRwi2_"
      },
      "source": [
        "with open(\"Macbeth.txt\", \"w\") as f:\n",
        "    f.write(nltk.corpus.gutenberg.raw('shakespeare-macbeth.txt'))"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfNqBTTlxZZN"
      },
      "source": [
        "with open(\"Macbeth.txt\") as f:\n",
        "  text = f.readlines()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AG_7hLkZxg5-"
      },
      "source": [
        "from string import punctuation\n",
        "\n",
        "article_count = 0\n",
        "for line in text:\n",
        "  for word in line.split(\" \"):\n",
        "    if word == \"\\n\":\n",
        "      pass\n",
        "    elif word.lower() in ['a', 'an', 'the']:\n",
        "      word = word.strip(punctuation)\n",
        "      article_count += 1"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRfU7vcVyH1g",
        "outputId": "7448ad07-7330-4dc2-cc4e-67358cf910ce"
      },
      "source": [
        "article_count"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4p6BYnejyXFh"
      },
      "source": [
        "with open(\"Macbeth-ArticlesRemoved.txt\", \"w\") as f:\n",
        "  for line in text:\n",
        "    f.write(line)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84bvzly-yl_N"
      },
      "source": [
        "import re\n",
        "\n",
        "updated_text = []\n",
        "for line in text:\n",
        "  # Replace punctuations with whitespace\n",
        "  line_pr = re.sub(\"[^A-Za-z0-9\\s]\", \" \", line)\n",
        "  # Replace multiple continous whitespaces with a single whitespace\n",
        "  line_pr = re.sub(\"\\s+\", \" \", line_pr)\n",
        "  updated_text.append(line_pr)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOTidUFUz22q"
      },
      "source": [
        "with open(\"Macbeth-punctuationsRemoved.txt\", \"w\") as f:\n",
        "  for line in updated_text:\n",
        "    f.write(line)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wu7jSnAh0Iig",
        "outputId": "a3258746-1dba-4895-b7b2-84d32acdcfea"
      },
      "source": [
        "nltk.download('names')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package names to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/names.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SchBzSGH0NPN"
      },
      "source": [
        "names_text = nltk.corpus.names.raw()\n",
        "names_list = names_text.split(\"\\n\")"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0HbOyXL0dTm",
        "outputId": "64a97337-67e8-40f6-8796-eee8a5e848f9"
      },
      "source": [
        "names_list[:10]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Abagael',\n",
              " 'Abagail',\n",
              " 'Abbe',\n",
              " 'Abbey',\n",
              " 'Abbi',\n",
              " 'Abbie',\n",
              " 'Abby',\n",
              " 'Abigael',\n",
              " 'Abigail',\n",
              " 'Abigale']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KODGMJrc0fhX",
        "outputId": "e8ffe210-d834-476d-8f8b-06f9c3c9aa21"
      },
      "source": [
        "len(names_list)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7945"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3w-gg_i0mZC",
        "outputId": "bfd26dcd-8224-4500-d80e-9d3281b7277f"
      },
      "source": [
        "start_with_counter = {}\n",
        "for name in names_list:\n",
        "  if len(name) > 0:\n",
        "    start = name.lower()[0]\n",
        "    if start not in start_with_counter.keys():\n",
        "      start_with_counter[start] = 1\n",
        "    else:\n",
        "      start_with_counter[start] += 1\n",
        "\n",
        "start_with_counter"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a': 656,\n",
              " 'b': 419,\n",
              " 'c': 635,\n",
              " 'd': 454,\n",
              " 'e': 370,\n",
              " 'f': 231,\n",
              " 'g': 369,\n",
              " 'h': 287,\n",
              " 'i': 128,\n",
              " 'j': 437,\n",
              " 'k': 346,\n",
              " 'l': 445,\n",
              " 'm': 684,\n",
              " 'n': 235,\n",
              " 'o': 118,\n",
              " 'p': 222,\n",
              " 'q': 24,\n",
              " 'r': 447,\n",
              " 's': 547,\n",
              " 't': 386,\n",
              " 'u': 36,\n",
              " 'v': 155,\n",
              " 'w': 205,\n",
              " 'x': 12,\n",
              " 'y': 34,\n",
              " 'z': 62}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Bmka0Weq1ESw",
        "outputId": "24085dab-777b-4d62-e9b1-75c792a0a1d7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "plt.bar(start_with_counter.keys(), start_with_counter.values())\n",
        "plt.xlabel('Starting Alphabet')\n",
        "plt.ylabel('Frequency of Names')\n",
        "plt.title('Frequency of Names of Each Alphabet')\n",
        "plt.savefig('FrequencyOfNamesOfEachAlphabet.png')\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}