remover-duplicadas-acento.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remover palavras que só diferem de outras pela presença ou ausência de acentos ou cedilhas ou caixa alta/baixa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7776, 'a', 'zunzum')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('7776palavras.txt', encoding='utf8') as fp:\n",
    "    palavras = [p.strip() for p in fp]\n",
    "    \n",
    "len(palavras), palavras[0], palavras[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import unicodedata\n",
    "\n",
    "def shave_marks(txt):\n",
    "    \"\"\"Remove all diacritic marks\"\"\"\n",
    "    norm_txt = unicodedata.normalize('NFD', txt)\n",
    "    shaved = ''.join(c for c in norm_txt if not unicodedata.combining(c))\n",
    "    return unicodedata.normalize('NFC', shaved)\n",
    "\n",
    "\n",
    "def normalizar(txt):\n",
    "    return shave_marks(txt).lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "normals = [normalizar(p) for p in palavras]\n",
    "\n",
    "for p in palavras:\n",
    "    if normals.count(normalizar(p)) > 1:\n",
    "        print(p, end=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escolhidas_para_sair = '''\n",
    "galã\n",
    "periodo\n",
    "trançar\n",
    "'''.strip().split()\n",
    "\n",
    "len(escolhidas_para_sair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7776"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(palavras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7773"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palavras_set = set(palavras)\n",
    "palavras_set -= set(escolhidas_para_sair)\n",
    "len(palavras_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 'acórdão', 'guaraná')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('fontes/candidatas.txt', encoding='utf8') as fp:\n",
    "    candidatas = [p.strip() for p in fp]\n",
    "    \n",
    "len(candidatas), candidatas[0], candidatas[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from random import shuffle\n",
    "shuffle(candidatas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('criança', 'esforço')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidatas[0], candidatas[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "while len(palavras_set) < 7776:\n",
    "    palavras_set.add(candidatas.pop())\n",
    "    \n",
    "len(candidatas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "palavras = sorted(palavras_set, key=normalizar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7776, 'a', 'zunzum')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(palavras), palavras[0], palavras[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('7776palavras.txt', 'wt', encoding='utf8') as fp:\n",
    "    fp.write('\\n'.join(palavras) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('fontes/candidatas.txt', 'wt', encoding='utf8') as fp:\n",
    "    fp.write('\\n'.join(candidatas) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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