Exercicios.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testes iniciais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python\n",
      "is cool\n",
      "python ... is cool\n",
      "(1, 2, 3)\n",
      "[1, 2, 3]\n",
      "[2, 3]\n",
      "{'host': 'earth'}\n",
      "host earth host\n"
     ]
    }
   ],
   "source": [
    "pystr='python\\nis cool'\n",
    "pystr\n",
    "print(pystr)\n",
    "pystr2 = '''python ... is cool'''\n",
    "print(pystr2)\n",
    "tup=(1,2,3)\n",
    "print(tup)\n",
    "lis=[1,2,3]\n",
    "print(lis)\n",
    "lis=lis[1:3]\n",
    "print(lis)\n",
    "aDict = {'host': 'earth'} \n",
    "print(aDict)\n",
    "for key in aDict: \n",
    "    print(key, aDict[key]+\" \"+key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NLP Problem Set 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['String', 'lista', 'Cão', 'comida', 'António', 'alfabeto']\n"
     ]
    }
   ],
   "source": [
    "stringlist= [\"alfabeto\", \"Cão\", \"comida\", \"António\", \"String\", \"lista\"]\n",
    "stringlist.sort(key=str.lower,reverse=True)\n",
    "print(stringlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a b c d e f g\n"
     ]
    }
   ],
   "source": [
    "texto=\"    a b c d e f g      \"\n",
    "x=0\n",
    "j=0\n",
    "checker=False\n",
    "while checker==False:\n",
    "    if texto[x]==\" \":\n",
    "        j=x\n",
    "    else:\n",
    "        checker=True\n",
    "    x=x+1\n",
    "texto=texto[j+1:]\n",
    "texto=texto[::-1]\n",
    "checker=False\n",
    "while checker==False:\n",
    "    if texto[x]==\" \":\n",
    "        j=x\n",
    "    else:\n",
    "        checker=True\n",
    "    x=x+1\n",
    "texto=texto[j+1:]\n",
    "texto=texto[::-1]\n",
    "print(texto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ahpla\n"
     ]
    }
   ],
   "source": [
    "x=\"alpha\"\n",
    "x=x[::-1]\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_strstrip(txt):\n",
    "    def remove_trail(txt):\n",
    "        i = 0\n",
    "        while txt[i] == \" \":\n",
    "          i += 1\n",
    "        txt = txt[i:]\n",
    "        return txt\n",
    "    txt = remove_trail(txt)\n",
    "    txt = txt[::-1]\n",
    "    txt = remove_trail(txt)\n",
    "    txt = txt[::-1]\n",
    "    return txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a b c d e f g\n"
     ]
    }
   ],
   "source": [
    "texto = \"    a b c d e f g      \"\n",
    "print(my_strstrip(texto))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord('a')-ord('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def subpos(string, pos, newchar):\n",
    "    if pos == 0:\n",
    "        string = newchar + string[1:]\n",
    "    elif pos == len(txt)-1:\n",
    "        string = string[:len(txt)-1] + newchar\n",
    "    else:\n",
    "        string = string[:pos] + newchar + string[pos+1:]\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def invertedcaps(txt):\n",
    "    i = -1\n",
    "    for c in txt:\n",
    "        i += 1\n",
    "        if txt[i].islower() == True:\n",
    "            var1 = chr(ord(txt[i])-32)\n",
    "        elif txt[i].isupper() == True:\n",
    "            var1 = chr(ord(txt[i])+32)\n",
    "        else:\n",
    "            var1 = txt[i]\n",
    "        txt = subpos(txt, i, var1)\n",
    "    return txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abaCate, PepiNO\n",
      "ABAcATE, pEPIno\n"
     ]
    }
   ],
   "source": [
    "txt=\"abaCate, PepiNO\"\n",
    "print(txt)\n",
    "print(invertedcaps(txt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def findchr(string, char):\n",
    "    i = -1\n",
    "    j = -1\n",
    "    while i < len(string)-1:\n",
    "        i += 1\n",
    "        if string[i] == char:\n",
    "            j = i\n",
    "            break\n",
    "    return j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def findallchr(string, char):\n",
    "    j = -1\n",
    "    vec=[-1]\n",
    "    for i in range(len(string)):\n",
    "        if string[i] == char:\n",
    "            j += 1\n",
    "            if j == 0:\n",
    "                vec[0] = i\n",
    "            else:\n",
    "                vec.append(i)\n",
    "    return vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findchr(\"batata\", \"c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findallchr(\"batata\", \"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def subfirstchr(string, origchar, newchar):\n",
    "    i = findchr(string,origchar)\n",
    "    string = subpos(string, i, newchar)\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def suballchr(string, origchar, newchar):\n",
    "    lista = findallchr(string, origchar)\n",
    "    for eachnum in lista:\n",
    "        string = subpos(string, eachnum, newchar)\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bctata'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subfirstchr(\"batata\", \"a\", \"c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bctctc fritc'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suballchr(\"batata frita\",\"a\",\"c\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'almoço': 'Massa', 'Bebida': 'Água', 'Face': 'Cara', 'Amanhecer': 'Nascer do sol'}\n",
      "<class 'dict'>\n",
      "['Amanhecer', 'Bebida', 'Face', 'almoço']\n",
      "['almoço', 'Amanhecer', 'Bebida', 'Face']\n"
     ]
    }
   ],
   "source": [
    "dic={\"almoço\" : \"Massa\", \"Bebida\" : \"Água\", \"Face\" : \"Cara\", \"Amanhecer\" : \"Nascer do sol\"}\n",
    "print(dic)\n",
    "print(type(dic))\n",
    "print(sorted(dic))\n",
    "#para indiferenciar maiúsculas ou minúsculas\n",
    "lista = list(dic.keys())\n",
    "lista.sort(key = str.lower)\n",
    "print(lista)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def switchkeysvalues(dictionary):\n",
    "    listkeys = list(dictionary.keys())\n",
    "    listvalues = list(dictionary.values())\n",
    "    newdictionary = dict()\n",
    "    for i in range(len(listkeys)):\n",
    "        newdictionary[listvalues[i]] = listkeys[i]\n",
    "    return newdictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(switchkeysvalues(dic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def squaredict():\n",
    "    newdictionary = dict()\n",
    "    for i in range(11) :\n",
    "        newdictionary[i] = i ** 2\n",
    "    return newdictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100}"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squaredict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I returned from the City about three o'clock on that \n",
      "May afternoon pretty well disgusted with life. \n",
      "I had been three months in the Old Country, and was \n",
      "fed up with it. \n",
      "If anyone had told me a year ago that I would have \n",
      "been feeling like that I should have laughed at him; \n",
      "but there was the fact. \n",
      "The weather made me liverish, \n",
      "the talk of the ordinary Englishman made me sick, \n",
      "I couldn't get enough exercise, and the amusements \n",
      "of London seemed as flat as soda-water that \n",
      "has been standing in the sun. \n",
      "'Richard Hannay,' I kept telling myself, 'you \n",
      "have got into the wrong ditch, my friend, and \n",
      "you had better climb out.'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f = open(\"C:/Users/sergiojesus/Desktop/sample.txt\",\"r\")\n",
    "l = f.read()\n",
    "print(l)\n",
    "f.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def countwordsfile(filepath):\n",
    "    with open(filepath,\"r\") as file:\n",
    "        text = file.read()\n",
    "        text = list(text.split())\n",
    "        print(len(text))\n",
    "        freq = list()\n",
    "        wordsfreq = dict()\n",
    "        words = list(set(text))\n",
    "        for i in range(len(words)):\n",
    "            freq.append(text.count(words[i]))\n",
    "    return [words,freq] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116\n",
      "[['Country,', 'kept', 'but', 'ago', 'you', 'of', 'into', 'the', 'fact.', \"o'clock\", 'myself,', 'London', 'City', 'sick,', 'that', 'three', 'on', 'with', 'was', 'me', 'my', 'sun.', 'has', 'ditch,', \"out.'\", 'friend,', 'Old', 'about', 'ordinary', 'get', 'it.', 'like', 'up', 'in', 'I', 'Englishman', 'anyone', 'exercise,', \"Hannay,'\", 'have', 'at', 'would', \"'you\", 'from', 'and', 'got', 'year', 'The', 'enough', 'climb', 'as', 'If', 'pretty', 'soda-water', 'him;', 'should', 'fed', 'wrong', 'a', 'well', 'feeling', 'liverish,', 'laughed', 'afternoon', 'weather', 'talk', 'had', 'there', 'flat', 'returned', 'telling', \"'Richard\", 'been', 'made', 'standing', 'May', \"couldn't\", 'months', 'amusements', 'disgusted', 'seemed', 'better', 'told', 'life.'], [1, 1, 1, 1, 1, 2, 1, 8, 1, 1, 1, 1, 1, 1, 4, 2, 1, 2, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 6, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]\n"
     ]
    }
   ],
   "source": [
    "dados=countwordsfile(\"C:/Users/sergiojesus/Desktop/sample.txt\")\n",
    "print(dados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84\n"
     ]
    }
   ],
   "source": [
    "soma=0\n",
    "for i in range(len(contador[1])):\n",
    "    soma += contador[1][i]\n",
    "soma\n",
    "print(len(dados))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJAAAAJQCAYAAADR6J6nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACAkYAEAAAAwEhAAgAAAGD0fwGZ/lAZ0PL7EQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160e11a1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.hist(list(dados[1]),bins = 8, align=\"mid\", rwidth = 0.1)\n",
    "plt.xlabel(\"Palavras\")\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}