├── .ipynb_checkpoints
    ├── Aula_01 Jupyter-checkpoint.ipynb
    ├── Aula_02_ Exercício-checkpoint.ipynb
    ├── Aula_02_Python_Introdução-checkpoint.ipynb
    ├── Aula_03_Numpy-checkpoint.ipynb
    ├── Aula_03_Numpy_Exercicios-checkpoint.ipynb
    ├── Aula_04_Pandas-checkpoint.ipynb
    ├── Aula_04_Pandas_Exercicios-checkpoint.ipynb
    ├── Aula_05_Visualização-checkpoint.ipynb
    ├── Aula_06_01_Regressão Linear-checkpoint.ipynb
    ├── Aula_06_02_KNN-checkpoint.ipynb
    ├── Aula_06_03_SVR-checkpoint.ipynb
    ├── Untitled-checkpoint.ipynb
    └── Untitled1-checkpoint.ipynb
├── Aula_01 Jupyter.ipynb
├── Aula_02_ Exercício.ipynb
├── Aula_02_Python_Introdução.ipynb
├── Aula_03_Numpy.ipynb
├── Aula_03_Numpy_Exercicios.ipynb
├── Aula_04_Pandas.ipynb
├── Aula_04_Pandas_Exercicios.ipynb
├── Aula_05_Visualização.ipynb
├── Aula_06_01_Regressão Linear.ipynb
├── Aula_06_02_KNN.ipynb
├── Aula_06_03_SVR.ipynb
├── Livros.pdf
├── Projeto
    ├── erbs.csv
    ├── medicoes.csv
    ├── projeto.odt
    └── testLoc.csv
├── Untitled.ipynb
├── Untitled1.ipynb
├── cal_housing.data
├── fig1.jpg
├── imagens
    ├── jupyter-sq-text.png
    └── white_nav_logo.svg
└── videos
    └── video1.mp4


/.ipynb_checkpoints/Aula_01 Jupyter-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Jupyter\n",
  8 |     "---------------"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "markdown",
 13 |    "metadata": {},
 14 |    "source": [
 15 |     "## Instalação e Execução\n",
 16 |     "------"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "* [Instalar Pacote Anaconda](https://www.anaconda.com/download/)\n",
 24 |     "* Ir para o diretório de dados \n",
 25 |     "* Executar no prompt de comando ´jupyter notebook`"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "markdown",
 30 |    "metadata": {},
 31 |    "source": [
 32 |     "### Jupyter Kernels\n",
 33 |     "--------------------"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "markdown",
 38 |    "metadata": {},
 39 |    "source": [
 40 |     "* Python\n",
 41 |     "* Julia\n",
 42 |     "* R\n",
 43 |     "* C#\n",
 44 |     "* F#\n",
 45 |     "* Scala\n",
 46 |     "* Haskell\n",
 47 |     "* etc...\n",
 48 |     "\n",
 49 |     "Você pode verificar os Kernels em [Jupyter Kernels](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels)"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "markdown",
 54 |    "metadata": {},
 55 |    "source": [
 56 |     "## Jupyter Cells (Markdown vs Code)"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "markdown",
 61 |    "metadata": {},
 62 |    "source": [
 63 |     "* Markdown (short cut = <mark>M</mark>)\n",
 64 |     "* Code (short cut = <mark>Y</mark>)\n",
 65 |     "* Executar Células (short cut = <mark>Shift + Enter</mark>)"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "### Latex Markdown\n",
 73 |     "\n",
 74 |     "$e^{i\\pi} + 1 = 0$"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "markdown",
 79 |    "metadata": {},
 80 |    "source": [
 81 |     "### Código Markdown"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "markdown",
 86 |    "metadata": {},
 87 |    "source": [
 88 |     "Exemplo : Python\n",
 89 |     "```python\n",
 90 |     "print \"Hello World\"\n",
 91 |     "```\n",
 92 |     "Exemplo : Javascript\n",
 93 |     "```javascript\n",
 94 |     "console.log(\"Hello World\")\n",
 95 |     "```"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "markdown",
100 |    "metadata": {},
101 |    "source": [
102 |     "### Tabelas\n",
103 |     "\n",
104 |     "| Aluno | Prova | Nota         \n",
105 |     "| :- |-------------: | :-:\n",
106 |     "|Robson Dias | PCOM  | 8.5\n",
107 |     "| Daniel | PCOM | 10.0\n",
108 |     "| Lizandro | PCOM2 | 10.0\n"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "markdown",
113 |    "metadata": {},
114 |    "source": [
115 |     "## Videos\n",
116 |     "<video controls src=\"./videos/video1.mp4\" />"
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "markdown",
121 |    "metadata": {},
122 |    "source": [
123 |     "## Imagens\n",
124 |     "\n",
125 |     "<img style=\"float: left;\" src=\"./imagens/jupyter-sq-text.png\",width=60,height=60/>\n"
126 |    ]
127 |   },
128 |   {
129 |    "cell_type": "markdown",
130 |    "metadata": {},
131 |    "source": [
132 |     "### Principais Atalhos"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "markdown",
137 |    "metadata": {},
138 |    "source": [
139 |     "* Executar Célula (<mark>CTR + ENTER</mark>)\n",
140 |     "* Inserir Célula Acima (<mark>A</mark>)\n",
141 |     "* Inserir Célula Abaixo (<mark>B</mark>)\n",
142 |     "* Markdown (<mark>M</mark>)\n",
143 |     "* Código (<mark>Y</mark>)\n",
144 |     "* Apagar Céluda (<mark>D+D</mark>)\n",
145 |     "* Retorna última Celula apagada (<mark>Z</mark>)\n",
146 |     "* Copiar Cálula (<mark>C</mark>)\n"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "markdown",
151 |    "metadata": {},
152 |    "source": [
153 |     "## M"
154 |    ]
155 |   },
156 |   {
157 |    "cell_type": "code",
158 |    "execution_count": null,
159 |    "metadata": {
160 |     "collapsed": true
161 |    },
162 |    "outputs": [],
163 |    "source": []
164 |   }
165 |  ],
166 |  "metadata": {
167 |   "kernelspec": {
168 |    "display_name": "Python 3",
169 |    "language": "python",
170 |    "name": "python3"
171 |   },
172 |   "language_info": {
173 |    "codemirror_mode": {
174 |     "name": "ipython",
175 |     "version": 3
176 |    },
177 |    "file_extension": ".py",
178 |    "mimetype": "text/x-python",
179 |    "name": "python",
180 |    "nbconvert_exporter": "python",
181 |    "pygments_lexer": "ipython3",
182 |    "version": "3.6.1"
183 |   }
184 |  },
185 |  "nbformat": 4,
186 |  "nbformat_minor": 2
187 | }
188 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Aula_02_ Exercício-checkpoint.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "### Exercício \n",
 8 |     "------------------\n",
 9 |     "Faça uma função em Python que receba um string (frase) e:\n",
10 |     "* a) Passe Todas as letras iniciais de palavras para maiúsculas;\n",
11 |     "* b) Todas as outras letras devem ficar com exceção das inicias devem ficar em minusculas;\n",
12 |     "* c) Os artigos isolados, exceto se for a primeira letra da frase, devem ficar em minúsculo.\n",
13 |     "\n",
14 |     "**Exemplo 1** : \n",
15 |     "* entrada : \"a grande partidA\"\n",
16 |     "* Saída : \"A Grande Partida\"\n",
17 |     "\n",
18 |     "**Exemplo 2** : \n",
19 |     "* entrada : \"o livro E o garato\"\n",
20 |     "* Saída : \"O Livro  e o Garoto\"\n",
21 |     "\n"
22 |    ]
23 |   },
24 |   {
25 |    "cell_type": "code",
26 |    "execution_count": null,
27 |    "metadata": {
28 |     "collapsed": true
29 |    },
30 |    "outputs": [],
31 |    "source": []
32 |   }
33 |  ],
34 |  "metadata": {
35 |   "kernelspec": {
36 |    "display_name": "Python 3",
37 |    "language": "python",
38 |    "name": "python3"
39 |   },
40 |   "language_info": {
41 |    "codemirror_mode": {
42 |     "name": "ipython",
43 |     "version": 3
44 |    },
45 |    "file_extension": ".py",
46 |    "mimetype": "text/x-python",
47 |    "name": "python",
48 |    "nbconvert_exporter": "python",
49 |    "pygments_lexer": "ipython3",
50 |    "version": "3.6.1"
51 |   }
52 |  },
53 |  "nbformat": 4,
54 |  "nbformat_minor": 2
55 | }
56 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Aula_02_Python_Introdução-checkpoint.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Python\n",
   8 |     "\n",
   9 |     "\n",
  10 |     "Sumário\n",
  11 |     "\n",
  12 |     "* Tipos de Dados\n",
  13 |     "    * Numbers\n",
  14 |     "    * Strings\n",
  15 |     "    * Printing\n",
  16 |     "    * Lists\n",
  17 |     "    * Dictionaries\n",
  18 |     "    * Booleans\n",
  19 |     "    * Tuples \n",
  20 |     "    * Sets\n",
  21 |     "* Comparadores\n",
  22 |     "* if,elif, else Statements\n",
  23 |     "* for Loops\n",
  24 |     "* while Loops\n",
  25 |     "* range()\n",
  26 |     "* list comprehension\n",
  27 |     "* Funções\n",
  28 |     "* lambda expressions\n",
  29 |     "* Mapa e Filtros\n",
  30 |     "* Métodos\n",
  31 |     "____"
  32 |    ]
  33 |   },
  34 |   {
  35 |    "cell_type": "markdown",
  36 |    "metadata": {},
  37 |    "source": [
  38 |     "## Tipos de Dados\n",
  39 |     "\n",
  40 |     "### Números"
  41 |    ]
  42 |   },
  43 |   {
  44 |    "cell_type": "code",
  45 |    "execution_count": null,
  46 |    "metadata": {},
  47 |    "outputs": [],
  48 |    "source": [
  49 |     "1 + 5"
  50 |    ]
  51 |   },
  52 |   {
  53 |    "cell_type": "code",
  54 |    "execution_count": null,
  55 |    "metadata": {},
  56 |    "outputs": [],
  57 |    "source": [
  58 |     "2 * 3"
  59 |    ]
  60 |   },
  61 |   {
  62 |    "cell_type": "code",
  63 |    "execution_count": null,
  64 |    "metadata": {},
  65 |    "outputs": [],
  66 |    "source": [
  67 |     "1 / 4"
  68 |    ]
  69 |   },
  70 |   {
  71 |    "cell_type": "code",
  72 |    "execution_count": null,
  73 |    "metadata": {},
  74 |    "outputs": [],
  75 |    "source": [
  76 |     "2 ** 3"
  77 |    ]
  78 |   },
  79 |   {
  80 |    "cell_type": "code",
  81 |    "execution_count": null,
  82 |    "metadata": {},
  83 |    "outputs": [],
  84 |    "source": [
  85 |     "16 % 2"
  86 |    ]
  87 |   },
  88 |   {
  89 |    "cell_type": "code",
  90 |    "execution_count": null,
  91 |    "metadata": {},
  92 |    "outputs": [],
  93 |    "source": [
  94 |     "5 % 3"
  95 |    ]
  96 |   },
  97 |   {
  98 |    "cell_type": "code",
  99 |    "execution_count": null,
 100 |    "metadata": {},
 101 |    "outputs": [],
 102 |    "source": [
 103 |     "(2 + 4) * (4 + 5)"
 104 |    ]
 105 |   },
 106 |   {
 107 |    "cell_type": "markdown",
 108 |    "metadata": {},
 109 |    "source": [
 110 |     "### Variáveis"
 111 |    ]
 112 |   },
 113 |   {
 114 |    "cell_type": "code",
 115 |    "execution_count": null,
 116 |    "metadata": {
 117 |     "collapsed": true
 118 |    },
 119 |    "outputs": [],
 120 |    "source": [
 121 |     "# Não pode iniciar com números e caracteres especiais \n",
 122 |     "var1 = 2"
 123 |    ]
 124 |   },
 125 |   {
 126 |    "cell_type": "code",
 127 |    "execution_count": null,
 128 |    "metadata": {
 129 |     "collapsed": true
 130 |    },
 131 |    "outputs": [],
 132 |    "source": [
 133 |     "x = 2\n",
 134 |     "y = 3"
 135 |    ]
 136 |   },
 137 |   {
 138 |    "cell_type": "code",
 139 |    "execution_count": null,
 140 |    "metadata": {},
 141 |    "outputs": [],
 142 |    "source": [
 143 |     "z = (x^2 + y^2)**(0.5)\n",
 144 |     "z"
 145 |    ]
 146 |   },
 147 |   {
 148 |    "cell_type": "code",
 149 |    "execution_count": null,
 150 |    "metadata": {},
 151 |    "outputs": [],
 152 |    "source": [
 153 |     "z"
 154 |    ]
 155 |   },
 156 |   {
 157 |    "cell_type": "markdown",
 158 |    "metadata": {},
 159 |    "source": [
 160 |     "### Strings"
 161 |    ]
 162 |   },
 163 |   {
 164 |    "cell_type": "code",
 165 |    "execution_count": null,
 166 |    "metadata": {},
 167 |    "outputs": [],
 168 |    "source": [
 169 |     "'aspas simples'"
 170 |    ]
 171 |   },
 172 |   {
 173 |    "cell_type": "code",
 174 |    "execution_count": null,
 175 |    "metadata": {},
 176 |    "outputs": [],
 177 |    "source": [
 178 |     "\"aspas duplas\""
 179 |    ]
 180 |   },
 181 |   {
 182 |    "cell_type": "code",
 183 |    "execution_count": null,
 184 |    "metadata": {},
 185 |    "outputs": [],
 186 |    "source": [
 187 |     "\"robson's car is broken\""
 188 |    ]
 189 |   },
 190 |   {
 191 |    "cell_type": "markdown",
 192 |    "metadata": {},
 193 |    "source": [
 194 |     "### Impressão"
 195 |    ]
 196 |   },
 197 |   {
 198 |    "cell_type": "code",
 199 |    "execution_count": null,
 200 |    "metadata": {
 201 |     "collapsed": true
 202 |    },
 203 |    "outputs": [],
 204 |    "source": [
 205 |     "x = 'hello word!'"
 206 |    ]
 207 |   },
 208 |   {
 209 |    "cell_type": "code",
 210 |    "execution_count": null,
 211 |    "metadata": {},
 212 |    "outputs": [],
 213 |    "source": [
 214 |     "x"
 215 |    ]
 216 |   },
 217 |   {
 218 |    "cell_type": "code",
 219 |    "execution_count": null,
 220 |    "metadata": {},
 221 |    "outputs": [],
 222 |    "source": [
 223 |     "print(x)"
 224 |    ]
 225 |   },
 226 |   {
 227 |    "cell_type": "code",
 228 |    "execution_count": null,
 229 |    "metadata": {
 230 |     "collapsed": true
 231 |    },
 232 |    "outputs": [],
 233 |    "source": [
 234 |     "nrTitulo = 5\n",
 235 |     "pais = 'Brasil'"
 236 |    ]
 237 |   },
 238 |   {
 239 |    "cell_type": "code",
 240 |    "execution_count": null,
 241 |    "metadata": {},
 242 |    "outputs": [],
 243 |    "source": [
 244 |     "print('O {pais} foi campeão {nrTitulo} vezes'.format(pais=pais,nrTitulo=nrTitulo))"
 245 |    ]
 246 |   },
 247 |   {
 248 |    "cell_type": "code",
 249 |    "execution_count": null,
 250 |    "metadata": {},
 251 |    "outputs": [],
 252 |    "source": [
 253 |     "print('A {} foi campeão {} vezes'.format(pais,nrTitulo))"
 254 |    ]
 255 |   },
 256 |   {
 257 |    "cell_type": "markdown",
 258 |    "metadata": {},
 259 |    "source": [
 260 |     "### Listas"
 261 |    ]
 262 |   },
 263 |   {
 264 |    "cell_type": "code",
 265 |    "execution_count": null,
 266 |    "metadata": {},
 267 |    "outputs": [],
 268 |    "source": [
 269 |     "[2,4,8]"
 270 |    ]
 271 |   },
 272 |   {
 273 |    "cell_type": "code",
 274 |    "execution_count": null,
 275 |    "metadata": {},
 276 |    "outputs": [],
 277 |    "source": [
 278 |     "['Oi',1,[1,2],\"a\"]"
 279 |    ]
 280 |   },
 281 |   {
 282 |    "cell_type": "code",
 283 |    "execution_count": null,
 284 |    "metadata": {
 285 |     "collapsed": true
 286 |    },
 287 |    "outputs": [],
 288 |    "source": [
 289 |     "lista1 = ['a','b','c','d']"
 290 |    ]
 291 |   },
 292 |   {
 293 |    "cell_type": "code",
 294 |    "execution_count": null,
 295 |    "metadata": {
 296 |     "collapsed": true
 297 |    },
 298 |    "outputs": [],
 299 |    "source": [
 300 |     "lista1.append('e')"
 301 |    ]
 302 |   },
 303 |   {
 304 |    "cell_type": "code",
 305 |    "execution_count": null,
 306 |    "metadata": {},
 307 |    "outputs": [],
 308 |    "source": [
 309 |     "lista1[0]"
 310 |    ]
 311 |   },
 312 |   {
 313 |    "cell_type": "code",
 314 |    "execution_count": null,
 315 |    "metadata": {},
 316 |    "outputs": [],
 317 |    "source": [
 318 |     "lista1[1:]"
 319 |    ]
 320 |   },
 321 |   {
 322 |    "cell_type": "code",
 323 |    "execution_count": null,
 324 |    "metadata": {},
 325 |    "outputs": [],
 326 |    "source": [
 327 |     "my_list[:1] # último não incluído"
 328 |    ]
 329 |   },
 330 |   {
 331 |    "cell_type": "code",
 332 |    "execution_count": null,
 333 |    "metadata": {
 334 |     "collapsed": true
 335 |    },
 336 |    "outputs": [],
 337 |    "source": [
 338 |     "lista1[0] = 'Novo'"
 339 |    ]
 340 |   },
 341 |   {
 342 |    "cell_type": "code",
 343 |    "execution_count": null,
 344 |    "metadata": {
 345 |     "collapsed": true
 346 |    },
 347 |    "outputs": [],
 348 |    "source": [
 349 |     "# Lista Encadeiada\n",
 350 |     "lista2 = [1,2,3,[4,5,['alvo']]]"
 351 |    ]
 352 |   },
 353 |   {
 354 |    "cell_type": "code",
 355 |    "execution_count": null,
 356 |    "metadata": {},
 357 |    "outputs": [],
 358 |    "source": [
 359 |     "lista2[3]"
 360 |    ]
 361 |   },
 362 |   {
 363 |    "cell_type": "code",
 364 |    "execution_count": null,
 365 |    "metadata": {},
 366 |    "outputs": [],
 367 |    "source": [
 368 |     "lista2[3][2]"
 369 |    ]
 370 |   },
 371 |   {
 372 |    "cell_type": "code",
 373 |    "execution_count": null,
 374 |    "metadata": {
 375 |     "scrolled": false
 376 |    },
 377 |    "outputs": [],
 378 |    "source": [
 379 |     "lista2[3][2][0]"
 380 |    ]
 381 |   },
 382 |   {
 383 |    "cell_type": "code",
 384 |    "execution_count": null,
 385 |    "metadata": {
 386 |     "collapsed": true
 387 |    },
 388 |    "outputs": [],
 389 |    "source": [
 390 |     "'x' in ['x','y','z']"
 391 |    ]
 392 |   },
 393 |   {
 394 |    "cell_type": "markdown",
 395 |    "metadata": {},
 396 |    "source": [
 397 |     "### Dicionários"
 398 |    ]
 399 |   },
 400 |   {
 401 |    "cell_type": "code",
 402 |    "execution_count": 80,
 403 |    "metadata": {
 404 |     "collapsed": true
 405 |    },
 406 |    "outputs": [],
 407 |    "source": [
 408 |     "dic = {'key1':'item1','key2':'item2'}"
 409 |    ]
 410 |   },
 411 |   {
 412 |    "cell_type": "code",
 413 |    "execution_count": null,
 414 |    "metadata": {},
 415 |    "outputs": [],
 416 |    "source": [
 417 |     "dic"
 418 |    ]
 419 |   },
 420 |   {
 421 |    "cell_type": "code",
 422 |    "execution_count": null,
 423 |    "metadata": {},
 424 |    "outputs": [],
 425 |    "source": [
 426 |     "dic['key1']"
 427 |    ]
 428 |   },
 429 |   {
 430 |    "cell_type": "code",
 431 |    "execution_count": 81,
 432 |    "metadata": {},
 433 |    "outputs": [
 434 |     {
 435 |      "data": {
 436 |       "text/plain": [
 437 |        "dict_keys(['key1', 'key2'])"
 438 |       ]
 439 |      },
 440 |      "execution_count": 81,
 441 |      "metadata": {},
 442 |      "output_type": "execute_result"
 443 |     }
 444 |    ],
 445 |    "source": [
 446 |     "dic.keys()"
 447 |    ]
 448 |   },
 449 |   {
 450 |    "cell_type": "code",
 451 |    "execution_count": null,
 452 |    "metadata": {
 453 |     "collapsed": true
 454 |    },
 455 |    "outputs": [],
 456 |    "source": [
 457 |     "dic.items()"
 458 |    ]
 459 |   },
 460 |   {
 461 |    "cell_type": "markdown",
 462 |    "metadata": {},
 463 |    "source": [
 464 |     "### Booleans"
 465 |    ]
 466 |   },
 467 |   {
 468 |    "cell_type": "code",
 469 |    "execution_count": null,
 470 |    "metadata": {},
 471 |    "outputs": [],
 472 |    "source": [
 473 |     "True"
 474 |    ]
 475 |   },
 476 |   {
 477 |    "cell_type": "code",
 478 |    "execution_count": null,
 479 |    "metadata": {},
 480 |    "outputs": [],
 481 |    "source": [
 482 |     "False"
 483 |    ]
 484 |   },
 485 |   {
 486 |    "cell_type": "markdown",
 487 |    "metadata": {},
 488 |    "source": [
 489 |     "### Tuplas "
 490 |    ]
 491 |   },
 492 |   {
 493 |    "cell_type": "code",
 494 |    "execution_count": null,
 495 |    "metadata": {
 496 |     "collapsed": true
 497 |    },
 498 |    "outputs": [],
 499 |    "source": [
 500 |     "vogais = ('a','e','i','o','u')"
 501 |    ]
 502 |   },
 503 |   {
 504 |    "cell_type": "code",
 505 |    "execution_count": null,
 506 |    "metadata": {},
 507 |    "outputs": [],
 508 |    "source": [
 509 |     "vogais[0]"
 510 |    ]
 511 |   },
 512 |   {
 513 |    "cell_type": "code",
 514 |    "execution_count": null,
 515 |    "metadata": {},
 516 |    "outputs": [],
 517 |    "source": [
 518 |     "vogais[0] = 'Nova'"
 519 |    ]
 520 |   },
 521 |   {
 522 |    "cell_type": "markdown",
 523 |    "metadata": {},
 524 |    "source": [
 525 |     "### Sets "
 526 |    ]
 527 |   },
 528 |   {
 529 |    "cell_type": "code",
 530 |    "execution_count": null,
 531 |    "metadata": {},
 532 |    "outputs": [],
 533 |    "source": [
 534 |     "{1,2,3}"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": null,
 540 |    "metadata": {},
 541 |    "outputs": [],
 542 |    "source": [
 543 |     "{1,2,2,1,2,1,2,3,3,2,3,2,2,2,1,2,4}"
 544 |    ]
 545 |   },
 546 |   {
 547 |    "cell_type": "markdown",
 548 |    "metadata": {},
 549 |    "source": [
 550 |     "## Comparadores"
 551 |    ]
 552 |   },
 553 |   {
 554 |    "cell_type": "code",
 555 |    "execution_count": null,
 556 |    "metadata": {},
 557 |    "outputs": [],
 558 |    "source": [
 559 |     "4 > 4"
 560 |    ]
 561 |   },
 562 |   {
 563 |    "cell_type": "code",
 564 |    "execution_count": null,
 565 |    "metadata": {},
 566 |    "outputs": [],
 567 |    "source": [
 568 |     "1 < 4"
 569 |    ]
 570 |   },
 571 |   {
 572 |    "cell_type": "code",
 573 |    "execution_count": null,
 574 |    "metadata": {},
 575 |    "outputs": [],
 576 |    "source": [
 577 |     "1 >= 1"
 578 |    ]
 579 |   },
 580 |   {
 581 |    "cell_type": "code",
 582 |    "execution_count": null,
 583 |    "metadata": {},
 584 |    "outputs": [],
 585 |    "source": [
 586 |     "1 <= 4"
 587 |    ]
 588 |   },
 589 |   {
 590 |    "cell_type": "code",
 591 |    "execution_count": null,
 592 |    "metadata": {},
 593 |    "outputs": [],
 594 |    "source": [
 595 |     "1 == 4"
 596 |    ]
 597 |   },
 598 |   {
 599 |    "cell_type": "code",
 600 |    "execution_count": null,
 601 |    "metadata": {},
 602 |    "outputs": [],
 603 |    "source": [
 604 |     "'ok' == 'bye'"
 605 |    ]
 606 |   },
 607 |   {
 608 |    "cell_type": "markdown",
 609 |    "metadata": {},
 610 |    "source": [
 611 |     "## Operadores Lógicos"
 612 |    ]
 613 |   },
 614 |   {
 615 |    "cell_type": "code",
 616 |    "execution_count": null,
 617 |    "metadata": {},
 618 |    "outputs": [],
 619 |    "source": [
 620 |     "(1 > 2) and (2 < 3)"
 621 |    ]
 622 |   },
 623 |   {
 624 |    "cell_type": "code",
 625 |    "execution_count": null,
 626 |    "metadata": {},
 627 |    "outputs": [],
 628 |    "source": [
 629 |     "(1 > 2) or (2 < 3)"
 630 |    ]
 631 |   },
 632 |   {
 633 |    "cell_type": "code",
 634 |    "execution_count": null,
 635 |    "metadata": {},
 636 |    "outputs": [],
 637 |    "source": [
 638 |     "(1 == 2) or (2 == 3) or (4 == 4)"
 639 |    ]
 640 |   },
 641 |   {
 642 |    "cell_type": "markdown",
 643 |    "metadata": {},
 644 |    "source": [
 645 |     "## if,elif, else Statements"
 646 |    ]
 647 |   },
 648 |   {
 649 |    "cell_type": "code",
 650 |    "execution_count": null,
 651 |    "metadata": {},
 652 |    "outputs": [],
 653 |    "source": [
 654 |     "if 1 < 2:\n",
 655 |     "    print('Ok!')"
 656 |    ]
 657 |   },
 658 |   {
 659 |    "cell_type": "code",
 660 |    "execution_count": null,
 661 |    "metadata": {},
 662 |    "outputs": [],
 663 |    "source": [
 664 |     "if 1 < 2:\n",
 665 |     "    print('Não OK')"
 666 |    ]
 667 |   },
 668 |   {
 669 |    "cell_type": "code",
 670 |    "execution_count": null,
 671 |    "metadata": {},
 672 |    "outputs": [],
 673 |    "source": [
 674 |     "if 1 < 2:\n",
 675 |     "    print('1º')\n",
 676 |     "else:\n",
 677 |     "    print('2º')"
 678 |    ]
 679 |   },
 680 |   {
 681 |    "cell_type": "code",
 682 |    "execution_count": null,
 683 |    "metadata": {},
 684 |    "outputs": [],
 685 |    "source": [
 686 |     "if 1 > 2:\n",
 687 |     "    print('1º')\n",
 688 |     "else:\n",
 689 |     "    print('2º')"
 690 |    ]
 691 |   },
 692 |   {
 693 |    "cell_type": "code",
 694 |    "execution_count": null,
 695 |    "metadata": {},
 696 |    "outputs": [],
 697 |    "source": [
 698 |     "if 1 == 2:\n",
 699 |     "    print('1º')\n",
 700 |     "elif 3 == 3:\n",
 701 |     "    print('2º')\n",
 702 |     "else:\n",
 703 |     "    print('3º')"
 704 |    ]
 705 |   },
 706 |   {
 707 |    "cell_type": "markdown",
 708 |    "metadata": {},
 709 |    "source": [
 710 |     "## for Loops"
 711 |    ]
 712 |   },
 713 |   {
 714 |    "cell_type": "code",
 715 |    "execution_count": null,
 716 |    "metadata": {},
 717 |    "outputs": [],
 718 |    "source": [
 719 |     "seq = [1,2,3,4,5,6]"
 720 |    ]
 721 |   },
 722 |   {
 723 |    "cell_type": "code",
 724 |    "execution_count": null,
 725 |    "metadata": {
 726 |     "scrolled": true
 727 |    },
 728 |    "outputs": [],
 729 |    "source": [
 730 |     "for n in seq:\n",
 731 |     "    print(n)"
 732 |    ]
 733 |   },
 734 |   {
 735 |    "cell_type": "code",
 736 |    "execution_count": null,
 737 |    "metadata": {},
 738 |    "outputs": [],
 739 |    "source": [
 740 |     "for n in seq:\n",
 741 |     "    print('OK')"
 742 |    ]
 743 |   },
 744 |   {
 745 |    "cell_type": "code",
 746 |    "execution_count": null,
 747 |    "metadata": {},
 748 |    "outputs": [],
 749 |    "source": [
 750 |     "for m in seq:\n",
 751 |     "    print(m**2)"
 752 |    ]
 753 |   },
 754 |   {
 755 |    "cell_type": "markdown",
 756 |    "metadata": {},
 757 |    "source": [
 758 |     "## while Loops"
 759 |    ]
 760 |   },
 761 |   {
 762 |    "cell_type": "code",
 763 |    "execution_count": 53,
 764 |    "metadata": {},
 765 |    "outputs": [
 766 |     {
 767 |      "name": "stdout",
 768 |      "output_type": "stream",
 769 |      "text": [
 770 |       "i é igual a : 1\n",
 771 |       "i é igual a : 2\n",
 772 |       "i é igual a : 3\n",
 773 |       "i é igual a : 4\n"
 774 |      ]
 775 |     }
 776 |    ],
 777 |    "source": [
 778 |     "i = 1\n",
 779 |     "while i < 5:\n",
 780 |     "    print('i é igual a : {}'.format(i))\n",
 781 |     "    i = i+1"
 782 |    ]
 783 |   },
 784 |   {
 785 |    "cell_type": "markdown",
 786 |    "metadata": {},
 787 |    "source": [
 788 |     "## range()"
 789 |    ]
 790 |   },
 791 |   {
 792 |    "cell_type": "code",
 793 |    "execution_count": 59,
 794 |    "metadata": {},
 795 |    "outputs": [
 796 |     {
 797 |      "data": {
 798 |       "text/plain": [
 799 |        "4"
 800 |       ]
 801 |      },
 802 |      "execution_count": 59,
 803 |      "metadata": {},
 804 |      "output_type": "execute_result"
 805 |     }
 806 |    ],
 807 |    "source": [
 808 |     "range(1,5)"
 809 |    ]
 810 |   },
 811 |   {
 812 |    "cell_type": "code",
 813 |    "execution_count": null,
 814 |    "metadata": {},
 815 |    "outputs": [],
 816 |    "source": [
 817 |     "for i in range(5):\n",
 818 |     "    print(i)"
 819 |    ]
 820 |   },
 821 |   {
 822 |    "cell_type": "code",
 823 |    "execution_count": null,
 824 |    "metadata": {},
 825 |    "outputs": [],
 826 |    "source": [
 827 |     "list(range(5))"
 828 |    ]
 829 |   },
 830 |   {
 831 |    "cell_type": "markdown",
 832 |    "metadata": {},
 833 |    "source": [
 834 |     "## list comprehension"
 835 |    ]
 836 |   },
 837 |   {
 838 |    "cell_type": "code",
 839 |    "execution_count": null,
 840 |    "metadata": {
 841 |     "collapsed": true
 842 |    },
 843 |    "outputs": [],
 844 |    "source": [
 845 |     "x = [1,2,3,4]"
 846 |    ]
 847 |   },
 848 |   {
 849 |    "cell_type": "code",
 850 |    "execution_count": null,
 851 |    "metadata": {},
 852 |    "outputs": [],
 853 |    "source": [
 854 |     "out = []\n",
 855 |     "for item in x:\n",
 856 |     "    out.append(item**2)\n",
 857 |     "print(out)"
 858 |    ]
 859 |   },
 860 |   {
 861 |    "cell_type": "code",
 862 |    "execution_count": null,
 863 |    "metadata": {},
 864 |    "outputs": [],
 865 |    "source": [
 866 |     "[item**2 for item in x]"
 867 |    ]
 868 |   },
 869 |   {
 870 |    "cell_type": "markdown",
 871 |    "metadata": {},
 872 |    "source": [
 873 |     "## Funções"
 874 |    ]
 875 |   },
 876 |   {
 877 |    "cell_type": "code",
 878 |    "execution_count": 60,
 879 |    "metadata": {
 880 |     "collapsed": true
 881 |    },
 882 |    "outputs": [],
 883 |    "source": [
 884 |     "def pot(n=2):\n",
 885 |     "    \"\"\"\n",
 886 |     "    Potência do Número\n",
 887 |     "    \"\"\"\n",
 888 |     "    return n**2"
 889 |    ]
 890 |   },
 891 |   {
 892 |    "cell_type": "code",
 893 |    "execution_count": null,
 894 |    "metadata": {
 895 |     "scrolled": true
 896 |    },
 897 |    "outputs": [],
 898 |    "source": [
 899 |     "y = pot(2)"
 900 |    ]
 901 |   },
 902 |   {
 903 |    "cell_type": "markdown",
 904 |    "metadata": {},
 905 |    "source": [
 906 |     "## lambda expressions"
 907 |    ]
 908 |   },
 909 |   {
 910 |    "cell_type": "code",
 911 |    "execution_count": 63,
 912 |    "metadata": {
 913 |     "collapsed": true
 914 |    },
 915 |    "outputs": [],
 916 |    "source": [
 917 |     "def dobrar(x):\n",
 918 |     "    return x*2"
 919 |    ]
 920 |   },
 921 |   {
 922 |    "cell_type": "code",
 923 |    "execution_count": 64,
 924 |    "metadata": {},
 925 |    "outputs": [
 926 |     {
 927 |      "data": {
 928 |       "text/plain": [
 929 |        "4"
 930 |       ]
 931 |      },
 932 |      "execution_count": 64,
 933 |      "metadata": {},
 934 |      "output_type": "execute_result"
 935 |     }
 936 |    ],
 937 |    "source": [
 938 |     "dobrar(2)"
 939 |    ]
 940 |   },
 941 |   {
 942 |    "cell_type": "code",
 943 |    "execution_count": 69,
 944 |    "metadata": {},
 945 |    "outputs": [
 946 |     {
 947 |      "data": {
 948 |       "text/plain": [
 949 |        "4"
 950 |       ]
 951 |      },
 952 |      "execution_count": 69,
 953 |      "metadata": {},
 954 |      "output_type": "execute_result"
 955 |     }
 956 |    ],
 957 |    "source": [
 958 |     "dobrar =lambda x=2: x*2\n",
 959 |     "dobrar(2)"
 960 |    ]
 961 |   },
 962 |   {
 963 |    "cell_type": "markdown",
 964 |    "metadata": {},
 965 |    "source": [
 966 |     "## Mapas e Filtros"
 967 |    ]
 968 |   },
 969 |   {
 970 |    "cell_type": "code",
 971 |    "execution_count": 70,
 972 |    "metadata": {
 973 |     "collapsed": true
 974 |    },
 975 |    "outputs": [],
 976 |    "source": [
 977 |     "seq = [1,2,3]"
 978 |    ]
 979 |   },
 980 |   {
 981 |    "cell_type": "code",
 982 |    "execution_count": 71,
 983 |    "metadata": {},
 984 |    "outputs": [
 985 |     {
 986 |      "data": {
 987 |       "text/plain": [
 988 |        "<map at 0x23a74e2cc88>"
 989 |       ]
 990 |      },
 991 |      "execution_count": 71,
 992 |      "metadata": {},
 993 |      "output_type": "execute_result"
 994 |     }
 995 |    ],
 996 |    "source": [
 997 |     "map(dobrar,seq) # Posição de memória"
 998 |    ]
 999 |   },
1000 |   {
1001 |    "cell_type": "code",
1002 |    "execution_count": 72,
1003 |    "metadata": {},
1004 |    "outputs": [
1005 |     {
1006 |      "data": {
1007 |       "text/plain": [
1008 |        "[2, 4, 6]"
1009 |       ]
1010 |      },
1011 |      "execution_count": 72,
1012 |      "metadata": {},
1013 |      "output_type": "execute_result"
1014 |     }
1015 |    ],
1016 |    "source": [
1017 |     "list(map(dobrar,seq)) # Passa para lista"
1018 |    ]
1019 |   },
1020 |   {
1021 |    "cell_type": "code",
1022 |    "execution_count": 73,
1023 |    "metadata": {},
1024 |    "outputs": [
1025 |     {
1026 |      "data": {
1027 |       "text/plain": [
1028 |        "[2, 4, 6]"
1029 |       ]
1030 |      },
1031 |      "execution_count": 73,
1032 |      "metadata": {},
1033 |      "output_type": "execute_result"
1034 |     }
1035 |    ],
1036 |    "source": [
1037 |     "list(map(lambda x: x*2,seq)) # Juntando tudo"
1038 |    ]
1039 |   },
1040 |   {
1041 |    "cell_type": "code",
1042 |    "execution_count": 74,
1043 |    "metadata": {},
1044 |    "outputs": [
1045 |     {
1046 |      "data": {
1047 |       "text/plain": [
1048 |        "<filter at 0x23a74e2ca20>"
1049 |       ]
1050 |      },
1051 |      "execution_count": 74,
1052 |      "metadata": {},
1053 |      "output_type": "execute_result"
1054 |     }
1055 |    ],
1056 |    "source": [
1057 |     "filter(lambda x: x%2 == 0,seq)"
1058 |    ]
1059 |   },
1060 |   {
1061 |    "cell_type": "code",
1062 |    "execution_count": 75,
1063 |    "metadata": {},
1064 |    "outputs": [
1065 |     {
1066 |      "data": {
1067 |       "text/plain": [
1068 |        "[2]"
1069 |       ]
1070 |      },
1071 |      "execution_count": 75,
1072 |      "metadata": {},
1073 |      "output_type": "execute_result"
1074 |     }
1075 |    ],
1076 |    "source": [
1077 |     "list(filter(lambda item: item%2 == 0,seq))"
1078 |    ]
1079 |   }
1080 |  ],
1081 |  "metadata": {
1082 |   "kernelspec": {
1083 |    "display_name": "Python 3",
1084 |    "language": "python",
1085 |    "name": "python3"
1086 |   },
1087 |   "language_info": {
1088 |    "codemirror_mode": {
1089 |     "name": "ipython",
1090 |     "version": 3
1091 |    },
1092 |    "file_extension": ".py",
1093 |    "mimetype": "text/x-python",
1094 |    "name": "python",
1095 |    "nbconvert_exporter": "python",
1096 |    "pygments_lexer": "ipython3",
1097 |    "version": "3.6.1"
1098 |   }
1099 |  },
1100 |  "nbformat": 4,
1101 |  "nbformat_minor": 1
1102 | }
1103 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Aula_03_Numpy-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### Numpy : Linear Algebra Library for Python"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "## Instalação"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": null,
 20 |    "metadata": {
 21 |     "collapsed": true
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "# Verificar versão do numpy\n",
 26 |     "import numpy\n",
 27 |     "numpy.version.version"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "### Comandos\n",
 35 |     "-----------------\n",
 36 |     "* conda install numpy\n",
 37 |     "* pip install numpy"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "metadata": {},
 43 |    "source": [
 44 |     "## Numpy Arrays"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "markdown",
 49 |    "metadata": {},
 50 |    "source": [
 51 |     "### Vetores"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": null,
 57 |    "metadata": {
 58 |     "collapsed": true
 59 |    },
 60 |    "outputs": [],
 61 |    "source": [
 62 |     "# Criando 1D\n",
 63 |     "import numpy as np\n",
 64 |     "list = [1,2,3,4]\n",
 65 |     "np_arr = np.array(list1)\n"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "### Matrizes"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": null,
 78 |    "metadata": {
 79 |     "collapsed": true
 80 |    },
 81 |    "outputs": [],
 82 |    "source": [
 83 |     "# Criando 2D\n",
 84 |     "mat1 = [[1,2,3],[2,4,8],[3,9,27]]\n",
 85 |     "np_arr2 = np.array(mat1)\n"
 86 |    ]
 87 |   },
 88 |   {
 89 |    "cell_type": "code",
 90 |    "execution_count": null,
 91 |    "metadata": {
 92 |     "collapsed": true
 93 |    },
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "np_arr2"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "markdown",
101 |    "metadata": {},
102 |    "source": [
103 |     "### Numpy Arange"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": null,
109 |    "metadata": {
110 |     "collapsed": true
111 |    },
112 |    "outputs": [],
113 |    "source": [
114 |     "np.arange(1,10)"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "code",
119 |    "execution_count": null,
120 |    "metadata": {
121 |     "collapsed": true
122 |    },
123 |    "outputs": [],
124 |    "source": [
125 |     "np.arange(0,21,2)"
126 |    ]
127 |   },
128 |   {
129 |    "cell_type": "code",
130 |    "execution_count": null,
131 |    "metadata": {
132 |     "collapsed": true
133 |    },
134 |    "outputs": [],
135 |    "source": [
136 |     "np.arange(20,1.-2)"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "markdown",
141 |    "metadata": {},
142 |    "source": [
143 |     "## Matrizes Especiais"
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "markdown",
148 |    "metadata": {},
149 |    "source": [
150 |     "### Matriz Identidade, Zeros e Uns"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "code",
155 |    "execution_count": null,
156 |    "metadata": {
157 |     "collapsed": true
158 |    },
159 |    "outputs": [],
160 |    "source": [
161 |     "np.eye(3)"
162 |    ]
163 |   },
164 |   {
165 |    "cell_type": "code",
166 |    "execution_count": null,
167 |    "metadata": {
168 |     "collapsed": true
169 |    },
170 |    "outputs": [],
171 |    "source": [
172 |     "# Zeros\n",
173 |     "np.zeros(3) # vetor\n",
174 |     "np.zeros((4,3)) # matriz"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "code",
179 |    "execution_count": null,
180 |    "metadata": {
181 |     "collapsed": true
182 |    },
183 |    "outputs": [],
184 |    "source": [
185 |     "# Um\n",
186 |     "np.ones(3) # vetor\n",
187 |     "np.ones((4,3)) # matriz\n",
188 |     "\n"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "markdown",
193 |    "metadata": {},
194 |    "source": [
195 |     "### linspace"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": null,
201 |    "metadata": {
202 |     "collapsed": true
203 |    },
204 |    "outputs": [],
205 |    "source": [
206 |     "np.linspace(0,10,100) # Não confudir com arage"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "markdown",
211 |    "metadata": {},
212 |    "source": [
213 |     "Mini tarefa 3.1\n",
214 |     "-------------\n",
215 |     "* a) Faça um matriz identidade, zero e de uns (3x3)\n",
216 |     "* b) Crie um vetor de inteiros de 0 a 100;\n",
217 |     "* c) Crie uma matriz 5x5 com todos elementos igual 5."
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "markdown",
222 |    "metadata": {},
223 |    "source": [
224 |     "## Geração Números Aleatórios"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "markdown",
229 |    "metadata": {},
230 |    "source": [
231 |     "#### Inteiros"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": null,
237 |    "metadata": {
238 |     "collapsed": true
239 |    },
240 |    "outputs": [],
241 |    "source": [
242 |     "# Escolhendo um número de 1 a 20\n",
243 |     "np.random.randint(1,21)  # Pressione shif+tab para ver opções"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": null,
249 |    "metadata": {
250 |     "collapsed": true
251 |    },
252 |    "outputs": [],
253 |    "source": [
254 |     "# Escolhendo cinco números de 1 a 20\n",
255 |     "np.random.randint(1,21,15)"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "markdown",
260 |    "metadata": {},
261 |    "source": [
262 |     "#### Distribuição Uniforme"
263 |    ]
264 |   },
265 |   {
266 |    "cell_type": "code",
267 |    "execution_count": null,
268 |    "metadata": {
269 |     "collapsed": true
270 |    },
271 |    "outputs": [],
272 |    "source": [
273 |     "np.random.rand(3) # Distribuiçõa uniforme 0-1"
274 |    ]
275 |   },
276 |   {
277 |    "cell_type": "code",
278 |    "execution_count": null,
279 |    "metadata": {
280 |     "collapsed": true
281 |    },
282 |    "outputs": [],
283 |    "source": [
284 |     "np.random.rand(2,3) # Não usa Tupla"
285 |    ]
286 |   },
287 |   {
288 |    "cell_type": "markdown",
289 |    "metadata": {},
290 |    "source": [
291 |     "#### Distribuição Normal"
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "code",
296 |    "execution_count": null,
297 |    "metadata": {
298 |     "collapsed": true
299 |    },
300 |    "outputs": [],
301 |    "source": [
302 |     "np.random.randn(2,3) #m=0,sd=1"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "markdown",
307 |    "metadata": {},
308 |    "source": [
309 |     "### Manipulção de Matrizes/Vetores"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "markdown",
314 |    "metadata": {},
315 |    "source": [
316 |     "#### Max, Min e Média"
317 |    ]
318 |   },
319 |   {
320 |    "cell_type": "code",
321 |    "execution_count": null,
322 |    "metadata": {
323 |     "collapsed": true
324 |    },
325 |    "outputs": [],
326 |    "source": [
327 |     "\n",
328 |     "m = np.random.randint(1,100,10)\n",
329 |     "print(\"Máximo:{}, Mínimo:{} e Média:{}\".format(m.max(),m.min(),m.mean()))"
330 |    ]
331 |   },
332 |   {
333 |    "cell_type": "code",
334 |    "execution_count": null,
335 |    "metadata": {
336 |     "collapsed": true
337 |    },
338 |    "outputs": [],
339 |    "source": [
340 |     "# Posição Min,Max \n",
341 |     "m.argmax()\n",
342 |     "print(\"O máixmo é {}, e está no index {}.\".format(m.max(),m.argmax()))\n",
343 |     "m"
344 |    ]
345 |   },
346 |   {
347 |    "cell_type": "markdown",
348 |    "metadata": {},
349 |    "source": [
350 |     "### Dimensão e Tipos"
351 |    ]
352 |   },
353 |   {
354 |    "cell_type": "code",
355 |    "execution_count": null,
356 |    "metadata": {
357 |     "collapsed": true
358 |    },
359 |    "outputs": [],
360 |    "source": [
361 |     "# Dimensão\n",
362 |     "v = np.random.randint(0,100,50)\n",
363 |     "v.shape"
364 |    ]
365 |   },
366 |   {
367 |    "cell_type": "code",
368 |    "execution_count": null,
369 |    "metadata": {
370 |     "collapsed": true
371 |    },
372 |    "outputs": [],
373 |    "source": [
374 |     "# Alterando Dimensão\n",
375 |     "m = v.reshape(5,10)\n",
376 |     "m.shape"
377 |    ]
378 |   },
379 |   {
380 |    "cell_type": "code",
381 |    "execution_count": null,
382 |    "metadata": {
383 |     "collapsed": true
384 |    },
385 |    "outputs": [],
386 |    "source": [
387 |     "#Tipo\n",
388 |     "m.dtype"
389 |    ]
390 |   },
391 |   {
392 |    "cell_type": "markdown",
393 |    "metadata": {},
394 |    "source": [
395 |     "Mini tarefa 3.2\n",
396 |     "-------------\n",
397 |     "* a) Crie um matriz 3x3, escolhendo os números de uma distribuição uniforme de 0 a 1;\n",
398 |     "* b) Crie um vetor com 10 itens escolhidos distribuição normal(m=0, sd=1);\n",
399 |     "* c) Faça uma função que represente o lançamento de um dado;"
400 |    ]
401 |   },
402 |   {
403 |    "cell_type": "markdown",
404 |    "metadata": {},
405 |    "source": [
406 |     "## Seleção Numpy (slice)"
407 |    ]
408 |   },
409 |   {
410 |    "cell_type": "markdown",
411 |    "metadata": {},
412 |    "source": [
413 |     "#### Vetores"
414 |    ]
415 |   },
416 |   {
417 |    "cell_type": "code",
418 |    "execution_count": null,
419 |    "metadata": {
420 |     "collapsed": true
421 |    },
422 |    "outputs": [],
423 |    "source": [
424 |     "a = np.arange(1,21)\n",
425 |     "a"
426 |    ]
427 |   },
428 |   {
429 |    "cell_type": "code",
430 |    "execution_count": null,
431 |    "metadata": {
432 |     "collapsed": true
433 |    },
434 |    "outputs": [],
435 |    "source": [
436 |     "a[2]"
437 |    ]
438 |   },
439 |   {
440 |    "cell_type": "code",
441 |    "execution_count": null,
442 |    "metadata": {
443 |     "collapsed": true
444 |    },
445 |    "outputs": [],
446 |    "source": [
447 |     "a[0:3] "
448 |    ]
449 |   },
450 |   {
451 |    "cell_type": "code",
452 |    "execution_count": null,
453 |    "metadata": {
454 |     "collapsed": true
455 |    },
456 |    "outputs": [],
457 |    "source": [
458 |     "a[:3] "
459 |    ]
460 |   },
461 |   {
462 |    "cell_type": "code",
463 |    "execution_count": null,
464 |    "metadata": {
465 |     "collapsed": true
466 |    },
467 |    "outputs": [],
468 |    "source": [
469 |     "a[3:]"
470 |    ]
471 |   },
472 |   {
473 |    "cell_type": "code",
474 |    "execution_count": null,
475 |    "metadata": {
476 |     "collapsed": true
477 |    },
478 |    "outputs": [],
479 |    "source": [
480 |     "a[0:18]"
481 |    ]
482 |   },
483 |   {
484 |    "cell_type": "code",
485 |    "execution_count": null,
486 |    "metadata": {
487 |     "collapsed": true
488 |    },
489 |    "outputs": [],
490 |    "source": [
491 |     "a[:-2]"
492 |    ]
493 |   },
494 |   {
495 |    "cell_type": "markdown",
496 |    "metadata": {},
497 |    "source": [
498 |     "#### Matrizes"
499 |    ]
500 |   },
501 |   {
502 |    "cell_type": "code",
503 |    "execution_count": null,
504 |    "metadata": {
505 |     "collapsed": true
506 |    },
507 |    "outputs": [],
508 |    "source": [
509 |     "m[0] # Linha"
510 |    ]
511 |   },
512 |   {
513 |    "cell_type": "code",
514 |    "execution_count": null,
515 |    "metadata": {},
516 |    "outputs": [],
517 |    "source": [
518 |     "m[:][0] # Coluna"
519 |    ]
520 |   },
521 |   {
522 |    "cell_type": "code",
523 |    "execution_count": null,
524 |    "metadata": {
525 |     "collapsed": true
526 |    },
527 |    "outputs": [],
528 |    "source": [
529 |     "m[0][0] #Numero"
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": null,
535 |    "metadata": {},
536 |    "outputs": [],
537 |    "source": [
538 |     "m[0:2][0:3] #Sub matriz "
539 |    ]
540 |   },
541 |   {
542 |    "cell_type": "code",
543 |    "execution_count": null,
544 |    "metadata": {
545 |     "collapsed": true
546 |    },
547 |    "outputs": [],
548 |    "source": [
549 |     "# Seleção Opção 2"
550 |    ]
551 |   },
552 |   {
553 |    "cell_type": "code",
554 |    "execution_count": null,
555 |    "metadata": {
556 |     "collapsed": true
557 |    },
558 |    "outputs": [],
559 |    "source": [
560 |     "m[1,:] # Linha (Recomendado)"
561 |    ]
562 |   },
563 |   {
564 |    "cell_type": "code",
565 |    "execution_count": null,
566 |    "metadata": {
567 |     "collapsed": true
568 |    },
569 |    "outputs": [],
570 |    "source": [
571 |     "m[1,1] # Numero (Recomendado)"
572 |    ]
573 |   },
574 |   {
575 |    "cell_type": "code",
576 |    "execution_count": null,
577 |    "metadata": {
578 |     "collapsed": true
579 |    },
580 |    "outputs": [],
581 |    "source": [
582 |     "m[:,1] # Coluna (Recomendado)"
583 |    ]
584 |   },
585 |   {
586 |    "cell_type": "code",
587 |    "execution_count": null,
588 |    "metadata": {
589 |     "collapsed": true
590 |    },
591 |    "outputs": [],
592 |    "source": [
593 |     "m[0:2,0:3] #Sub matriz \n"
594 |    ]
595 |   },
596 |   {
597 |    "cell_type": "markdown",
598 |    "metadata": {},
599 |    "source": [
600 |     "# MINI TAREFA 3.3\n",
601 |     "Dada a matriz q:\n",
602 |     "\n",
603 |     "q = np.arange(50).reshape(5,10)\n",
604 |     "\n",
605 |     "Selecione a submatrix que contenha os números:\n",
606 |     "\n",
607 |     "* a) 23, 24, 25\n",
608 |     "* b) 16, 26, 36\n",
609 |     "* c) 23, 24, 25, 33, 34 e 35."
610 |    ]
611 |   },
612 |   {
613 |    "cell_type": "markdown",
614 |    "metadata": {},
615 |    "source": [
616 |     "### Broadcast e Copy"
617 |    ]
618 |   },
619 |   {
620 |    "cell_type": "code",
621 |    "execution_count": null,
622 |    "metadata": {
623 |     "collapsed": true
624 |    },
625 |    "outputs": [],
626 |    "source": [
627 |     "b = a[0:5] "
628 |    ]
629 |   },
630 |   {
631 |    "cell_type": "code",
632 |    "execution_count": null,
633 |    "metadata": {
634 |     "collapsed": true,
635 |     "scrolled": false
636 |    },
637 |    "outputs": [],
638 |    "source": [
639 |     "b[0:5] = 30"
640 |    ]
641 |   },
642 |   {
643 |    "cell_type": "code",
644 |    "execution_count": null,
645 |    "metadata": {
646 |     "collapsed": true
647 |    },
648 |    "outputs": [],
649 |    "source": [
650 |     "a = np.arange(1,21)\n",
651 |     "b = a.copy()[0:5]\n",
652 |     "b[0:5] = 30"
653 |    ]
654 |   },
655 |   {
656 |    "cell_type": "code",
657 |    "execution_count": null,
658 |    "metadata": {
659 |     "collapsed": true
660 |    },
661 |    "outputs": [],
662 |    "source": [
663 |     "a"
664 |    ]
665 |   },
666 |   {
667 |    "cell_type": "code",
668 |    "execution_count": null,
669 |    "metadata": {
670 |     "collapsed": true
671 |    },
672 |    "outputs": [],
673 |    "source": [
674 |     "b"
675 |    ]
676 |   },
677 |   {
678 |    "cell_type": "markdown",
679 |    "metadata": {},
680 |    "source": [
681 |     "## Seleçao com Boolean"
682 |    ]
683 |   },
684 |   {
685 |    "cell_type": "code",
686 |    "execution_count": null,
687 |    "metadata": {
688 |     "collapsed": true
689 |    },
690 |    "outputs": [],
691 |    "source": [
692 |     " c = np.arange(1,100)\n",
693 |     "    "
694 |    ]
695 |   },
696 |   {
697 |    "cell_type": "code",
698 |    "execution_count": null,
699 |    "metadata": {
700 |     "collapsed": true
701 |    },
702 |    "outputs": [],
703 |    "source": [
704 |     "bool_sel = c > 50"
705 |    ]
706 |   },
707 |   {
708 |    "cell_type": "code",
709 |    "execution_count": null,
710 |    "metadata": {},
711 |    "outputs": [],
712 |    "source": [
713 |     "c[bool_sel]"
714 |    ]
715 |   },
716 |   {
717 |    "cell_type": "markdown",
718 |    "metadata": {},
719 |    "source": [
720 |     "Mini tarefa 3.4\n",
721 |     "-------------\n",
722 |     "* a) Faça o filtro acima em um só passo;\n",
723 |     "* b) Faça um filtro para selecionar somente os números pares;\n",
724 |     "* c) Considerando $m$ como a média de um vetor $v$, e $v_i$ como o $i$-ésimo item de $v$. Faça um função f, que substitua $v_i$ por zero caso $v_i < m$. \n",
725 |     " "
726 |    ]
727 |   },
728 |   {
729 |    "cell_type": "markdown",
730 |    "metadata": {
731 |     "collapsed": true
732 |    },
733 |    "source": [
734 |     "### Operações com Numpy"
735 |    ]
736 |   },
737 |   {
738 |    "cell_type": "code",
739 |    "execution_count": null,
740 |    "metadata": {},
741 |    "outputs": [],
742 |    "source": [
743 |     "# Soma\n",
744 |     "a = np.arange(0,5)\n",
745 |     "b = np.arange(0,5)\n",
746 |     "soma = a + b\n",
747 |     "print(\"{}+{}={}\".format(a,b,soma))"
748 |    ]
749 |   },
750 |   {
751 |    "cell_type": "code",
752 |    "execution_count": null,
753 |    "metadata": {},
754 |    "outputs": [],
755 |    "source": [
756 |     "# Subtração\n",
757 |     "sub = a - b\n",
758 |     "print(\"{}-{}={}\".format(a,b,sub))"
759 |    ]
760 |   },
761 |   {
762 |    "cell_type": "code",
763 |    "execution_count": null,
764 |    "metadata": {},
765 |    "outputs": [],
766 |    "source": [
767 |     "# Multiplicação\n",
768 |     "mul = a * b\n",
769 |     "print(\"{}*{}={}\".format(a,b,mul))"
770 |    ]
771 |   },
772 |   {
773 |    "cell_type": "code",
774 |    "execution_count": 26,
775 |    "metadata": {},
776 |    "outputs": [
777 |     {
778 |      "name": "stdout",
779 |      "output_type": "stream",
780 |      "text": [
781 |       "[0 1 2 3 4]**[0 1 2 3 4]=[  1   1   4  27 256]\n"
782 |      ]
783 |     }
784 |    ],
785 |    "source": [
786 |     "#Potência\n",
787 |     "pot = a ** b\n",
788 |     "print(\"{}**{}={}\".format(a,b,pot))"
789 |    ]
790 |   },
791 |   {
792 |    "cell_type": "code",
793 |    "execution_count": 27,
794 |    "metadata": {},
795 |    "outputs": [
796 |     {
797 |      "name": "stdout",
798 |      "output_type": "stream",
799 |      "text": [
800 |       "[0 1 2 3 4]*2=[2 3 4 5 6]\n",
801 |       "[0 1 2 3 4]-2=[-2 -1  0  1  2]\n",
802 |       "[0 1 2 3 4]*2=[0 2 4 6 8]\n",
803 |       "[0 1 2 3 4]**2=[ 0  1  4  9 16]\n"
804 |      ]
805 |     }
806 |    ],
807 |    "source": [
808 |     "#Usando escalar\n",
809 |     "c = 2\n",
810 |     "soma= a + c\n",
811 |     "sub = a - c\n",
812 |     "mul = a * c\n",
813 |     "pot = a ** c \n",
814 |     "print(\"{}*{}={}\".format(a,c,soma))\n",
815 |     "print(\"{}-{}={}\".format(a,c,sub))\n",
816 |     "print(\"{}*{}={}\".format(a,c,mul))\n",
817 |     "print(\"{}**{}={}\".format(a,c,pot))\n"
818 |    ]
819 |   },
820 |   {
821 |    "cell_type": "code",
822 |    "execution_count": null,
823 |    "metadata": {},
824 |    "outputs": [],
825 |    "source": [
826 |     " # Divisão (cuidado com Erros na Operação)\n",
827 |     "div=a/a\n",
828 |     "print(\"{}/{}={}\".format(a,a,div))"
829 |    ]
830 |   },
831 |   {
832 |    "cell_type": "code",
833 |    "execution_count": 24,
834 |    "metadata": {},
835 |    "outputs": [
836 |     {
837 |      "name": "stdout",
838 |      "output_type": "stream",
839 |      "text": [
840 |       "1/[0 1 2 3 4]=[        inf  1.          0.5         0.33333333  0.25      ]\n"
841 |      ]
842 |     },
843 |     {
844 |      "name": "stderr",
845 |      "output_type": "stream",
846 |      "text": [
847 |       "C:\\Program Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
848 |       "  \"\"\"Entry point for launching an IPython kernel.\n"
849 |      ]
850 |     }
851 |    ],
852 |    "source": [
853 |     "div2=1/a\n",
854 |     "print(\"1/{}={}\".format(a,div2))"
855 |    ]
856 |   },
857 |   {
858 |    "cell_type": "markdown",
859 |    "metadata": {},
860 |    "source": [
861 |     "# Funções Vetorizadas (Universal Functions)"
862 |    ]
863 |   },
864 |   {
865 |    "cell_type": "code",
866 |    "execution_count": 33,
867 |    "metadata": {
868 |     "scrolled": true
869 |    },
870 |    "outputs": [
871 |     {
872 |      "name": "stdout",
873 |      "output_type": "stream",
874 |      "text": [
875 |       "Sumário: Max=4, Min=0, Média=2.0\n"
876 |      ]
877 |     }
878 |    ],
879 |    "source": [
880 |     "# Exemplo:\n",
881 |     "max=np.max(a)\n",
882 |     "min=np.min(a)\n",
883 |     "media=np.mean(a)\n",
884 |     "print(\"Sumário: Max={}, Min={}, Média={}\".format(max,min,media))"
885 |    ]
886 |   },
887 |   {
888 |    "cell_type": "markdown",
889 |    "metadata": {},
890 |    "source": [
891 |     "Lista de Funções que podem ser aplicadas a vetores e matrizes:\n",
892 |     "\n",
893 |     "Universal functions:[https://docs.scipy.org/doc/numpy-1.13.0/reference/ufuncs.html]"
894 |    ]
895 |   },
896 |   {
897 |    "cell_type": "code",
898 |    "execution_count": 46,
899 |    "metadata": {},
900 |    "outputs": [
901 |     {
902 |      "name": "stdout",
903 |      "output_type": "stream",
904 |      "text": [
905 |       "[[0 1 2]\n",
906 |       " [3 4 5]\n",
907 |       " [6 7 8]]\n",
908 |       "colSum:[ 9 12 15] e rowSum:[ 3 12 21]\n"
909 |      ]
910 |     }
911 |    ],
912 |    "source": [
913 |     "# Aplicando as funções em linhas ou colunas\n",
914 |     "m = np.arange(0,9).reshape(3,3)\n",
915 |     "m\n",
916 |     "colSum = m.sum(0)\n",
917 |     "rowSum = m.sum(1)\n",
918 |     "print(m)\n",
919 |     "print(\"colSum:{} e rowSum:{}\".format(colSum,rowSum))"
920 |    ]
921 |   },
922 |   {
923 |    "cell_type": "markdown",
924 |    "metadata": {},
925 |    "source": [
926 |     "# MINI TAREFA 3.4"
927 |    ]
928 |   },
929 |   {
930 |    "cell_type": "markdown",
931 |    "metadata": {},
932 |    "source": [
933 |     "Faça uma função que receba um vetor $v$ e normalize seus valores utilizando a média e o desvio padrão. \n",
934 |     "Confome abaixo:\n",
935 |     "\n",
936 |     "$v_i^{'} = \\frac{v_i-m}{\\sigma}$\n",
937 |     "\n",
938 |     "Onde $v_i$ é $i$-ésimo elemento de $v$, $v_i^{'}$ o $i$-ésimo elemento do novo vetor, $m$ é a média de $v$ e $\\sigma$ o desvio padrão de $v$."
939 |    ]
940 |   },
941 |   {
942 |    "cell_type": "code",
943 |    "execution_count": null,
944 |    "metadata": {
945 |     "collapsed": true
946 |    },
947 |    "outputs": [],
948 |    "source": []
949 |   }
950 |  ],
951 |  "metadata": {
952 |   "kernelspec": {
953 |    "display_name": "Python 3",
954 |    "language": "python",
955 |    "name": "python3"
956 |   },
957 |   "language_info": {
958 |    "codemirror_mode": {
959 |     "name": "ipython",
960 |     "version": 3
961 |    },
962 |    "file_extension": ".py",
963 |    "mimetype": "text/x-python",
964 |    "name": "python",
965 |    "nbconvert_exporter": "python",
966 |    "pygments_lexer": "ipython3",
967 |    "version": "3.6.1"
968 |   }
969 |  },
970 |  "nbformat": 4,
971 |  "nbformat_minor": 2
972 | }
973 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Aula_03_Numpy_Exercicios-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "**Faça  importação de Numpy como np**"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 4,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": []
 16 |   },
 17 |   {
 18 |    "cell_type": "markdown",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     "** Crie uma vetor de 20 uns** "
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 6,
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "data": {
 31 |       "text/plain": [
 32 |        "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
 33 |        "        1.,  1.,  1.,  1.,  1.,  1.,  1.])"
 34 |       ]
 35 |      },
 36 |      "execution_count": 6,
 37 |      "metadata": {},
 38 |      "output_type": "execute_result"
 39 |     }
 40 |    ],
 41 |    "source": []
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {},
 46 |    "source": [
 47 |     "** Crie uma vetor de 5 zeros** "
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 8,
 53 |    "metadata": {},
 54 |    "outputs": [
 55 |     {
 56 |      "data": {
 57 |       "text/plain": [
 58 |        "array([ 0.,  0.,  0.,  0.,  0.])"
 59 |       ]
 60 |      },
 61 |      "execution_count": 8,
 62 |      "metadata": {},
 63 |      "output_type": "execute_result"
 64 |     }
 65 |    ],
 66 |    "source": []
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "*** Crie um vetor com 25 elemetos, todos igual 5.**"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 13,
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "data": {
 82 |       "text/plain": [
 83 |        "array([ 5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,\n",
 84 |        "        5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.])"
 85 |       ]
 86 |      },
 87 |      "execution_count": 13,
 88 |      "metadata": {},
 89 |      "output_type": "execute_result"
 90 |     }
 91 |    ],
 92 |    "source": []
 93 |   },
 94 |   {
 95 |    "cell_type": "markdown",
 96 |    "metadata": {},
 97 |    "source": [
 98 |     "*** Crie uma matriz 5x5 com todos elementos iguais a 5.***"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 11,
104 |    "metadata": {},
105 |    "outputs": [
106 |     {
107 |      "data": {
108 |       "text/plain": [
109 |        "array([[ 5.,  5.,  5.,  5.,  5.],\n",
110 |        "       [ 5.,  5.,  5.,  5.,  5.],\n",
111 |        "       [ 5.,  5.,  5.,  5.,  5.],\n",
112 |        "       [ 5.,  5.,  5.,  5.,  5.],\n",
113 |        "       [ 5.,  5.,  5.,  5.,  5.]])"
114 |       ]
115 |      },
116 |      "execution_count": 11,
117 |      "metadata": {},
118 |      "output_type": "execute_result"
119 |     }
120 |    ],
121 |    "source": []
122 |   },
123 |   {
124 |    "cell_type": "markdown",
125 |    "metadata": {},
126 |    "source": [
127 |     "**Crie um vetor de inteiros de 0 a 15**"
128 |    ]
129 |   },
130 |   {
131 |    "cell_type": "code",
132 |    "execution_count": 14,
133 |    "metadata": {},
134 |    "outputs": [
135 |     {
136 |      "data": {
137 |       "text/plain": [
138 |        "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
139 |       ]
140 |      },
141 |      "execution_count": 14,
142 |      "metadata": {},
143 |      "output_type": "execute_result"
144 |     }
145 |    ],
146 |    "source": []
147 |   },
148 |   {
149 |    "cell_type": "markdown",
150 |    "metadata": {},
151 |    "source": [
152 |     "**Crie um vetor com todos os numeros pares de 5 a 60**"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 20,
158 |    "metadata": {},
159 |    "outputs": [
160 |     {
161 |      "data": {
162 |       "text/plain": [
163 |        "array([ 6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38,\n",
164 |        "       40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60])"
165 |       ]
166 |      },
167 |      "execution_count": 20,
168 |      "metadata": {},
169 |      "output_type": "execute_result"
170 |     }
171 |    ],
172 |    "source": []
173 |   },
174 |   {
175 |    "cell_type": "markdown",
176 |    "metadata": {},
177 |    "source": [
178 |     "**Crie uma matriz identidade 5x5**"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 24,
184 |    "metadata": {},
185 |    "outputs": [
186 |     {
187 |      "data": {
188 |       "text/plain": [
189 |        "array([[ 1.,  0.,  0.,  0.,  0.],\n",
190 |        "       [ 0.,  1.,  0.,  0.,  0.],\n",
191 |        "       [ 0.,  0.,  1.,  0.,  0.],\n",
192 |        "       [ 0.,  0.,  0.,  1.,  0.],\n",
193 |        "       [ 0.,  0.,  0.,  0.,  1.]])"
194 |       ]
195 |      },
196 |      "execution_count": 24,
197 |      "metadata": {},
198 |      "output_type": "execute_result"
199 |     }
200 |    ],
201 |    "source": []
202 |   },
203 |   {
204 |    "cell_type": "markdown",
205 |    "metadata": {},
206 |    "source": [
207 |     "**Crie uma matriz 4x4 com elementos de 0 a 15**"
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 23,
213 |    "metadata": {},
214 |    "outputs": [
215 |     {
216 |      "data": {
217 |       "text/plain": [
218 |        "array([[ 0,  1,  2,  3],\n",
219 |        "       [ 4,  5,  6,  7],\n",
220 |        "       [ 8,  9, 10, 11],\n",
221 |        "       [12, 13, 14, 15]])"
222 |       ]
223 |      },
224 |      "execution_count": 23,
225 |      "metadata": {},
226 |      "output_type": "execute_result"
227 |     }
228 |    ],
229 |    "source": []
230 |   },
231 |   {
232 |    "cell_type": "markdown",
233 |    "metadata": {},
234 |    "source": [
235 |     "***Gere um número aleatório de 0 a 1***\n",
236 |     "\n",
237 |     "use antes do código *np.random.seed(100)* "
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 31,
243 |    "metadata": {},
244 |    "outputs": [
245 |     {
246 |      "data": {
247 |       "text/plain": [
248 |        "array([ 0.54340494])"
249 |       ]
250 |      },
251 |      "execution_count": 31,
252 |      "metadata": {},
253 |      "output_type": "execute_result"
254 |     }
255 |    ],
256 |    "source": [
257 |     "\n"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "markdown",
262 |    "metadata": {},
263 |    "source": [
264 |     "** Gere a Matriz abaixo **"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 38,
270 |    "metadata": {},
271 |    "outputs": [
272 |     {
273 |      "data": {
274 |       "text/plain": [
275 |        "array([[ 0.01,  0.02,  0.03,  0.04,  0.05,  0.06,  0.07,  0.08,  0.09,  0.1 ],\n",
276 |        "       [ 0.11,  0.12,  0.13,  0.14,  0.15,  0.16,  0.17,  0.18,  0.19,  0.2 ],\n",
277 |        "       [ 0.21,  0.22,  0.23,  0.24,  0.25,  0.26,  0.27,  0.28,  0.29,  0.3 ],\n",
278 |        "       [ 0.31,  0.32,  0.33,  0.34,  0.35,  0.36,  0.37,  0.38,  0.39,  0.4 ],\n",
279 |        "       [ 0.41,  0.42,  0.43,  0.44,  0.45,  0.46,  0.47,  0.48,  0.49,  0.5 ],\n",
280 |        "       [ 0.51,  0.52,  0.53,  0.54,  0.55,  0.56,  0.57,  0.58,  0.59,  0.6 ],\n",
281 |        "       [ 0.61,  0.62,  0.63,  0.64,  0.65,  0.66,  0.67,  0.68,  0.69,  0.7 ],\n",
282 |        "       [ 0.71,  0.72,  0.73,  0.74,  0.75,  0.76,  0.77,  0.78,  0.79,  0.8 ],\n",
283 |        "       [ 0.81,  0.82,  0.83,  0.84,  0.85,  0.86,  0.87,  0.88,  0.89,  0.9 ],\n",
284 |        "       [ 0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99,  1.  ]])"
285 |       ]
286 |      },
287 |      "execution_count": 38,
288 |      "metadata": {},
289 |      "output_type": "execute_result"
290 |     }
291 |    ],
292 |    "source": []
293 |   },
294 |   {
295 |    "cell_type": "markdown",
296 |    "metadata": {},
297 |    "source": [
298 |     "***Dada a matriz m, selecione a submatriz abaixo apartir de m*** \n"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": 56,
304 |    "metadata": {},
305 |    "outputs": [
306 |     {
307 |      "data": {
308 |       "text/plain": [
309 |        "array([[12, 13, 14],\n",
310 |        "       [18, 19, 20]])"
311 |       ]
312 |      },
313 |      "execution_count": 56,
314 |      "metadata": {},
315 |      "output_type": "execute_result"
316 |     }
317 |    ],
318 |    "source": [
319 |     "# Matriz m\n",
320 |     "m = np.arange(24).reshape(4,6)"
321 |    ]
322 |   },
323 |   {
324 |    "cell_type": "markdown",
325 |    "metadata": {},
326 |    "source": [
327 |     "***Calcule o desvio padrão das colunas da matriz m do exemplo acima*** "
328 |    ]
329 |   },
330 |   {
331 |    "cell_type": "code",
332 |    "execution_count": 59,
333 |    "metadata": {},
334 |    "outputs": [
335 |     {
336 |      "data": {
337 |       "text/plain": [
338 |        "array([ 1.70782513,  1.70782513,  1.70782513,  1.70782513])"
339 |       ]
340 |      },
341 |      "execution_count": 59,
342 |      "metadata": {},
343 |      "output_type": "execute_result"
344 |     }
345 |    ],
346 |    "source": []
347 |   }
348 |  ],
349 |  "metadata": {
350 |   "kernelspec": {
351 |    "display_name": "Python 3",
352 |    "language": "python",
353 |    "name": "python3"
354 |   },
355 |   "language_info": {
356 |    "codemirror_mode": {
357 |     "name": "ipython",
358 |     "version": 3
359 |    },
360 |    "file_extension": ".py",
361 |    "mimetype": "text/x-python",
362 |    "name": "python",
363 |    "nbconvert_exporter": "python",
364 |    "pygments_lexer": "ipython3",
365 |    "version": "3.6.1"
366 |   }
367 |  },
368 |  "nbformat": 4,
369 |  "nbformat_minor": 2
370 | }
371 | 


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/.ipynb_checkpoints/Aula_04_Pandas_Exercicios-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### Faça a importação do panda como pd\n",
  8 |     "### Faça a importação do numpy como np"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": null,
 14 |    "metadata": {
 15 |     "collapsed": true
 16 |    },
 17 |    "outputs": [],
 18 |    "source": []
 19 |   },
 20 |   {
 21 |    "cell_type": "markdown",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "* **Ajuste o seed do numpy para 201 **\n",
 25 |     "* **Crie uma série em panda a partir de vetor de 100 números aleatórios de 0 a 1. **\n"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 7,
 31 |    "metadata": {},
 32 |    "outputs": [],
 33 |    "source": [
 34 |     "# Dadas as Séries \n",
 35 |     "nProva1 = pd.Series(data=[5,10,9], index=['AL01','AL02','AL03'])\n",
 36 |     "nProva2 = pd.Series(data=[4,6,4], index=['AL01','AL02','AL03'])\n"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "markdown",
 41 |    "metadata": {},
 42 |    "source": [
 43 |     "* (a) Calcule a Nota percentual de Cada Aluno nas Provas 1 e 2 em relação à Nota Máxima;\n",
 44 |     "* (b) Calcule a Nota Normalizada de Cada Aluno nas Provas 1 e 2;"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 8,
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "data": {
 54 |       "text/html": [
 55 |        "<div>\n",
 56 |        "<style>\n",
 57 |        "    .dataframe thead tr:only-child th {\n",
 58 |        "        text-align: right;\n",
 59 |        "    }\n",
 60 |        "\n",
 61 |        "    .dataframe thead th {\n",
 62 |        "        text-align: left;\n",
 63 |        "    }\n",
 64 |        "\n",
 65 |        "    .dataframe tbody tr th {\n",
 66 |        "        vertical-align: top;\n",
 67 |        "    }\n",
 68 |        "</style>\n",
 69 |        "<table border=\"1\" class=\"dataframe\">\n",
 70 |        "  <thead>\n",
 71 |        "    <tr style=\"text-align: right;\">\n",
 72 |        "      <th></th>\n",
 73 |        "      <th>P1</th>\n",
 74 |        "      <th>P2</th>\n",
 75 |        "      <th>P3</th>\n",
 76 |        "    </tr>\n",
 77 |        "  </thead>\n",
 78 |        "  <tbody>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>AL01</th>\n",
 81 |        "      <td>5.434049</td>\n",
 82 |        "      <td>2.783694</td>\n",
 83 |        "      <td>4.245176</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>AL02</th>\n",
 87 |        "      <td>8.447761</td>\n",
 88 |        "      <td>0.047189</td>\n",
 89 |        "      <td>1.215691</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <th>AL03</th>\n",
 93 |        "      <td>6.707491</td>\n",
 94 |        "      <td>8.258528</td>\n",
 95 |        "      <td>1.367066</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>AL04</th>\n",
 99 |        "      <td>5.750933</td>\n",
100 |        "      <td>8.913220</td>\n",
101 |        "      <td>2.092021</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>AL05</th>\n",
105 |        "      <td>1.853282</td>\n",
106 |        "      <td>1.083769</td>\n",
107 |        "      <td>2.196975</td>\n",
108 |        "    </tr>\n",
109 |        "  </tbody>\n",
110 |        "</table>\n",
111 |        "</div>"
112 |       ],
113 |       "text/plain": [
114 |        "            P1        P2        P3\n",
115 |        "AL01  5.434049  2.783694  4.245176\n",
116 |        "AL02  8.447761  0.047189  1.215691\n",
117 |        "AL03  6.707491  8.258528  1.367066\n",
118 |        "AL04  5.750933  8.913220  2.092021\n",
119 |        "AL05  1.853282  1.083769  2.196975"
120 |       ]
121 |      },
122 |      "execution_count": 8,
123 |      "metadata": {},
124 |      "output_type": "execute_result"
125 |     }
126 |    ],
127 |    "source": [
128 |     "# Dadas o dataframe\n",
129 |     "np.random.seed(100)\n",
130 |     "dados = np.random.rand(5,3)*10\n",
131 |     "colunas = ['P1','P2','P3']\n",
132 |     "index = ['AL01','AL02','AL03','AL04','AL05']\n",
133 |     "df = pd.DataFrame(dados,index,colunas)\n",
134 |     "df\n"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "markdown",
139 |    "metadata": {},
140 |    "source": [
141 |     "* (a) Calcue a média de cada prova;\n",
142 |     "* (b) Calcule a média de cada aluno.\n",
143 |     "* (c) Liste os alunos com nota em P2 abaixo da média.\n",
144 |     "* (d) Ordene o dataframe por P3\n",
145 |     "* (e) Crie um nova coluna com a média do aluno."
146 |    ]
147 |   },
148 |   {
149 |    "cell_type": "code",
150 |    "execution_count": 14,
151 |    "metadata": {},
152 |    "outputs": [
153 |     {
154 |      "data": {
155 |       "text/html": [
156 |        "<div>\n",
157 |        "<style>\n",
158 |        "    .dataframe thead tr:only-child th {\n",
159 |        "        text-align: right;\n",
160 |        "    }\n",
161 |        "\n",
162 |        "    .dataframe thead th {\n",
163 |        "        text-align: left;\n",
164 |        "    }\n",
165 |        "\n",
166 |        "    .dataframe tbody tr th {\n",
167 |        "        vertical-align: top;\n",
168 |        "    }\n",
169 |        "</style>\n",
170 |        "<table border=\"1\" class=\"dataframe\">\n",
171 |        "  <thead>\n",
172 |        "    <tr style=\"text-align: right;\">\n",
173 |        "      <th></th>\n",
174 |        "      <th>A</th>\n",
175 |        "      <th>B</th>\n",
176 |        "      <th>C</th>\n",
177 |        "      <th>D</th>\n",
178 |        "    </tr>\n",
179 |        "  </thead>\n",
180 |        "  <tbody>\n",
181 |        "    <tr>\n",
182 |        "      <th>0</th>\n",
183 |        "      <td>1.0</td>\n",
184 |        "      <td>2.0</td>\n",
185 |        "      <td>3</td>\n",
186 |        "      <td>NaN</td>\n",
187 |        "    </tr>\n",
188 |        "    <tr>\n",
189 |        "      <th>1</th>\n",
190 |        "      <td>2.0</td>\n",
191 |        "      <td>4.0</td>\n",
192 |        "      <td>4</td>\n",
193 |        "      <td>1.0</td>\n",
194 |        "    </tr>\n",
195 |        "    <tr>\n",
196 |        "      <th>2</th>\n",
197 |        "      <td>NaN</td>\n",
198 |        "      <td>NaN</td>\n",
199 |        "      <td>4</td>\n",
200 |        "      <td>2.0</td>\n",
201 |        "    </tr>\n",
202 |        "  </tbody>\n",
203 |        "</table>\n",
204 |        "</div>"
205 |       ],
206 |       "text/plain": [
207 |        "     A    B  C    D\n",
208 |        "0  1.0  2.0  3  NaN\n",
209 |        "1  2.0  4.0  4  1.0\n",
210 |        "2  NaN  NaN  4  2.0"
211 |       ]
212 |      },
213 |      "execution_count": 14,
214 |      "metadata": {},
215 |      "output_type": "execute_result"
216 |     }
217 |    ],
218 |    "source": [
219 |     "# Dados o dataFrame abaixo\n",
220 |     "dic = {'A':[1,2,np.nan],'B':[2,4,np.nan],'C':[3,4,4],'D':[np.nan,1,2]}\n",
221 |     "df = pd.DataFrame(dic)\n",
222 |     "df"
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "markdown",
227 |    "metadata": {},
228 |    "source": [
229 |     "* **Selecione as linhas sem NAN**\n",
230 |     "* **Selecione as colunas sem NAN**"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": null,
236 |    "metadata": {
237 |     "collapsed": true
238 |    },
239 |    "outputs": [],
240 |    "source": [
241 |     "# Dados o dataFrame abaixo\n",
242 |     "dic = {'Metodo':['KNN','SVR','ANN','KNN','SVR','ANN'],\n",
243 |     "        'Cenario':['C1','C2','C1','C2','C1','C2'],\n",
244 |     "        'Erro':[2.1,2.1,1.5,1.7,1.8,1.9]}\n",
245 |     "df = pd.DataFrame(dic)"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "markdown",
250 |    "metadata": {},
251 |    "source": [
252 |     "**Calcule:**\n",
253 |     "\n",
254 |     "* Erro média de cada método;\n",
255 |     "* Erro médio de cada cenário.\n"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "code",
260 |    "execution_count": null,
261 |    "metadata": {
262 |     "collapsed": true
263 |    },
264 |    "outputs": [],
265 |    "source": []
266 |   }
267 |  ],
268 |  "metadata": {
269 |   "kernelspec": {
270 |    "display_name": "Python 3",
271 |    "language": "python",
272 |    "name": "python3"
273 |   },
274 |   "language_info": {
275 |    "codemirror_mode": {
276 |     "name": "ipython",
277 |     "version": 3
278 |    },
279 |    "file_extension": ".py",
280 |    "mimetype": "text/x-python",
281 |    "name": "python",
282 |    "nbconvert_exporter": "python",
283 |    "pygments_lexer": "ipython3",
284 |    "version": "3.6.1"
285 |   }
286 |  },
287 |  "nbformat": 4,
288 |  "nbformat_minor": 2
289 | }
290 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Aula_05_Visualização-checkpoint.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Visualização de Dados\n",
   8 |     "----------------------------------------"
   9 |    ]
  10 |   },
  11 |   {
  12 |    "cell_type": "markdown",
  13 |    "metadata": {},
  14 |    "source": [
  15 |     "### Matplotlib\n",
  16 |     "-----------------"
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "code",
  21 |    "execution_count": null,
  22 |    "metadata": {
  23 |     "collapsed": true
  24 |    },
  25 |    "outputs": [],
  26 |    "source": [
  27 |     "# Instalação\n",
  28 |     "conda install matplotlib\n",
  29 |     "pip install matplotlib"
  30 |    ]
  31 |   },
  32 |   {
  33 |    "cell_type": "code",
  34 |    "execution_count": null,
  35 |    "metadata": {
  36 |     "collapsed": true
  37 |    },
  38 |    "outputs": [],
  39 |    "source": [
  40 |     "# Importação\n",
  41 |     "import matplotlib.pyplot as plt"
  42 |    ]
  43 |   },
  44 |   {
  45 |    "cell_type": "code",
  46 |    "execution_count": null,
  47 |    "metadata": {
  48 |     "collapsed": true
  49 |    },
  50 |    "outputs": [],
  51 |    "source": [
  52 |     "# Permite visualisação dos Plot no Jupyter Notebook\n",
  53 |     "%matplotlib inline"
  54 |    ]
  55 |   },
  56 |   {
  57 |    "cell_type": "code",
  58 |    "execution_count": null,
  59 |    "metadata": {
  60 |     "collapsed": true
  61 |    },
  62 |    "outputs": [],
  63 |    "source": [
  64 |     "# Importando Numpy para Exemplos\n",
  65 |     "import numpy as np\n"
  66 |    ]
  67 |   },
  68 |   {
  69 |    "cell_type": "code",
  70 |    "execution_count": null,
  71 |    "metadata": {
  72 |     "collapsed": true
  73 |    },
  74 |    "outputs": [],
  75 |    "source": [
  76 |     "### API Funcional\n",
  77 |     "x = np.linspace(-10,10,100)\n",
  78 |     "y = x ** 3"
  79 |    ]
  80 |   },
  81 |   {
  82 |    "cell_type": "code",
  83 |    "execution_count": null,
  84 |    "metadata": {},
  85 |    "outputs": [],
  86 |    "source": [
  87 |     "#Exemplo 01\n",
  88 |     "plt.plot(x,y) # Fora do jupyter use plt.show() para visualizar o plot\n",
  89 |     "plt.show()"
  90 |    ]
  91 |   },
  92 |   {
  93 |    "cell_type": "code",
  94 |    "execution_count": null,
  95 |    "metadata": {},
  96 |    "outputs": [],
  97 |    "source": [
  98 |     "plt.plot(x,y,'g--')\n",
  99 |     "plt.xlabel('Eixo X')\n",
 100 |     "plt.ylabel('Eixo Y')\n",
 101 |     "plt.title(\"Exemplo 01\")"
 102 |    ]
 103 |   },
 104 |   {
 105 |    "cell_type": "markdown",
 106 |    "metadata": {},
 107 |    "source": [
 108 |     "### Mini Tarefa\n"
 109 |    ]
 110 |   },
 111 |   {
 112 |    "cell_type": "markdown",
 113 |    "metadata": {},
 114 |    "source": [
 115 |     "**Faça o plotter da função $sen(x)$ no intervalo de 0 a $2\\pi$**."
 116 |    ]
 117 |   },
 118 |   {
 119 |    "cell_type": "markdown",
 120 |    "metadata": {},
 121 |    "source": [
 122 |     "## SubPlots"
 123 |    ]
 124 |   },
 125 |   {
 126 |    "cell_type": "code",
 127 |    "execution_count": null,
 128 |    "metadata": {},
 129 |    "outputs": [],
 130 |    "source": [
 131 |     "x = np.linspace(0,2*np.pi,100)\n",
 132 |     "y1 = np.sin(x)\n",
 133 |     "y2 = np.cos(x)\n",
 134 |     "plt.subplot(1,2,1) # Linha 1 coluna 2 gráfico 1\n",
 135 |     "plt.plot(x,y1,'g')\n",
 136 |     "plt.subplot(1,2,2) # Linha 1 coluna 2 gráfico 2\n",
 137 |     "plt.plot(x,y2,'b')\n",
 138 |     "plt.show()"
 139 |    ]
 140 |   },
 141 |   {
 142 |    "cell_type": "code",
 143 |    "execution_count": null,
 144 |    "metadata": {
 145 |     "scrolled": true
 146 |    },
 147 |    "outputs": [],
 148 |    "source": [
 149 |     "# Utilizado orienção a Obejto\n",
 150 |     "fig1 = plt.figure()\n",
 151 |     "axes= fig1.add_axes([0.1,0.1,0.8,0.8])\n",
 152 |     "axes.plot(x,y1)\n",
 153 |     "axes.set_xlabel('x')\n",
 154 |     "axes.set_ylabel('y')\n",
 155 |     "axes.set_title(\"Exemplo 02\")"
 156 |    ]
 157 |   },
 158 |   {
 159 |    "cell_type": "markdown",
 160 |    "metadata": {},
 161 |    "source": [
 162 |     "### Mini Tarefa"
 163 |    ]
 164 |   },
 165 |   {
 166 |    "cell_type": "markdown",
 167 |    "metadata": {},
 168 |    "source": [
 169 |     "**Faça o plotter da função $cos(x)$ no intervalo de 0 a $2\\pi$** utilizando OO."
 170 |    ]
 171 |   },
 172 |   {
 173 |    "cell_type": "markdown",
 174 |    "metadata": {},
 175 |    "source": [
 176 |     "### SubPlot com OO"
 177 |    ]
 178 |   },
 179 |   {
 180 |    "cell_type": "code",
 181 |    "execution_count": null,
 182 |    "metadata": {},
 183 |    "outputs": [],
 184 |    "source": [
 185 |     "# Eixos\n",
 186 |     "fig1 = plt.figure()\n",
 187 |     "axes1 = fig1.add_axes([0.1,0.1,0.8,0.8])\n",
 188 |     "axes2 = fig1.add_axes([0.2,0.4,0.4,0.4])\n"
 189 |    ]
 190 |   },
 191 |   {
 192 |    "cell_type": "code",
 193 |    "execution_count": null,
 194 |    "metadata": {},
 195 |    "outputs": [],
 196 |    "source": [
 197 |     "# Exemplo 03\n",
 198 |     "fig1 = plt.figure()\n",
 199 |     "axes1 = fig1.add_axes([0.1,0.1,0.8,0.8])\n",
 200 |     "axes2 = fig1.add_axes([0.2,0.4,0.4,0.4])\n",
 201 |     "x = np.linspace(0,2*np.pi,100)\n",
 202 |     "y1= np.sin(x)\n",
 203 |     "axes1.plot(x,y1)\n",
 204 |     "axes2.plot(y1,x)"
 205 |    ]
 206 |   },
 207 |   {
 208 |    "cell_type": "markdown",
 209 |    "metadata": {},
 210 |    "source": [
 211 |     "### Mini Tarefa"
 212 |    ]
 213 |   },
 214 |   {
 215 |    "cell_type": "markdown",
 216 |    "metadata": {},
 217 |    "source": [
 218 |     "**Ajuste o Plotter do exemplo 03 para que ambos os gráficos sejam exibidos**"
 219 |    ]
 220 |   },
 221 |   {
 222 |    "cell_type": "code",
 223 |    "execution_count": null,
 224 |    "metadata": {},
 225 |    "outputs": [],
 226 |    "source": [
 227 |     "fig,axes = plt.subplots(2,2)\n",
 228 |     "plt.tight_layout() # Ajusta overlap"
 229 |    ]
 230 |   },
 231 |   {
 232 |    "cell_type": "code",
 233 |    "execution_count": null,
 234 |    "metadata": {},
 235 |    "outputs": [],
 236 |    "source": [
 237 |     "x = np.linspace(0,5,10)\n",
 238 |     "y = x ** 2\n",
 239 |     "for ax in axes:\n",
 240 |     "    ax.plot(x,y)\n",
 241 |     "fig"
 242 |    ]
 243 |   },
 244 |   {
 245 |    "cell_type": "code",
 246 |    "execution_count": null,
 247 |    "metadata": {},
 248 |    "outputs": [],
 249 |    "source": [
 250 |     "# Utilizando Index\n",
 251 |     "axes[0,1].plot(x,2*y)\n",
 252 |     "fig"
 253 |    ]
 254 |   },
 255 |   {
 256 |    "cell_type": "markdown",
 257 |    "metadata": {},
 258 |    "source": [
 259 |     "### Mini Tarefa"
 260 |    ]
 261 |   },
 262 |   {
 263 |    "cell_type": "markdown",
 264 |    "metadata": {},
 265 |    "source": [
 266 |     "(a) ** Faça um subplot 1x2 com as funções $sen(x)$ e $cos(x)$ de 0 a $2\\pi$.**\n",
 267 |     "\n",
 268 |     "(b) ** Faça um subplo 2x2 com as funções: $x^{2}$, $x^{3}$, $sen(x)$ e $cos(x)$ de 0 a $2\\pi$ **."
 269 |    ]
 270 |   },
 271 |   {
 272 |    "cell_type": "markdown",
 273 |    "metadata": {},
 274 |    "source": [
 275 |     "### Tamanho e DPI"
 276 |    ]
 277 |   },
 278 |   {
 279 |    "cell_type": "code",
 280 |    "execution_count": null,
 281 |    "metadata": {},
 282 |    "outputs": [],
 283 |    "source": [
 284 |     "fig = plt.figure(figsize=(2,2))\n",
 285 |     "axes = fig.add_axes([0.1,0.1,1,1])\n",
 286 |     "x = np.linspace(0,1,10)\n",
 287 |     "y = x**2\n",
 288 |     "axes.plot(x,y)"
 289 |    ]
 290 |   },
 291 |   {
 292 |    "cell_type": "code",
 293 |    "execution_count": null,
 294 |    "metadata": {},
 295 |    "outputs": [],
 296 |    "source": [
 297 |     "#Ajustando Tamanhos em subplots\n",
 298 |     "fig,axes = plt.subplots(1,2,figsize=(8,2))\n",
 299 |     "x = np.linspace(0,2*np.pi,100)\n",
 300 |     "y1 = np.sin(x)\n",
 301 |     "y2 = np.cos(x)\n",
 302 |     "axes[0].plot(x,y1)\n",
 303 |     "axes[1].plot(x,y2)\n",
 304 |     "plt.tight_layout()"
 305 |    ]
 306 |   },
 307 |   {
 308 |    "cell_type": "markdown",
 309 |    "metadata": {},
 310 |    "source": [
 311 |     "## Mini Tarefa"
 312 |    ]
 313 |   },
 314 |   {
 315 |    "cell_type": "markdown",
 316 |    "metadata": {},
 317 |    "source": [
 318 |     "**Altere o código acima de modo que os gráficos fiquem um abaixo do outro**"
 319 |    ]
 320 |   },
 321 |   {
 322 |    "cell_type": "code",
 323 |    "execution_count": null,
 324 |    "metadata": {
 325 |     "collapsed": true
 326 |    },
 327 |    "outputs": [],
 328 |    "source": [
 329 |     "# Salvando a figura\n",
 330 |     "fig.savefig(\"fig1.jpg\",dpi=100)"
 331 |    ]
 332 |   },
 333 |   {
 334 |    "cell_type": "markdown",
 335 |    "metadata": {
 336 |     "collapsed": true
 337 |    },
 338 |    "source": [
 339 |     "### Legendas"
 340 |    ]
 341 |   },
 342 |   {
 343 |    "cell_type": "code",
 344 |    "execution_count": null,
 345 |    "metadata": {},
 346 |    "outputs": [],
 347 |    "source": [
 348 |     "fig = plt.figure()\n",
 349 |     "axes = fig.add_axes([0.1,0.1,0.8,0.8])\n",
 350 |     "x = np.linspace(0,10,10)\n",
 351 |     "y1 = x**2\n",
 352 |     "y2 = 2*x + 1\n",
 353 |     "axes.plot(x,y1,label='y1')\n",
 354 |     "axes.plot(x,y2,label='y2')\n",
 355 |     "axes.legend(loc=0)\n",
 356 |     "#Valores loc\n",
 357 |     "#'best'         : 0, (only implemented for axes legends)\n",
 358 |     "#'upper right'  : 1,\n",
 359 |     "#'upper left'   : 2,\n",
 360 |     "#'lower left'   : 3,\n",
 361 |     "#'lower right'  : 4,\n",
 362 |     "#'right'        : 5,\n",
 363 |     "#'center left'  : 6,\n",
 364 |     "#'center right' : 7,\n",
 365 |     "#'lower center' : 8,\n",
 366 |     "#'upper center' : 9,\n",
 367 |     "#'center'       : 10,\n",
 368 |     "\n",
 369 |     "# Com tuplas\n",
 370 |     "axes.legend(loc=(0.05,0.8))"
 371 |    ]
 372 |   },
 373 |   {
 374 |    "cell_type": "markdown",
 375 |    "metadata": {},
 376 |    "source": [
 377 |     "## Mini Tarefa"
 378 |    ]
 379 |   },
 380 |   {
 381 |    "cell_type": "markdown",
 382 |    "metadata": {},
 383 |    "source": [
 384 |     "** Sendo a perda em espaço livre dada por $PL=20\\times log_{10}{(d)}+20\\times log_{10}{(f)} +32.45 $ dB** \n",
 385 |     "\n",
 386 |     "Onde $d$ é a distância em $km$ e $f$ a frequência em MHz.\n",
 387 |     "\n",
 388 |     "Faça plots com legendas para as frequência de 1,10 e 100 MHz, para distâncias de 0 a 5 $Km$.\n"
 389 |    ]
 390 |   },
 391 |   {
 392 |    "cell_type": "markdown",
 393 |    "metadata": {},
 394 |    "source": [
 395 |     "## Customização"
 396 |    ]
 397 |   },
 398 |   {
 399 |    "cell_type": "code",
 400 |    "execution_count": null,
 401 |    "metadata": {},
 402 |    "outputs": [],
 403 |    "source": [
 404 |     "fig = plt.figure()\n",
 405 |     "ax = fig.add_axes([0.1,0.1,0.8,0.8])\n",
 406 |     "ax.plot(x,y1,color='red',linewidth=3,alpha=0.7,linestyle='--',marker='o',markersize=5) #Opções style= \"-\", \"--\",\"-.\", \":\" steps\n",
 407 |     "ax.plot(x,y2,color='#000000',lw=3,alpha=0.7,ls='--',marker='o',markersize=10) "
 408 |    ]
 409 |   },
 410 |   {
 411 |    "cell_type": "code",
 412 |    "execution_count": null,
 413 |    "metadata": {},
 414 |    "outputs": [],
 415 |    "source": [
 416 |     "# Limites eixos\n",
 417 |     "fig = plt.figure()\n",
 418 |     "ax = fig.add_axes([0.1,0.1,0.8,0.8])\n",
 419 |     "x = np.linspace(0,10,10)\n",
 420 |     "y = x+1\n",
 421 |     "ax.plot(x,y)\n",
 422 |     "ax.set_xlim([2,8])\n",
 423 |     "ax.set_ylim([2,8])\n",
 424 |     "\n"
 425 |    ]
 426 |   },
 427 |   {
 428 |    "cell_type": "markdown",
 429 |    "metadata": {},
 430 |    "source": [
 431 |     "Tutorial :https://www.labri.fr/perso/nrougier/teaching/matplotlib/"
 432 |    ]
 433 |   },
 434 |   {
 435 |    "cell_type": "markdown",
 436 |    "metadata": {},
 437 |    "source": [
 438 |     "### Seaborn\n",
 439 |     "-----------------\n",
 440 |     "https://seaborn.pydata.org/"
 441 |    ]
 442 |   },
 443 |   {
 444 |    "cell_type": "code",
 445 |    "execution_count": null,
 446 |    "metadata": {
 447 |     "collapsed": true
 448 |    },
 449 |    "outputs": [],
 450 |    "source": [
 451 |     "# Instalação\n",
 452 |     "#conda install seaborn\n",
 453 |     "#pip install seaborn"
 454 |    ]
 455 |   },
 456 |   {
 457 |    "cell_type": "code",
 458 |    "execution_count": null,
 459 |    "metadata": {
 460 |     "collapsed": true
 461 |    },
 462 |    "outputs": [],
 463 |    "source": [
 464 |     "# importação\n",
 465 |     "import seaborn as sns\n",
 466 |     "import numpy as np\n",
 467 |     "import pandas as pd"
 468 |    ]
 469 |   },
 470 |   {
 471 |    "cell_type": "code",
 472 |    "execution_count": null,
 473 |    "metadata": {
 474 |     "collapsed": true
 475 |    },
 476 |    "outputs": [],
 477 |    "source": [
 478 |     "%matplotlib inline"
 479 |    ]
 480 |   },
 481 |   {
 482 |    "cell_type": "markdown",
 483 |    "metadata": {},
 484 |    "source": [
 485 |     "## Distribuição"
 486 |    ]
 487 |   },
 488 |   {
 489 |    "cell_type": "code",
 490 |    "execution_count": null,
 491 |    "metadata": {
 492 |     "collapsed": true
 493 |    },
 494 |    "outputs": [],
 495 |    "source": [
 496 |     "iris = sns.load_dataset('iris')\n",
 497 |     "#https://github.com/mwaskom/seaborn-data"
 498 |    ]
 499 |   },
 500 |   {
 501 |    "cell_type": "code",
 502 |    "execution_count": null,
 503 |    "metadata": {},
 504 |    "outputs": [],
 505 |    "source": [
 506 |     "type(iris)"
 507 |    ]
 508 |   },
 509 |   {
 510 |    "cell_type": "code",
 511 |    "execution_count": null,
 512 |    "metadata": {},
 513 |    "outputs": [],
 514 |    "source": [
 515 |     "iris.head()"
 516 |    ]
 517 |   },
 518 |   {
 519 |    "cell_type": "code",
 520 |    "execution_count": null,
 521 |    "metadata": {},
 522 |    "outputs": [],
 523 |    "source": [
 524 |     "sns.distplot(tips['sepal_width'])"
 525 |    ]
 526 |   },
 527 |   {
 528 |    "cell_type": "code",
 529 |    "execution_count": null,
 530 |    "metadata": {
 531 |     "scrolled": true
 532 |    },
 533 |    "outputs": [],
 534 |    "source": [
 535 |     "sns.distplot(tips['sepal_width'],kde=False,bins=20)"
 536 |    ]
 537 |   },
 538 |   {
 539 |    "cell_type": "markdown",
 540 |    "metadata": {},
 541 |    "source": [
 542 |     "## Mini Tarefa\n",
 543 |     "** Carregue o dataset tips da lib seaborn e plot a distribuiçãod a coluna total_bill***"
 544 |    ]
 545 |   },
 546 |   {
 547 |    "cell_type": "markdown",
 548 |    "metadata": {},
 549 |    "source": [
 550 |     "## Join Plot"
 551 |    ]
 552 |   },
 553 |   {
 554 |    "cell_type": "code",
 555 |    "execution_count": null,
 556 |    "metadata": {
 557 |     "collapsed": true
 558 |    },
 559 |    "outputs": [],
 560 |    "source": [
 561 |     "tips = sns.load_dataset('tips')"
 562 |    ]
 563 |   },
 564 |   {
 565 |    "cell_type": "code",
 566 |    "execution_count": null,
 567 |    "metadata": {},
 568 |    "outputs": [],
 569 |    "source": [
 570 |     "tips.head()"
 571 |    ]
 572 |   },
 573 |   {
 574 |    "cell_type": "code",
 575 |    "execution_count": null,
 576 |    "metadata": {},
 577 |    "outputs": [],
 578 |    "source": [
 579 |     "sns.jointplot(x='total_bill', y= 'tip',data=tips)"
 580 |    ]
 581 |   },
 582 |   {
 583 |    "cell_type": "code",
 584 |    "execution_count": null,
 585 |    "metadata": {},
 586 |    "outputs": [],
 587 |    "source": [
 588 |     "sns.jointplot(x='total_bill', y= 'tip',data=tips,kind='reg') # Kinds=hex,reg,kde"
 589 |    ]
 590 |   },
 591 |   {
 592 |    "cell_type": "markdown",
 593 |    "metadata": {},
 594 |    "source": [
 595 |     "### Pair Plots"
 596 |    ]
 597 |   },
 598 |   {
 599 |    "cell_type": "code",
 600 |    "execution_count": null,
 601 |    "metadata": {},
 602 |    "outputs": [],
 603 |    "source": [
 604 |     "sns.pairplot(tips)"
 605 |    ]
 606 |   },
 607 |   {
 608 |    "cell_type": "code",
 609 |    "execution_count": null,
 610 |    "metadata": {},
 611 |    "outputs": [],
 612 |    "source": [
 613 |     "tips.head()"
 614 |    ]
 615 |   },
 616 |   {
 617 |    "cell_type": "code",
 618 |    "execution_count": null,
 619 |    "metadata": {},
 620 |    "outputs": [],
 621 |    "source": [
 622 |     "sns.pairplot(tips,hue='sex')"
 623 |    ]
 624 |   },
 625 |   {
 626 |    "cell_type": "code",
 627 |    "execution_count": null,
 628 |    "metadata": {},
 629 |    "outputs": [],
 630 |    "source": [
 631 |     "sns.pairplot(tips,hue='sex',palette=\"RdBu_r\")\n",
 632 |     "#http://seaborn.pydata.org/tutorial/color_palettes.html"
 633 |    ]
 634 |   },
 635 |   {
 636 |    "cell_type": "markdown",
 637 |    "metadata": {},
 638 |    "source": [
 639 |     "## Rug Plots"
 640 |    ]
 641 |   },
 642 |   {
 643 |    "cell_type": "code",
 644 |    "execution_count": null,
 645 |    "metadata": {},
 646 |    "outputs": [],
 647 |    "source": [
 648 |     "sns.rugplot(tips['total_bill'])"
 649 |    ]
 650 |   },
 651 |   {
 652 |    "cell_type": "markdown",
 653 |    "metadata": {},
 654 |    "source": [
 655 |     "## Bar Plots"
 656 |    ]
 657 |   },
 658 |   {
 659 |    "cell_type": "code",
 660 |    "execution_count": null,
 661 |    "metadata": {},
 662 |    "outputs": [],
 663 |    "source": [
 664 |     "tips.head()\n"
 665 |    ]
 666 |   },
 667 |   {
 668 |    "cell_type": "code",
 669 |    "execution_count": null,
 670 |    "metadata": {},
 671 |    "outputs": [],
 672 |    "source": [
 673 |     "sns.barplot(x='smoker',y='total_bill',data=tips)"
 674 |    ]
 675 |   },
 676 |   {
 677 |    "cell_type": "code",
 678 |    "execution_count": null,
 679 |    "metadata": {},
 680 |    "outputs": [],
 681 |    "source": [
 682 |     "sns.barplot(x='smoker',y='total_bill',data=tips,estimator=np.sum)"
 683 |    ]
 684 |   },
 685 |   {
 686 |    "cell_type": "code",
 687 |    "execution_count": null,
 688 |    "metadata": {},
 689 |    "outputs": [],
 690 |    "source": [
 691 |     "## Bar plot especial\n",
 692 |     "sns.countplot(x='smoker',data=tips)"
 693 |    ]
 694 |   },
 695 |   {
 696 |    "cell_type": "markdown",
 697 |    "metadata": {},
 698 |    "source": [
 699 |     "## Mini Tarefa"
 700 |    ]
 701 |   },
 702 |   {
 703 |    "cell_type": "markdown",
 704 |    "metadata": {},
 705 |    "source": [
 706 |     "** Como podemos fazer um countplot utilizando barplot ?** "
 707 |    ]
 708 |   },
 709 |   {
 710 |    "cell_type": "markdown",
 711 |    "metadata": {},
 712 |    "source": [
 713 |     "### Boxplots"
 714 |    ]
 715 |   },
 716 |   {
 717 |    "cell_type": "code",
 718 |    "execution_count": null,
 719 |    "metadata": {},
 720 |    "outputs": [],
 721 |    "source": [
 722 |     "sns.boxplot(x='sex',y='total_bill',data=tips)"
 723 |    ]
 724 |   },
 725 |   {
 726 |    "cell_type": "code",
 727 |    "execution_count": null,
 728 |    "metadata": {},
 729 |    "outputs": [],
 730 |    "source": [
 731 |     "sns.boxplot(x='sex',y='total_bill',data=tips,hue='smoker')"
 732 |    ]
 733 |   },
 734 |   {
 735 |    "cell_type": "markdown",
 736 |    "metadata": {},
 737 |    "source": [
 738 |     "## Mini Tarefa"
 739 |    ]
 740 |   },
 741 |   {
 742 |    "cell_type": "markdown",
 743 |    "metadata": {},
 744 |    "source": [
 745 |     "** (a) Qual o dia da semana em que os clientes fornecem melhores gorjetas ?**\n",
 746 |     "\n",
 747 |     "** (b) No dia acima, quem é mais generoso, os homens ou as mulheres?  **"
 748 |    ]
 749 |   },
 750 |   {
 751 |    "cell_type": "markdown",
 752 |    "metadata": {},
 753 |    "source": [
 754 |     "## Violin Plots"
 755 |    ]
 756 |   },
 757 |   {
 758 |    "cell_type": "code",
 759 |    "execution_count": null,
 760 |    "metadata": {},
 761 |    "outputs": [],
 762 |    "source": [
 763 |     "sns.violinplot(x='sex',y='total_bill',data=tips)"
 764 |    ]
 765 |   },
 766 |   {
 767 |    "cell_type": "code",
 768 |    "execution_count": null,
 769 |    "metadata": {},
 770 |    "outputs": [],
 771 |    "source": [
 772 |     "sns.violinplot(x='sex',y='total_bill',data=tips,hue='day')"
 773 |    ]
 774 |   },
 775 |   {
 776 |    "cell_type": "code",
 777 |    "execution_count": null,
 778 |    "metadata": {},
 779 |    "outputs": [],
 780 |    "source": [
 781 |     "# Usando split\n",
 782 |     "sns.violinplot(x='day',y='total_bill',data=tips,hue='sex')"
 783 |    ]
 784 |   },
 785 |   {
 786 |    "cell_type": "code",
 787 |    "execution_count": null,
 788 |    "metadata": {},
 789 |    "outputs": [],
 790 |    "source": [
 791 |     "sns.violinplot(x='day',y='total_bill',data=tips,hue='sex',split=True)"
 792 |    ]
 793 |   },
 794 |   {
 795 |    "cell_type": "markdown",
 796 |    "metadata": {},
 797 |    "source": [
 798 |     "## StripPlots"
 799 |    ]
 800 |   },
 801 |   {
 802 |    "cell_type": "code",
 803 |    "execution_count": null,
 804 |    "metadata": {},
 805 |    "outputs": [],
 806 |    "source": [
 807 |     "sns.stripplot(x='sex',y='tip',data=tips) #"
 808 |    ]
 809 |   },
 810 |   {
 811 |    "cell_type": "code",
 812 |    "execution_count": null,
 813 |    "metadata": {},
 814 |    "outputs": [],
 815 |    "source": [
 816 |     "sns.stripplot(x='sex',y='tip',data=tips,jitter=True)"
 817 |    ]
 818 |   },
 819 |   {
 820 |    "cell_type": "code",
 821 |    "execution_count": null,
 822 |    "metadata": {},
 823 |    "outputs": [],
 824 |    "source": [
 825 |     "sns.stripplot(x='day',y='tip',data=tips,jitter=True,hue='sex')"
 826 |    ]
 827 |   },
 828 |   {
 829 |    "cell_type": "markdown",
 830 |    "metadata": {},
 831 |    "source": [
 832 |     "### Swarm Plot"
 833 |    ]
 834 |   },
 835 |   {
 836 |    "cell_type": "code",
 837 |    "execution_count": null,
 838 |    "metadata": {},
 839 |    "outputs": [],
 840 |    "source": [
 841 |     "gr= sns.swarmplot(x='day',y='tip',data=tips)"
 842 |    ]
 843 |   },
 844 |   {
 845 |    "cell_type": "markdown",
 846 |    "metadata": {},
 847 |    "source": [
 848 |     "## Factor Plots"
 849 |    ]
 850 |   },
 851 |   {
 852 |    "cell_type": "code",
 853 |    "execution_count": null,
 854 |    "metadata": {},
 855 |    "outputs": [],
 856 |    "source": [
 857 |     "sns.factorplot(x='day',y='tip',data=tips,kind='bar')\n",
 858 |     "#https://seaborn.pydata.org/generated/seaborn.factorplot.html"
 859 |    ]
 860 |   },
 861 |   {
 862 |    "cell_type": "code",
 863 |    "execution_count": null,
 864 |    "metadata": {},
 865 |    "outputs": [],
 866 |    "source": [
 867 |     "sns.factorplot(x='day',y='tip',data=tips,kind='violin',hue='sex')"
 868 |    ]
 869 |   },
 870 |   {
 871 |    "cell_type": "code",
 872 |    "execution_count": null,
 873 |    "metadata": {},
 874 |    "outputs": [],
 875 |    "source": [
 876 |     "sns.factorplot(x=\"day\", y=\"tip\",col=\"sex\", data=tips,kind=\"bar\")\n"
 877 |    ]
 878 |   },
 879 |   {
 880 |    "cell_type": "markdown",
 881 |    "metadata": {},
 882 |    "source": [
 883 |     "## Regression plots"
 884 |    ]
 885 |   },
 886 |   {
 887 |    "cell_type": "code",
 888 |    "execution_count": null,
 889 |    "metadata": {},
 890 |    "outputs": [],
 891 |    "source": [
 892 |     "# Regressão\n",
 893 |     "ax =sns.lmplot(x='total_bill',y='tip',data=tips)\n",
 894 |     "ax.set_xlabels(\"Total Bill\")\n",
 895 |     "ax.set_ylabels(\"Tip\")"
 896 |    ]
 897 |   },
 898 |   {
 899 |    "cell_type": "code",
 900 |    "execution_count": null,
 901 |    "metadata": {},
 902 |    "outputs": [],
 903 |    "source": [
 904 |     "# Usando Hue\n",
 905 |     "ax =sns.lmplot(x='total_bill',y='tip',data=tips,hue='sex') #makers=['o','v]\n",
 906 |     "ax.set_xlabels(\"Total Bill\")\n",
 907 |     "ax.set_ylabels(\"Tip\")"
 908 |    ]
 909 |   },
 910 |   {
 911 |    "cell_type": "code",
 912 |    "execution_count": null,
 913 |    "metadata": {},
 914 |    "outputs": [],
 915 |    "source": [
 916 |     "# Usando col\n",
 917 |     "ax =sns.lmplot(x='total_bill',y='tip',data=tips,col='sex') #makers=['o','v]\n",
 918 |     "ax.set_xlabels(\"Total Bill\")\n",
 919 |     "ax.set_ylabels(\"Tip\")"
 920 |    ]
 921 |   },
 922 |   {
 923 |    "cell_type": "code",
 924 |    "execution_count": null,
 925 |    "metadata": {},
 926 |    "outputs": [],
 927 |    "source": [
 928 |     "# Usando col e row\n",
 929 |     "ax =sns.lmplot(x='total_bill',y='tip',data=tips,col='sex',row='day') #makers=['o','v]\n",
 930 |     "ax.set_xlabels(\"Total Bill\")\n",
 931 |     "ax.set_ylabels(\"Tip\")"
 932 |    ]
 933 |   },
 934 |   {
 935 |    "cell_type": "code",
 936 |    "execution_count": null,
 937 |    "metadata": {},
 938 |    "outputs": [],
 939 |    "source": [
 940 |     "# Usando col, row e hue\n",
 941 |     "ax =sns.lmplot(x='total_bill',y='tip',data=tips,col='day',hue='sex') #makers=['o','v]\n",
 942 |     "ax.set_xlabels(\"Total Bill\")\n",
 943 |     "ax.set_ylabels(\"Tip\")"
 944 |    ]
 945 |   },
 946 |   {
 947 |    "cell_type": "markdown",
 948 |    "metadata": {},
 949 |    "source": [
 950 |     "## PairGrid\n"
 951 |    ]
 952 |   },
 953 |   {
 954 |    "cell_type": "code",
 955 |    "execution_count": null,
 956 |    "metadata": {},
 957 |    "outputs": [],
 958 |    "source": [
 959 |     "iris = sns.load_dataset('iris')\n",
 960 |     "g = sns.pairplot(iris)"
 961 |    ]
 962 |   },
 963 |   {
 964 |    "cell_type": "code",
 965 |    "execution_count": null,
 966 |    "metadata": {},
 967 |    "outputs": [],
 968 |    "source": [
 969 |     "g = sns.PairGrid(iris)\n",
 970 |     "g.map(plt.scatter)\n"
 971 |    ]
 972 |   },
 973 |   {
 974 |    "cell_type": "code",
 975 |    "execution_count": null,
 976 |    "metadata": {},
 977 |    "outputs": [],
 978 |    "source": [
 979 |     "# Customizando\n",
 980 |     "g = sns.PairGrid(iris)\n",
 981 |     "#g.map_diag(sns.distplot)\n",
 982 |     "#g.map_lower(plt.scatter)\n",
 983 |     "#g.map_upper(sns.kdeplot)"
 984 |    ]
 985 |   },
 986 |   {
 987 |    "cell_type": "markdown",
 988 |    "metadata": {},
 989 |    "source": [
 990 |     "## FacetGird"
 991 |    ]
 992 |   },
 993 |   {
 994 |    "cell_type": "code",
 995 |    "execution_count": null,
 996 |    "metadata": {},
 997 |    "outputs": [],
 998 |    "source": [
 999 |     "g = sns.FacetGrid(data=tips,col='time',row='sex')\n",
1000 |     "g.map(sns.distplot,'tip')"
1001 |    ]
1002 |   },
1003 |   {
1004 |    "cell_type": "code",
1005 |    "execution_count": null,
1006 |    "metadata": {
1007 |     "collapsed": true
1008 |    },
1009 |    "outputs": [],
1010 |    "source": []
1011 |   }
1012 |  ],
1013 |  "metadata": {
1014 |   "kernelspec": {
1015 |    "display_name": "Python 3",
1016 |    "language": "python",
1017 |    "name": "python3"
1018 |   },
1019 |   "language_info": {
1020 |    "codemirror_mode": {
1021 |     "name": "ipython",
1022 |     "version": 3
1023 |    },
1024 |    "file_extension": ".py",
1025 |    "mimetype": "text/x-python",
1026 |    "name": "python",
1027 |    "nbconvert_exporter": "python",
1028 |    "pygments_lexer": "ipython3",
1029 |    "version": "3.6.1"
1030 |   }
1031 |  },
1032 |  "nbformat": 4,
1033 |  "nbformat_minor": 2
1034 | }
1035 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Aula_06_01_Regressão Linear-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### Versão sckitlearn"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": null,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "import sklearn\n",
 17 |     "print('Minha versão do sckit é {}.'.format(sklearn.__version__))"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "markdown",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "### Importações\n",
 25 |     "-------------"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": null,
 31 |    "metadata": {
 32 |     "collapsed": true
 33 |    },
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "#Imports \n",
 37 |     "import numpy as np\n",
 38 |     "import pandas as pd\n",
 39 |     "from matplotlib import pyplot as plt\n",
 40 |     "import seaborn as sns\n",
 41 |     "%matplotlib inline"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {},
 47 |    "source": [
 48 |     "### Carregando Dataset\n",
 49 |     "-------------------------"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": null,
 55 |    "metadata": {
 56 |     "collapsed": true
 57 |    },
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "# Dataset (a partir de csv)\n",
 61 |     "#df =pd.read_csv('meuarquivo.data')"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": null,
 67 |    "metadata": {
 68 |     "scrolled": true
 69 |    },
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "# A a partir do Sckit\n",
 73 |     "from sklearn.datasets import load_boston\n",
 74 |     "boston = load_boston()\n",
 75 |     "print(boston.DESCR)"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": null,
 81 |    "metadata": {
 82 |     "scrolled": true
 83 |    },
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "# Tranformado em um dataframe Pandas\n",
 87 |     "df = pd.DataFrame(boston.data)\n",
 88 |     "df.head()"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": null,
 94 |    "metadata": {
 95 |     "scrolled": true
 96 |    },
 97 |    "outputs": [],
 98 |    "source": [
 99 |     "df.columns = boston.feature_names\n",
100 |     "df.head()"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "code",
105 |    "execution_count": null,
106 |    "metadata": {},
107 |    "outputs": [],
108 |    "source": [
109 |     "df['PRICE'] =boston.target\n",
110 |     "df.head()"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": null,
116 |    "metadata": {
117 |     "collapsed": true
118 |    },
119 |    "outputs": [],
120 |    "source": [
121 |     "# Info\n",
122 |     "df.info()"
123 |    ]
124 |   },
125 |   {
126 |    "cell_type": "code",
127 |    "execution_count": null,
128 |    "metadata": {
129 |     "collapsed": true
130 |    },
131 |    "outputs": [],
132 |    "source": [
133 |     "# Describe\n",
134 |     "df.describe()"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": null,
140 |    "metadata": {
141 |     "collapsed": true
142 |    },
143 |    "outputs": [],
144 |    "source": [
145 |     "# PLots\n",
146 |     "# Verificando relação par a par\n",
147 |     "sns.pairplot(df)\n"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": null,
153 |    "metadata": {
154 |     "scrolled": true
155 |    },
156 |    "outputs": [],
157 |    "source": [
158 |     "# Verificando a distribuição do alvo\n",
159 |     "sns.distplot(df['PRICE'])"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": null,
165 |    "metadata": {
166 |     "collapsed": true
167 |    },
168 |    "outputs": [],
169 |    "source": [
170 |     "# Verificando a correlação\n",
171 |     "df.corr()\n",
172 |     "sns.heatmap(df.corr())"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "code",
177 |    "execution_count": null,
178 |    "metadata": {
179 |     "collapsed": true,
180 |     "scrolled": true
181 |    },
182 |    "outputs": [],
183 |    "source": [
184 |     "# Melhorando o plot\n",
185 |     "plt.rc(\"figure\", figsize=(12,8))\n",
186 |     "sns.heatmap(df.corr(),annot=True)"
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "markdown",
191 |    "metadata": {},
192 |    "source": [
193 |     "### Dados de Treino e Dados de Teste"
194 |    ]
195 |   },
196 |   {
197 |    "cell_type": "code",
198 |    "execution_count": null,
199 |    "metadata": {
200 |     "collapsed": true
201 |    },
202 |    "outputs": [],
203 |    "source": [
204 |     "# Separando Treino e Teste\n",
205 |     "from sklearn.model_selection import train_test_split\n",
206 |     "\n",
207 |     "X = df.drop('PRICE',axis=1)\n",
208 |     "y = df['PRICE']\n",
209 |     "\n",
210 |     "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=100)\n"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "markdown",
215 |    "metadata": {},
216 |    "source": [
217 |     "### Regressão Linear"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "code",
222 |    "execution_count": null,
223 |    "metadata": {
224 |     "collapsed": true
225 |    },
226 |    "outputs": [],
227 |    "source": [
228 |     "from sklearn.linear_model import LinearRegression\n",
229 |     "lm = LinearRegression()\n",
230 |     "lm.fit(X_train,y_train)"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": null,
236 |    "metadata": {
237 |     "collapsed": true
238 |    },
239 |    "outputs": [],
240 |    "source": [
241 |     "# Verificando Interceptação e Coeficiêntes\n",
242 |     "print('Interceptação: {}'.format(lm.intercept_))\n",
243 |     "pd.DataFrame(lm.coef_,X.columns,columns=['Coeff'])"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": null,
249 |    "metadata": {
250 |     "collapsed": true
251 |    },
252 |    "outputs": [],
253 |    "source": [
254 |     "# Fazendo predições\n",
255 |     "y_pred = lm.predict(X_test)\n",
256 |     "y_pred"
257 |    ]
258 |   },
259 |   {
260 |    "cell_type": "code",
261 |    "execution_count": null,
262 |    "metadata": {
263 |     "collapsed": true
264 |    },
265 |    "outputs": [],
266 |    "source": [
267 |     "# Verificando a predição\n",
268 |     "plt.scatter(y_pred,y_test)\n",
269 |     "plt.xlabel(\"Predito\")\n",
270 |     "plt.ylabel(\"Real\")"
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": null,
276 |    "metadata": {
277 |     "collapsed": true
278 |    },
279 |    "outputs": [],
280 |    "source": [
281 |     "sns.distplot(y_pred-y_test)"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "markdown",
286 |    "metadata": {},
287 |    "source": [
288 |     "## Métricas Regressão"
289 |    ]
290 |   },
291 |   {
292 |    "cell_type": "markdown",
293 |    "metadata": {},
294 |    "source": [
295 |     "### Mean Absolute Error (MAE)\n",
296 |     "\n",
297 |     "\n",
298 |     "$MAE=\\frac{1}{n}\\sum\\limits^{n}_{i=1}|y_i - \\hat{y}_i|$\n",
299 |     "\n",
300 |     "\n",
301 |     "### Mean Square Error (MSE)\n",
302 |     "\n",
303 |     "$MSE=\\frac{1}{n}\\sum\\limits^{n}_{i=1}(y_i - \\hat{y}_i)^2$\n",
304 |     "\n",
305 |     "*Obs: erros mais altos são punidos*\n",
306 |     "\n",
307 |     "### Root Mean Square Error (RMSE)\n",
308 |     "\n",
309 |     "$RMSE=\\sqrt{\\frac{1}{n}\\sum\\limits^{n}_{i=1}(y_i - \\hat{y}_i)^2}$\n",
310 |     "\n",
311 |     "*Obs:tem mesma unidade que $y$*\n",
312 |     "\n",
313 |     "*Todas são loss function*\n"
314 |    ]
315 |   },
316 |   {
317 |    "cell_type": "code",
318 |    "execution_count": null,
319 |    "metadata": {},
320 |    "outputs": [],
321 |    "source": [
322 |     "# Calculando\n",
323 |     "from sklearn import metrics\n",
324 |     "print(\"MAE:\",metrics.mean_absolute_error(y_test,y_pred))\n",
325 |     "print(\"MSE:\",metrics.mean_squared_error(y_test,y_pred))\n",
326 |     "print(\"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
327 |    ]
328 |   },
329 |   {
330 |    "cell_type": "markdown",
331 |    "metadata": {},
332 |    "source": [
333 |     "**Mini Tarefa**\n",
334 |     "\n",
335 |     "Para o Dataframe tips faça:\n",
336 |     "- Gráfico de distribuição do alvo (tips)\n",
337 |     "- Mapa de Calor\n",
338 |     "- Modelo de Regressão Linear para predição de tips\n",
339 |     "- Imprima a interceptação e os coeficientes\n",
340 |     "- Calcule MAE, MSE e RMSE para o modelo"
341 |    ]
342 |   },
343 |   {
344 |    "cell_type": "code",
345 |    "execution_count": null,
346 |    "metadata": {
347 |     "collapsed": true
348 |    },
349 |    "outputs": [],
350 |    "source": []
351 |   }
352 |  ],
353 |  "metadata": {
354 |   "kernelspec": {
355 |    "display_name": "Python 3",
356 |    "language": "python",
357 |    "name": "python3"
358 |   },
359 |   "language_info": {
360 |    "codemirror_mode": {
361 |     "name": "ipython",
362 |     "version": 3
363 |    },
364 |    "file_extension": ".py",
365 |    "mimetype": "text/x-python",
366 |    "name": "python",
367 |    "nbconvert_exporter": "python",
368 |    "pygments_lexer": "ipython3",
369 |    "version": "3.6.1"
370 |   }
371 |  },
372 |  "nbformat": 4,
373 |  "nbformat_minor": 2
374 | }
375 | 


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/.ipynb_checkpoints/Untitled-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/.ipynb_checkpoints/Untitled1-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Aula_01 Jupyter.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Jupyter\n",
  8 |     "---------------"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "markdown",
 13 |    "metadata": {},
 14 |    "source": [
 15 |     "## Instalação e Execução\n",
 16 |     "------"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "* [Instalar Pacote Anaconda](https://www.anaconda.com/download/)\n",
 24 |     "* Ir para o diretório de dados \n",
 25 |     "* Executar no prompt de comando ´jupyter notebook`"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "markdown",
 30 |    "metadata": {},
 31 |    "source": [
 32 |     "### Jupyter Kernels\n",
 33 |     "--------------------"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "markdown",
 38 |    "metadata": {},
 39 |    "source": [
 40 |     "* Python\n",
 41 |     "* Julia\n",
 42 |     "* R\n",
 43 |     "* C#\n",
 44 |     "* F#\n",
 45 |     "* Scala\n",
 46 |     "* Haskell\n",
 47 |     "* etc...\n",
 48 |     "\n",
 49 |     "Você pode verificar os Kernels em [Jupyter Kernels](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels)"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "markdown",
 54 |    "metadata": {},
 55 |    "source": [
 56 |     "## Jupyter Cells (Markdown vs Code)"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "markdown",
 61 |    "metadata": {},
 62 |    "source": [
 63 |     "* Markdown (short cut = <mark>M</mark>)\n",
 64 |     "* Code (short cut = <mark>Y</mark>)\n",
 65 |     "* Executar Células (short cut = <mark>Shift + Enter</mark>)"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "### Latex Markdown\n",
 73 |     "\n",
 74 |     "$e^{i\\pi} + 1 = 0$"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "markdown",
 79 |    "metadata": {},
 80 |    "source": [
 81 |     "### Código Markdown"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "markdown",
 86 |    "metadata": {},
 87 |    "source": [
 88 |     "Exemplo : Python\n",
 89 |     "```python\n",
 90 |     "print \"Hello World\"\n",
 91 |     "```\n",
 92 |     "Exemplo : Javascript\n",
 93 |     "```javascript\n",
 94 |     "console.log(\"Hello World\")\n",
 95 |     "```"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "markdown",
100 |    "metadata": {},
101 |    "source": [
102 |     "### Tabelas\n",
103 |     "\n",
104 |     "| Aluno | Prova | Nota         \n",
105 |     "| :- |-------------: | :-:\n",
106 |     "|Robson Dias | PCOM  | 8.5\n",
107 |     "| Daniel | PCOM | 10.0\n",
108 |     "| Lizandro | PCOM2 | 10.0\n"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "markdown",
113 |    "metadata": {},
114 |    "source": [
115 |     "## Videos\n",
116 |     "<video controls src=\"./videos/video1.mp4\" />"
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "markdown",
121 |    "metadata": {},
122 |    "source": [
123 |     "## Imagens\n",
124 |     "\n",
125 |     "<img style=\"float: left;\" src=\"./imagens/jupyter-sq-text.png\",width=60,height=60/>\n"
126 |    ]
127 |   },
128 |   {
129 |    "cell_type": "markdown",
130 |    "metadata": {},
131 |    "source": [
132 |     "### Principais Atalhos"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "markdown",
137 |    "metadata": {},
138 |    "source": [
139 |     "* Executar Célula (<mark>CTR + ENTER</mark>)\n",
140 |     "* Inserir Célula Acima (<mark>A</mark>)\n",
141 |     "* Inserir Célula Abaixo (<mark>B</mark>)\n",
142 |     "* Markdown (<mark>M</mark>)\n",
143 |     "* Código (<mark>Y</mark>)\n",
144 |     "* Apagar Céluda (<mark>D+D</mark>)\n",
145 |     "* Retorna última Celula apagada (<mark>Z</mark>)\n",
146 |     "* Copiar Célula (<mark>C</mark>)\n",
147 |     "* Salvar (<mark>CTR + S</mark>)\n"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "markdown",
152 |    "metadata": {},
153 |    "source": [
154 |     "## M"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "code",
159 |    "execution_count": null,
160 |    "metadata": {
161 |     "collapsed": true
162 |    },
163 |    "outputs": [],
164 |    "source": []
165 |   }
166 |  ],
167 |  "metadata": {
168 |   "kernelspec": {
169 |    "display_name": "Python 3",
170 |    "language": "python",
171 |    "name": "python3"
172 |   },
173 |   "language_info": {
174 |    "codemirror_mode": {
175 |     "name": "ipython",
176 |     "version": 3
177 |    },
178 |    "file_extension": ".py",
179 |    "mimetype": "text/x-python",
180 |    "name": "python",
181 |    "nbconvert_exporter": "python",
182 |    "pygments_lexer": "ipython3",
183 |    "version": "3.6.1"
184 |   }
185 |  },
186 |  "nbformat": 4,
187 |  "nbformat_minor": 2
188 | }
189 | 


--------------------------------------------------------------------------------
/Aula_02_ Exercício.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "### Exercício \n",
 8 |     "------------------\n",
 9 |     "Faça uma função em Python que receba um string (frase) e:\n",
10 |     "* a) Passe Todas as letras iniciais de palavras para maiúsculas;\n",
11 |     "* b) Todas as outras letras devem ficar com exceção das inicias devem ficar em minusculas;\n",
12 |     "* c) Os artigos isolados, exceto se for a primeira letra da frase, devem ficar em minúsculo.\n",
13 |     "\n",
14 |     "**Exemplo 1** : \n",
15 |     "* entrada : \"a grande partidA\"\n",
16 |     "* Saída : \"A Grande Partida\"\n",
17 |     "\n",
18 |     "**Exemplo 2** : \n",
19 |     "* entrada : \"o livro E o garato\"\n",
20 |     "* Saída : \"O Livro  e o Garoto\"\n",
21 |     "\n"
22 |    ]
23 |   },
24 |   {
25 |    "cell_type": "code",
26 |    "execution_count": null,
27 |    "metadata": {
28 |     "collapsed": true
29 |    },
30 |    "outputs": [],
31 |    "source": []
32 |   }
33 |  ],
34 |  "metadata": {
35 |   "kernelspec": {
36 |    "display_name": "Python 3",
37 |    "language": "python",
38 |    "name": "python3"
39 |   },
40 |   "language_info": {
41 |    "codemirror_mode": {
42 |     "name": "ipython",
43 |     "version": 3
44 |    },
45 |    "file_extension": ".py",
46 |    "mimetype": "text/x-python",
47 |    "name": "python",
48 |    "nbconvert_exporter": "python",
49 |    "pygments_lexer": "ipython3",
50 |    "version": "3.6.1"
51 |   }
52 |  },
53 |  "nbformat": 4,
54 |  "nbformat_minor": 2
55 | }
56 | 


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/Aula_02_Python_Introdução.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Python\n",
   8 |     "\n",
   9 |     "\n",
  10 |     "Sumário\n",
  11 |     "\n",
  12 |     "* Tipos de Dados\n",
  13 |     "    * Numbers\n",
  14 |     "    * Strings\n",
  15 |     "    * Printing\n",
  16 |     "    * Lists\n",
  17 |     "    * Dictionaries\n",
  18 |     "    * Booleans\n",
  19 |     "    * Tuples \n",
  20 |     "    * Sets\n",
  21 |     "* Comparadores\n",
  22 |     "* if,elif, else Statements\n",
  23 |     "* for Loops\n",
  24 |     "* while Loops\n",
  25 |     "* range()\n",
  26 |     "* list comprehension\n",
  27 |     "* Funções\n",
  28 |     "* lambda expressions\n",
  29 |     "* Mapa e Filtros\n",
  30 |     "* Métodos\n",
  31 |     "____"
  32 |    ]
  33 |   },
  34 |   {
  35 |    "cell_type": "markdown",
  36 |    "metadata": {},
  37 |    "source": [
  38 |     "## Tipos de Dados\n",
  39 |     "\n",
  40 |     "### Números"
  41 |    ]
  42 |   },
  43 |   {
  44 |    "cell_type": "code",
  45 |    "execution_count": null,
  46 |    "metadata": {},
  47 |    "outputs": [],
  48 |    "source": [
  49 |     "1 + 5"
  50 |    ]
  51 |   },
  52 |   {
  53 |    "cell_type": "code",
  54 |    "execution_count": null,
  55 |    "metadata": {},
  56 |    "outputs": [],
  57 |    "source": [
  58 |     "2 * 3"
  59 |    ]
  60 |   },
  61 |   {
  62 |    "cell_type": "code",
  63 |    "execution_count": null,
  64 |    "metadata": {},
  65 |    "outputs": [],
  66 |    "source": [
  67 |     "1 / 4"
  68 |    ]
  69 |   },
  70 |   {
  71 |    "cell_type": "code",
  72 |    "execution_count": null,
  73 |    "metadata": {},
  74 |    "outputs": [],
  75 |    "source": [
  76 |     "2 ** 3"
  77 |    ]
  78 |   },
  79 |   {
  80 |    "cell_type": "code",
  81 |    "execution_count": null,
  82 |    "metadata": {},
  83 |    "outputs": [],
  84 |    "source": [
  85 |     "16 % 2"
  86 |    ]
  87 |   },
  88 |   {
  89 |    "cell_type": "code",
  90 |    "execution_count": null,
  91 |    "metadata": {},
  92 |    "outputs": [],
  93 |    "source": [
  94 |     "5 % 3"
  95 |    ]
  96 |   },
  97 |   {
  98 |    "cell_type": "code",
  99 |    "execution_count": null,
 100 |    "metadata": {},
 101 |    "outputs": [],
 102 |    "source": [
 103 |     "(2 + 4) * (4 + 5)"
 104 |    ]
 105 |   },
 106 |   {
 107 |    "cell_type": "markdown",
 108 |    "metadata": {},
 109 |    "source": [
 110 |     "### Variáveis"
 111 |    ]
 112 |   },
 113 |   {
 114 |    "cell_type": "code",
 115 |    "execution_count": null,
 116 |    "metadata": {
 117 |     "collapsed": true
 118 |    },
 119 |    "outputs": [],
 120 |    "source": [
 121 |     "# Não pode iniciar com números e caracteres especiais \n",
 122 |     "var1 = 2"
 123 |    ]
 124 |   },
 125 |   {
 126 |    "cell_type": "code",
 127 |    "execution_count": null,
 128 |    "metadata": {
 129 |     "collapsed": true
 130 |    },
 131 |    "outputs": [],
 132 |    "source": [
 133 |     "x = 2\n",
 134 |     "y = 3"
 135 |    ]
 136 |   },
 137 |   {
 138 |    "cell_type": "code",
 139 |    "execution_count": null,
 140 |    "metadata": {},
 141 |    "outputs": [],
 142 |    "source": [
 143 |     "z = (x^2 + y^2)**(0.5)\n",
 144 |     "z"
 145 |    ]
 146 |   },
 147 |   {
 148 |    "cell_type": "code",
 149 |    "execution_count": null,
 150 |    "metadata": {},
 151 |    "outputs": [],
 152 |    "source": [
 153 |     "z"
 154 |    ]
 155 |   },
 156 |   {
 157 |    "cell_type": "markdown",
 158 |    "metadata": {},
 159 |    "source": [
 160 |     "### Strings"
 161 |    ]
 162 |   },
 163 |   {
 164 |    "cell_type": "code",
 165 |    "execution_count": null,
 166 |    "metadata": {},
 167 |    "outputs": [],
 168 |    "source": [
 169 |     "'aspas simples'"
 170 |    ]
 171 |   },
 172 |   {
 173 |    "cell_type": "code",
 174 |    "execution_count": null,
 175 |    "metadata": {},
 176 |    "outputs": [],
 177 |    "source": [
 178 |     "\"aspas duplas\""
 179 |    ]
 180 |   },
 181 |   {
 182 |    "cell_type": "code",
 183 |    "execution_count": null,
 184 |    "metadata": {},
 185 |    "outputs": [],
 186 |    "source": [
 187 |     "\"robson's car is broken\""
 188 |    ]
 189 |   },
 190 |   {
 191 |    "cell_type": "markdown",
 192 |    "metadata": {},
 193 |    "source": [
 194 |     "### Impressão"
 195 |    ]
 196 |   },
 197 |   {
 198 |    "cell_type": "code",
 199 |    "execution_count": null,
 200 |    "metadata": {
 201 |     "collapsed": true
 202 |    },
 203 |    "outputs": [],
 204 |    "source": [
 205 |     "x = 'hello word!'"
 206 |    ]
 207 |   },
 208 |   {
 209 |    "cell_type": "code",
 210 |    "execution_count": null,
 211 |    "metadata": {},
 212 |    "outputs": [],
 213 |    "source": [
 214 |     "x"
 215 |    ]
 216 |   },
 217 |   {
 218 |    "cell_type": "code",
 219 |    "execution_count": null,
 220 |    "metadata": {},
 221 |    "outputs": [],
 222 |    "source": [
 223 |     "print(x)"
 224 |    ]
 225 |   },
 226 |   {
 227 |    "cell_type": "code",
 228 |    "execution_count": null,
 229 |    "metadata": {
 230 |     "collapsed": true
 231 |    },
 232 |    "outputs": [],
 233 |    "source": [
 234 |     "nrTitulo = 5\n",
 235 |     "pais = 'Brasil'"
 236 |    ]
 237 |   },
 238 |   {
 239 |    "cell_type": "code",
 240 |    "execution_count": null,
 241 |    "metadata": {},
 242 |    "outputs": [],
 243 |    "source": [
 244 |     "print('O {pais} foi campeão {nrTitulo} vezes'.format(pais=pais,nrTitulo=nrTitulo))"
 245 |    ]
 246 |   },
 247 |   {
 248 |    "cell_type": "code",
 249 |    "execution_count": null,
 250 |    "metadata": {},
 251 |    "outputs": [],
 252 |    "source": [
 253 |     "print('A {} foi campeão {} vezes'.format(pais,nrTitulo))"
 254 |    ]
 255 |   },
 256 |   {
 257 |    "cell_type": "markdown",
 258 |    "metadata": {},
 259 |    "source": [
 260 |     "### Listas"
 261 |    ]
 262 |   },
 263 |   {
 264 |    "cell_type": "code",
 265 |    "execution_count": null,
 266 |    "metadata": {},
 267 |    "outputs": [],
 268 |    "source": [
 269 |     "[2,4,8]"
 270 |    ]
 271 |   },
 272 |   {
 273 |    "cell_type": "code",
 274 |    "execution_count": null,
 275 |    "metadata": {},
 276 |    "outputs": [],
 277 |    "source": [
 278 |     "['Oi',1,[1,2],\"a\"]"
 279 |    ]
 280 |   },
 281 |   {
 282 |    "cell_type": "code",
 283 |    "execution_count": null,
 284 |    "metadata": {
 285 |     "collapsed": true
 286 |    },
 287 |    "outputs": [],
 288 |    "source": [
 289 |     "lista1 = ['a','b','c','d']"
 290 |    ]
 291 |   },
 292 |   {
 293 |    "cell_type": "code",
 294 |    "execution_count": null,
 295 |    "metadata": {
 296 |     "collapsed": true
 297 |    },
 298 |    "outputs": [],
 299 |    "source": [
 300 |     "lista1.append('e')"
 301 |    ]
 302 |   },
 303 |   {
 304 |    "cell_type": "code",
 305 |    "execution_count": null,
 306 |    "metadata": {},
 307 |    "outputs": [],
 308 |    "source": [
 309 |     "lista1[0]"
 310 |    ]
 311 |   },
 312 |   {
 313 |    "cell_type": "code",
 314 |    "execution_count": null,
 315 |    "metadata": {},
 316 |    "outputs": [],
 317 |    "source": [
 318 |     "lista1[1:]"
 319 |    ]
 320 |   },
 321 |   {
 322 |    "cell_type": "code",
 323 |    "execution_count": null,
 324 |    "metadata": {},
 325 |    "outputs": [],
 326 |    "source": [
 327 |     "my_list[:1] # último não incluído"
 328 |    ]
 329 |   },
 330 |   {
 331 |    "cell_type": "code",
 332 |    "execution_count": null,
 333 |    "metadata": {
 334 |     "collapsed": true
 335 |    },
 336 |    "outputs": [],
 337 |    "source": [
 338 |     "lista1[0] = 'Novo'"
 339 |    ]
 340 |   },
 341 |   {
 342 |    "cell_type": "code",
 343 |    "execution_count": null,
 344 |    "metadata": {
 345 |     "collapsed": true
 346 |    },
 347 |    "outputs": [],
 348 |    "source": [
 349 |     "# Lista Encadeiada\n",
 350 |     "lista2 = [1,2,3,[4,5,['alvo']]]"
 351 |    ]
 352 |   },
 353 |   {
 354 |    "cell_type": "code",
 355 |    "execution_count": null,
 356 |    "metadata": {},
 357 |    "outputs": [],
 358 |    "source": [
 359 |     "lista2[3]"
 360 |    ]
 361 |   },
 362 |   {
 363 |    "cell_type": "code",
 364 |    "execution_count": null,
 365 |    "metadata": {},
 366 |    "outputs": [],
 367 |    "source": [
 368 |     "lista2[3][2]"
 369 |    ]
 370 |   },
 371 |   {
 372 |    "cell_type": "code",
 373 |    "execution_count": null,
 374 |    "metadata": {
 375 |     "scrolled": false
 376 |    },
 377 |    "outputs": [],
 378 |    "source": [
 379 |     "lista2[3][2][0]"
 380 |    ]
 381 |   },
 382 |   {
 383 |    "cell_type": "code",
 384 |    "execution_count": null,
 385 |    "metadata": {
 386 |     "collapsed": true
 387 |    },
 388 |    "outputs": [],
 389 |    "source": [
 390 |     "'x' in ['x','y','z']"
 391 |    ]
 392 |   },
 393 |   {
 394 |    "cell_type": "markdown",
 395 |    "metadata": {},
 396 |    "source": [
 397 |     "### Dicionários"
 398 |    ]
 399 |   },
 400 |   {
 401 |    "cell_type": "code",
 402 |    "execution_count": 80,
 403 |    "metadata": {
 404 |     "collapsed": true
 405 |    },
 406 |    "outputs": [],
 407 |    "source": [
 408 |     "dic = {'key1':'item1','key2':'item2'}"
 409 |    ]
 410 |   },
 411 |   {
 412 |    "cell_type": "code",
 413 |    "execution_count": null,
 414 |    "metadata": {},
 415 |    "outputs": [],
 416 |    "source": [
 417 |     "dic"
 418 |    ]
 419 |   },
 420 |   {
 421 |    "cell_type": "code",
 422 |    "execution_count": null,
 423 |    "metadata": {},
 424 |    "outputs": [],
 425 |    "source": [
 426 |     "dic['key1']"
 427 |    ]
 428 |   },
 429 |   {
 430 |    "cell_type": "code",
 431 |    "execution_count": 81,
 432 |    "metadata": {},
 433 |    "outputs": [
 434 |     {
 435 |      "data": {
 436 |       "text/plain": [
 437 |        "dict_keys(['key1', 'key2'])"
 438 |       ]
 439 |      },
 440 |      "execution_count": 81,
 441 |      "metadata": {},
 442 |      "output_type": "execute_result"
 443 |     }
 444 |    ],
 445 |    "source": [
 446 |     "dic.keys()"
 447 |    ]
 448 |   },
 449 |   {
 450 |    "cell_type": "code",
 451 |    "execution_count": null,
 452 |    "metadata": {
 453 |     "collapsed": true
 454 |    },
 455 |    "outputs": [],
 456 |    "source": [
 457 |     "dic.items()"
 458 |    ]
 459 |   },
 460 |   {
 461 |    "cell_type": "markdown",
 462 |    "metadata": {},
 463 |    "source": [
 464 |     "### Booleans"
 465 |    ]
 466 |   },
 467 |   {
 468 |    "cell_type": "code",
 469 |    "execution_count": null,
 470 |    "metadata": {},
 471 |    "outputs": [],
 472 |    "source": [
 473 |     "True"
 474 |    ]
 475 |   },
 476 |   {
 477 |    "cell_type": "code",
 478 |    "execution_count": null,
 479 |    "metadata": {},
 480 |    "outputs": [],
 481 |    "source": [
 482 |     "False"
 483 |    ]
 484 |   },
 485 |   {
 486 |    "cell_type": "markdown",
 487 |    "metadata": {},
 488 |    "source": [
 489 |     "### Tuplas "
 490 |    ]
 491 |   },
 492 |   {
 493 |    "cell_type": "code",
 494 |    "execution_count": null,
 495 |    "metadata": {
 496 |     "collapsed": true
 497 |    },
 498 |    "outputs": [],
 499 |    "source": [
 500 |     "vogais = ('a','e','i','o','u')"
 501 |    ]
 502 |   },
 503 |   {
 504 |    "cell_type": "code",
 505 |    "execution_count": null,
 506 |    "metadata": {},
 507 |    "outputs": [],
 508 |    "source": [
 509 |     "vogais[0]"
 510 |    ]
 511 |   },
 512 |   {
 513 |    "cell_type": "code",
 514 |    "execution_count": null,
 515 |    "metadata": {},
 516 |    "outputs": [],
 517 |    "source": [
 518 |     "vogais[0] = 'Nova'"
 519 |    ]
 520 |   },
 521 |   {
 522 |    "cell_type": "markdown",
 523 |    "metadata": {},
 524 |    "source": [
 525 |     "### Sets "
 526 |    ]
 527 |   },
 528 |   {
 529 |    "cell_type": "code",
 530 |    "execution_count": null,
 531 |    "metadata": {},
 532 |    "outputs": [],
 533 |    "source": [
 534 |     "{1,2,3}"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": null,
 540 |    "metadata": {},
 541 |    "outputs": [],
 542 |    "source": [
 543 |     "{1,2,2,1,2,1,2,3,3,2,3,2,2,2,1,2,4}"
 544 |    ]
 545 |   },
 546 |   {
 547 |    "cell_type": "markdown",
 548 |    "metadata": {},
 549 |    "source": [
 550 |     "## Comparadores"
 551 |    ]
 552 |   },
 553 |   {
 554 |    "cell_type": "code",
 555 |    "execution_count": null,
 556 |    "metadata": {},
 557 |    "outputs": [],
 558 |    "source": [
 559 |     "4 > 4"
 560 |    ]
 561 |   },
 562 |   {
 563 |    "cell_type": "code",
 564 |    "execution_count": null,
 565 |    "metadata": {},
 566 |    "outputs": [],
 567 |    "source": [
 568 |     "1 < 4"
 569 |    ]
 570 |   },
 571 |   {
 572 |    "cell_type": "code",
 573 |    "execution_count": null,
 574 |    "metadata": {},
 575 |    "outputs": [],
 576 |    "source": [
 577 |     "1 >= 1"
 578 |    ]
 579 |   },
 580 |   {
 581 |    "cell_type": "code",
 582 |    "execution_count": null,
 583 |    "metadata": {},
 584 |    "outputs": [],
 585 |    "source": [
 586 |     "1 <= 4"
 587 |    ]
 588 |   },
 589 |   {
 590 |    "cell_type": "code",
 591 |    "execution_count": null,
 592 |    "metadata": {},
 593 |    "outputs": [],
 594 |    "source": [
 595 |     "1 == 4"
 596 |    ]
 597 |   },
 598 |   {
 599 |    "cell_type": "code",
 600 |    "execution_count": null,
 601 |    "metadata": {},
 602 |    "outputs": [],
 603 |    "source": [
 604 |     "'ok' == 'bye'"
 605 |    ]
 606 |   },
 607 |   {
 608 |    "cell_type": "markdown",
 609 |    "metadata": {},
 610 |    "source": [
 611 |     "## Operadores Lógicos"
 612 |    ]
 613 |   },
 614 |   {
 615 |    "cell_type": "code",
 616 |    "execution_count": null,
 617 |    "metadata": {},
 618 |    "outputs": [],
 619 |    "source": [
 620 |     "(1 > 2) and (2 < 3)"
 621 |    ]
 622 |   },
 623 |   {
 624 |    "cell_type": "code",
 625 |    "execution_count": null,
 626 |    "metadata": {},
 627 |    "outputs": [],
 628 |    "source": [
 629 |     "(1 > 2) or (2 < 3)"
 630 |    ]
 631 |   },
 632 |   {
 633 |    "cell_type": "code",
 634 |    "execution_count": null,
 635 |    "metadata": {},
 636 |    "outputs": [],
 637 |    "source": [
 638 |     "(1 == 2) or (2 == 3) or (4 == 4)"
 639 |    ]
 640 |   },
 641 |   {
 642 |    "cell_type": "markdown",
 643 |    "metadata": {},
 644 |    "source": [
 645 |     "## if,elif, else Statements"
 646 |    ]
 647 |   },
 648 |   {
 649 |    "cell_type": "code",
 650 |    "execution_count": null,
 651 |    "metadata": {},
 652 |    "outputs": [],
 653 |    "source": [
 654 |     "if 1 < 2:\n",
 655 |     "    print('Ok!')"
 656 |    ]
 657 |   },
 658 |   {
 659 |    "cell_type": "code",
 660 |    "execution_count": null,
 661 |    "metadata": {},
 662 |    "outputs": [],
 663 |    "source": [
 664 |     "if 1 < 2:\n",
 665 |     "    print('Não OK')"
 666 |    ]
 667 |   },
 668 |   {
 669 |    "cell_type": "code",
 670 |    "execution_count": null,
 671 |    "metadata": {},
 672 |    "outputs": [],
 673 |    "source": [
 674 |     "if 1 < 2:\n",
 675 |     "    print('1º')\n",
 676 |     "else:\n",
 677 |     "    print('2º')"
 678 |    ]
 679 |   },
 680 |   {
 681 |    "cell_type": "code",
 682 |    "execution_count": null,
 683 |    "metadata": {},
 684 |    "outputs": [],
 685 |    "source": [
 686 |     "if 1 > 2:\n",
 687 |     "    print('1º')\n",
 688 |     "else:\n",
 689 |     "    print('2º')"
 690 |    ]
 691 |   },
 692 |   {
 693 |    "cell_type": "code",
 694 |    "execution_count": null,
 695 |    "metadata": {},
 696 |    "outputs": [],
 697 |    "source": [
 698 |     "if 1 == 2:\n",
 699 |     "    print('1º')\n",
 700 |     "elif 3 == 3:\n",
 701 |     "    print('2º')\n",
 702 |     "else:\n",
 703 |     "    print('3º')"
 704 |    ]
 705 |   },
 706 |   {
 707 |    "cell_type": "markdown",
 708 |    "metadata": {},
 709 |    "source": [
 710 |     "## for Loops"
 711 |    ]
 712 |   },
 713 |   {
 714 |    "cell_type": "code",
 715 |    "execution_count": null,
 716 |    "metadata": {},
 717 |    "outputs": [],
 718 |    "source": [
 719 |     "seq = [1,2,3,4,5,6]"
 720 |    ]
 721 |   },
 722 |   {
 723 |    "cell_type": "code",
 724 |    "execution_count": null,
 725 |    "metadata": {
 726 |     "scrolled": true
 727 |    },
 728 |    "outputs": [],
 729 |    "source": [
 730 |     "for n in seq:\n",
 731 |     "    print(n)"
 732 |    ]
 733 |   },
 734 |   {
 735 |    "cell_type": "code",
 736 |    "execution_count": null,
 737 |    "metadata": {},
 738 |    "outputs": [],
 739 |    "source": [
 740 |     "for n in seq:\n",
 741 |     "    print('OK')"
 742 |    ]
 743 |   },
 744 |   {
 745 |    "cell_type": "code",
 746 |    "execution_count": null,
 747 |    "metadata": {},
 748 |    "outputs": [],
 749 |    "source": [
 750 |     "for m in seq:\n",
 751 |     "    print(m**2)"
 752 |    ]
 753 |   },
 754 |   {
 755 |    "cell_type": "markdown",
 756 |    "metadata": {},
 757 |    "source": [
 758 |     "## while Loops"
 759 |    ]
 760 |   },
 761 |   {
 762 |    "cell_type": "code",
 763 |    "execution_count": 53,
 764 |    "metadata": {},
 765 |    "outputs": [
 766 |     {
 767 |      "name": "stdout",
 768 |      "output_type": "stream",
 769 |      "text": [
 770 |       "i é igual a : 1\n",
 771 |       "i é igual a : 2\n",
 772 |       "i é igual a : 3\n",
 773 |       "i é igual a : 4\n"
 774 |      ]
 775 |     }
 776 |    ],
 777 |    "source": [
 778 |     "i = 1\n",
 779 |     "while i < 5:\n",
 780 |     "    print('i é igual a : {}'.format(i))\n",
 781 |     "    i = i+1"
 782 |    ]
 783 |   },
 784 |   {
 785 |    "cell_type": "markdown",
 786 |    "metadata": {},
 787 |    "source": [
 788 |     "## range()"
 789 |    ]
 790 |   },
 791 |   {
 792 |    "cell_type": "code",
 793 |    "execution_count": 59,
 794 |    "metadata": {},
 795 |    "outputs": [
 796 |     {
 797 |      "data": {
 798 |       "text/plain": [
 799 |        "4"
 800 |       ]
 801 |      },
 802 |      "execution_count": 59,
 803 |      "metadata": {},
 804 |      "output_type": "execute_result"
 805 |     }
 806 |    ],
 807 |    "source": [
 808 |     "range(1,5)"
 809 |    ]
 810 |   },
 811 |   {
 812 |    "cell_type": "code",
 813 |    "execution_count": null,
 814 |    "metadata": {},
 815 |    "outputs": [],
 816 |    "source": [
 817 |     "for i in range(5):\n",
 818 |     "    print(i)"
 819 |    ]
 820 |   },
 821 |   {
 822 |    "cell_type": "code",
 823 |    "execution_count": null,
 824 |    "metadata": {},
 825 |    "outputs": [],
 826 |    "source": [
 827 |     "list(range(5))"
 828 |    ]
 829 |   },
 830 |   {
 831 |    "cell_type": "markdown",
 832 |    "metadata": {},
 833 |    "source": [
 834 |     "## list comprehension"
 835 |    ]
 836 |   },
 837 |   {
 838 |    "cell_type": "code",
 839 |    "execution_count": null,
 840 |    "metadata": {
 841 |     "collapsed": true
 842 |    },
 843 |    "outputs": [],
 844 |    "source": [
 845 |     "x = [1,2,3,4]"
 846 |    ]
 847 |   },
 848 |   {
 849 |    "cell_type": "code",
 850 |    "execution_count": null,
 851 |    "metadata": {},
 852 |    "outputs": [],
 853 |    "source": [
 854 |     "out = []\n",
 855 |     "for item in x:\n",
 856 |     "    out.append(item**2)\n",
 857 |     "print(out)"
 858 |    ]
 859 |   },
 860 |   {
 861 |    "cell_type": "code",
 862 |    "execution_count": null,
 863 |    "metadata": {},
 864 |    "outputs": [],
 865 |    "source": [
 866 |     "[item**2 for item in x]"
 867 |    ]
 868 |   },
 869 |   {
 870 |    "cell_type": "markdown",
 871 |    "metadata": {},
 872 |    "source": [
 873 |     "## Funções"
 874 |    ]
 875 |   },
 876 |   {
 877 |    "cell_type": "code",
 878 |    "execution_count": 60,
 879 |    "metadata": {
 880 |     "collapsed": true
 881 |    },
 882 |    "outputs": [],
 883 |    "source": [
 884 |     "def pot(n=2):\n",
 885 |     "    \"\"\"\n",
 886 |     "    Potência do Número\n",
 887 |     "    \"\"\"\n",
 888 |     "    return n**2"
 889 |    ]
 890 |   },
 891 |   {
 892 |    "cell_type": "code",
 893 |    "execution_count": null,
 894 |    "metadata": {
 895 |     "scrolled": true
 896 |    },
 897 |    "outputs": [],
 898 |    "source": [
 899 |     "y = pot(2)"
 900 |    ]
 901 |   },
 902 |   {
 903 |    "cell_type": "markdown",
 904 |    "metadata": {},
 905 |    "source": [
 906 |     "## lambda expressions"
 907 |    ]
 908 |   },
 909 |   {
 910 |    "cell_type": "code",
 911 |    "execution_count": 63,
 912 |    "metadata": {
 913 |     "collapsed": true
 914 |    },
 915 |    "outputs": [],
 916 |    "source": [
 917 |     "def dobrar(x):\n",
 918 |     "    return x*2"
 919 |    ]
 920 |   },
 921 |   {
 922 |    "cell_type": "code",
 923 |    "execution_count": 64,
 924 |    "metadata": {},
 925 |    "outputs": [
 926 |     {
 927 |      "data": {
 928 |       "text/plain": [
 929 |        "4"
 930 |       ]
 931 |      },
 932 |      "execution_count": 64,
 933 |      "metadata": {},
 934 |      "output_type": "execute_result"
 935 |     }
 936 |    ],
 937 |    "source": [
 938 |     "dobrar(2)"
 939 |    ]
 940 |   },
 941 |   {
 942 |    "cell_type": "code",
 943 |    "execution_count": 69,
 944 |    "metadata": {},
 945 |    "outputs": [
 946 |     {
 947 |      "data": {
 948 |       "text/plain": [
 949 |        "4"
 950 |       ]
 951 |      },
 952 |      "execution_count": 69,
 953 |      "metadata": {},
 954 |      "output_type": "execute_result"
 955 |     }
 956 |    ],
 957 |    "source": [
 958 |     "dobrar =lambda x=2: x*2\n",
 959 |     "dobrar(2)"
 960 |    ]
 961 |   },
 962 |   {
 963 |    "cell_type": "markdown",
 964 |    "metadata": {},
 965 |    "source": [
 966 |     "## Mapas e Filtros"
 967 |    ]
 968 |   },
 969 |   {
 970 |    "cell_type": "code",
 971 |    "execution_count": 70,
 972 |    "metadata": {
 973 |     "collapsed": true
 974 |    },
 975 |    "outputs": [],
 976 |    "source": [
 977 |     "seq = [1,2,3]"
 978 |    ]
 979 |   },
 980 |   {
 981 |    "cell_type": "code",
 982 |    "execution_count": 71,
 983 |    "metadata": {},
 984 |    "outputs": [
 985 |     {
 986 |      "data": {
 987 |       "text/plain": [
 988 |        "<map at 0x23a74e2cc88>"
 989 |       ]
 990 |      },
 991 |      "execution_count": 71,
 992 |      "metadata": {},
 993 |      "output_type": "execute_result"
 994 |     }
 995 |    ],
 996 |    "source": [
 997 |     "map(dobrar,seq) # Posição de memória"
 998 |    ]
 999 |   },
1000 |   {
1001 |    "cell_type": "code",
1002 |    "execution_count": 72,
1003 |    "metadata": {},
1004 |    "outputs": [
1005 |     {
1006 |      "data": {
1007 |       "text/plain": [
1008 |        "[2, 4, 6]"
1009 |       ]
1010 |      },
1011 |      "execution_count": 72,
1012 |      "metadata": {},
1013 |      "output_type": "execute_result"
1014 |     }
1015 |    ],
1016 |    "source": [
1017 |     "list(map(dobrar,seq)) # Passa para lista"
1018 |    ]
1019 |   },
1020 |   {
1021 |    "cell_type": "code",
1022 |    "execution_count": 73,
1023 |    "metadata": {},
1024 |    "outputs": [
1025 |     {
1026 |      "data": {
1027 |       "text/plain": [
1028 |        "[2, 4, 6]"
1029 |       ]
1030 |      },
1031 |      "execution_count": 73,
1032 |      "metadata": {},
1033 |      "output_type": "execute_result"
1034 |     }
1035 |    ],
1036 |    "source": [
1037 |     "list(map(lambda x: x*2,seq)) # Juntando tudo"
1038 |    ]
1039 |   },
1040 |   {
1041 |    "cell_type": "code",
1042 |    "execution_count": 74,
1043 |    "metadata": {},
1044 |    "outputs": [
1045 |     {
1046 |      "data": {
1047 |       "text/plain": [
1048 |        "<filter at 0x23a74e2ca20>"
1049 |       ]
1050 |      },
1051 |      "execution_count": 74,
1052 |      "metadata": {},
1053 |      "output_type": "execute_result"
1054 |     }
1055 |    ],
1056 |    "source": [
1057 |     "filter(lambda x: x%2 == 0,seq)"
1058 |    ]
1059 |   },
1060 |   {
1061 |    "cell_type": "code",
1062 |    "execution_count": 75,
1063 |    "metadata": {},
1064 |    "outputs": [
1065 |     {
1066 |      "data": {
1067 |       "text/plain": [
1068 |        "[2]"
1069 |       ]
1070 |      },
1071 |      "execution_count": 75,
1072 |      "metadata": {},
1073 |      "output_type": "execute_result"
1074 |     }
1075 |    ],
1076 |    "source": [
1077 |     "list(filter(lambda item: item%2 == 0,seq))"
1078 |    ]
1079 |   }
1080 |  ],
1081 |  "metadata": {
1082 |   "kernelspec": {
1083 |    "display_name": "Python 3",
1084 |    "language": "python",
1085 |    "name": "python3"
1086 |   },
1087 |   "language_info": {
1088 |    "codemirror_mode": {
1089 |     "name": "ipython",
1090 |     "version": 3
1091 |    },
1092 |    "file_extension": ".py",
1093 |    "mimetype": "text/x-python",
1094 |    "name": "python",
1095 |    "nbconvert_exporter": "python",
1096 |    "pygments_lexer": "ipython3",
1097 |    "version": "3.6.1"
1098 |   }
1099 |  },
1100 |  "nbformat": 4,
1101 |  "nbformat_minor": 1
1102 | }
1103 | 


--------------------------------------------------------------------------------
/Aula_03_Numpy.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### Numpy : Linear Algebra Library for Python"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "## Instalação"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": null,
 20 |    "metadata": {
 21 |     "collapsed": true
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "# Verificar versão do numpy\n",
 26 |     "import numpy\n",
 27 |     "numpy.version.version"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "### Comandos\n",
 35 |     "-----------------\n",
 36 |     "* conda install numpy\n",
 37 |     "* pip install numpy"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "metadata": {},
 43 |    "source": [
 44 |     "## Numpy Arrays"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "markdown",
 49 |    "metadata": {},
 50 |    "source": [
 51 |     "### Vetores"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": null,
 57 |    "metadata": {
 58 |     "collapsed": true
 59 |    },
 60 |    "outputs": [],
 61 |    "source": [
 62 |     "# Criando 1D\n",
 63 |     "import numpy as np\n",
 64 |     "list = [1,2,3,4]\n",
 65 |     "np_arr = np.array(list1)\n"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "### Matrizes"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": null,
 78 |    "metadata": {
 79 |     "collapsed": true
 80 |    },
 81 |    "outputs": [],
 82 |    "source": [
 83 |     "# Criando 2D\n",
 84 |     "mat1 = [[1,2,3],[2,4,8],[3,9,27]]\n",
 85 |     "np_arr2 = np.array(mat1)\n"
 86 |    ]
 87 |   },
 88 |   {
 89 |    "cell_type": "code",
 90 |    "execution_count": null,
 91 |    "metadata": {
 92 |     "collapsed": true
 93 |    },
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "np_arr2"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "markdown",
101 |    "metadata": {},
102 |    "source": [
103 |     "### Numpy Arange"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": null,
109 |    "metadata": {
110 |     "collapsed": true
111 |    },
112 |    "outputs": [],
113 |    "source": [
114 |     "np.arange(1,10)"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "code",
119 |    "execution_count": null,
120 |    "metadata": {
121 |     "collapsed": true
122 |    },
123 |    "outputs": [],
124 |    "source": [
125 |     "np.arange(0,21,2)"
126 |    ]
127 |   },
128 |   {
129 |    "cell_type": "code",
130 |    "execution_count": null,
131 |    "metadata": {
132 |     "collapsed": true
133 |    },
134 |    "outputs": [],
135 |    "source": [
136 |     "np.arange(20,1.-2)"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "markdown",
141 |    "metadata": {},
142 |    "source": [
143 |     "## Matrizes Especiais"
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "markdown",
148 |    "metadata": {},
149 |    "source": [
150 |     "### Matriz Identidade, Zeros e Uns"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "code",
155 |    "execution_count": null,
156 |    "metadata": {
157 |     "collapsed": true
158 |    },
159 |    "outputs": [],
160 |    "source": [
161 |     "np.eye(3)"
162 |    ]
163 |   },
164 |   {
165 |    "cell_type": "code",
166 |    "execution_count": null,
167 |    "metadata": {
168 |     "collapsed": true
169 |    },
170 |    "outputs": [],
171 |    "source": [
172 |     "# Zeros\n",
173 |     "np.zeros(3) # vetor\n",
174 |     "np.zeros((4,3)) # matriz"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "code",
179 |    "execution_count": null,
180 |    "metadata": {
181 |     "collapsed": true
182 |    },
183 |    "outputs": [],
184 |    "source": [
185 |     "# Um\n",
186 |     "np.ones(3) # vetor\n",
187 |     "np.ones((4,3)) # matriz\n",
188 |     "\n"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "markdown",
193 |    "metadata": {},
194 |    "source": [
195 |     "### linspace"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": null,
201 |    "metadata": {
202 |     "collapsed": true
203 |    },
204 |    "outputs": [],
205 |    "source": [
206 |     "np.linspace(0,10,100) # Não confudir com arage"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "markdown",
211 |    "metadata": {},
212 |    "source": [
213 |     "Mini tarefa 3.1\n",
214 |     "-------------\n",
215 |     "* a) Faça um matriz identidade, zero e de uns (3x3)\n",
216 |     "* b) Crie um vetor de inteiros de 0 a 100;\n",
217 |     "* c) Crie uma matriz 5x5 com todos elementos igual 5."
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "markdown",
222 |    "metadata": {},
223 |    "source": [
224 |     "## Geração Números Aleatórios"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "markdown",
229 |    "metadata": {},
230 |    "source": [
231 |     "#### Inteiros"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": null,
237 |    "metadata": {
238 |     "collapsed": true
239 |    },
240 |    "outputs": [],
241 |    "source": [
242 |     "# Escolhendo um número de 1 a 20\n",
243 |     "np.random.randint(1,21)  # Pressione shif+tab para ver opções"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": null,
249 |    "metadata": {
250 |     "collapsed": true
251 |    },
252 |    "outputs": [],
253 |    "source": [
254 |     "# Escolhendo cinco números de 1 a 20\n",
255 |     "np.random.randint(1,21,15)"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "markdown",
260 |    "metadata": {},
261 |    "source": [
262 |     "#### Distribuição Uniforme"
263 |    ]
264 |   },
265 |   {
266 |    "cell_type": "code",
267 |    "execution_count": null,
268 |    "metadata": {
269 |     "collapsed": true
270 |    },
271 |    "outputs": [],
272 |    "source": [
273 |     "np.random.rand(3) # Distribuiçõa uniforme 0-1"
274 |    ]
275 |   },
276 |   {
277 |    "cell_type": "code",
278 |    "execution_count": null,
279 |    "metadata": {
280 |     "collapsed": true
281 |    },
282 |    "outputs": [],
283 |    "source": [
284 |     "np.random.rand(2,3) # Não usa Tupla"
285 |    ]
286 |   },
287 |   {
288 |    "cell_type": "markdown",
289 |    "metadata": {},
290 |    "source": [
291 |     "#### Distribuição Normal"
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "code",
296 |    "execution_count": null,
297 |    "metadata": {
298 |     "collapsed": true
299 |    },
300 |    "outputs": [],
301 |    "source": [
302 |     "np.random.randn(2,3) #m=0,sd=1"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "markdown",
307 |    "metadata": {},
308 |    "source": [
309 |     "### Manipulção de Matrizes/Vetores"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "markdown",
314 |    "metadata": {},
315 |    "source": [
316 |     "#### Max, Min e Média"
317 |    ]
318 |   },
319 |   {
320 |    "cell_type": "code",
321 |    "execution_count": null,
322 |    "metadata": {
323 |     "collapsed": true
324 |    },
325 |    "outputs": [],
326 |    "source": [
327 |     "\n",
328 |     "m = np.random.randint(1,100,10)\n",
329 |     "print(\"Máximo:{}, Mínimo:{} e Média:{}\".format(m.max(),m.min(),m.mean()))"
330 |    ]
331 |   },
332 |   {
333 |    "cell_type": "code",
334 |    "execution_count": null,
335 |    "metadata": {
336 |     "collapsed": true
337 |    },
338 |    "outputs": [],
339 |    "source": [
340 |     "# Posição Min,Max \n",
341 |     "m.argmax()\n",
342 |     "print(\"O máixmo é {}, e está no index {}.\".format(m.max(),m.argmax()))\n",
343 |     "m"
344 |    ]
345 |   },
346 |   {
347 |    "cell_type": "markdown",
348 |    "metadata": {},
349 |    "source": [
350 |     "### Dimensão e Tipos"
351 |    ]
352 |   },
353 |   {
354 |    "cell_type": "code",
355 |    "execution_count": null,
356 |    "metadata": {
357 |     "collapsed": true
358 |    },
359 |    "outputs": [],
360 |    "source": [
361 |     "# Dimensão\n",
362 |     "v = np.random.randint(0,100,50)\n",
363 |     "v.shape"
364 |    ]
365 |   },
366 |   {
367 |    "cell_type": "code",
368 |    "execution_count": null,
369 |    "metadata": {
370 |     "collapsed": true
371 |    },
372 |    "outputs": [],
373 |    "source": [
374 |     "# Alterando Dimensão\n",
375 |     "m = v.reshape(5,10)\n",
376 |     "m.shape"
377 |    ]
378 |   },
379 |   {
380 |    "cell_type": "code",
381 |    "execution_count": null,
382 |    "metadata": {
383 |     "collapsed": true
384 |    },
385 |    "outputs": [],
386 |    "source": [
387 |     "#Tipo\n",
388 |     "m.dtype"
389 |    ]
390 |   },
391 |   {
392 |    "cell_type": "markdown",
393 |    "metadata": {},
394 |    "source": [
395 |     "Mini tarefa 3.2\n",
396 |     "-------------\n",
397 |     "* a) Crie um matriz 3x3, escolhendo os números de uma distribuição uniforme de 0 a 1;\n",
398 |     "* b) Crie um vetor com 10 itens escolhidos distribuição normal(m=0, sd=1);\n",
399 |     "* c) Faça uma função que represente o lançamento de um dado;"
400 |    ]
401 |   },
402 |   {
403 |    "cell_type": "markdown",
404 |    "metadata": {},
405 |    "source": [
406 |     "## Seleção Numpy (slice)"
407 |    ]
408 |   },
409 |   {
410 |    "cell_type": "markdown",
411 |    "metadata": {},
412 |    "source": [
413 |     "#### Vetores"
414 |    ]
415 |   },
416 |   {
417 |    "cell_type": "code",
418 |    "execution_count": null,
419 |    "metadata": {
420 |     "collapsed": true
421 |    },
422 |    "outputs": [],
423 |    "source": [
424 |     "a = np.arange(1,21)\n",
425 |     "a"
426 |    ]
427 |   },
428 |   {
429 |    "cell_type": "code",
430 |    "execution_count": null,
431 |    "metadata": {
432 |     "collapsed": true
433 |    },
434 |    "outputs": [],
435 |    "source": [
436 |     "a[2]"
437 |    ]
438 |   },
439 |   {
440 |    "cell_type": "code",
441 |    "execution_count": null,
442 |    "metadata": {
443 |     "collapsed": true
444 |    },
445 |    "outputs": [],
446 |    "source": [
447 |     "a[0:3] "
448 |    ]
449 |   },
450 |   {
451 |    "cell_type": "code",
452 |    "execution_count": null,
453 |    "metadata": {
454 |     "collapsed": true
455 |    },
456 |    "outputs": [],
457 |    "source": [
458 |     "a[:3] "
459 |    ]
460 |   },
461 |   {
462 |    "cell_type": "code",
463 |    "execution_count": null,
464 |    "metadata": {
465 |     "collapsed": true
466 |    },
467 |    "outputs": [],
468 |    "source": [
469 |     "a[3:]"
470 |    ]
471 |   },
472 |   {
473 |    "cell_type": "code",
474 |    "execution_count": null,
475 |    "metadata": {
476 |     "collapsed": true
477 |    },
478 |    "outputs": [],
479 |    "source": [
480 |     "a[0:18]"
481 |    ]
482 |   },
483 |   {
484 |    "cell_type": "code",
485 |    "execution_count": null,
486 |    "metadata": {
487 |     "collapsed": true
488 |    },
489 |    "outputs": [],
490 |    "source": [
491 |     "a[:-2]"
492 |    ]
493 |   },
494 |   {
495 |    "cell_type": "markdown",
496 |    "metadata": {},
497 |    "source": [
498 |     "#### Matrizes"
499 |    ]
500 |   },
501 |   {
502 |    "cell_type": "code",
503 |    "execution_count": null,
504 |    "metadata": {
505 |     "collapsed": true
506 |    },
507 |    "outputs": [],
508 |    "source": [
509 |     "m[0] # Linha"
510 |    ]
511 |   },
512 |   {
513 |    "cell_type": "code",
514 |    "execution_count": null,
515 |    "metadata": {},
516 |    "outputs": [],
517 |    "source": [
518 |     "m[:][0] # Coluna"
519 |    ]
520 |   },
521 |   {
522 |    "cell_type": "code",
523 |    "execution_count": null,
524 |    "metadata": {
525 |     "collapsed": true
526 |    },
527 |    "outputs": [],
528 |    "source": [
529 |     "m[0][0] #Numero"
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": null,
535 |    "metadata": {},
536 |    "outputs": [],
537 |    "source": [
538 |     "m[0:2][0:3] #Sub matriz "
539 |    ]
540 |   },
541 |   {
542 |    "cell_type": "code",
543 |    "execution_count": null,
544 |    "metadata": {
545 |     "collapsed": true
546 |    },
547 |    "outputs": [],
548 |    "source": [
549 |     "# Seleção Opção 2"
550 |    ]
551 |   },
552 |   {
553 |    "cell_type": "code",
554 |    "execution_count": null,
555 |    "metadata": {
556 |     "collapsed": true
557 |    },
558 |    "outputs": [],
559 |    "source": [
560 |     "m[1,:] # Linha (Recomendado)"
561 |    ]
562 |   },
563 |   {
564 |    "cell_type": "code",
565 |    "execution_count": null,
566 |    "metadata": {
567 |     "collapsed": true
568 |    },
569 |    "outputs": [],
570 |    "source": [
571 |     "m[1,1] # Numero (Recomendado)"
572 |    ]
573 |   },
574 |   {
575 |    "cell_type": "code",
576 |    "execution_count": null,
577 |    "metadata": {
578 |     "collapsed": true
579 |    },
580 |    "outputs": [],
581 |    "source": [
582 |     "m[:,1] # Coluna (Recomendado)"
583 |    ]
584 |   },
585 |   {
586 |    "cell_type": "code",
587 |    "execution_count": null,
588 |    "metadata": {
589 |     "collapsed": true
590 |    },
591 |    "outputs": [],
592 |    "source": [
593 |     "m[0:2,0:3] #Sub matriz \n"
594 |    ]
595 |   },
596 |   {
597 |    "cell_type": "markdown",
598 |    "metadata": {},
599 |    "source": [
600 |     "# MINI TAREFA 3.3\n",
601 |     "Dada a matriz q:\n",
602 |     "\n",
603 |     "q = np.arange(50).reshape(5,10)\n",
604 |     "\n",
605 |     "Selecione a submatrix que contenha os números:\n",
606 |     "\n",
607 |     "* a) 23, 24, 25\n",
608 |     "* b) 16, 26, 36\n",
609 |     "* c) 23, 24, 25, 33, 34 e 35."
610 |    ]
611 |   },
612 |   {
613 |    "cell_type": "markdown",
614 |    "metadata": {},
615 |    "source": [
616 |     "### Broadcast e Copy"
617 |    ]
618 |   },
619 |   {
620 |    "cell_type": "code",
621 |    "execution_count": null,
622 |    "metadata": {
623 |     "collapsed": true
624 |    },
625 |    "outputs": [],
626 |    "source": [
627 |     "b = a[0:5] "
628 |    ]
629 |   },
630 |   {
631 |    "cell_type": "code",
632 |    "execution_count": null,
633 |    "metadata": {
634 |     "collapsed": true,
635 |     "scrolled": false
636 |    },
637 |    "outputs": [],
638 |    "source": [
639 |     "b[0:5] = 30"
640 |    ]
641 |   },
642 |   {
643 |    "cell_type": "code",
644 |    "execution_count": null,
645 |    "metadata": {
646 |     "collapsed": true
647 |    },
648 |    "outputs": [],
649 |    "source": [
650 |     "a = np.arange(1,21)\n",
651 |     "b = a.copy()[0:5]\n",
652 |     "b[0:5] = 30"
653 |    ]
654 |   },
655 |   {
656 |    "cell_type": "code",
657 |    "execution_count": null,
658 |    "metadata": {
659 |     "collapsed": true
660 |    },
661 |    "outputs": [],
662 |    "source": [
663 |     "a"
664 |    ]
665 |   },
666 |   {
667 |    "cell_type": "code",
668 |    "execution_count": null,
669 |    "metadata": {
670 |     "collapsed": true
671 |    },
672 |    "outputs": [],
673 |    "source": [
674 |     "b"
675 |    ]
676 |   },
677 |   {
678 |    "cell_type": "markdown",
679 |    "metadata": {},
680 |    "source": [
681 |     "## Seleçao com Boolean"
682 |    ]
683 |   },
684 |   {
685 |    "cell_type": "code",
686 |    "execution_count": null,
687 |    "metadata": {
688 |     "collapsed": true
689 |    },
690 |    "outputs": [],
691 |    "source": [
692 |     " c = np.arange(1,100)\n",
693 |     "    "
694 |    ]
695 |   },
696 |   {
697 |    "cell_type": "code",
698 |    "execution_count": null,
699 |    "metadata": {
700 |     "collapsed": true
701 |    },
702 |    "outputs": [],
703 |    "source": [
704 |     "bool_sel = c > 50"
705 |    ]
706 |   },
707 |   {
708 |    "cell_type": "code",
709 |    "execution_count": null,
710 |    "metadata": {},
711 |    "outputs": [],
712 |    "source": [
713 |     "c[bool_sel]"
714 |    ]
715 |   },
716 |   {
717 |    "cell_type": "markdown",
718 |    "metadata": {},
719 |    "source": [
720 |     "Mini tarefa 3.4\n",
721 |     "-------------\n",
722 |     "* a) Faça o filtro acima em um só passo;\n",
723 |     "* b) Faça um filtro para selecionar somente os números pares;\n",
724 |     "* c) Considerando $m$ como a média de um vetor $v$, e $v_i$ como o $i$-ésimo item de $v$. Faça um função f, que substitua $v_i$ por zero caso $v_i < m$. \n",
725 |     " "
726 |    ]
727 |   },
728 |   {
729 |    "cell_type": "markdown",
730 |    "metadata": {
731 |     "collapsed": true
732 |    },
733 |    "source": [
734 |     "### Operações com Numpy"
735 |    ]
736 |   },
737 |   {
738 |    "cell_type": "code",
739 |    "execution_count": null,
740 |    "metadata": {},
741 |    "outputs": [],
742 |    "source": [
743 |     "# Soma\n",
744 |     "a = np.arange(0,5)\n",
745 |     "b = np.arange(0,5)\n",
746 |     "soma = a + b\n",
747 |     "print(\"{}+{}={}\".format(a,b,soma))"
748 |    ]
749 |   },
750 |   {
751 |    "cell_type": "code",
752 |    "execution_count": null,
753 |    "metadata": {},
754 |    "outputs": [],
755 |    "source": [
756 |     "# Subtração\n",
757 |     "sub = a - b\n",
758 |     "print(\"{}-{}={}\".format(a,b,sub))"
759 |    ]
760 |   },
761 |   {
762 |    "cell_type": "code",
763 |    "execution_count": null,
764 |    "metadata": {},
765 |    "outputs": [],
766 |    "source": [
767 |     "# Multiplicação\n",
768 |     "mul = a * b\n",
769 |     "print(\"{}*{}={}\".format(a,b,mul))"
770 |    ]
771 |   },
772 |   {
773 |    "cell_type": "code",
774 |    "execution_count": 26,
775 |    "metadata": {},
776 |    "outputs": [
777 |     {
778 |      "name": "stdout",
779 |      "output_type": "stream",
780 |      "text": [
781 |       "[0 1 2 3 4]**[0 1 2 3 4]=[  1   1   4  27 256]\n"
782 |      ]
783 |     }
784 |    ],
785 |    "source": [
786 |     "#Potência\n",
787 |     "pot = a ** b\n",
788 |     "print(\"{}**{}={}\".format(a,b,pot))"
789 |    ]
790 |   },
791 |   {
792 |    "cell_type": "code",
793 |    "execution_count": 27,
794 |    "metadata": {},
795 |    "outputs": [
796 |     {
797 |      "name": "stdout",
798 |      "output_type": "stream",
799 |      "text": [
800 |       "[0 1 2 3 4]*2=[2 3 4 5 6]\n",
801 |       "[0 1 2 3 4]-2=[-2 -1  0  1  2]\n",
802 |       "[0 1 2 3 4]*2=[0 2 4 6 8]\n",
803 |       "[0 1 2 3 4]**2=[ 0  1  4  9 16]\n"
804 |      ]
805 |     }
806 |    ],
807 |    "source": [
808 |     "#Usando escalar\n",
809 |     "c = 2\n",
810 |     "soma= a + c\n",
811 |     "sub = a - c\n",
812 |     "mul = a * c\n",
813 |     "pot = a ** c \n",
814 |     "print(\"{}*{}={}\".format(a,c,soma))\n",
815 |     "print(\"{}-{}={}\".format(a,c,sub))\n",
816 |     "print(\"{}*{}={}\".format(a,c,mul))\n",
817 |     "print(\"{}**{}={}\".format(a,c,pot))\n"
818 |    ]
819 |   },
820 |   {
821 |    "cell_type": "code",
822 |    "execution_count": null,
823 |    "metadata": {},
824 |    "outputs": [],
825 |    "source": [
826 |     " # Divisão (cuidado com Erros na Operação)\n",
827 |     "div=a/a\n",
828 |     "print(\"{}/{}={}\".format(a,a,div))"
829 |    ]
830 |   },
831 |   {
832 |    "cell_type": "code",
833 |    "execution_count": 24,
834 |    "metadata": {},
835 |    "outputs": [
836 |     {
837 |      "name": "stdout",
838 |      "output_type": "stream",
839 |      "text": [
840 |       "1/[0 1 2 3 4]=[        inf  1.          0.5         0.33333333  0.25      ]\n"
841 |      ]
842 |     },
843 |     {
844 |      "name": "stderr",
845 |      "output_type": "stream",
846 |      "text": [
847 |       "C:\\Program Files\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
848 |       "  \"\"\"Entry point for launching an IPython kernel.\n"
849 |      ]
850 |     }
851 |    ],
852 |    "source": [
853 |     "div2=1/a\n",
854 |     "print(\"1/{}={}\".format(a,div2))"
855 |    ]
856 |   },
857 |   {
858 |    "cell_type": "markdown",
859 |    "metadata": {},
860 |    "source": [
861 |     "# Funções Vetorizadas (Universal Functions)"
862 |    ]
863 |   },
864 |   {
865 |    "cell_type": "code",
866 |    "execution_count": 33,
867 |    "metadata": {
868 |     "scrolled": true
869 |    },
870 |    "outputs": [
871 |     {
872 |      "name": "stdout",
873 |      "output_type": "stream",
874 |      "text": [
875 |       "Sumário: Max=4, Min=0, Média=2.0\n"
876 |      ]
877 |     }
878 |    ],
879 |    "source": [
880 |     "# Exemplo:\n",
881 |     "max=np.max(a)\n",
882 |     "min=np.min(a)\n",
883 |     "media=np.mean(a)\n",
884 |     "print(\"Sumário: Max={}, Min={}, Média={}\".format(max,min,media))"
885 |    ]
886 |   },
887 |   {
888 |    "cell_type": "markdown",
889 |    "metadata": {},
890 |    "source": [
891 |     "Lista de Funções que podem ser aplicadas a vetores e matrizes:\n",
892 |     "\n",
893 |     "Universal functions:[https://docs.scipy.org/doc/numpy-1.13.0/reference/ufuncs.html]"
894 |    ]
895 |   },
896 |   {
897 |    "cell_type": "code",
898 |    "execution_count": 46,
899 |    "metadata": {},
900 |    "outputs": [
901 |     {
902 |      "name": "stdout",
903 |      "output_type": "stream",
904 |      "text": [
905 |       "[[0 1 2]\n",
906 |       " [3 4 5]\n",
907 |       " [6 7 8]]\n",
908 |       "colSum:[ 9 12 15] e rowSum:[ 3 12 21]\n"
909 |      ]
910 |     }
911 |    ],
912 |    "source": [
913 |     "# Aplicando as funções em linhas ou colunas\n",
914 |     "m = np.arange(0,9).reshape(3,3)\n",
915 |     "m\n",
916 |     "colSum = m.sum(0)\n",
917 |     "rowSum = m.sum(1)\n",
918 |     "print(m)\n",
919 |     "print(\"colSum:{} e rowSum:{}\".format(colSum,rowSum))"
920 |    ]
921 |   },
922 |   {
923 |    "cell_type": "markdown",
924 |    "metadata": {},
925 |    "source": [
926 |     "# MINI TAREFA 3.4"
927 |    ]
928 |   },
929 |   {
930 |    "cell_type": "markdown",
931 |    "metadata": {},
932 |    "source": [
933 |     "Faça uma função que receba um vetor $v$ e normalize seus valores utilizando a média e o desvio padrão. \n",
934 |     "Confome abaixo:\n",
935 |     "\n",
936 |     "$v_i^{'} = \\frac{v_i-m}{\\sigma}$\n",
937 |     "\n",
938 |     "Onde $v_i$ é $i$-ésimo elemento de $v$, $v_i^{'}$ o $i$-ésimo elemento do novo vetor, $m$ é a média de $v$ e $\\sigma$ o desvio padrão de $v$."
939 |    ]
940 |   },
941 |   {
942 |    "cell_type": "code",
943 |    "execution_count": null,
944 |    "metadata": {
945 |     "collapsed": true
946 |    },
947 |    "outputs": [],
948 |    "source": []
949 |   }
950 |  ],
951 |  "metadata": {
952 |   "kernelspec": {
953 |    "display_name": "Python 3",
954 |    "language": "python",
955 |    "name": "python3"
956 |   },
957 |   "language_info": {
958 |    "codemirror_mode": {
959 |     "name": "ipython",
960 |     "version": 3
961 |    },
962 |    "file_extension": ".py",
963 |    "mimetype": "text/x-python",
964 |    "name": "python",
965 |    "nbconvert_exporter": "python",
966 |    "pygments_lexer": "ipython3",
967 |    "version": "3.6.1"
968 |   }
969 |  },
970 |  "nbformat": 4,
971 |  "nbformat_minor": 2
972 | }
973 | 


--------------------------------------------------------------------------------
/Aula_03_Numpy_Exercicios.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "**Faça  importação de Numpy como np**"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 4,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": []
 16 |   },
 17 |   {
 18 |    "cell_type": "markdown",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     "** Crie uma vetor de 20 uns** "
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 6,
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "data": {
 31 |       "text/plain": [
 32 |        "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
 33 |        "        1.,  1.,  1.,  1.,  1.,  1.,  1.])"
 34 |       ]
 35 |      },
 36 |      "execution_count": 6,
 37 |      "metadata": {},
 38 |      "output_type": "execute_result"
 39 |     }
 40 |    ],
 41 |    "source": []
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {},
 46 |    "source": [
 47 |     "** Crie uma vetor de 5 zeros** "
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 8,
 53 |    "metadata": {},
 54 |    "outputs": [
 55 |     {
 56 |      "data": {
 57 |       "text/plain": [
 58 |        "array([ 0.,  0.,  0.,  0.,  0.])"
 59 |       ]
 60 |      },
 61 |      "execution_count": 8,
 62 |      "metadata": {},
 63 |      "output_type": "execute_result"
 64 |     }
 65 |    ],
 66 |    "source": []
 67 |   },
 68 |   {
 69 |    "cell_type": "markdown",
 70 |    "metadata": {},
 71 |    "source": [
 72 |     "*** Crie um vetor com 25 elemetos, todos igual 5.**"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 13,
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "data": {
 82 |       "text/plain": [
 83 |        "array([ 5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,\n",
 84 |        "        5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.])"
 85 |       ]
 86 |      },
 87 |      "execution_count": 13,
 88 |      "metadata": {},
 89 |      "output_type": "execute_result"
 90 |     }
 91 |    ],
 92 |    "source": []
 93 |   },
 94 |   {
 95 |    "cell_type": "markdown",
 96 |    "metadata": {},
 97 |    "source": [
 98 |     "*** Crie uma matriz 5x5 com todos elementos iguais a 5.***"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 11,
104 |    "metadata": {},
105 |    "outputs": [
106 |     {
107 |      "data": {
108 |       "text/plain": [
109 |        "array([[ 5.,  5.,  5.,  5.,  5.],\n",
110 |        "       [ 5.,  5.,  5.,  5.,  5.],\n",
111 |        "       [ 5.,  5.,  5.,  5.,  5.],\n",
112 |        "       [ 5.,  5.,  5.,  5.,  5.],\n",
113 |        "       [ 5.,  5.,  5.,  5.,  5.]])"
114 |       ]
115 |      },
116 |      "execution_count": 11,
117 |      "metadata": {},
118 |      "output_type": "execute_result"
119 |     }
120 |    ],
121 |    "source": []
122 |   },
123 |   {
124 |    "cell_type": "markdown",
125 |    "metadata": {},
126 |    "source": [
127 |     "**Crie um vetor de inteiros de 0 a 15**"
128 |    ]
129 |   },
130 |   {
131 |    "cell_type": "code",
132 |    "execution_count": 14,
133 |    "metadata": {},
134 |    "outputs": [
135 |     {
136 |      "data": {
137 |       "text/plain": [
138 |        "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
139 |       ]
140 |      },
141 |      "execution_count": 14,
142 |      "metadata": {},
143 |      "output_type": "execute_result"
144 |     }
145 |    ],
146 |    "source": []
147 |   },
148 |   {
149 |    "cell_type": "markdown",
150 |    "metadata": {},
151 |    "source": [
152 |     "**Crie um vetor com todos os numeros pares de 5 a 60**"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 20,
158 |    "metadata": {},
159 |    "outputs": [
160 |     {
161 |      "data": {
162 |       "text/plain": [
163 |        "array([ 6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38,\n",
164 |        "       40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60])"
165 |       ]
166 |      },
167 |      "execution_count": 20,
168 |      "metadata": {},
169 |      "output_type": "execute_result"
170 |     }
171 |    ],
172 |    "source": []
173 |   },
174 |   {
175 |    "cell_type": "markdown",
176 |    "metadata": {},
177 |    "source": [
178 |     "**Crie uma matriz identidade 5x5**"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 24,
184 |    "metadata": {},
185 |    "outputs": [
186 |     {
187 |      "data": {
188 |       "text/plain": [
189 |        "array([[ 1.,  0.,  0.,  0.,  0.],\n",
190 |        "       [ 0.,  1.,  0.,  0.,  0.],\n",
191 |        "       [ 0.,  0.,  1.,  0.,  0.],\n",
192 |        "       [ 0.,  0.,  0.,  1.,  0.],\n",
193 |        "       [ 0.,  0.,  0.,  0.,  1.]])"
194 |       ]
195 |      },
196 |      "execution_count": 24,
197 |      "metadata": {},
198 |      "output_type": "execute_result"
199 |     }
200 |    ],
201 |    "source": []
202 |   },
203 |   {
204 |    "cell_type": "markdown",
205 |    "metadata": {},
206 |    "source": [
207 |     "**Crie uma matriz 4x4 com elementos de 0 a 15**"
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 23,
213 |    "metadata": {},
214 |    "outputs": [
215 |     {
216 |      "data": {
217 |       "text/plain": [
218 |        "array([[ 0,  1,  2,  3],\n",
219 |        "       [ 4,  5,  6,  7],\n",
220 |        "       [ 8,  9, 10, 11],\n",
221 |        "       [12, 13, 14, 15]])"
222 |       ]
223 |      },
224 |      "execution_count": 23,
225 |      "metadata": {},
226 |      "output_type": "execute_result"
227 |     }
228 |    ],
229 |    "source": []
230 |   },
231 |   {
232 |    "cell_type": "markdown",
233 |    "metadata": {},
234 |    "source": [
235 |     "***Gere um número aleatório de 0 a 1***\n",
236 |     "\n",
237 |     "use antes do código *np.random.seed(100)* "
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 31,
243 |    "metadata": {},
244 |    "outputs": [
245 |     {
246 |      "data": {
247 |       "text/plain": [
248 |        "array([ 0.54340494])"
249 |       ]
250 |      },
251 |      "execution_count": 31,
252 |      "metadata": {},
253 |      "output_type": "execute_result"
254 |     }
255 |    ],
256 |    "source": [
257 |     "\n"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "markdown",
262 |    "metadata": {},
263 |    "source": [
264 |     "** Gere a Matriz abaixo **"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 38,
270 |    "metadata": {},
271 |    "outputs": [
272 |     {
273 |      "data": {
274 |       "text/plain": [
275 |        "array([[ 0.01,  0.02,  0.03,  0.04,  0.05,  0.06,  0.07,  0.08,  0.09,  0.1 ],\n",
276 |        "       [ 0.11,  0.12,  0.13,  0.14,  0.15,  0.16,  0.17,  0.18,  0.19,  0.2 ],\n",
277 |        "       [ 0.21,  0.22,  0.23,  0.24,  0.25,  0.26,  0.27,  0.28,  0.29,  0.3 ],\n",
278 |        "       [ 0.31,  0.32,  0.33,  0.34,  0.35,  0.36,  0.37,  0.38,  0.39,  0.4 ],\n",
279 |        "       [ 0.41,  0.42,  0.43,  0.44,  0.45,  0.46,  0.47,  0.48,  0.49,  0.5 ],\n",
280 |        "       [ 0.51,  0.52,  0.53,  0.54,  0.55,  0.56,  0.57,  0.58,  0.59,  0.6 ],\n",
281 |        "       [ 0.61,  0.62,  0.63,  0.64,  0.65,  0.66,  0.67,  0.68,  0.69,  0.7 ],\n",
282 |        "       [ 0.71,  0.72,  0.73,  0.74,  0.75,  0.76,  0.77,  0.78,  0.79,  0.8 ],\n",
283 |        "       [ 0.81,  0.82,  0.83,  0.84,  0.85,  0.86,  0.87,  0.88,  0.89,  0.9 ],\n",
284 |        "       [ 0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99,  1.  ]])"
285 |       ]
286 |      },
287 |      "execution_count": 38,
288 |      "metadata": {},
289 |      "output_type": "execute_result"
290 |     }
291 |    ],
292 |    "source": []
293 |   },
294 |   {
295 |    "cell_type": "markdown",
296 |    "metadata": {},
297 |    "source": [
298 |     "***Dada a matriz m, selecione a submatriz abaixo apartir de m*** \n"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": 56,
304 |    "metadata": {},
305 |    "outputs": [
306 |     {
307 |      "data": {
308 |       "text/plain": [
309 |        "array([[12, 13, 14],\n",
310 |        "       [18, 19, 20]])"
311 |       ]
312 |      },
313 |      "execution_count": 56,
314 |      "metadata": {},
315 |      "output_type": "execute_result"
316 |     }
317 |    ],
318 |    "source": [
319 |     "# Matriz m\n",
320 |     "m = np.arange(24).reshape(4,6)"
321 |    ]
322 |   },
323 |   {
324 |    "cell_type": "markdown",
325 |    "metadata": {},
326 |    "source": [
327 |     "***Calcule o desvio padrão das colunas da matriz m do exemplo acima*** "
328 |    ]
329 |   },
330 |   {
331 |    "cell_type": "code",
332 |    "execution_count": 59,
333 |    "metadata": {},
334 |    "outputs": [
335 |     {
336 |      "data": {
337 |       "text/plain": [
338 |        "array([ 1.70782513,  1.70782513,  1.70782513,  1.70782513])"
339 |       ]
340 |      },
341 |      "execution_count": 59,
342 |      "metadata": {},
343 |      "output_type": "execute_result"
344 |     }
345 |    ],
346 |    "source": []
347 |   }
348 |  ],
349 |  "metadata": {
350 |   "kernelspec": {
351 |    "display_name": "Python 3",
352 |    "language": "python",
353 |    "name": "python3"
354 |   },
355 |   "language_info": {
356 |    "codemirror_mode": {
357 |     "name": "ipython",
358 |     "version": 3
359 |    },
360 |    "file_extension": ".py",
361 |    "mimetype": "text/x-python",
362 |    "name": "python",
363 |    "nbconvert_exporter": "python",
364 |    "pygments_lexer": "ipython3",
365 |    "version": "3.6.1"
366 |   }
367 |  },
368 |  "nbformat": 4,
369 |  "nbformat_minor": 2
370 | }
371 | 


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/Aula_04_Pandas_Exercicios.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### Faça a importação do panda como pd\n",
  8 |     "### Faça a importação do numpy como np"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": null,
 14 |    "metadata": {
 15 |     "collapsed": true
 16 |    },
 17 |    "outputs": [],
 18 |    "source": []
 19 |   },
 20 |   {
 21 |    "cell_type": "markdown",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "* **Ajuste o seed do numpy para 201 **\n",
 25 |     "* **Crie uma série em panda a partir de vetor de 100 números aleatórios de 0 a 1. **\n"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 7,
 31 |    "metadata": {},
 32 |    "outputs": [],
 33 |    "source": [
 34 |     "# Dadas as Séries \n",
 35 |     "nProva1 = pd.Series(data=[5,10,9], index=['AL01','AL02','AL03'])\n",
 36 |     "nProva2 = pd.Series(data=[4,6,4], index=['AL01','AL02','AL03'])\n"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "markdown",
 41 |    "metadata": {},
 42 |    "source": [
 43 |     "* (a) Calcule a Nota percentual de Cada Aluno nas Provas 1 e 2 em relação à Nota Máxima;\n",
 44 |     "* (b) Calcule a Nota Normalizada de Cada Aluno nas Provas 1 e 2;"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 8,
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "data": {
 54 |       "text/html": [
 55 |        "<div>\n",
 56 |        "<style>\n",
 57 |        "    .dataframe thead tr:only-child th {\n",
 58 |        "        text-align: right;\n",
 59 |        "    }\n",
 60 |        "\n",
 61 |        "    .dataframe thead th {\n",
 62 |        "        text-align: left;\n",
 63 |        "    }\n",
 64 |        "\n",
 65 |        "    .dataframe tbody tr th {\n",
 66 |        "        vertical-align: top;\n",
 67 |        "    }\n",
 68 |        "</style>\n",
 69 |        "<table border=\"1\" class=\"dataframe\">\n",
 70 |        "  <thead>\n",
 71 |        "    <tr style=\"text-align: right;\">\n",
 72 |        "      <th></th>\n",
 73 |        "      <th>P1</th>\n",
 74 |        "      <th>P2</th>\n",
 75 |        "      <th>P3</th>\n",
 76 |        "    </tr>\n",
 77 |        "  </thead>\n",
 78 |        "  <tbody>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>AL01</th>\n",
 81 |        "      <td>5.434049</td>\n",
 82 |        "      <td>2.783694</td>\n",
 83 |        "      <td>4.245176</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>AL02</th>\n",
 87 |        "      <td>8.447761</td>\n",
 88 |        "      <td>0.047189</td>\n",
 89 |        "      <td>1.215691</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <th>AL03</th>\n",
 93 |        "      <td>6.707491</td>\n",
 94 |        "      <td>8.258528</td>\n",
 95 |        "      <td>1.367066</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>AL04</th>\n",
 99 |        "      <td>5.750933</td>\n",
100 |        "      <td>8.913220</td>\n",
101 |        "      <td>2.092021</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>AL05</th>\n",
105 |        "      <td>1.853282</td>\n",
106 |        "      <td>1.083769</td>\n",
107 |        "      <td>2.196975</td>\n",
108 |        "    </tr>\n",
109 |        "  </tbody>\n",
110 |        "</table>\n",
111 |        "</div>"
112 |       ],
113 |       "text/plain": [
114 |        "            P1        P2        P3\n",
115 |        "AL01  5.434049  2.783694  4.245176\n",
116 |        "AL02  8.447761  0.047189  1.215691\n",
117 |        "AL03  6.707491  8.258528  1.367066\n",
118 |        "AL04  5.750933  8.913220  2.092021\n",
119 |        "AL05  1.853282  1.083769  2.196975"
120 |       ]
121 |      },
122 |      "execution_count": 8,
123 |      "metadata": {},
124 |      "output_type": "execute_result"
125 |     }
126 |    ],
127 |    "source": [
128 |     "# Dadas o dataframe\n",
129 |     "np.random.seed(100)\n",
130 |     "dados = np.random.rand(5,3)*10\n",
131 |     "colunas = ['P1','P2','P3']\n",
132 |     "index = ['AL01','AL02','AL03','AL04','AL05']\n",
133 |     "df = pd.DataFrame(dados,index,colunas)\n",
134 |     "df\n"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "markdown",
139 |    "metadata": {},
140 |    "source": [
141 |     "* (a) Calcue a média de cada prova;\n",
142 |     "* (b) Calcule a média de cada aluno.\n",
143 |     "* (c) Liste os alunos com nota em P2 abaixo da média.\n",
144 |     "* (d) Ordene o dataframe por P3\n",
145 |     "* (e) Crie um nova coluna com a média do aluno."
146 |    ]
147 |   },
148 |   {
149 |    "cell_type": "code",
150 |    "execution_count": 14,
151 |    "metadata": {},
152 |    "outputs": [
153 |     {
154 |      "data": {
155 |       "text/html": [
156 |        "<div>\n",
157 |        "<style>\n",
158 |        "    .dataframe thead tr:only-child th {\n",
159 |        "        text-align: right;\n",
160 |        "    }\n",
161 |        "\n",
162 |        "    .dataframe thead th {\n",
163 |        "        text-align: left;\n",
164 |        "    }\n",
165 |        "\n",
166 |        "    .dataframe tbody tr th {\n",
167 |        "        vertical-align: top;\n",
168 |        "    }\n",
169 |        "</style>\n",
170 |        "<table border=\"1\" class=\"dataframe\">\n",
171 |        "  <thead>\n",
172 |        "    <tr style=\"text-align: right;\">\n",
173 |        "      <th></th>\n",
174 |        "      <th>A</th>\n",
175 |        "      <th>B</th>\n",
176 |        "      <th>C</th>\n",
177 |        "      <th>D</th>\n",
178 |        "    </tr>\n",
179 |        "  </thead>\n",
180 |        "  <tbody>\n",
181 |        "    <tr>\n",
182 |        "      <th>0</th>\n",
183 |        "      <td>1.0</td>\n",
184 |        "      <td>2.0</td>\n",
185 |        "      <td>3</td>\n",
186 |        "      <td>NaN</td>\n",
187 |        "    </tr>\n",
188 |        "    <tr>\n",
189 |        "      <th>1</th>\n",
190 |        "      <td>2.0</td>\n",
191 |        "      <td>4.0</td>\n",
192 |        "      <td>4</td>\n",
193 |        "      <td>1.0</td>\n",
194 |        "    </tr>\n",
195 |        "    <tr>\n",
196 |        "      <th>2</th>\n",
197 |        "      <td>NaN</td>\n",
198 |        "      <td>NaN</td>\n",
199 |        "      <td>4</td>\n",
200 |        "      <td>2.0</td>\n",
201 |        "    </tr>\n",
202 |        "  </tbody>\n",
203 |        "</table>\n",
204 |        "</div>"
205 |       ],
206 |       "text/plain": [
207 |        "     A    B  C    D\n",
208 |        "0  1.0  2.0  3  NaN\n",
209 |        "1  2.0  4.0  4  1.0\n",
210 |        "2  NaN  NaN  4  2.0"
211 |       ]
212 |      },
213 |      "execution_count": 14,
214 |      "metadata": {},
215 |      "output_type": "execute_result"
216 |     }
217 |    ],
218 |    "source": [
219 |     "# Dados o dataFrame abaixo\n",
220 |     "dic = {'A':[1,2,np.nan],'B':[2,4,np.nan],'C':[3,4,4],'D':[np.nan,1,2]}\n",
221 |     "df = pd.DataFrame(dic)\n",
222 |     "df"
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "markdown",
227 |    "metadata": {},
228 |    "source": [
229 |     "* **Selecione as linhas sem NAN**\n",
230 |     "* **Selecione as colunas sem NAN**"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": null,
236 |    "metadata": {
237 |     "collapsed": true
238 |    },
239 |    "outputs": [],
240 |    "source": [
241 |     "# Dados o dataFrame abaixo\n",
242 |     "dic = {'Metodo':['KNN','SVR','ANN','KNN','SVR','ANN'],\n",
243 |     "        'Cenario':['C1','C2','C1','C2','C1','C2'],\n",
244 |     "        'Erro':[2.1,2.1,1.5,1.7,1.8,1.9]}\n",
245 |     "df = pd.DataFrame(dic)"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "markdown",
250 |    "metadata": {},
251 |    "source": [
252 |     "**Calcule:**\n",
253 |     "\n",
254 |     "* Erro média de cada método;\n",
255 |     "* Erro médio de cada cenário.\n"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "code",
260 |    "execution_count": null,
261 |    "metadata": {
262 |     "collapsed": true
263 |    },
264 |    "outputs": [],
265 |    "source": []
266 |   }
267 |  ],
268 |  "metadata": {
269 |   "kernelspec": {
270 |    "display_name": "Python 3",
271 |    "language": "python",
272 |    "name": "python3"
273 |   },
274 |   "language_info": {
275 |    "codemirror_mode": {
276 |     "name": "ipython",
277 |     "version": 3
278 |    },
279 |    "file_extension": ".py",
280 |    "mimetype": "text/x-python",
281 |    "name": "python",
282 |    "nbconvert_exporter": "python",
283 |    "pygments_lexer": "ipython3",
284 |    "version": "3.6.1"
285 |   }
286 |  },
287 |  "nbformat": 4,
288 |  "nbformat_minor": 2
289 | }
290 | 


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/Aula_06_01_Regressão Linear.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### Versão sckitlearn"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": null,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "import sklearn\n",
 17 |     "print('Minha versão do sckit é {}.'.format(sklearn.__version__))"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "markdown",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "### Importações\n",
 25 |     "-------------"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": null,
 31 |    "metadata": {
 32 |     "collapsed": true
 33 |    },
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "#Imports \n",
 37 |     "import numpy as np\n",
 38 |     "import pandas as pd\n",
 39 |     "from matplotlib import pyplot as plt\n",
 40 |     "import seaborn as sns\n",
 41 |     "%matplotlib inline"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {},
 47 |    "source": [
 48 |     "### Carregando Dataset\n",
 49 |     "-------------------------"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": null,
 55 |    "metadata": {
 56 |     "collapsed": true
 57 |    },
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "# Dataset (a partir de csv)\n",
 61 |     "#df =pd.read_csv('meuarquivo.data')"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": null,
 67 |    "metadata": {
 68 |     "scrolled": true
 69 |    },
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "# A a partir do Sckit\n",
 73 |     "from sklearn.datasets import load_boston\n",
 74 |     "boston = load_boston()\n",
 75 |     "print(boston.DESCR)"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": null,
 81 |    "metadata": {
 82 |     "scrolled": true
 83 |    },
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "# Tranformado em um dataframe Pandas\n",
 87 |     "df = pd.DataFrame(boston.data)\n",
 88 |     "df.head()"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": null,
 94 |    "metadata": {
 95 |     "scrolled": true
 96 |    },
 97 |    "outputs": [],
 98 |    "source": [
 99 |     "df.columns = boston.feature_names\n",
100 |     "df.head()"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "code",
105 |    "execution_count": null,
106 |    "metadata": {},
107 |    "outputs": [],
108 |    "source": [
109 |     "df['PRICE'] =boston.target\n",
110 |     "df.head()"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": null,
116 |    "metadata": {
117 |     "collapsed": true
118 |    },
119 |    "outputs": [],
120 |    "source": [
121 |     "# Info\n",
122 |     "df.info()"
123 |    ]
124 |   },
125 |   {
126 |    "cell_type": "code",
127 |    "execution_count": null,
128 |    "metadata": {
129 |     "collapsed": true
130 |    },
131 |    "outputs": [],
132 |    "source": [
133 |     "# Describe\n",
134 |     "df.describe()"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": null,
140 |    "metadata": {
141 |     "collapsed": true
142 |    },
143 |    "outputs": [],
144 |    "source": [
145 |     "# PLots\n",
146 |     "# Verificando relação par a par\n",
147 |     "sns.pairplot(df)\n"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": null,
153 |    "metadata": {
154 |     "scrolled": true
155 |    },
156 |    "outputs": [],
157 |    "source": [
158 |     "# Verificando a distribuição do alvo\n",
159 |     "sns.distplot(df['PRICE'])"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": null,
165 |    "metadata": {
166 |     "collapsed": true
167 |    },
168 |    "outputs": [],
169 |    "source": [
170 |     "# Verificando a correlação\n",
171 |     "df.corr()\n",
172 |     "sns.heatmap(df.corr())"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "code",
177 |    "execution_count": null,
178 |    "metadata": {
179 |     "collapsed": true,
180 |     "scrolled": true
181 |    },
182 |    "outputs": [],
183 |    "source": [
184 |     "# Melhorando o plot\n",
185 |     "plt.rc(\"figure\", figsize=(12,8))\n",
186 |     "sns.heatmap(df.corr(),annot=True)"
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "markdown",
191 |    "metadata": {},
192 |    "source": [
193 |     "### Dados de Treino e Dados de Teste"
194 |    ]
195 |   },
196 |   {
197 |    "cell_type": "code",
198 |    "execution_count": null,
199 |    "metadata": {
200 |     "collapsed": true
201 |    },
202 |    "outputs": [],
203 |    "source": [
204 |     "# Separando Treino e Teste\n",
205 |     "from sklearn.model_selection import train_test_split\n",
206 |     "\n",
207 |     "X = df.drop('PRICE',axis=1)\n",
208 |     "y = df['PRICE']\n",
209 |     "\n",
210 |     "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=100)\n"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "markdown",
215 |    "metadata": {},
216 |    "source": [
217 |     "### Regressão Linear"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "code",
222 |    "execution_count": null,
223 |    "metadata": {
224 |     "collapsed": true
225 |    },
226 |    "outputs": [],
227 |    "source": [
228 |     "from sklearn.linear_model import LinearRegression\n",
229 |     "lm = LinearRegression()\n",
230 |     "lm.fit(X_train,y_train)"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": null,
236 |    "metadata": {
237 |     "collapsed": true
238 |    },
239 |    "outputs": [],
240 |    "source": [
241 |     "# Verificando Interceptação e Coeficiêntes\n",
242 |     "print('Interceptação: {}'.format(lm.intercept_))\n",
243 |     "pd.DataFrame(lm.coef_,X.columns,columns=['Coeff'])"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": null,
249 |    "metadata": {
250 |     "collapsed": true
251 |    },
252 |    "outputs": [],
253 |    "source": [
254 |     "# Fazendo predições\n",
255 |     "y_pred = lm.predict(X_test)\n",
256 |     "y_pred"
257 |    ]
258 |   },
259 |   {
260 |    "cell_type": "code",
261 |    "execution_count": null,
262 |    "metadata": {
263 |     "collapsed": true
264 |    },
265 |    "outputs": [],
266 |    "source": [
267 |     "# Verificando a predição\n",
268 |     "plt.scatter(y_pred,y_test)\n",
269 |     "plt.xlabel(\"Predito\")\n",
270 |     "plt.ylabel(\"Real\")"
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": null,
276 |    "metadata": {
277 |     "collapsed": true
278 |    },
279 |    "outputs": [],
280 |    "source": [
281 |     "sns.distplot(y_pred-y_test)"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "markdown",
286 |    "metadata": {},
287 |    "source": [
288 |     "## Métricas Regressão"
289 |    ]
290 |   },
291 |   {
292 |    "cell_type": "markdown",
293 |    "metadata": {},
294 |    "source": [
295 |     "### Mean Absolute Error (MAE)\n",
296 |     "\n",
297 |     "\n",
298 |     "$MAE=\\frac{1}{n}\\sum\\limits^{n}_{i=1}|y_i - \\hat{y}_i|$\n",
299 |     "\n",
300 |     "\n",
301 |     "### Mean Square Error (MSE)\n",
302 |     "\n",
303 |     "$MSE=\\frac{1}{n}\\sum\\limits^{n}_{i=1}(y_i - \\hat{y}_i)^2$\n",
304 |     "\n",
305 |     "*Obs: erros mais altos são punidos*\n",
306 |     "\n",
307 |     "### Root Mean Square Error (RMSE)\n",
308 |     "\n",
309 |     "$RMSE=\\sqrt{\\frac{1}{n}\\sum\\limits^{n}_{i=1}(y_i - \\hat{y}_i)^2}$\n",
310 |     "\n",
311 |     "*Obs:tem mesma unidade que $y$*\n",
312 |     "\n",
313 |     "*Todas são loss function*\n"
314 |    ]
315 |   },
316 |   {
317 |    "cell_type": "code",
318 |    "execution_count": null,
319 |    "metadata": {},
320 |    "outputs": [],
321 |    "source": [
322 |     "# Calculando\n",
323 |     "from sklearn import metrics\n",
324 |     "print(\"MAE:\",metrics.mean_absolute_error(y_test,y_pred))\n",
325 |     "print(\"MSE:\",metrics.mean_squared_error(y_test,y_pred))\n",
326 |     "print(\"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
327 |    ]
328 |   },
329 |   {
330 |    "cell_type": "markdown",
331 |    "metadata": {},
332 |    "source": [
333 |     "**Mini Tarefa**\n",
334 |     "\n",
335 |     "Para o Dataframe tips faça:\n",
336 |     "- Gráfico de distribuição do alvo (tips)\n",
337 |     "- Mapa de Calor\n",
338 |     "- Modelo de Regressão Linear para predição de tips\n",
339 |     "- Imprima a interceptação e os coeficientes\n",
340 |     "- Calcule MAE, MSE e RMSE para o modelo"
341 |    ]
342 |   },
343 |   {
344 |    "cell_type": "code",
345 |    "execution_count": null,
346 |    "metadata": {
347 |     "collapsed": true
348 |    },
349 |    "outputs": [],
350 |    "source": []
351 |   }
352 |  ],
353 |  "metadata": {
354 |   "kernelspec": {
355 |    "display_name": "Python 3",
356 |    "language": "python",
357 |    "name": "python3"
358 |   },
359 |   "language_info": {
360 |    "codemirror_mode": {
361 |     "name": "ipython",
362 |     "version": 3
363 |    },
364 |    "file_extension": ".py",
365 |    "mimetype": "text/x-python",
366 |    "name": "python",
367 |    "nbconvert_exporter": "python",
368 |    "pygments_lexer": "ipython3",
369 |    "version": "3.6.1"
370 |   }
371 |  },
372 |  "nbformat": 4,
373 |  "nbformat_minor": 2
374 | }
375 | 


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/Projeto/erbs.csv:
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1 | "nr","name","lat","lon","band","bcch","eirp"
2 | "BTS1","136_2",-8.068361111,-34.892722222,"GSM1800",662,55.59
3 | "BTS2","137_3",-8.075916667,-34.894611111,"GSM1800",806,55.59
4 | "BTS3","197_1",-8.076361111,-34.908,"GSM1800",666,55.59
5 | "BTS4","137_1",-8.075916667,-34.8946111116,"GSM1800",690,55.59
6 | "BTS5","138_2",-8.066,-34.8894444444444,"GSM1800",673,55.59
7 | "BTS6","245_2",-8.06458333333333,-34.8945833333333,"GSM1800",682,55.59
8 | 


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/Projeto/testLoc.csv:
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  1 | lat,lon,RSSI_1,RSSI_2,RSSI_3,RSSI_4,RSSI_5,RSSI_6
  2 | -8.07752,-34.899162,-67.3,-24.7,-73.4,-67.2,-79.2666666666667,-80.6
  3 | -8.077514,-34.899326,-64.8,-52.75,-65.35,-77.55,-75.1,-84.15
  4 | -8.07751,-34.89904,-67.9666666666667,-27.5,-64.7666666666667,-70.5666666666667,-74.7666666666667,-83.8333333333333
  5 | -8.077504,-34.899284,-65.2333333333333,-42.1666666666667,-60.4,-72.0666666666667,-79.0666666666667,-78.7666666666667
  6 | -8.077503,-34.899101,-67.4333333333333,-23.4333333333333,-62.7666666666667,-65.3666666666667,-74,-85.7
  7 | -8.077502,-34.899303,-67.6666666666667,-35.5666666666667,-62.9,-73.6,-78.5333333333333,-80.9666666666667
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 10 | -8.077494,-34.899319,-63,-46.75,-54.3,-76,-78.05,-82
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--------------------------------------------------------------------------------
/Untitled.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import numpy as np\n",
 12 |     "import pandas as pd"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "metadata": {
 19 |     "collapsed": true
 20 |    },
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "labels = ['a','b','c']\n",
 24 |     "my_data =[10,20,30]\n",
 25 |     "arr = np.array(my_data)\n",
 26 |     "d = {'a':10,'b':20,'c':30}"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 3,
 32 |    "metadata": {},
 33 |    "outputs": [
 34 |     {
 35 |      "data": {
 36 |       "text/plain": [
 37 |        "array([10, 20, 30])"
 38 |       ]
 39 |      },
 40 |      "execution_count": 3,
 41 |      "metadata": {},
 42 |      "output_type": "execute_result"
 43 |     }
 44 |    ],
 45 |    "source": [
 46 |     "arr"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": 4,
 52 |    "metadata": {},
 53 |    "outputs": [
 54 |     {
 55 |      "data": {
 56 |       "text/plain": [
 57 |        "0    10\n",
 58 |        "1    20\n",
 59 |        "2    30\n",
 60 |        "dtype: int64"
 61 |       ]
 62 |      },
 63 |      "execution_count": 4,
 64 |      "metadata": {},
 65 |      "output_type": "execute_result"
 66 |     }
 67 |    ],
 68 |    "source": [
 69 |     "pd.Series(data= my_data)"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": 5,
 75 |    "metadata": {},
 76 |    "outputs": [
 77 |     {
 78 |      "data": {
 79 |       "text/plain": [
 80 |        "a    10\n",
 81 |        "b    20\n",
 82 |        "c    30\n",
 83 |        "dtype: int64"
 84 |       ]
 85 |      },
 86 |      "execution_count": 5,
 87 |      "metadata": {},
 88 |      "output_type": "execute_result"
 89 |     }
 90 |    ],
 91 |    "source": [
 92 |     "pd.Series(data=my_data,index=labels)"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": 11,
 98 |    "metadata": {},
 99 |    "outputs": [],
100 |    "source": [
101 |     "pList = [1,2,3]\n",
102 |     "npList = np.array(pList)"
103 |    ]
104 |   },
105 |   {
106 |    "cell_type": "code",
107 |    "execution_count": 7,
108 |    "metadata": {
109 |     "collapsed": true
110 |    },
111 |    "outputs": [],
112 |    "source": [
113 |     "def mudar(a):\n",
114 |     "    a[1]=40"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "code",
119 |    "execution_count": 12,
120 |    "metadata": {},
121 |    "outputs": [],
122 |    "source": [
123 |     "mudar(pList)"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "code",
128 |    "execution_count": 13,
129 |    "metadata": {},
130 |    "outputs": [
131 |     {
132 |      "data": {
133 |       "text/plain": [
134 |        "[1, 40, 3]"
135 |       ]
136 |      },
137 |      "execution_count": 13,
138 |      "metadata": {},
139 |      "output_type": "execute_result"
140 |     }
141 |    ],
142 |    "source": [
143 |     "pList"
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "code",
148 |    "execution_count": 14,
149 |    "metadata": {},
150 |    "outputs": [
151 |     {
152 |      "data": {
153 |       "text/plain": [
154 |        "a    10\n",
155 |        "b    20\n",
156 |        "c    30\n",
157 |        "dtype: int64"
158 |       ]
159 |      },
160 |      "execution_count": 14,
161 |      "metadata": {},
162 |      "output_type": "execute_result"
163 |     }
164 |    ],
165 |    "source": [
166 |     "pd.Series(my_data,labels)"
167 |    ]
168 |   },
169 |   {
170 |    "cell_type": "code",
171 |    "execution_count": 15,
172 |    "metadata": {},
173 |    "outputs": [
174 |     {
175 |      "data": {
176 |       "text/plain": [
177 |        "0    10\n",
178 |        "1    40\n",
179 |        "2    30\n",
180 |        "dtype: int32"
181 |       ]
182 |      },
183 |      "execution_count": 15,
184 |      "metadata": {},
185 |      "output_type": "execute_result"
186 |     }
187 |    ],
188 |    "source": [
189 |     "pd.Series(arr)"
190 |    ]
191 |   },
192 |   {
193 |    "cell_type": "code",
194 |    "execution_count": 16,
195 |    "metadata": {},
196 |    "outputs": [
197 |     {
198 |      "data": {
199 |       "text/plain": [
200 |        "a    10\n",
201 |        "b    20\n",
202 |        "c    30\n",
203 |        "dtype: int64"
204 |       ]
205 |      },
206 |      "execution_count": 16,
207 |      "metadata": {},
208 |      "output_type": "execute_result"
209 |     }
210 |    ],
211 |    "source": [
212 |     "pd.Series(d)"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "code",
217 |    "execution_count": 17,
218 |    "metadata": {
219 |     "collapsed": true
220 |    },
221 |    "outputs": [],
222 |    "source": [
223 |     "ser1 = pd.Series([1,2,3,4],['PE','PB','CE','MG'])"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": 18,
229 |    "metadata": {},
230 |    "outputs": [
231 |     {
232 |      "data": {
233 |       "text/plain": [
234 |        "PE    1\n",
235 |        "PB    2\n",
236 |        "CE    3\n",
237 |        "MG    4\n",
238 |        "dtype: int64"
239 |       ]
240 |      },
241 |      "execution_count": 18,
242 |      "metadata": {},
243 |      "output_type": "execute_result"
244 |     }
245 |    ],
246 |    "source": [
247 |     "ser1"
248 |    ]
249 |   },
250 |   {
251 |    "cell_type": "code",
252 |    "execution_count": 19,
253 |    "metadata": {
254 |     "collapsed": true
255 |    },
256 |    "outputs": [],
257 |    "source": [
258 |     "ser2 = pd.Series([1,2,3,4],['PE','PB','CE','SP'])"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "code",
263 |    "execution_count": 20,
264 |    "metadata": {},
265 |    "outputs": [
266 |     {
267 |      "data": {
268 |       "text/plain": [
269 |        "PE    1\n",
270 |        "PB    2\n",
271 |        "CE    3\n",
272 |        "SP    4\n",
273 |        "dtype: int64"
274 |       ]
275 |      },
276 |      "execution_count": 20,
277 |      "metadata": {},
278 |      "output_type": "execute_result"
279 |     }
280 |    ],
281 |    "source": [
282 |     "ser2"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 21,
288 |    "metadata": {},
289 |    "outputs": [
290 |     {
291 |      "data": {
292 |       "text/plain": [
293 |        "CE    6.0\n",
294 |        "MG    NaN\n",
295 |        "PB    4.0\n",
296 |        "PE    2.0\n",
297 |        "SP    NaN\n",
298 |        "dtype: float64"
299 |       ]
300 |      },
301 |      "execution_count": 21,
302 |      "metadata": {},
303 |      "output_type": "execute_result"
304 |     }
305 |    ],
306 |    "source": [
307 |     "ser1 + ser2"
308 |    ]
309 |   },
310 |   {
311 |    "cell_type": "code",
312 |    "execution_count": 22,
313 |    "metadata": {},
314 |    "outputs": [
315 |     {
316 |      "data": {
317 |       "text/plain": [
318 |        "CE    9.0\n",
319 |        "MG    NaN\n",
320 |        "PB    4.0\n",
321 |        "PE    1.0\n",
322 |        "SP    NaN\n",
323 |        "dtype: float64"
324 |       ]
325 |      },
326 |      "execution_count": 22,
327 |      "metadata": {},
328 |      "output_type": "execute_result"
329 |     }
330 |    ],
331 |    "source": [
332 |     "ser1*ser2"
333 |    ]
334 |   },
335 |   {
336 |    "cell_type": "code",
337 |    "execution_count": 23,
338 |    "metadata": {},
339 |    "outputs": [
340 |     {
341 |      "data": {
342 |       "text/plain": [
343 |        "3.141592653589793"
344 |       ]
345 |      },
346 |      "execution_count": 23,
347 |      "metadata": {},
348 |      "output_type": "execute_result"
349 |     }
350 |    ],
351 |    "source": [
352 |     "np.pi"
353 |    ]
354 |   },
355 |   {
356 |    "cell_type": "code",
357 |    "execution_count": null,
358 |    "metadata": {
359 |     "collapsed": true
360 |    },
361 |    "outputs": [],
362 |    "source": []
363 |   }
364 |  ],
365 |  "metadata": {
366 |   "kernelspec": {
367 |    "display_name": "Python 3",
368 |    "language": "python",
369 |    "name": "python3"
370 |   },
371 |   "language_info": {
372 |    "codemirror_mode": {
373 |     "name": "ipython",
374 |     "version": 3
375 |    },
376 |    "file_extension": ".py",
377 |    "mimetype": "text/x-python",
378 |    "name": "python",
379 |    "nbconvert_exporter": "python",
380 |    "pygments_lexer": "ipython3",
381 |    "version": "3.6.1"
382 |   }
383 |  },
384 |  "nbformat": 4,
385 |  "nbformat_minor": 2
386 | }
387 | 


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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import numpy as np"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "metadata": {
 18 |     "collapsed": true
 19 |    },
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "def pathloss(dist):\n",
 23 |     "        return 32.44 + 20*np.log10(dist) + 20*np.log10(dist)"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 3,
 29 |    "metadata": {},
 30 |    "outputs": [
 31 |     {
 32 |      "data": {
 33 |       "text/plain": [
 34 |        "32.439999999999998"
 35 |       ]
 36 |      },
 37 |      "execution_count": 3,
 38 |      "metadata": {},
 39 |      "output_type": "execute_result"
 40 |     }
 41 |    ],
 42 |    "source": [
 43 |     "pathloss(1)"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": 4,
 49 |    "metadata": {},
 50 |    "outputs": [
 51 |     {
 52 |      "data": {
 53 |       "text/plain": [
 54 |        "array([ 32.44      ,  44.48119983])"
 55 |       ]
 56 |      },
 57 |      "execution_count": 4,
 58 |      "metadata": {},
 59 |      "output_type": "execute_result"
 60 |     }
 61 |    ],
 62 |    "source": [
 63 |     "pathloss([1,2])"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 6,
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "name": "stderr",
 73 |      "output_type": "stream",
 74 |      "text": [
 75 |       "'C:\\Program' não é reconhecido como um comando interno\n",
 76 |       "ou externo, um programa operável ou um arquivo em lotes.\n"
 77 |      ]
 78 |     }
 79 |    ],
 80 |    "source": [
 81 |     "import sys\n",
 82 |     "!{sys.executable} -m pip install geopy"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": null,
 88 |    "metadata": {
 89 |     "collapsed": true
 90 |    },
 91 |    "outputs": [],
 92 |    "source": []
 93 |   }
 94 |  ],
 95 |  "metadata": {
 96 |   "kernelspec": {
 97 |    "display_name": "Python 3",
 98 |    "language": "python",
 99 |    "name": "python3"
100 |   },
101 |   "language_info": {
102 |    "codemirror_mode": {
103 |     "name": "ipython",
104 |     "version": 3
105 |    },
106 |    "file_extension": ".py",
107 |    "mimetype": "text/x-python",
108 |    "name": "python",
109 |    "nbconvert_exporter": "python",
110 |    "pygments_lexer": "ipython3",
111 |    "version": "3.6.1"
112 |   }
113 |  },
114 |  "nbformat": 4,
115 |  "nbformat_minor": 2
116 | }
117 | 


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/videos/video1.mp4:
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https://raw.githubusercontent.com/timotrob/Curso_Python/53ed2be30d5f4d451b113786745365b681435c95/videos/video1.mp4


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