├── README.md ├── movies.dat └── 01 INTRODUCTION TO VARIABLES.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # PRE-PROCESSING-DATA 2 | -------------------------------------------------------------------------------- /movies.dat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ssilvacris/PRE-PROCESSING-DATA/main/movies.dat -------------------------------------------------------------------------------- /01 INTRODUCTION TO VARIABLES.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": { 5 | "IT%20Logo.png": { 6 | "image/png": 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7 | } 8 | }, 9 | "cell_type": "markdown", 10 | "metadata": {}, 11 | "source": [ 12 | "![IT%20Logo.png](attachment:IT%20Logo.png)" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "
Introduction to Variables
\n", 20 | "\n", 21 | "\\begin{align*}Alex\\:Kumenius\\end{align*}\n", 22 | "\\begin{align*}Business\\hspace{2mm}Intelligence\\hspace{2mm}and\\hspace{2mm}Data\\hspace{2mm}Scientist\\hspace{2mm}Project\\hspace{2mm}Integrator\\end{align*}\n", 23 | "$%$ \n", 24 | "\\begin{align*}Date : Gener\\hspace{2mm}2021\\end{align*}
" 25 | ] 26 | }, 27 | { 28 | "cell_type": "markdown", 29 | "metadata": {}, 30 | "source": [ 31 | "# TYPES OF VARIABLES" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "Generally, in Math and Statistics variables may be Numerical or Categorical Variables.\n", 39 | "\n", 40 | "A Variable is a quantity whose value changes. " 41 | ] 42 | }, 43 | { 44 | "attachments": { 45 | "Types%20of%20Variables.jpg": { 46 | "image/jpeg": 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47 | } 48 | }, 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "![Types%20of%20Variables.jpg](attachment:Types%20of%20Variables.jpg)" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "### NUMERICAL" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "A Numerical variable can take a wide range of numerical values, and it is sensible to add, subtract, or take averages with those values. On the other hand, we would not classify a variable reporting \"telephone area codes\" as numerical since there is **no** sense to *average, sum*, and *difference*." 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "#### Discrete Variables" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "A Discrete variable is a variable whose value is obtained by counting. \n", 81 | "\n", 82 | "Over a particular range of real values ($\\:\\mathbb {R}\\:$) is any value in the range that the variable is permitted to take on, there is a positive minimum distance to the nearest other permissible value. The number of permitted values is either finite or countably infinite. \n", 83 | "\n", 84 | "Common examples are variables that must be integers, non-negative integers, positive integers, or only the integers 0 and 1." 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "Examples: \n", 92 | "- number of students present\n", 93 | "- number of red marbles in a jar\n", 94 | "- number of heads when flipping three coins\n", 95 | "- students’ grade level" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "#### Continuous Variables" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "A Continuous variable is a variable whose value is obtained by measuring." 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "A Continuous variable is one which can take on infinitely many, uncountable values.\n", 117 | "\n", 118 | "For example, a variable over a non-empty range of the real numbers ($\\:\\mathbb {R}\\:$) ***a*** and ***b*** is continuous, if it can take on *any value in that range*. The reason is that any range of real numbers between ***a*** and ***b*** with$\\hspace{3mm}$${\\displaystyle a,b\\in \\mathbb {R} ; \n", 119 | "\\hspace{4mm}a\\neq b}$ is infinite and uncountable." 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "Examples: \n", 127 | "- height of students in class\n", 128 | "- weight of students in class\n", 129 | "- time it takes to get to school\n", 130 | "- distance traveled between classes" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "metadata": {}, 136 | "source": [ 137 | "### CATEGORICAL" 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "metadata": {}, 143 | "source": [ 144 | "A Categorical Variable takes on a limited, and usually fixed, number of possible values, categories; and the possible values are call the variable's levels." 145 | ] 146 | }, 147 | { 148 | "cell_type": "markdown", 149 | "metadata": {}, 150 | "source": [ 151 | "Categorical Variables where their levels have a natural order are \"Ordinal Variables\". \n", 152 | "\n", 153 | "1- The ``categories`` are deduced from the data . \n", 154 | "2- The ``categories`` are messy . \n", 155 | "\n", 156 | "Examples are *gender, social class, blood type, country affiliation, observation time or rating via Likert scales*. " 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": {}, 162 | "source": [ 163 | "Categorical Variables without this type of special ordering is called \"Nominal Variable\"." 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "Categorical data might have an order (e.g. ‘strongly agree’ vs ‘agree’ or ‘first observation’ vs. ‘second observation’), but **numerical operations** (additions, divisions, …) are not possible." 171 | ] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "metadata": {}, 176 | "source": [ 177 | "#### Categorical - \"Ordinal Variables\"" 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": null, 183 | "metadata": { 184 | "ExecuteTime": { 185 | "end_time": "2021-01-26T19:52:05.746971Z", 186 | "start_time": "2021-01-26T19:52:05.742982Z" 187 | } 188 | }, 189 | "outputs": [], 190 | "source": [ 191 | "import os\n", 192 | "import pandas as pd\n", 193 | "import numpy as np" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "metadata": { 200 | "ExecuteTime": { 201 | "end_time": "2021-01-26T18:43:52.984928Z", 202 | "start_time": "2021-01-26T18:43:52.474293Z" 203 | } 204 | }, 205 | "outputs": [], 206 | "source": [ 207 | "df = pd.DataFrame({\"A\": [\"a\", \"b\", \"c\", \"a\"]})\n", 208 | "df" 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": null, 214 | "metadata": { 215 | "ExecuteTime": { 216 | "end_time": "2020-10-07T19:05:33.105215Z", 217 | "start_time": "2020-10-07T19:05:33.098229Z" 218 | } 219 | }, 220 | "outputs": [], 221 | "source": [ 222 | "type(df)" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": null, 228 | "metadata": { 229 | "ExecuteTime": { 230 | "end_time": "2020-10-07T19:05:44.793545Z", 231 | "start_time": "2020-10-07T19:05:44.784570Z" 232 | } 233 | }, 234 | "outputs": [], 235 | "source": [ 236 | "df.dtypes" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "metadata": { 243 | "ExecuteTime": { 244 | "end_time": "2020-10-07T19:09:05.228799Z", 245 | "start_time": "2020-10-07T19:09:05.208853Z" 246 | } 247 | }, 248 | "outputs": [], 249 | "source": [ 250 | "# passing astype('category'), as the default behavior\n", 251 | "df[\"B\"] = df[\"A\"].astype('category')\n", 252 | "df" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": null, 258 | "metadata": { 259 | "ExecuteTime": { 260 | "end_time": "2020-10-07T19:09:24.257645Z", 261 | "start_time": "2020-10-07T19:09:24.251662Z" 262 | } 263 | }, 264 | "outputs": [], 265 | "source": [ 266 | "type(df)" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": null, 272 | "metadata": { 273 | "ExecuteTime": { 274 | "end_time": "2020-10-07T19:09:37.949002Z", 275 | "start_time": "2020-10-07T19:09:37.940032Z" 276 | } 277 | }, 278 | "outputs": [], 279 | "source": [ 280 | "df.dtypes" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": null, 286 | "metadata": { 287 | "ExecuteTime": { 288 | "end_time": "2020-10-07T19:09:55.301924Z", 289 | "start_time": "2020-10-07T19:09:55.292946Z" 290 | } 291 | }, 292 | "outputs": [], 293 | "source": [ 294 | "df['A']" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": null, 300 | "metadata": { 301 | "ExecuteTime": { 302 | "end_time": "2020-10-07T19:10:04.229271Z", 303 | "start_time": "2020-10-07T19:10:04.218298Z" 304 | } 305 | }, 306 | "outputs": [], 307 | "source": [ 308 | "df['B']" 309 | ] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "metadata": {}, 314 | "source": [ 315 | "#### Categorical - \"Nominal Variables\"" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "How we control the behavior of a Categorical - \"Nominal Variable\"?." 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": null, 328 | "metadata": { 329 | "ExecuteTime": { 330 | "end_time": "2020-10-07T19:18:25.445046Z", 331 | "start_time": "2020-10-07T19:18:25.441060Z" 332 | } 333 | }, 334 | "outputs": [], 335 | "source": [ 336 | "from pandas.api.types import CategoricalDtype" 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": null, 342 | "metadata": { 343 | "ExecuteTime": { 344 | "end_time": "2020-10-07T19:18:27.155759Z", 345 | "start_time": "2020-10-07T19:18:27.143791Z" 346 | } 347 | }, 348 | "outputs": [], 349 | "source": [ 350 | "s = pd.Series([\"Wednesday\", \"Monday\", \"Thursday\", \"Sunday\", \"Friday\"])\n", 351 | "s\n", 352 | "s.sort_values(inplace=True)\n", 353 | "s" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": null, 359 | "metadata": { 360 | "ExecuteTime": { 361 | "end_time": "2020-10-07T19:18:28.398988Z", 362 | "start_time": "2020-10-07T19:18:28.384027Z" 363 | } 364 | }, 365 | "outputs": [], 366 | "source": [ 367 | "sc = pd.Series([\"Wednesday\", \"Saturday\", \"Monday\", \"Sunday\", \"Thursday\", \"Tuesday\", \"Friday\"], \n", 368 | " dtype=\"category\")\n", 369 | "sc\n", 370 | "sc.sort_values(inplace=True)\n", 371 | "sc" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": null, 377 | "metadata": { 378 | "ExecuteTime": { 379 | "end_time": "2020-10-07T19:18:30.706258Z", 380 | "start_time": "2020-10-07T19:18:30.697284Z" 381 | } 382 | }, 383 | "outputs": [], 384 | "source": [ 385 | "cat_s = CategoricalDtype(categories=[\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"], \n", 386 | " ordered=True)\n", 387 | "cat_s" 388 | ] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": null, 393 | "metadata": { 394 | "ExecuteTime": { 395 | "end_time": "2020-10-07T19:19:37.669725Z", 396 | "start_time": "2020-10-07T19:19:37.658755Z" 397 | } 398 | }, 399 | "outputs": [], 400 | "source": [ 401 | "s_cat =s.astype(cat_s)\n", 402 | "s_cat.sort_values(inplace=True)\n", 403 | "s_cat" 404 | ] 405 | }, 406 | { 407 | "cell_type": "markdown", 408 | "metadata": {}, 409 | "source": [ 410 | "The categorical data type is useful in the following ``cases``:\n", 411 | "\n", 412 | "- A string variable consisting of only a few different values. \n", 413 | "Converting such a string variable to a categorical variable will save some memory. \n", 414 | "$%$\n", 415 | "- The lexical order of a variable is not the same as the logical order (“one”, “two”, “three”). \n", 416 | "By converting to a categorical and specifying an order on the categories, sorting and min/max will use the logical order instead of the lexical order. \n", 417 | "$%$\n", 418 | "- As a signal to other Python libraries that this column should be treated as a categorical variable \n", 419 | "e.g. to use suitable statistical methods or plot types." 420 | ] 421 | }, 422 | { 423 | "attachments": { 424 | "IT%20Logo.png": { 425 | "image/png": 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426 | } 427 | }, 428 | "cell_type": "markdown", 429 | "metadata": {}, 430 | "source": [ 431 | "![IT%20Logo.png](attachment:IT%20Logo.png)" 432 | ] 433 | }, 434 | { 435 | "cell_type": "markdown", 436 | "metadata": {}, 437 | "source": [ 438 | "# Detectando y Filtrando Outliers" 439 | ] 440 | }, 441 | { 442 | "cell_type": "markdown", 443 | "metadata": {}, 444 | "source": [ 445 | "Filtrar o transformar Outliers - Valores Atípicos es en gran medida la aplicación de operaciones de ``matriz - arrays``." 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": null, 451 | "metadata": { 452 | "ExecuteTime": { 453 | "end_time": "2021-01-26T19:38:04.526327Z", 454 | "start_time": "2021-01-26T19:38:04.521341Z" 455 | } 456 | }, 457 | "outputs": [], 458 | "source": [ 459 | "data = pd.DataFrame(np.random.randn(1000, 4))" 460 | ] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": null, 465 | "metadata": { 466 | "ExecuteTime": { 467 | "end_time": "2021-01-26T19:38:09.397310Z", 468 | "start_time": "2021-01-26T19:38:09.386337Z" 469 | } 470 | }, 471 | "outputs": [], 472 | "source": [ 473 | "data.shape" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": null, 479 | "metadata": { 480 | "ExecuteTime": { 481 | "end_time": "2021-01-26T19:38:11.196500Z", 482 | "start_time": "2021-01-26T19:38:11.186527Z" 483 | } 484 | }, 485 | "outputs": [], 486 | "source": [ 487 | "data.info()" 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "execution_count": null, 493 | "metadata": { 494 | "ExecuteTime": { 495 | "end_time": "2021-01-26T19:38:15.867015Z", 496 | "start_time": "2021-01-26T19:38:15.851058Z" 497 | } 498 | }, 499 | "outputs": [], 500 | "source": [ 501 | "data.head()" 502 | ] 503 | }, 504 | { 505 | "cell_type": "code", 506 | "execution_count": null, 507 | "metadata": { 508 | "ExecuteTime": { 509 | "end_time": "2021-01-26T19:38:19.857350Z", 510 | "start_time": "2021-01-26T19:38:19.824441Z" 511 | } 512 | }, 513 | "outputs": [], 514 | "source": [ 515 | "data.describe()" 516 | ] 517 | }, 518 | { 519 | "cell_type": "markdown", 520 | "metadata": {}, 521 | "source": [ 522 | "Queremos encontrar en una de las columnas, valores que contengan el número 3 en ``valor absoluto``." 523 | ] 524 | }, 525 | { 526 | "cell_type": "code", 527 | "execution_count": null, 528 | "metadata": { 529 | "ExecuteTime": { 530 | "end_time": "2021-01-26T19:38:31.166125Z", 531 | "start_time": "2021-01-26T19:38:31.161139Z" 532 | } 533 | }, 534 | "outputs": [], 535 | "source": [ 536 | "col = data[1]" 537 | ] 538 | }, 539 | { 540 | "cell_type": "code", 541 | "execution_count": null, 542 | "metadata": { 543 | "ExecuteTime": { 544 | "end_time": "2021-01-26T19:38:33.032140Z", 545 | "start_time": "2021-01-26T19:38:33.020170Z" 546 | } 547 | }, 548 | "outputs": [], 549 | "source": [ 550 | "col[np.abs(col) > 3]" 551 | ] 552 | }, 553 | { 554 | "cell_type": "markdown", 555 | "metadata": {}, 556 | "source": [ 557 | "Para selecionar todas las observaciones - cases con valores que excedan los limites ó rangos ``3`` o ``-3``, usaremos el método any() en un DataFrame Boleano : " 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": null, 563 | "metadata": { 564 | "ExecuteTime": { 565 | "end_time": "2021-01-26T19:38:46.240833Z", 566 | "start_time": "2021-01-26T19:38:46.217894Z" 567 | } 568 | }, 569 | "outputs": [], 570 | "source": [ 571 | "data[(np.abs(data) > 3).any(1)]" 572 | ] 573 | }, 574 | { 575 | "cell_type": "markdown", 576 | "metadata": {}, 577 | "source": [ 578 | "Los valores se pueden establecer en función de estos criterios. \n", 579 | " \n", 580 | "Si queremos limitar los valores del intervalo a ``–3`` a ``3``:" 581 | ] 582 | }, 583 | { 584 | "cell_type": "code", 585 | "execution_count": null, 586 | "metadata": { 587 | "ExecuteTime": { 588 | "end_time": "2021-01-26T19:38:59.391682Z", 589 | "start_time": "2021-01-26T19:38:59.375726Z" 590 | } 591 | }, 592 | "outputs": [], 593 | "source": [ 594 | "data[np.abs(data) > 3] = np.sign(data) * 3" 595 | ] 596 | }, 597 | { 598 | "cell_type": "code", 599 | "execution_count": null, 600 | "metadata": {}, 601 | "outputs": [], 602 | "source": [ 603 | "np.sign(data)" 604 | ] 605 | }, 606 | { 607 | "cell_type": "code", 608 | "execution_count": null, 609 | "metadata": { 610 | "ExecuteTime": { 611 | "end_time": "2021-01-26T19:42:20.168046Z", 612 | "start_time": "2021-01-26T19:42:20.153085Z" 613 | } 614 | }, 615 | "outputs": [], 616 | "source": [ 617 | "data.head()" 618 | ] 619 | }, 620 | { 621 | "cell_type": "code", 622 | "execution_count": null, 623 | "metadata": { 624 | "ExecuteTime": { 625 | "end_time": "2021-01-26T19:40:33.842241Z", 626 | "start_time": "2021-01-26T19:40:33.767435Z" 627 | } 628 | }, 629 | "outputs": [], 630 | "source": [ 631 | "# observamos 'min' y 'max' en el resumen estadístico,\n", 632 | "# no supera el intervalo -3 y 3\n", 633 | "data.describe()" 634 | ] 635 | }, 636 | { 637 | "cell_type": "markdown", 638 | "metadata": {}, 639 | "source": [ 640 | "La declaración np.sign(data) produce valores ``1`` y ``-1``, basandose si los valores en data son positivos o negativos :" 641 | ] 642 | }, 643 | { 644 | "cell_type": "code", 645 | "execution_count": null, 646 | "metadata": { 647 | "ExecuteTime": { 648 | "end_time": "2021-01-26T19:41:44.333824Z", 649 | "start_time": "2021-01-26T19:41:44.317867Z" 650 | } 651 | }, 652 | "outputs": [], 653 | "source": [ 654 | "np.sign(data).head()" 655 | ] 656 | }, 657 | { 658 | "cell_type": "code", 659 | "execution_count": null, 660 | "metadata": { 661 | "ExecuteTime": { 662 | "end_time": "2021-01-26T19:42:47.525923Z", 663 | "start_time": "2021-01-26T19:42:47.505976Z" 664 | } 665 | }, 666 | "outputs": [], 667 | "source": [ 668 | "(np.sign(data) * 3).head()" 669 | ] 670 | }, 671 | { 672 | "cell_type": "markdown", 673 | "metadata": {}, 674 | "source": [ 675 | "# Computing Indicator/Dummy Variables" 676 | ] 677 | }, 678 | { 679 | "cell_type": "markdown", 680 | "metadata": {}, 681 | "source": [ 682 | "Otro tipo de transformación para aplicaciones de Statistical Modeling o Machine Learning es convertir una variable Categórica en una matriz \"dummy / ficticia\" o \"indicator / indicadora\". \n", 683 | "\n", 684 | "Si una columna en un DataFrame tiene k valores distintos, derivaría una matriz o DataFrame con k columnas que contienen todos los 1s y 0s. Pandas tiene una función get_dummies() para hacerlo :" 685 | ] 686 | }, 687 | { 688 | "cell_type": "markdown", 689 | "metadata": {}, 690 | "source": [ 691 | "## pd.get_dummies() method" 692 | ] 693 | }, 694 | { 695 | "cell_type": "code", 696 | "execution_count": null, 697 | "metadata": { 698 | "ExecuteTime": { 699 | "end_time": "2021-01-26T19:45:31.598953Z", 700 | "start_time": "2021-01-26T19:45:31.584992Z" 701 | } 702 | }, 703 | "outputs": [], 704 | "source": [ 705 | "df = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],\n", 706 | " 'data1': range(6)})\n", 707 | "df" 708 | ] 709 | }, 710 | { 711 | "cell_type": "code", 712 | "execution_count": null, 713 | "metadata": { 714 | "ExecuteTime": { 715 | "end_time": "2021-01-26T19:46:19.737984Z", 716 | "start_time": "2021-01-26T19:46:19.732995Z" 717 | } 718 | }, 719 | "outputs": [], 720 | "source": [ 721 | "type(df)" 722 | ] 723 | }, 724 | { 725 | "cell_type": "code", 726 | "execution_count": null, 727 | "metadata": { 728 | "ExecuteTime": { 729 | "end_time": "2021-01-26T19:48:54.368489Z", 730 | "start_time": "2021-01-26T19:48:54.362506Z" 731 | } 732 | }, 733 | "outputs": [], 734 | "source": [ 735 | "df.shape" 736 | ] 737 | }, 738 | { 739 | "cell_type": "code", 740 | "execution_count": null, 741 | "metadata": { 742 | "ExecuteTime": { 743 | "end_time": "2021-01-26T19:46:34.025999Z", 744 | "start_time": "2021-01-26T19:46:34.011036Z" 745 | } 746 | }, 747 | "outputs": [], 748 | "source": [ 749 | "df_dummies = pd.get_dummies(df['key'])\n", 750 | "df_dummies" 751 | ] 752 | }, 753 | { 754 | "cell_type": "code", 755 | "execution_count": null, 756 | "metadata": { 757 | "ExecuteTime": { 758 | "end_time": "2021-01-26T19:49:23.562460Z", 759 | "start_time": "2021-01-26T19:49:23.556482Z" 760 | } 761 | }, 762 | "outputs": [], 763 | "source": [ 764 | "df_dummies.shape" 765 | ] 766 | }, 767 | { 768 | "cell_type": "markdown", 769 | "metadata": {}, 770 | "source": [ 771 | "Podemos agregar un prefijo a las columnas en el dummy DataFrame, que luego se puede combinar con los otros datos. get_dummies() tiene un argumento prefix para hacer esto:" 772 | ] 773 | }, 774 | { 775 | "cell_type": "code", 776 | "execution_count": null, 777 | "metadata": { 778 | "ExecuteTime": { 779 | "end_time": "2021-01-26T19:47:30.770852Z", 780 | "start_time": "2021-01-26T19:47:30.756890Z" 781 | } 782 | }, 783 | "outputs": [], 784 | "source": [ 785 | "dummies = pd.get_dummies(df['key'], prefix = 'cod')\n", 786 | "dummies" 787 | ] 788 | }, 789 | { 790 | "cell_type": "code", 791 | "execution_count": null, 792 | "metadata": { 793 | "ExecuteTime": { 794 | "end_time": "2021-01-26T19:49:42.793060Z", 795 | "start_time": "2021-01-26T19:49:42.787075Z" 796 | } 797 | }, 798 | "outputs": [], 799 | "source": [ 800 | "dummies.shape" 801 | ] 802 | }, 803 | { 804 | "cell_type": "code", 805 | "execution_count": null, 806 | "metadata": { 807 | "ExecuteTime": { 808 | "end_time": "2021-01-26T19:47:58.184658Z", 809 | "start_time": "2021-01-26T19:47:58.167706Z" 810 | } 811 | }, 812 | "outputs": [], 813 | "source": [ 814 | "df_con_dummies = df[['data1']].join(dummies)\n", 815 | "df_con_dummies" 816 | ] 817 | }, 818 | { 819 | "cell_type": "code", 820 | "execution_count": null, 821 | "metadata": { 822 | "ExecuteTime": { 823 | "end_time": "2021-01-26T19:50:01.634701Z", 824 | "start_time": "2021-01-26T19:50:01.628716Z" 825 | } 826 | }, 827 | "outputs": [], 828 | "source": [ 829 | "df_con_dummies.shape" 830 | ] 831 | }, 832 | { 833 | "cell_type": "code", 834 | "execution_count": null, 835 | "metadata": { 836 | "ExecuteTime": { 837 | "end_time": "2021-01-26T19:52:16.802424Z", 838 | "start_time": "2021-01-26T19:52:16.796440Z" 839 | } 840 | }, 841 | "outputs": [], 842 | "source": [ 843 | "os.getcwd()" 844 | ] 845 | }, 846 | { 847 | "cell_type": "code", 848 | "execution_count": null, 849 | "metadata": { 850 | "ExecuteTime": { 851 | "end_time": "2021-01-26T19:52:30.322285Z", 852 | "start_time": "2021-01-26T19:52:30.231530Z" 853 | } 854 | }, 855 | "outputs": [], 856 | "source": [ 857 | "os.chdir('D:\\\\Documents\\\\Python\\\\Python for Data Analysis-Pandas Jupyter Notebook\\\\pydata-Notebooks\\\\datasets\\\\movielens')" 858 | ] 859 | }, 860 | { 861 | "cell_type": "code", 862 | "execution_count": null, 863 | "metadata": { 864 | "ExecuteTime": { 865 | "end_time": "2021-01-26T19:52:43.313563Z", 866 | "start_time": "2021-01-26T19:52:43.307579Z" 867 | } 868 | }, 869 | "outputs": [], 870 | "source": [ 871 | "os.listdir()" 872 | ] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": null, 877 | "metadata": { 878 | "ExecuteTime": { 879 | "end_time": "2021-01-26T19:53:05.057445Z", 880 | "start_time": "2021-01-26T19:53:05.051462Z" 881 | } 882 | }, 883 | "outputs": [], 884 | "source": [ 885 | "mcabecera = ['movie_id', 'titulo', 'genero']\n", 886 | "mcabecera" 887 | ] 888 | }, 889 | { 890 | "cell_type": "code", 891 | "execution_count": null, 892 | "metadata": { 893 | "ExecuteTime": { 894 | "end_time": "2021-01-26T19:53:25.872811Z", 895 | "start_time": "2021-01-26T19:53:25.778064Z" 896 | } 897 | }, 898 | "outputs": [], 899 | "source": [ 900 | "movies = pd.read_table('movies.dat', sep = '::', header = None, names = mcabecera)\n", 901 | "movies.head()" 902 | ] 903 | }, 904 | { 905 | "cell_type": "code", 906 | "execution_count": null, 907 | "metadata": { 908 | "ExecuteTime": { 909 | "end_time": "2021-01-26T19:53:48.110373Z", 910 | "start_time": "2021-01-26T19:53:48.104392Z" 911 | } 912 | }, 913 | "outputs": [], 914 | "source": [ 915 | "movies.shape" 916 | ] 917 | }, 918 | { 919 | "cell_type": "code", 920 | "execution_count": null, 921 | "metadata": { 922 | "ExecuteTime": { 923 | "end_time": "2021-01-26T19:54:26.564593Z", 924 | "start_time": "2021-01-26T19:54:26.545644Z" 925 | } 926 | }, 927 | "outputs": [], 928 | "source": [ 929 | "movies.describe()" 930 | ] 931 | }, 932 | { 933 | "cell_type": "markdown", 934 | "metadata": {}, 935 | "source": [ 936 | "Agregar dummy variables para cada género requiere un poco de transformación. \n", 937 | "\n", 938 | "Primero, extraemos la lista de géneros únicos en el dataset:" 939 | ] 940 | }, 941 | { 942 | "cell_type": "code", 943 | "execution_count": null, 944 | "metadata": { 945 | "ExecuteTime": { 946 | "end_time": "2021-01-26T19:55:22.492109Z", 947 | "start_time": "2021-01-26T19:55:22.486126Z" 948 | } 949 | }, 950 | "outputs": [], 951 | "source": [ 952 | "todos_generos = []\n", 953 | "todos_generos" 954 | ] 955 | }, 956 | { 957 | "cell_type": "code", 958 | "execution_count": null, 959 | "metadata": { 960 | "ExecuteTime": { 961 | "end_time": "2021-01-26T19:55:36.133648Z", 962 | "start_time": "2021-01-26T19:55:36.125669Z" 963 | } 964 | }, 965 | "outputs": [], 966 | "source": [ 967 | "for x in movies.genero:\n", 968 | " todos_generos.extend(x.split('|'))" 969 | ] 970 | }, 971 | { 972 | "cell_type": "code", 973 | "execution_count": null, 974 | "metadata": { 975 | "ExecuteTime": { 976 | "end_time": "2021-01-26T19:56:10.333382Z", 977 | "start_time": "2021-01-26T19:56:10.327401Z" 978 | } 979 | }, 980 | "outputs": [], 981 | "source": [ 982 | "type(todos_generos)" 983 | ] 984 | }, 985 | { 986 | "cell_type": "code", 987 | "execution_count": null, 988 | "metadata": { 989 | "ExecuteTime": { 990 | "end_time": "2021-01-26T19:55:49.812087Z", 991 | "start_time": "2021-01-26T19:55:49.806104Z" 992 | } 993 | }, 994 | "outputs": [], 995 | "source": [ 996 | "todos_generos[:8]" 997 | ] 998 | }, 999 | { 1000 | "cell_type": "code", 1001 | "execution_count": null, 1002 | "metadata": { 1003 | "ExecuteTime": { 1004 | "end_time": "2021-01-26T19:56:33.090558Z", 1005 | "start_time": "2021-01-26T19:56:33.084574Z" 1006 | } 1007 | }, 1008 | "outputs": [], 1009 | "source": [ 1010 | "len(todos_generos)" 1011 | ] 1012 | }, 1013 | { 1014 | "cell_type": "code", 1015 | "execution_count": null, 1016 | "metadata": { 1017 | "ExecuteTime": { 1018 | "end_time": "2021-01-26T19:56:52.893627Z", 1019 | "start_time": "2021-01-26T19:56:52.884651Z" 1020 | } 1021 | }, 1022 | "outputs": [], 1023 | "source": [ 1024 | "generos = pd.unique(todos_generos)\n", 1025 | "generos" 1026 | ] 1027 | }, 1028 | { 1029 | "cell_type": "code", 1030 | "execution_count": null, 1031 | "metadata": { 1032 | "ExecuteTime": { 1033 | "end_time": "2021-01-26T19:57:06.461364Z", 1034 | "start_time": "2021-01-26T19:57:06.456376Z" 1035 | } 1036 | }, 1037 | "outputs": [], 1038 | "source": [ 1039 | "len(generos)" 1040 | ] 1041 | }, 1042 | { 1043 | "cell_type": "code", 1044 | "execution_count": null, 1045 | "metadata": { 1046 | "ExecuteTime": { 1047 | "end_time": "2021-01-26T19:57:44.381011Z", 1048 | "start_time": "2021-01-26T19:57:44.369044Z" 1049 | } 1050 | }, 1051 | "outputs": [], 1052 | "source": [ 1053 | "movies.head(10)" 1054 | ] 1055 | }, 1056 | { 1057 | "cell_type": "markdown", 1058 | "metadata": {}, 1059 | "source": [ 1060 | "Para construir un Dummy DataFrame, se empieza creando una matriz/array 'zeros', para finalmente crear un DaFrame de 'zeros' :" 1061 | ] 1062 | }, 1063 | { 1064 | "cell_type": "code", 1065 | "execution_count": null, 1066 | "metadata": { 1067 | "ExecuteTime": { 1068 | "end_time": "2021-01-26T19:58:32.461502Z", 1069 | "start_time": "2021-01-26T19:58:32.455517Z" 1070 | } 1071 | }, 1072 | "outputs": [], 1073 | "source": [ 1074 | "len(movies)" 1075 | ] 1076 | }, 1077 | { 1078 | "cell_type": "code", 1079 | "execution_count": null, 1080 | "metadata": { 1081 | "ExecuteTime": { 1082 | "end_time": "2021-01-26T19:58:46.455099Z", 1083 | "start_time": "2021-01-26T19:58:46.449115Z" 1084 | } 1085 | }, 1086 | "outputs": [], 1087 | "source": [ 1088 | "cero_matriz = np.zeros((len(movies), len(generos)))\n", 1089 | "cero_matriz.shape" 1090 | ] 1091 | }, 1092 | { 1093 | "cell_type": "code", 1094 | "execution_count": null, 1095 | "metadata": { 1096 | "ExecuteTime": { 1097 | "end_time": "2021-01-26T19:59:55.580340Z", 1098 | "start_time": "2021-01-26T19:59:55.573359Z" 1099 | } 1100 | }, 1101 | "outputs": [], 1102 | "source": [ 1103 | "cero_matriz" 1104 | ] 1105 | }, 1106 | { 1107 | "cell_type": "code", 1108 | "execution_count": null, 1109 | "metadata": { 1110 | "ExecuteTime": { 1111 | "end_time": "2021-01-26T19:59:19.859815Z", 1112 | "start_time": "2021-01-26T19:59:19.848844Z" 1113 | } 1114 | }, 1115 | "outputs": [], 1116 | "source": [ 1117 | "sum(cero_matriz)" 1118 | ] 1119 | }, 1120 | { 1121 | "cell_type": "code", 1122 | "execution_count": null, 1123 | "metadata": { 1124 | "ExecuteTime": { 1125 | "end_time": "2021-01-26T19:59:37.990355Z", 1126 | "start_time": "2021-01-26T19:59:37.979386Z" 1127 | } 1128 | }, 1129 | "outputs": [], 1130 | "source": [ 1131 | "len(sum(cero_matriz))" 1132 | ] 1133 | }, 1134 | { 1135 | "cell_type": "code", 1136 | "execution_count": null, 1137 | "metadata": { 1138 | "ExecuteTime": { 1139 | "end_time": "2021-01-26T20:00:12.919390Z", 1140 | "start_time": "2021-01-26T20:00:12.891466Z" 1141 | } 1142 | }, 1143 | "outputs": [], 1144 | "source": [ 1145 | "dummies = pd.DataFrame(cero_matriz, columns = generos)\n", 1146 | "dummies.head()" 1147 | ] 1148 | }, 1149 | { 1150 | "cell_type": "code", 1151 | "execution_count": null, 1152 | "metadata": { 1153 | "ExecuteTime": { 1154 | "end_time": "2021-01-26T20:00:33.202711Z", 1155 | "start_time": "2021-01-26T20:00:33.192738Z" 1156 | } 1157 | }, 1158 | "outputs": [], 1159 | "source": [ 1160 | "dummies.sum()" 1161 | ] 1162 | }, 1163 | { 1164 | "cell_type": "code", 1165 | "execution_count": null, 1166 | "metadata": { 1167 | "ExecuteTime": { 1168 | "end_time": "2021-01-26T20:01:12.865319Z", 1169 | "start_time": "2021-01-26T20:01:12.762592Z" 1170 | } 1171 | }, 1172 | "outputs": [], 1173 | "source": [ 1174 | "dummies.describe()" 1175 | ] 1176 | }, 1177 | { 1178 | "cell_type": "markdown", 1179 | "metadata": {}, 1180 | "source": [ 1181 | "Ahora, iteramos cada película y configuramos las entradas en cada fila de dummies a 1. Para hacer esto, usamos dummies.columns para calcular los índices de columna para cada género:" 1182 | ] 1183 | }, 1184 | { 1185 | "cell_type": "code", 1186 | "execution_count": null, 1187 | "metadata": { 1188 | "ExecuteTime": { 1189 | "end_time": "2021-01-26T20:02:06.012470Z", 1190 | "start_time": "2021-01-26T20:02:06.005490Z" 1191 | } 1192 | }, 1193 | "outputs": [], 1194 | "source": [ 1195 | "dummies.columns" 1196 | ] 1197 | }, 1198 | { 1199 | "cell_type": "code", 1200 | "execution_count": null, 1201 | "metadata": { 1202 | "ExecuteTime": { 1203 | "end_time": "2021-01-26T20:02:36.185479Z", 1204 | "start_time": "2021-01-26T20:02:36.178496Z" 1205 | } 1206 | }, 1207 | "outputs": [], 1208 | "source": [ 1209 | "gen = movies.genero[0]\n", 1210 | "gen" 1211 | ] 1212 | }, 1213 | { 1214 | "cell_type": "code", 1215 | "execution_count": null, 1216 | "metadata": { 1217 | "ExecuteTime": { 1218 | "end_time": "2021-01-26T20:02:57.306554Z", 1219 | "start_time": "2021-01-26T20:02:57.301568Z" 1220 | } 1221 | }, 1222 | "outputs": [], 1223 | "source": [ 1224 | "gen.split('|')" 1225 | ] 1226 | }, 1227 | { 1228 | "cell_type": "code", 1229 | "execution_count": null, 1230 | "metadata": { 1231 | "ExecuteTime": { 1232 | "end_time": "2021-01-26T20:03:10.256940Z", 1233 | "start_time": "2021-01-26T20:03:10.249960Z" 1234 | } 1235 | }, 1236 | "outputs": [], 1237 | "source": [ 1238 | "dummies.columns.get_indexer(gen.split('|'))" 1239 | ] 1240 | }, 1241 | { 1242 | "cell_type": "markdown", 1243 | "metadata": {}, 1244 | "source": [ 1245 | "Ahora, podemos utilizar .iloc para establecer valores basados en estos índices :" 1246 | ] 1247 | }, 1248 | { 1249 | "cell_type": "code", 1250 | "execution_count": null, 1251 | "metadata": { 1252 | "ExecuteTime": { 1253 | "end_time": "2021-01-26T20:04:18.141497Z", 1254 | "start_time": "2021-01-26T20:04:15.569373Z" 1255 | } 1256 | }, 1257 | "outputs": [], 1258 | "source": [ 1259 | "for i, gen in enumerate(movies.genero):\n", 1260 | " indices = dummies.columns.get_indexer(gen.split('|'))\n", 1261 | " dummies.iloc[i, indices] = 1" 1262 | ] 1263 | }, 1264 | { 1265 | "cell_type": "code", 1266 | "execution_count": null, 1267 | "metadata": { 1268 | "ExecuteTime": { 1269 | "end_time": "2021-01-26T20:04:26.117181Z", 1270 | "start_time": "2021-01-26T20:04:26.104216Z" 1271 | } 1272 | }, 1273 | "outputs": [], 1274 | "source": [ 1275 | "movies.head()" 1276 | ] 1277 | }, 1278 | { 1279 | "cell_type": "code", 1280 | "execution_count": null, 1281 | "metadata": { 1282 | "ExecuteTime": { 1283 | "end_time": "2021-01-26T20:04:43.303247Z", 1284 | "start_time": "2021-01-26T20:04:43.272330Z" 1285 | } 1286 | }, 1287 | "outputs": [], 1288 | "source": [ 1289 | "dummies.head()" 1290 | ] 1291 | }, 1292 | { 1293 | "cell_type": "code", 1294 | "execution_count": null, 1295 | "metadata": { 1296 | "ExecuteTime": { 1297 | "end_time": "2021-01-26T20:05:18.217925Z", 1298 | "start_time": "2021-01-26T20:05:18.208948Z" 1299 | } 1300 | }, 1301 | "outputs": [], 1302 | "source": [ 1303 | "dummies.sum()" 1304 | ] 1305 | }, 1306 | { 1307 | "cell_type": "markdown", 1308 | "metadata": {}, 1309 | "source": [ 1310 | "Finalmente podemos combinar 'dummies', con 'movies'" 1311 | ] 1312 | }, 1313 | { 1314 | "cell_type": "code", 1315 | "execution_count": null, 1316 | "metadata": { 1317 | "ExecuteTime": { 1318 | "end_time": "2021-01-26T20:05:58.156177Z", 1319 | "start_time": "2021-01-26T20:05:58.116286Z" 1320 | } 1321 | }, 1322 | "outputs": [], 1323 | "source": [ 1324 | "movies_dummies = movies.join(dummies.add_prefix('Genero_'))\n", 1325 | "movies_dummies.head()" 1326 | ] 1327 | }, 1328 | { 1329 | "cell_type": "code", 1330 | "execution_count": null, 1331 | "metadata": { 1332 | "ExecuteTime": { 1333 | "end_time": "2021-01-26T20:06:25.775396Z", 1334 | "start_time": "2021-01-26T20:06:25.767415Z" 1335 | } 1336 | }, 1337 | "outputs": [], 1338 | "source": [ 1339 | "movies_dummies.iloc[1]" 1340 | ] 1341 | }, 1342 | { 1343 | "cell_type": "code", 1344 | "execution_count": null, 1345 | "metadata": { 1346 | "ExecuteTime": { 1347 | "end_time": "2021-01-26T20:07:41.326460Z", 1348 | "start_time": "2021-01-26T20:07:41.322473Z" 1349 | } 1350 | }, 1351 | "outputs": [], 1352 | "source": [ 1353 | "np.random.seed(12345)" 1354 | ] 1355 | }, 1356 | { 1357 | "cell_type": "code", 1358 | "execution_count": null, 1359 | "metadata": { 1360 | "ExecuteTime": { 1361 | "end_time": "2021-01-26T20:07:43.477712Z", 1362 | "start_time": "2021-01-26T20:07:43.469733Z" 1363 | } 1364 | }, 1365 | "outputs": [], 1366 | "source": [ 1367 | "values = np.random.rand(10)\n", 1368 | "values" 1369 | ] 1370 | }, 1371 | { 1372 | "cell_type": "code", 1373 | "execution_count": null, 1374 | "metadata": { 1375 | "ExecuteTime": { 1376 | "end_time": "2021-01-26T20:08:01.157459Z", 1377 | "start_time": "2021-01-26T20:08:01.151471Z" 1378 | } 1379 | }, 1380 | "outputs": [], 1381 | "source": [ 1382 | "bins = [0, 0.2, 0.4, 0.6, 0.8, 1]\n", 1383 | "bins" 1384 | ] 1385 | }, 1386 | { 1387 | 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