├── Codes ├── Chapter 1.ipynb ├── Chapter 10.ipynb ├── Chapter 2 .ipynb ├── Chapter 3.ipynb ├── Chapter 4.ipynb ├── Chapter 5.ipynb ├── Chapter 6.ipynb ├── Chapter 7.ipynb ├── Chapter 8.ipynb ├── Chapter 9.ipynb ├── Hands On Data Visualization Project.ipynb └── temp ├── Data Visualization eBook final.pdf └── Data ├── customer_churn.csv ├── empty ├── google_data.csv ├── iris_data.csv ├── iris_data.tsv └── titanic_data.csv /Codes/Chapter 1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Chapter 1: Introduction" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "### 1.3.1. Writing Your First Program" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "name": "stdout", 24 | "output_type": "stream", 25 | "text": [ 26 | "Welcome to Data Visualization with Python\n" 27 | ] 28 | } 29 | ], 30 | "source": [ 31 | "print(\"Welcome to Data Visualization with Python\")" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "### 1.3.2. Python Variables and Data Types" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 1, 44 | "metadata": {}, 45 | "outputs": [ 46 | { 47 | "name": "stdout", 48 | "output_type": "stream", 49 | "text": [ 50 | "\n", 51 | "\n", 52 | "\n", 53 | "\n", 54 | "\n", 55 | "\n", 56 | "\n" 57 | ] 58 | } 59 | ], 60 | "source": [ 61 | "# A string Variable\n", 62 | "first_name = \"Joseph\" \n", 63 | "print(type(first_name))\n", 64 | "\n", 65 | "# An Integer Variable\n", 66 | "age = 20 \n", 67 | "print(type(age))\n", 68 | "\n", 69 | "# A floating point variable\n", 70 | "weight = 70.35\n", 71 | "print(type(weight))\n", 72 | "\n", 73 | "# A boolean variable\n", 74 | "married = False\n", 75 | "print(type(married))\n", 76 | "\n", 77 | "#List\n", 78 | "cars = [\"Honda\", \"Toyota\", \"Suzuki\"]\n", 79 | "print(type(cars))\n", 80 | "\n", 81 | "#Tuples\n", 82 | "days = (\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\")\n", 83 | "print(type(days))\n", 84 | "\n", 85 | "#Dictionaries\n", 86 | "days2 = {1:\"Sunday\", 2:\"Monday\", 3:\"Tuesday\", 4:\"Wednesday\", 5:\"Thursday\", 6:\"Friday\", 7:\"Saturday\"}\n", 87 | "print(type(days2))\n" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "### 1.3.3. Python Operations" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "#### Arithmetic Operators" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 2, 107 | "metadata": {}, 108 | "outputs": [ 109 | { 110 | "name": "stdout", 111 | "output_type": "stream", 112 | "text": [ 113 | "30\n", 114 | "10\n", 115 | "200\n", 116 | "2.0\n", 117 | "10240000000000\n" 118 | ] 119 | } 120 | ], 121 | "source": [ 122 | "\n", 123 | "X = 20 \n", 124 | "Y = 10 \n", 125 | "print(X + Y) \n", 126 | "print(X - Y) \n", 127 | "print(X * Y) \n", 128 | "print(X / Y) \n", 129 | "print(X ** Y)\n" 130 | ] 131 | }, 132 | { 133 | "cell_type": "markdown", 134 | "metadata": {}, 135 | "source": [ 136 | "#### Logical Operators" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 3, 142 | "metadata": {}, 143 | "outputs": [ 144 | { 145 | "name": "stdout", 146 | "output_type": "stream", 147 | "text": [ 148 | "False\n", 149 | "True\n", 150 | "True\n" 151 | ] 152 | } 153 | ], 154 | "source": [ 155 | "X = True \n", 156 | "Y = False \n", 157 | "print(X and Y) \n", 158 | "print(X or Y) \n", 159 | "print(not(X and Y))\n" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "#### Comparison Operators" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 4, 172 | "metadata": {}, 173 | "outputs": [ 174 | { 175 | "name": "stdout", 176 | "output_type": "stream", 177 | "text": [ 178 | "False\n", 179 | "True\n", 180 | "False\n", 181 | "True\n", 182 | "False\n", 183 | "True\n" 184 | ] 185 | } 186 | ], 187 | "source": [ 188 | "\n", 189 | "X = 20\n", 190 | "Y = 35\n", 191 | "\n", 192 | "print(X == Y) \n", 193 | "print(X != Y) \n", 194 | "print(X > Y) \n", 195 | "print(X < Y) \n", 196 | "print(X >= Y) \n", 197 | "print(X <= Y)\n" 198 | ] 199 | }, 200 | { 201 | "cell_type": "markdown", 202 | "metadata": {}, 203 | "source": [ 204 | "#### Assignment Operators" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 5, 210 | "metadata": {}, 211 | "outputs": [ 212 | { 213 | "name": "stdout", 214 | "output_type": "stream", 215 | "text": [ 216 | "30\n", 217 | "30\n", 218 | "10\n", 219 | "200\n", 220 | "2.0\n", 221 | "0\n", 222 | "10240000000000\n" 223 | ] 224 | } 225 | ], 226 | "source": [ 227 | "X = 20; Y = 10\n", 228 | "R = X + Y \n", 229 | "print(R)\n", 230 | "\n", 231 | "X = 20; \n", 232 | "Y = 10 \n", 233 | "X += Y\n", 234 | "print(X)\n", 235 | "\n", 236 | "X = 20; \n", 237 | "Y = 10 \n", 238 | "X -= Y \n", 239 | "print(X) \n", 240 | "\n", 241 | "X = 20;\n", 242 | "Y = 10 \n", 243 | "X *= Y \n", 244 | "print(X)\n", 245 | "\n", 246 | "X = 20;\n", 247 | "Y = 10 \n", 248 | "X /= Y \n", 249 | "print(X) \n", 250 | "\n", 251 | "X = 20;\n", 252 | "Y = 10 \n", 253 | "X %= Y \n", 254 | "print(X) \n", 255 | "\n", 256 | "X = 20; \n", 257 | "Y = 10 \n", 258 | "X **= Y \n", 259 | "print(X)\n" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "#### Membership Operators" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 6, 272 | "metadata": {}, 273 | "outputs": [ 274 | { 275 | "name": "stdout", 276 | "output_type": "stream", 277 | "text": [ 278 | "True\n" 279 | ] 280 | } 281 | ], 282 | "source": [ 283 | "days = (\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\")\n", 284 | "print('Sunday' in days)\n" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": 7, 290 | "metadata": {}, 291 | "outputs": [ 292 | { 293 | "name": "stdout", 294 | "output_type": "stream", 295 | "text": [ 296 | "True\n" 297 | ] 298 | } 299 | ], 300 | "source": [ 301 | "days = (\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\")\n", 302 | "print('Xunday' not in days)\n" 303 | ] 304 | }, 305 | { 306 | "cell_type": "markdown", 307 | "metadata": {}, 308 | "source": [ 309 | "### 1.3.4. Conditional Statements" 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": 8, 315 | "metadata": {}, 316 | "outputs": [ 317 | { 318 | "name": "stdout", 319 | "output_type": "stream", 320 | "text": [ 321 | "Ten is greater than 10\n" 322 | ] 323 | } 324 | ], 325 | "source": [ 326 | "\n", 327 | "# The if statment\n", 328 | "\n", 329 | "if 10 > 5:\n", 330 | " print(\"Ten is greater than 10\")\n", 331 | "\n", 332 | " " 333 | ] 334 | }, 335 | { 336 | "cell_type": "code", 337 | "execution_count": 11, 338 | "metadata": {}, 339 | "outputs": [ 340 | { 341 | "name": "stdout", 342 | "output_type": "stream", 343 | "text": [ 344 | "10 is greater than 5\n" 345 | ] 346 | } 347 | ], 348 | "source": [ 349 | "# if-else statement\n", 350 | "\n", 351 | "if 5 > 10:\n", 352 | " print(\"5 is greater than 10\")\n", 353 | "else:\n", 354 | " print(\"10 is greater than 5\")\n" 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "execution_count": 12, 360 | "metadata": {}, 361 | "outputs": [ 362 | { 363 | "name": "stdout", 364 | "output_type": "stream", 365 | "text": [ 366 | "5 is not greater than 10 and 8 is not smaller than 4\n" 367 | ] 368 | } 369 | ], 370 | "source": [ 371 | "#if-elif and else\n", 372 | "\n", 373 | "if 5 > 10:\n", 374 | " print(\"5 is greater than 10\")\n", 375 | "elif 8 < 4:\n", 376 | " print(\"8 is smaller than 4\")\n", 377 | "else:\n", 378 | " print(\"5 is not greater than 10 and 8 is not smaller than 4\")\n" 379 | ] 380 | }, 381 | { 382 | "cell_type": "markdown", 383 | "metadata": {}, 384 | "source": [ 385 | "### 1.3.5. Iteration Statements" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": 13, 391 | "metadata": {}, 392 | "outputs": [ 393 | { 394 | "name": "stdout", 395 | "output_type": "stream", 396 | "text": [ 397 | "0\n", 398 | "1\n", 399 | "2\n", 400 | "3\n", 401 | "4\n" 402 | ] 403 | } 404 | ], 405 | "source": [ 406 | "items = range(5)\n", 407 | "for item in items:\n", 408 | " print(item)\n" 409 | ] 410 | }, 411 | { 412 | "cell_type": "code", 413 | "execution_count": 14, 414 | "metadata": {}, 415 | "outputs": [ 416 | { 417 | "name": "stdout", 418 | "output_type": "stream", 419 | "text": [ 420 | "0\n", 421 | "1\n", 422 | "2\n", 423 | "3\n", 424 | "4\n", 425 | "5\n", 426 | "6\n", 427 | "7\n", 428 | "8\n", 429 | "9\n" 430 | ] 431 | } 432 | ], 433 | "source": [ 434 | "c = 0\n", 435 | "while c < 10:\n", 436 | " print(c)\n", 437 | " c = c +1\n" 438 | ] 439 | }, 440 | { 441 | "cell_type": "markdown", 442 | "metadata": {}, 443 | "source": [ 444 | "### 1.3.6. Functions" 445 | ] 446 | }, 447 | { 448 | "cell_type": "code", 449 | "execution_count": 15, 450 | "metadata": {}, 451 | "outputs": [ 452 | { 453 | "name": "stdout", 454 | "output_type": "stream", 455 | "text": [ 456 | "This is a simple function\n" 457 | ] 458 | } 459 | ], 460 | "source": [ 461 | "def myfunc():\n", 462 | " print(\"This is a simple function\")\n", 463 | "\n", 464 | "### function call\n", 465 | "myfunc()\n" 466 | ] 467 | }, 468 | { 469 | "cell_type": "code", 470 | "execution_count": 16, 471 | "metadata": {}, 472 | "outputs": [ 473 | { 474 | "name": "stdout", 475 | "output_type": "stream", 476 | "text": [ 477 | "This is a function with parameter value: Parameter 1\n" 478 | ] 479 | } 480 | ], 481 | "source": [ 482 | "def myfuncparam(num):\n", 483 | " print(\"This is a function with parameter value: \"+num)\n", 484 | "\n", 485 | "### function call\n", 486 | "myfuncparam(\"Parameter 1\")\n" 487 | ] 488 | }, 489 | { 490 | "cell_type": "code", 491 | "execution_count": 17, 492 | "metadata": {}, 493 | "outputs": [ 494 | { 495 | "name": "stdout", 496 | "output_type": "stream", 497 | "text": [ 498 | "This function returns a value\n" 499 | ] 500 | } 501 | ], 502 | "source": [ 503 | "def myreturnfunc():\n", 504 | " return \"This function returns a value\"\n", 505 | "\n", 506 | "val = myreturnfunc()\n", 507 | "print(val)\n" 508 | ] 509 | }, 510 | { 511 | "cell_type": "markdown", 512 | "metadata": {}, 513 | "source": [ 514 | "### 1.3.7. Objects and Classes" 515 | ] 516 | }, 517 | { 518 | "cell_type": "code", 519 | "execution_count": 18, 520 | "metadata": {}, 521 | "outputs": [ 522 | { 523 | "name": "stdout", 524 | "output_type": "stream", 525 | "text": [ 526 | "Fruit has been eaten\n", 527 | "apple\n", 528 | "10\n" 529 | ] 530 | } 531 | ], 532 | "source": [ 533 | "class Fruit:\n", 534 | "\n", 535 | " name = \"apple\"\n", 536 | " price = 10\n", 537 | "\n", 538 | " def eat_fruit(self):\n", 539 | " print(\"Fruit has been eaten\")\n", 540 | "\n", 541 | "\n", 542 | "f = Fruit()\n", 543 | "f.eat_fruit()\n", 544 | "print(f.name)\n", 545 | "print(f.price)\n" 546 | ] 547 | }, 548 | { 549 | "cell_type": "code", 550 | "execution_count": 19, 551 | "metadata": {}, 552 | "outputs": [ 553 | { 554 | "name": "stdout", 555 | "output_type": "stream", 556 | "text": [ 557 | "Fruit has been eaten\n", 558 | "Orange\n" 559 | ] 560 | } 561 | ], 562 | "source": [ 563 | "class Fruit:\n", 564 | "\n", 565 | " name = \"apple\"\n", 566 | " price = 10\n", 567 | "\n", 568 | " def __init__(self, fruit_name, fruit_price):\n", 569 | " Fruit.name = fruit_name\n", 570 | " Fruit.price = fruit_price\n", 571 | "\n", 572 | " def eat_fruit(self):\n", 573 | " print(\"Fruit has been eaten\")\n", 574 | "\n", 575 | "\n", 576 | "f = Fruit(\"Orange\", 15)\n", 577 | "f.eat_fruit()\n", 578 | "print(f.name)\n" 579 | ] 580 | }, 581 | { 582 | "cell_type": "markdown", 583 | "metadata": {}, 584 | "source": [ 585 | "## Exercis 1.1" 586 | ] 587 | }, 588 | { 589 | "cell_type": "markdown", 590 | "metadata": {}, 591 | "source": [ 592 | "### Question 1" 593 | ] 594 | }, 595 | { 596 | "cell_type": "markdown", 597 | "metadata": {}, 598 | "source": [ 599 | "Which iteration should be used when you want to repeatedly execute a code specific number of times?\n", 600 | "\n", 601 | "A- For Loop \\\n", 602 | "B- While Loop \\\n", 603 | "C- Both A & B \\\n", 604 | "D- None of the above \n", 605 | "\n", 606 | "Answer: A" 607 | ] 608 | }, 609 | { 610 | "cell_type": "markdown", 611 | "metadata": {}, 612 | "source": [ 613 | "### Question 2" 614 | ] 615 | }, 616 | { 617 | "cell_type": "markdown", 618 | "metadata": {}, 619 | "source": [ 620 | "What is the maximum number of values that a function can return in Python?\n", 621 | "\n", 622 | "A- Single Value \\\n", 623 | "B- Double Value \\\n", 624 | "C- More than two values \\\n", 625 | "D- None\n", 626 | "\n", 627 | "Answer: C" 628 | ] 629 | }, 630 | { 631 | "cell_type": "markdown", 632 | "metadata": {}, 633 | "source": [ 634 | "### Question 3" 635 | ] 636 | }, 637 | { 638 | "cell_type": "markdown", 639 | "metadata": {}, 640 | "source": [ 641 | "Which of the following membership operators are supported by Python?\n", 642 | "\n", 643 | "A- In \\\n", 644 | "B- Out \\\n", 645 | "C- Not In \\\n", 646 | "D- Both A and C \n", 647 | "\n", 648 | "Answer: D" 649 | ] 650 | }, 651 | { 652 | "cell_type": "markdown", 653 | "metadata": {}, 654 | "source": [ 655 | "## Exercis 1.2" 656 | ] 657 | }, 658 | { 659 | "cell_type": "markdown", 660 | "metadata": {}, 661 | "source": [ 662 | "Print the table of integer 9 using a while loop:" 663 | ] 664 | }, 665 | { 666 | "cell_type": "code", 667 | "execution_count": 19, 668 | "metadata": {}, 669 | "outputs": [ 670 | { 671 | "name": "stdout", 672 | "output_type": "stream", 673 | "text": [ 674 | "9 x 1 = 9\n", 675 | "9 x 2 = 18\n", 676 | "9 x 3 = 27\n", 677 | "9 x 4 = 36\n", 678 | "9 x 5 = 45\n", 679 | "9 x 6 = 54\n", 680 | "9 x 7 = 63\n", 681 | "9 x 8 = 72\n", 682 | "9 x 9 = 81\n", 683 | "9 x 10 = 90\n" 684 | ] 685 | } 686 | ], 687 | "source": [ 688 | "j=1\n", 689 | "while j< 11:\n", 690 | " print(\"9 x \"+str(j)+ \" = \"+ str(9*j))\n", 691 | " j=j+1" 692 | ] 693 | }, 694 | { 695 | "cell_type": "code", 696 | "execution_count": null, 697 | "metadata": {}, 698 | "outputs": [], 699 | "source": [] 700 | } 701 | ], 702 | "metadata": { 703 | "kernelspec": { 704 | "display_name": "Python 3", 705 | "language": "python", 706 | "name": "python3" 707 | }, 708 | "language_info": { 709 | "codemirror_mode": { 710 | "name": "ipython", 711 | "version": 3 712 | }, 713 | "file_extension": ".py", 714 | "mimetype": "text/x-python", 715 | "name": "python", 716 | "nbconvert_exporter": "python", 717 | "pygments_lexer": "ipython3", 718 | "version": "3.8.5" 719 | } 720 | }, 721 | "nbformat": 4, 722 | "nbformat_minor": 2 723 | } 724 | -------------------------------------------------------------------------------- /Codes/Chapter 10.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Chapter 10: Interactive Data Visualization with Plotly" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "### 10.1. Installation" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "ename": "SyntaxError", 24 | "evalue": "invalid syntax (, line 1)", 25 | "output_type": "error", 26 | "traceback": [ 27 | "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m $ pip install plotly \\\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "$ pip install plotly \\\n", 33 | "$ pip install cufflinks" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 3, 39 | "metadata": {}, 40 | "outputs": [], 41 | "source": [ 42 | "import pandas as pd\n", 43 | "import numpy as np\n", 44 | "%matplotlib inline\n", 45 | "import seaborn as sns" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 4, 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [ 54 | "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 5, 60 | "metadata": {}, 61 | "outputs": [ 62 | { 63 | "data": { 64 | "text/html": [ 65 | " \n", 80 | " " 81 | ] 82 | }, 83 | "metadata": {}, 84 | "output_type": "display_data" 85 | } 86 | ], 87 | "source": [ 88 | "init_notebook_mode(connected=True)" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 6, 94 | "metadata": {}, 95 | "outputs": [ 96 | { 97 | "data": { 98 | "text/html": [ 99 | " \n", 114 | " " 115 | ] 116 | }, 117 | "metadata": {}, 118 | "output_type": "display_data" 119 | } 120 | ], 121 | "source": [ 122 | "import cufflinks as cf\n", 123 | "cf.go_offline()" 124 | ] 125 | }, 126 | { 127 | "cell_type": "markdown", 128 | "metadata": {}, 129 | "source": [ 130 | "### 10.2. Line Plot" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": null, 136 | "metadata": {}, 137 | "outputs": [], 138 | "source": [ 139 | "flights_data = sns.load_dataset('flights')\n", 140 | "\n", 141 | "flights_data.head()" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": null, 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [ 150 | "dataset_filter = flights_data[[\"passengers\"]]\n", 151 | "dataset_filter.plot()" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": null, 157 | "metadata": {}, 158 | "outputs": [], 159 | "source": [ 160 | "dataset_filter.iplot()" 161 | ] 162 | }, 163 | { 164 | "cell_type": "markdown", 165 | "metadata": {}, 166 | "source": [ 167 | "### 10.3. Bar Plot" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": null, 173 | "metadata": {}, 174 | "outputs": [], 175 | "source": [ 176 | "flights_data.iplot(kind='bar', x=['month'],y= 'passengers')\n" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": null, 182 | "metadata": {}, 183 | "outputs": [], 184 | "source": [ 185 | "flights_data.iplot(kind='barh', x=['month'],y= 'passengers')" 186 | ] 187 | }, 188 | { 189 | "cell_type": "markdown", 190 | "metadata": {}, 191 | "source": [ 192 | "### 10.4. Scatter Plot" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": null, 198 | "metadata": {}, 199 | "outputs": [], 200 | "source": [ 201 | "flights_data.iplot(kind='scatter', x= 'month', y= 'passengers', mode= 'markers')" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": null, 207 | "metadata": {}, 208 | "outputs": [], 209 | "source": [ 210 | "import seaborn as sns\n", 211 | "\n", 212 | "tips_data = sns.load_dataset('tips')\n", 213 | "\n", 214 | "tips_data.head()" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": null, 220 | "metadata": {}, 221 | "outputs": [], 222 | "source": [ 223 | "tips_data.iplot(kind='scatter', x='total_bill', y='tip', mode='markers', color = 'blue')" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": {}, 229 | "source": [ 230 | "### 10.5. Box Plot" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": null, 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "tips_data.iplot(kind='box')" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "### 10.6. Hist Plot" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": null, 252 | "metadata": {}, 253 | "outputs": [], 254 | "source": [ 255 | "titanic_data = pd.read_csv(r\"E:\\Data Visualization with Python\\Datasets\\titanic_data.csv\")\n", 256 | "titanic_data.head()" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": null, 262 | "metadata": {}, 263 | "outputs": [], 264 | "source": [ 265 | "titanic_data['Age'].iplot(kind='hist',bins=25)" 266 | ] 267 | }, 268 | { 269 | "cell_type": "markdown", 270 | "metadata": {}, 271 | "source": [ 272 | "## Exercise 10.1 " 273 | ] 274 | }, 275 | { 276 | "cell_type": "markdown", 277 | "metadata": {}, 278 | "source": [ 279 | "### Question 1" 280 | ] 281 | }, 282 | { 283 | "cell_type": "markdown", 284 | "metadata": {}, 285 | "source": [ 286 | "Which top level function is used to plot interactive plots with Plotly using Cufflinks?\n", 287 | "\n", 288 | "A- plot() \\\n", 289 | "B- iplot() \\\n", 290 | "C- draw()() \\\n", 291 | "D- idraw()\n", 292 | "\n", 293 | "Answer: B" 294 | ] 295 | }, 296 | { 297 | "cell_type": "markdown", 298 | "metadata": {}, 299 | "source": [ 300 | "### Question 2" 301 | ] 302 | }, 303 | { 304 | "cell_type": "markdown", 305 | "metadata": {}, 306 | "source": [ 307 | "To plot a scatter plot with Plotly using Cufflinks, the value of ____ attribute should be set to _____:\n", 308 | "\n", 309 | "A- shape, markers \\\n", 310 | "B- shape, scatter \\\n", 311 | "C- mode, marker \\\n", 312 | "D- mode, scatter\n", 313 | "\n", 314 | "Answer: C" 315 | ] 316 | }, 317 | { 318 | "cell_type": "markdown", 319 | "metadata": {}, 320 | "source": [ 321 | "### Question 3" 322 | ] 323 | }, 324 | { 325 | "cell_type": "markdown", 326 | "metadata": {}, 327 | "source": [ 328 | "To plot a histogram with Plotly and cufflinks, the kind attribute of the iplot() function should be set to:\n", 329 | "\n", 330 | "A- histogram()\\\n", 331 | "B- histo() \\\n", 332 | "C- hist()\\\n", 333 | "D- none of the above\n", 334 | "\n", 335 | "Answer: C" 336 | ] 337 | }, 338 | { 339 | "cell_type": "markdown", 340 | "metadata": {}, 341 | "source": [ 342 | "## Exercise 10.2" 343 | ] 344 | }, 345 | { 346 | "cell_type": "markdown", 347 | "metadata": {}, 348 | "source": [ 349 | "Plot an interactive histogram for the PClass column of the titanic dataset." 350 | ] 351 | }, 352 | { 353 | "cell_type": "code", 354 | "execution_count": 7, 355 | "metadata": {}, 356 | "outputs": [ 357 | { 358 | "data": { 359 | "application/vnd.plotly.v1+json": { 360 | "config": { 361 | "linkText": "Export to plot.ly", 362 | "plotlyServerURL": "https://plot.ly", 363 | "showLink": true 364 | }, 365 | "data": [ 366 | { 367 | "histfunc": "count", 368 | "histnorm": "", 369 | "marker": { 370 | "color": "rgba(255, 153, 51, 1.0)", 371 | "line": { 372 | "color": "#4D5663", 373 | "width": 1.3 374 | } 375 | }, 376 | "name": "Pclass", 377 | "opacity": 0.8, 378 | "orientation": "v", 379 | "type": "histogram", 380 | "x": [ 381 | 3, 382 | 1, 383 | 3, 384 | 1, 385 | 3, 386 | 3, 387 | 1, 388 | 3, 389 | 3, 390 | 2, 391 | 3, 392 | 1, 393 | 3, 394 | 3, 395 | 3, 396 | 2, 397 | 3, 398 | 2, 399 | 3, 400 | 3, 401 | 2, 402 | 2, 403 | 3, 404 | 1, 405 | 3, 406 | 3, 407 | 3, 408 | 1, 409 | 3, 410 | 3, 411 | 1, 412 | 1, 413 | 3, 414 | 2, 415 | 1, 416 | 1, 417 | 3, 418 | 3, 419 | 3, 420 | 3, 421 | 3, 422 | 2, 423 | 3, 424 | 2, 425 | 3, 426 | 3, 427 | 3, 428 | 3, 429 | 3, 430 | 3, 431 | 3, 432 | 3, 433 | 1, 434 | 2, 435 | 1, 436 | 1, 437 | 2, 438 | 3, 439 | 2, 440 | 3, 441 | 3, 442 | 1, 443 | 1, 444 | 3, 445 | 1, 446 | 3, 447 | 2, 448 | 3, 449 | 3, 450 | 3, 451 | 2, 452 | 3, 453 | 2, 454 | 3, 455 | 3, 456 | 3, 457 | 3, 458 | 3, 459 | 2, 460 | 3, 461 | 3, 462 | 3, 463 | 3, 464 | 1, 465 | 2, 466 | 3, 467 | 3, 468 | 3, 469 | 1, 470 | 3, 471 | 3, 472 | 3, 473 | 1, 474 | 3, 475 | 3, 476 | 3, 477 | 1, 478 | 1, 479 | 2, 480 | 2, 481 | 3, 482 | 3, 483 | 1, 484 | 3, 485 | 3, 486 | 3, 487 | 3, 488 | 3, 489 | 3, 490 | 3, 491 | 1, 492 | 3, 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\n", 2127 | " \n", 2128 | " \n", 2129 | "
\n", 2130 | " \n", 2169 | "
" 2170 | ] 2171 | }, 2172 | "metadata": {}, 2173 | "output_type": "display_data" 2174 | } 2175 | ], 2176 | "source": [ 2177 | "titanic_data = pd.read_csv(r\"E:\\Data Visualization with Python\\Datasets\\titanic_data.csv\")\n", 2178 | "\n", 2179 | "titanic_data['Pclass'].iplot(kind='hist')" 2180 | ] 2181 | }, 2182 | { 2183 | "cell_type": "code", 2184 | "execution_count": null, 2185 | "metadata": {}, 2186 | "outputs": [], 2187 | "source": [] 2188 | } 2189 | ], 2190 | "metadata": { 2191 | "kernelspec": { 2192 | "display_name": "Python 3", 2193 | "language": "python", 2194 | "name": "python3" 2195 | }, 2196 | "language_info": { 2197 | "codemirror_mode": { 2198 | "name": "ipython", 2199 | "version": 3 2200 | }, 2201 | "file_extension": ".py", 2202 | "mimetype": "text/x-python", 2203 | "name": "python", 2204 | "nbconvert_exporter": "python", 2205 | "pygments_lexer": "ipython3", 2206 | "version": "3.7.4" 2207 | } 2208 | }, 2209 | "nbformat": 4, 2210 | "nbformat_minor": 2 2211 | } 2212 | -------------------------------------------------------------------------------- /Codes/Chapter 9.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Chapter 9: Interactive Data Visualization with Bokeh" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "### 9.1. Introduction|" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "### 9.2. Line Plots" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 2, 29 | "metadata": {}, 30 | "outputs": [ 31 | { 32 | "data": { 33 | "text/html": [ 34 | "
\n", 35 | "\n", 48 | "\n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | "
yearmonthpassengers
01949January112
11949February118
21949March132
31949April129
41949May121
\n", 90 | "
" 91 | ], 92 | "text/plain": [ 93 | " year month passengers\n", 94 | "0 1949 January 112\n", 95 | "1 1949 February 118\n", 96 | "2 1949 March 132\n", 97 | "3 1949 April 129\n", 98 | "4 1949 May 121" 99 | ] 100 | }, 101 | "execution_count": 2, 102 | "metadata": {}, 103 | "output_type": "execute_result" 104 | } 105 | ], 106 | "source": [ 107 | "import pandas as pd\n", 108 | "import numpy as np\n", 109 | "%matplotlib inline\n", 110 | "import seaborn as sns\n", 111 | "\n", 112 | "flights_data = sns.load_dataset('flights')\n", 113 | "\n", 114 | "flights_data.head()" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 3, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [ 123 | "\n", 124 | "from bokeh.plotting import figure, output_file, show" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 4, 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "output_file('E:/bokeh.html')" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 5, 139 | "metadata": {}, 140 | "outputs": [], 141 | "source": [ 142 | "plot = figure(\n", 143 | " title = 'Years vs Passengers',\n", 144 | " x_axis_label ='Year',\n", 145 | " y_axis_label ='Passengers',\n", 146 | " plot_width=600,\n", 147 | " plot_height=400\n", 148 | ")" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 6, 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "year = flights_data['year']\n", 158 | "passengers = flights_data['passengers']" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 7, 164 | "metadata": {}, 165 | "outputs": [ 166 | { 167 | "data": { 168 | "text/html": [ 169 | "
GlyphRenderer(
id = '1038', …)
data_source = ColumnDataSource(id='1035', ...),
glyph = Line(id='1036', ...),
hover_glyph = None,
js_event_callbacks = {},
js_property_callbacks = {},
level = 'glyph',
muted = False,
muted_glyph = None,
name = None,
nonselection_glyph = Line(id='1037', ...),
selection_glyph = None,
subscribed_events = [],
tags = [],
view = CDSView(id='1039', ...),
visible = True,
x_range_name = 'default',
y_range_name = 'default')
\n", 170 | "\n" 185 | ], 186 | "text/plain": [ 187 | "GlyphRenderer(id='1038', ...)" 188 | ] 189 | }, 190 | "execution_count": 7, 191 | "metadata": {}, 192 | "output_type": "execute_result" 193 | } 194 | ], 195 | "source": [ 196 | "plot.line(year,passengers, legend='Years vs Passengers', line_width=2)" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 8, 202 | "metadata": {}, 203 | "outputs": [], 204 | "source": [ 205 | "show(plot)" 206 | ] 207 | }, 208 | { 209 | "cell_type": "markdown", 210 | "metadata": {}, 211 | "source": [ 212 | "### 9.3. Bar Plots" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 9, 218 | "metadata": {}, 219 | "outputs": [ 220 | { 221 | "name": "stdout", 222 | "output_type": "stream", 223 | "text": [ 224 | "['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n", 225 | "[241.75, 235.0, 270.1666666666667, 267.0833333333333, 271.8333333333333, 311.6666666666667, 351.3333333333333, 351.0833333333333, 302.4166666666667, 266.5833333333333, 232.83333333333334, 261.8333333333333]\n" 226 | ] 227 | } 228 | ], 229 | "source": [ 230 | "month_passengers = flights_data.groupby(\"month\")[\"passengers\"].mean()\n", 231 | "print(month_passengers.index.tolist())\n", 232 | "print(month_passengers.tolist())" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 10, 238 | "metadata": {}, 239 | "outputs": [], 240 | "source": [ 241 | "plot2 = figure(\n", 242 | " x_range = month_passengers.index.tolist(),\n", 243 | " title = 'Month vs Passengers',\n", 244 | " x_axis_label ='Month',\n", 245 | " y_axis_label ='Passengers',\n", 246 | " plot_height=400\n", 247 | ")" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": 11, 253 | "metadata": {}, 254 | "outputs": [], 255 | "source": [ 256 | "plot2.vbar(x = month_passengers.index.tolist() , top = month_passengers.tolist(), legend='Months vs Passengers', width = 0.7)\n", 257 | "show(plot2)" 258 | ] 259 | }, 260 | { 261 | "cell_type": "markdown", 262 | "metadata": {}, 263 | "source": [ 264 | "### 9.4. Scatter Plots" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": 12, 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [ 273 | "plot3 = figure(\n", 274 | " title = 'Years vs Passengers',\n", 275 | " x_axis_label ='Year',\n", 276 | " y_axis_label ='Passengers',\n", 277 | " plot_width=600,\n", 278 | " plot_height=400\n", 279 | ")" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": 13, 285 | "metadata": {}, 286 | "outputs": [], 287 | "source": [ 288 | "year = flights_data['year']\n", 289 | "passengers = flights_data['passengers']" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": 14, 295 | "metadata": {}, 296 | "outputs": [], 297 | "source": [ 298 | "plot3.scatter(year,passengers, legend='Years vs Passengers', line_width=2)\n", 299 | "show(plot3)" 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "execution_count": 15, 305 | "metadata": {}, 306 | "outputs": [ 307 | { 308 | "data": { 309 | "text/html": [ 310 | "
\n", 311 | "\n", 324 | "\n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | "
total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\n", 390 | "
" 391 | ], 392 | "text/plain": [ 393 | " total_bill tip sex smoker day time size\n", 394 | "0 16.99 1.01 Female No Sun Dinner 2\n", 395 | "1 10.34 1.66 Male No Sun Dinner 3\n", 396 | "2 21.01 3.50 Male No Sun Dinner 3\n", 397 | "3 23.68 3.31 Male No Sun Dinner 2\n", 398 | "4 24.59 3.61 Female No Sun Dinner 4" 399 | ] 400 | }, 401 | "execution_count": 15, 402 | "metadata": {}, 403 | "output_type": "execute_result" 404 | } 405 | ], 406 | "source": [ 407 | "import seaborn as sns\n", 408 | "\n", 409 | "tips_data = sns.load_dataset('tips')\n", 410 | "\n", 411 | "tips_data.head()" 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "execution_count": 16, 417 | "metadata": {}, 418 | "outputs": [], 419 | "source": [ 420 | "plot4 = figure(\n", 421 | " title = 'Total Bill vs Tips',\n", 422 | " x_axis_label ='Totall Bill',\n", 423 | " y_axis_label ='Tips',\n", 424 | " plot_width=600,\n", 425 | " plot_height=400\n", 426 | ")" 427 | ] 428 | }, 429 | { 430 | "cell_type": "code", 431 | "execution_count": 17, 432 | "metadata": {}, 433 | "outputs": [], 434 | "source": [ 435 | "total_bill = tips_data['total_bill']\n", 436 | "tips = tips_data['tip']" 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "execution_count": 18, 442 | "metadata": {}, 443 | "outputs": [], 444 | "source": [ 445 | "plot4.scatter(total_bill, tips, legend='Total Bill vs Tip', line_width=2)\n", 446 | "show(plot4)" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": 19, 452 | "metadata": {}, 453 | "outputs": [], 454 | "source": [ 455 | "plot5 = figure(\n", 456 | " title = 'Total Bill vs Tips',\n", 457 | " x_axis_label ='Totall Bill',\n", 458 | " y_axis_label ='Tips',\n", 459 | " plot_width=600,\n", 460 | " plot_height=400\n", 461 | ")" 462 | ] 463 | }, 464 | { 465 | "cell_type": "code", 466 | "execution_count": 20, 467 | "metadata": {}, 468 | "outputs": [], 469 | "source": [ 470 | "plot5.circle(total_bill, tips, radius = 0.5)\n", 471 | "show(plot5)" 472 | ] 473 | }, 474 | { 475 | "cell_type": "markdown", 476 | "metadata": {}, 477 | "source": [ 478 | "## Exercise 9.1" 479 | ] 480 | }, 481 | { 482 | "cell_type": "markdown", 483 | "metadata": {}, 484 | "source": [ 485 | "### Question 1" 486 | ] 487 | }, 488 | { 489 | "cell_type": "markdown", 490 | "metadata": {}, 491 | "source": [ 492 | "Which object is used to set the width and height of a plot in bokeh:\n", 493 | "\n", 494 | "A- figure() \\\n", 495 | "B- width() \\\n", 496 | "C- height() \\\n", 497 | "D- None of the above\n", 498 | "\n", 499 | "Answer: A" 500 | ] 501 | }, 502 | { 503 | "cell_type": "markdown", 504 | "metadata": {}, 505 | "source": [ 506 | "### Question 2" 507 | ] 508 | }, 509 | { 510 | "cell_type": "markdown", 511 | "metadata": {}, 512 | "source": [ 513 | "Which attribute is used to set the width of the line in a line plot in bokeh:\n", 514 | "\n", 515 | "A- line \\\n", 516 | "B- width \\\n", 517 | "C- line_width \\\n", 518 | "D- length \n", 519 | "\n", 520 | "Answer: C" 521 | ] 522 | }, 523 | { 524 | "cell_type": "markdown", 525 | "metadata": {}, 526 | "source": [ 527 | "### Question 3" 528 | ] 529 | }, 530 | { 531 | "cell_type": "markdown", 532 | "metadata": {}, 533 | "source": [ 534 | "In the Bokeh library, the list of values used to plot bar plots is passed to the following attribute of the bar plot:\n", 535 | "\n", 536 | "A- values \\\n", 537 | "B- legends \\\n", 538 | "C- y \\\n", 539 | "D- top \n", 540 | "\n", 541 | "Answer: D" 542 | ] 543 | }, 544 | { 545 | "cell_type": "markdown", 546 | "metadata": {}, 547 | "source": [ 548 | "## Exercise 9.2" 549 | ] 550 | }, 551 | { 552 | "cell_type": "markdown", 553 | "metadata": {}, 554 | "source": [ 555 | "Plot a bar plot using titanic dataset that displays the average age of both male and female passengers." 556 | ] 557 | }, 558 | { 559 | "cell_type": "code", 560 | "execution_count": 22, 561 | "metadata": {}, 562 | "outputs": [], 563 | "source": [ 564 | "titanic_data = pd.read_csv(r\"E:\\Data Visualization with Python\\Datasets\\titanic_data.csv\")\n", 565 | "\n", 566 | "titanic_data = pd.read_csv(r\"E:\\Data Visualization with Python\\Datasets\\titanic_data.csv\")\n", 567 | "\n", 568 | "sex_mean = titanic_data.groupby(\"Sex\")[\"Age\"].mean()\n", 569 | "\n", 570 | "plotx = figure(\n", 571 | " x_range = sex_mean.index.tolist(),\n", 572 | " title = 'Sex vs Age',\n", 573 | " x_axis_label ='Sex',\n", 574 | " y_axis_label ='Age',\n", 575 | " plot_height=400\n", 576 | ")\n", 577 | "\n", 578 | "plotx.vbar(x = sex_mean.index.tolist() , top = sex_mean.tolist(), legend='Sex vs Age', width = 0.7)\n", 579 | "show(plotx)" 580 | ] 581 | }, 582 | { 583 | "cell_type": "code", 584 | "execution_count": null, 585 | "metadata": {}, 586 | "outputs": [], 587 | "source": [] 588 | } 589 | ], 590 | "metadata": { 591 | "kernelspec": { 592 | "display_name": "Python 3", 593 | "language": "python", 594 | "name": "python3" 595 | }, 596 | "language_info": { 597 | "codemirror_mode": { 598 | "name": "ipython", 599 | "version": 3 600 | }, 601 | "file_extension": ".py", 602 | "mimetype": "text/x-python", 603 | "name": "python", 604 | "nbconvert_exporter": "python", 605 | "pygments_lexer": "ipython3", 606 | "version": "3.7.4" 607 | } 608 | }, 609 | "nbformat": 4, 610 | "nbformat_minor": 2 611 | } 612 | -------------------------------------------------------------------------------- /Codes/temp: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Data Visualization eBook final.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AISPUBLISHING/Data-Visualization/b914dcb9c141937ea364d030f8ea52a2ecab068f/Data Visualization eBook final.pdf -------------------------------------------------------------------------------- /Data/empty: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Data/iris_data.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3.0,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5.0,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5.0,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3.0,1.4,0.1,setosa 15 | 4.3,3.0,1.1,0.1,setosa 16 | 5.8,4.0,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1.0,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5.0,3.0,1.6,0.2,setosa 28 | 5.0,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.1,setosa 37 | 5.0,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.1,1.5,0.1,setosa 40 | 4.4,3.0,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5.0,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5.0,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3.0,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5.0,3.3,1.4,0.2,setosa 52 | 7.0,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4.0,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1.0,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5.0,2.0,3.5,1.0,versicolor 63 | 5.9,3.0,4.2,1.5,versicolor 64 | 6.0,2.2,4.0,1.0,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3.0,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1.0,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4.0,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3.0,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3.0,5.0,1.7,versicolor 80 | 6.0,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1.0,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1.0,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6.0,2.7,5.1,1.6,versicolor 86 | 5.4,3.0,4.5,1.5,versicolor 87 | 6.0,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3.0,4.1,1.3,versicolor 91 | 5.5,2.5,4.0,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3.0,4.6,1.4,versicolor 94 | 5.8,2.6,4.0,1.2,versicolor 95 | 5.0,2.3,3.3,1.0,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3.0,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3.0,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6.0,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3.0,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3.0,5.8,2.2,virginica 107 | 7.6,3.0,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2.0,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3.0,5.5,2.1,virginica 115 | 5.7,2.5,5.0,2.0,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3.0,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6.0,2.2,5.0,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2.0,virginica 124 | 7.7,2.8,6.7,2.0,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6.0,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3.0,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3.0,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2.0,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3.0,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6.0,3.0,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3.0,5.2,2.3,virginica 148 | 6.3,2.5,5.0,1.9,virginica 149 | 6.5,3.0,5.2,2.0,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3.0,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- /Data/iris_data.tsv: -------------------------------------------------------------------------------- 1 | SepalLength SepalWidth PetalLength PetalWidth TrainingClass 2 | 5.1 3.5 1.4 0.2 Iris-setosa 3 | 4.9 3.0 1.4 0.2 Iris-setosa 4 | 4.7 3.2 1.3 0.2 Iris-setosa 5 | 4.6 3.1 1.5 0.2 Iris-setosa 6 | 5.0 3.6 1.4 0.2 Iris-setosa 7 | 5.4 3.9 1.7 0.4 Iris-setosa 8 | 4.6 3.4 1.4 0.3 Iris-setosa 9 | 5.0 3.4 1.5 0.2 Iris-setosa 10 | 4.4 2.9 1.4 0.2 Iris-setosa 11 | 4.9 3.1 1.5 0.1 Iris-setosa 12 | 5.4 3.7 1.5 0.2 Iris-setosa 13 | 4.8 3.4 1.6 0.2 Iris-setosa 14 | 4.8 3.0 1.4 0.1 Iris-setosa 15 | 4.3 3.0 1.1 0.1 Iris-setosa 16 | 5.8 4.0 1.2 0.2 Iris-setosa 17 | 5.7 4.4 1.5 0.4 Iris-setosa 18 | 5.4 3.9 1.3 0.4 Iris-setosa 19 | 5.1 3.5 1.4 0.3 Iris-setosa 20 | 5.7 3.8 1.7 0.3 Iris-setosa 21 | 5.1 3.8 1.5 0.3 Iris-setosa 22 | 5.4 3.4 1.7 0.2 Iris-setosa 23 | 5.1 3.7 1.5 0.4 Iris-setosa 24 | 4.6 3.6 1.0 0.2 Iris-setosa 25 | 5.1 3.3 1.7 0.5 Iris-setosa 26 | 4.8 3.4 1.9 0.2 Iris-setosa 27 | 5.0 3.0 1.6 0.2 Iris-setosa 28 | 5.0 3.4 1.6 0.4 Iris-setosa 29 | 5.2 3.5 1.5 0.2 Iris-setosa 30 | 5.2 3.4 1.4 0.2 Iris-setosa 31 | 4.7 3.2 1.6 0.2 Iris-setosa 32 | 4.8 3.1 1.6 0.2 Iris-setosa 33 | 5.4 3.4 1.5 0.4 Iris-setosa 34 | 5.2 4.1 1.5 0.1 Iris-setosa 35 | 5.5 4.2 1.4 0.2 Iris-setosa 36 | 4.9 3.1 1.5 0.1 Iris-setosa 37 | 5.0 3.2 1.2 0.2 Iris-setosa 38 | 5.5 3.5 1.3 0.2 Iris-setosa 39 | 4.9 3.1 1.5 0.1 Iris-setosa 40 | 4.4 3.0 1.3 0.2 Iris-setosa 41 | 5.1 3.4 1.5 0.2 Iris-setosa 42 | 5.0 3.5 1.3 0.3 Iris-setosa 43 | 4.5 2.3 1.3 0.3 Iris-setosa 44 | 4.4 3.2 1.3 0.2 Iris-setosa 45 | 5.0 3.5 1.6 0.6 Iris-setosa 46 | 5.1 3.8 1.9 0.4 Iris-setosa 47 | 4.8 3.0 1.4 0.3 Iris-setosa 48 | 5.1 3.8 1.6 0.2 Iris-setosa 49 | 4.6 3.2 1.4 0.2 Iris-setosa 50 | 5.3 3.7 1.5 0.2 Iris-setosa 51 | 5.0 3.3 1.4 0.2 Iris-setosa 52 | 7.0 3.2 4.7 1.4 Iris-versicolor 53 | 6.4 3.2 4.5 1.5 Iris-versicolor 54 | 6.9 3.1 4.9 1.5 Iris-versicolor 55 | 5.5 2.3 4.0 1.3 Iris-versicolor 56 | 6.5 2.8 4.6 1.5 Iris-versicolor 57 | 5.7 2.8 4.5 1.3 Iris-versicolor 58 | 6.3 3.3 4.7 1.6 Iris-versicolor 59 | 4.9 2.4 3.3 1.0 Iris-versicolor 60 | 6.6 2.9 4.6 1.3 Iris-versicolor 61 | 5.2 2.7 3.9 1.4 Iris-versicolor 62 | 5.0 2.0 3.5 1.0 Iris-versicolor 63 | 5.9 3.0 4.2 1.5 Iris-versicolor 64 | 6.0 2.2 4.0 1.0 Iris-versicolor 65 | 6.1 2.9 4.7 1.4 Iris-versicolor 66 | 5.6 2.9 3.6 1.3 Iris-versicolor 67 | 6.7 3.1 4.4 1.4 Iris-versicolor 68 | 5.6 3.0 4.5 1.5 Iris-versicolor 69 | 5.8 2.7 4.1 1.0 Iris-versicolor 70 | 6.2 2.2 4.5 1.5 Iris-versicolor 71 | 5.6 2.5 3.9 1.1 Iris-versicolor 72 | 5.9 3.2 4.8 1.8 Iris-versicolor 73 | 6.1 2.8 4.0 1.3 Iris-versicolor 74 | 6.3 2.5 4.9 1.5 Iris-versicolor 75 | 6.1 2.8 4.7 1.2 Iris-versicolor 76 | 6.4 2.9 4.3 1.3 Iris-versicolor 77 | 6.6 3.0 4.4 1.4 Iris-versicolor 78 | 6.8 2.8 4.8 1.4 Iris-versicolor 79 | 6.7 3.0 5.0 1.7 Iris-versicolor 80 | 6.0 2.9 4.5 1.5 Iris-versicolor 81 | 5.7 2.6 3.5 1.0 Iris-versicolor 82 | 5.5 2.4 3.8 1.1 Iris-versicolor 83 | 5.5 2.4 3.7 1.0 Iris-versicolor 84 | 5.8 2.7 3.9 1.2 Iris-versicolor 85 | 6.0 2.7 5.1 1.6 Iris-versicolor 86 | 5.4 3.0 4.5 1.5 Iris-versicolor 87 | 6.0 3.4 4.5 1.6 Iris-versicolor 88 | 6.7 3.1 4.7 1.5 Iris-versicolor 89 | 6.3 2.3 4.4 1.3 Iris-versicolor 90 | 5.6 3.0 4.1 1.3 Iris-versicolor 91 | 5.5 2.5 4.0 1.3 Iris-versicolor 92 | 5.5 2.6 4.4 1.2 Iris-versicolor 93 | 6.1 3.0 4.6 1.4 Iris-versicolor 94 | 5.8 2.6 4.0 1.2 Iris-versicolor 95 | 5.0 2.3 3.3 1.0 Iris-versicolor 96 | 5.6 2.7 4.2 1.3 Iris-versicolor 97 | 5.7 3.0 4.2 1.2 Iris-versicolor 98 | 5.7 2.9 4.2 1.3 Iris-versicolor 99 | 6.2 2.9 4.3 1.3 Iris-versicolor 100 | 5.1 2.5 3.0 1.1 Iris-versicolor 101 | 5.7 2.8 4.1 1.3 Iris-versicolor 102 | 6.3 3.3 6.0 2.5 Iris-virginica 103 | 5.8 2.7 5.1 1.9 Iris-virginica 104 | 7.1 3.0 5.9 2.1 Iris-virginica 105 | 6.3 2.9 5.6 1.8 Iris-virginica 106 | 6.5 3.0 5.8 2.2 Iris-virginica 107 | 7.6 3.0 6.6 2.1 Iris-virginica 108 | 4.9 2.5 4.5 1.7 Iris-virginica 109 | 7.3 2.9 6.3 1.8 Iris-virginica 110 | 6.7 2.5 5.8 1.8 Iris-virginica 111 | 7.2 3.6 6.1 2.5 Iris-virginica 112 | 6.5 3.2 5.1 2.0 Iris-virginica 113 | 6.4 2.7 5.3 1.9 Iris-virginica 114 | 6.8 3.0 5.5 2.1 Iris-virginica 115 | 5.7 2.5 5.0 2.0 Iris-virginica 116 | 5.8 2.8 5.1 2.4 Iris-virginica 117 | 6.4 3.2 5.3 2.3 Iris-virginica 118 | 6.5 3.0 5.5 1.8 Iris-virginica 119 | 7.7 3.8 6.7 2.2 Iris-virginica 120 | 7.7 2.6 6.9 2.3 Iris-virginica 121 | 6.0 2.2 5.0 1.5 Iris-virginica 122 | 6.9 3.2 5.7 2.3 Iris-virginica 123 | 5.6 2.8 4.9 2.0 Iris-virginica 124 | 7.7 2.8 6.7 2.0 Iris-virginica 125 | 6.3 2.7 4.9 1.8 Iris-virginica 126 | 6.7 3.3 5.7 2.1 Iris-virginica 127 | 7.2 3.2 6.0 1.8 Iris-virginica 128 | 6.2 2.8 4.8 1.8 Iris-virginica 129 | 6.1 3.0 4.9 1.8 Iris-virginica 130 | 6.4 2.8 5.6 2.1 Iris-virginica 131 | 7.2 3.0 5.8 1.6 Iris-virginica 132 | 7.4 2.8 6.1 1.9 Iris-virginica 133 | 7.9 3.8 6.4 2.0 Iris-virginica 134 | 6.4 2.8 5.6 2.2 Iris-virginica 135 | 6.3 2.8 5.1 1.5 Iris-virginica 136 | 6.1 2.6 5.6 1.4 Iris-virginica 137 | 7.7 3.0 6.1 2.3 Iris-virginica 138 | 6.3 3.4 5.6 2.4 Iris-virginica 139 | 6.4 3.1 5.5 1.8 Iris-virginica 140 | 6.0 3.0 4.8 1.8 Iris-virginica 141 | 6.9 3.1 5.4 2.1 Iris-virginica 142 | 6.7 3.1 5.6 2.4 Iris-virginica 143 | 6.9 3.1 5.1 2.3 Iris-virginica 144 | 5.8 2.7 5.1 1.9 Iris-virginica 145 | 6.8 3.2 5.9 2.3 Iris-virginica 146 | 6.7 3.3 5.7 2.5 Iris-virginica 147 | 6.7 3.0 5.2 2.3 Iris-virginica 148 | 6.3 2.5 5.0 1.9 Iris-virginica 149 | 6.5 3.0 5.2 2.0 Iris-virginica 150 | 6.2 3.4 5.4 2.3 Iris-virginica 151 | 5.9 3.0 5.1 1.8 Iris-virginica 152 | 153 | -------------------------------------------------------------------------------- /Data/titanic_data.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 4 | 3,1,3,"Heikkinen, Miss. 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