├── IntroToPythonForDataScience.ipynb
├── README.md
└── brics.csv


/IntroToPythonForDataScience.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Introduction to Python for Data Science\n",
   8 |     "\n",
   9 |     "This jupyter notebook is adapted from the [**FREE** Edx course](https://www.edx.org/course/introduction-python-data-science-microsoft-dat208x-6) of the same title. "
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 1,
  15 |    "metadata": {},
  16 |    "outputs": [
  17 |     {
  18 |      "data": {
  19 |       "text/html": [
  20 |        "<!-- This cell changes the alignment of Tables to the left --> \n",
  21 |        "<style>\n",
  22 |        "table {float:left}\n",
  23 |        "</style>"
  24 |       ],
  25 |       "text/plain": [
  26 |        "<IPython.core.display.HTML object>"
  27 |       ]
  28 |      },
  29 |      "metadata": {},
  30 |      "output_type": "display_data"
  31 |     }
  32 |    ],
  33 |    "source": [
  34 |     "%%html\n",
  35 |     "<!-- This cell changes the alignment of Tables to the left --> \n",
  36 |     "<style>\n",
  37 |     "table {float:left}\n",
  38 |     "</style>"
  39 |    ]
  40 |   },
  41 |   {
  42 |    "cell_type": "markdown",
  43 |    "metadata": {},
  44 |    "source": [
  45 |     "# 1.1. Hello Python\n",
  46 |     "\n",
  47 |     "## What you will learn\n",
  48 |     "- Python\n",
  49 |     "- Specifically for Data Science\n",
  50 |     "- Store data\n",
  51 |     "- Manipulate data\n",
  52 |     "- Tools for data analysis\n",
  53 |     "\n",
  54 |     "## Python\n",
  55 |     "- Guido Van Rossum\n",
  56 |     "- General Purpose: build anything\n",
  57 |     "- Open Source! Free!\n",
  58 |     "- Python Packages, also for Data Science\n",
  59 |     "- Many applications and fields\n",
  60 |     "- [Version 2.7 or 3.x](https://wiki.python.org/moin/Python2orPython3)"
  61 |    ]
  62 |   },
  63 |   {
  64 |    "cell_type": "markdown",
  65 |    "metadata": {},
  66 |    "source": [
  67 |     "# 1.2. Variables and Types\n",
  68 |     "\n",
  69 |     "- Specific, case-sensitive name\n",
  70 |     "- Call up value through variable name"
  71 |    ]
  72 |   },
  73 |   {
  74 |    "cell_type": "code",
  75 |    "execution_count": 2,
  76 |    "metadata": {},
  77 |    "outputs": [],
  78 |    "source": [
  79 |     "height = 1.79\n",
  80 |     "weight = 68.7"
  81 |    ]
  82 |   },
  83 |   {
  84 |    "cell_type": "code",
  85 |    "execution_count": 3,
  86 |    "metadata": {},
  87 |    "outputs": [
  88 |     {
  89 |      "data": {
  90 |       "text/plain": [
  91 |        "1.79"
  92 |       ]
  93 |      },
  94 |      "execution_count": 3,
  95 |      "metadata": {},
  96 |      "output_type": "execute_result"
  97 |     }
  98 |    ],
  99 |    "source": [
 100 |     "height"
 101 |    ]
 102 |   },
 103 |   {
 104 |    "cell_type": "markdown",
 105 |    "metadata": {},
 106 |    "source": [
 107 |     "## Calculate BMI\n",
 108 |     "\n",
 109 |     "- Definition: $ \\text{BMI} = \\frac{\\text{weight}}{\\text{height}^2} $"
 110 |    ]
 111 |   },
 112 |   {
 113 |    "cell_type": "code",
 114 |    "execution_count": 4,
 115 |    "metadata": {},
 116 |    "outputs": [
 117 |     {
 118 |      "name": "stdout",
 119 |      "output_type": "stream",
 120 |      "text": [
 121 |       "21.44127836209856\n"
 122 |      ]
 123 |     }
 124 |    ],
 125 |    "source": [
 126 |     "height = 1.79\n",
 127 |     "weight = 68.7\n",
 128 |     "bmi = weight / height ** 2\n",
 129 |     "print(bmi)"
 130 |    ]
 131 |   },
 132 |   {
 133 |    "cell_type": "code",
 134 |    "execution_count": 5,
 135 |    "metadata": {},
 136 |    "outputs": [
 137 |     {
 138 |      "name": "stdout",
 139 |      "output_type": "stream",
 140 |      "text": [
 141 |       "21.44127836209856\n"
 142 |      ]
 143 |     }
 144 |    ],
 145 |    "source": [
 146 |     "# changing height or weight does not alter bmi\n",
 147 |     "height = 1.34\n",
 148 |     "print(bmi)"
 149 |    ]
 150 |   },
 151 |   {
 152 |    "cell_type": "markdown",
 153 |    "metadata": {},
 154 |    "source": [
 155 |     "### Python Data Types\n",
 156 |     "\n",
 157 |     "- float - real numbers\n",
 158 |     "- int - integer numbers\n",
 159 |     "- str - string, text\n",
 160 |     "- bool - True, False"
 161 |    ]
 162 |   },
 163 |   {
 164 |    "cell_type": "code",
 165 |    "execution_count": 6,
 166 |    "metadata": {},
 167 |    "outputs": [
 168 |     {
 169 |      "data": {
 170 |       "text/plain": [
 171 |        "float"
 172 |       ]
 173 |      },
 174 |      "execution_count": 6,
 175 |      "metadata": {},
 176 |      "output_type": "execute_result"
 177 |     }
 178 |    ],
 179 |    "source": [
 180 |     "type(bmi)"
 181 |    ]
 182 |   },
 183 |   {
 184 |    "cell_type": "code",
 185 |    "execution_count": 7,
 186 |    "metadata": {},
 187 |    "outputs": [
 188 |     {
 189 |      "data": {
 190 |       "text/plain": [
 191 |        "int"
 192 |       ]
 193 |      },
 194 |      "execution_count": 7,
 195 |      "metadata": {},
 196 |      "output_type": "execute_result"
 197 |     }
 198 |    ],
 199 |    "source": [
 200 |     "day_of_week = 5\n",
 201 |     "type(day_of_week)"
 202 |    ]
 203 |   },
 204 |   {
 205 |    "cell_type": "code",
 206 |    "execution_count": 8,
 207 |    "metadata": {},
 208 |    "outputs": [
 209 |     {
 210 |      "data": {
 211 |       "text/plain": [
 212 |        "str"
 213 |       ]
 214 |      },
 215 |      "execution_count": 8,
 216 |      "metadata": {},
 217 |      "output_type": "execute_result"
 218 |     }
 219 |    ],
 220 |    "source": [
 221 |     "x = \"body mass index\"\n",
 222 |     "type(x)"
 223 |    ]
 224 |   },
 225 |   {
 226 |    "cell_type": "code",
 227 |    "execution_count": 9,
 228 |    "metadata": {},
 229 |    "outputs": [
 230 |     {
 231 |      "data": {
 232 |       "text/plain": [
 233 |        "str"
 234 |       ]
 235 |      },
 236 |      "execution_count": 9,
 237 |      "metadata": {},
 238 |      "output_type": "execute_result"
 239 |     }
 240 |    ],
 241 |    "source": [
 242 |     "y = 'this works too'\n",
 243 |     "type(y)"
 244 |    ]
 245 |   },
 246 |   {
 247 |    "cell_type": "code",
 248 |    "execution_count": 10,
 249 |    "metadata": {},
 250 |    "outputs": [
 251 |     {
 252 |      "data": {
 253 |       "text/plain": [
 254 |        "bool"
 255 |       ]
 256 |      },
 257 |      "execution_count": 10,
 258 |      "metadata": {},
 259 |      "output_type": "execute_result"
 260 |     }
 261 |    ],
 262 |    "source": [
 263 |     "z = True\n",
 264 |     "type(z)"
 265 |    ]
 266 |   },
 267 |   {
 268 |    "cell_type": "code",
 269 |    "execution_count": 11,
 270 |    "metadata": {},
 271 |    "outputs": [
 272 |     {
 273 |      "name": "stdout",
 274 |      "output_type": "stream",
 275 |      "text": [
 276 |       "5\n",
 277 |       "abcd\n"
 278 |      ]
 279 |     }
 280 |    ],
 281 |    "source": [
 282 |     "# Different type = different behavior!\n",
 283 |     "print(2 + 3)\n",
 284 |     "print('ab' + 'cd')"
 285 |    ]
 286 |   },
 287 |   {
 288 |    "cell_type": "markdown",
 289 |    "metadata": {},
 290 |    "source": [
 291 |     "# 2.1. Python `List`\n",
 292 |     "\n",
 293 |     "## Python Data Types\n",
 294 |     "- `float` - real numbers\n",
 295 |     "- `int` - integer numbers\n",
 296 |     "- `str` - string, text\n",
 297 |     "- `bool` - True, False\n",
 298 |     "- Each variable represents ***single*** value\n",
 299 |     "\n",
 300 |     "## Problem\n",
 301 |     "- Data Science: many data points\n",
 302 |     "- Height of entire family\n",
 303 |     "```Python\n",
 304 |     "In [3]: height1 = 1.73\n",
 305 |     "In [4]: height2 = 1.68\n",
 306 |     "In [5]: height3 = 1.71\n",
 307 |     "In [6]: height4 = 1.89\n",
 308 |     "```\n",
 309 |     "- Inconvenient\n",
 310 |     "\n",
 311 |     "## Python `List`\n",
 312 |     "- Name a collection of values\n",
 313 |     "- Contain any type\n",
 314 |     "- Contain different types"
 315 |    ]
 316 |   },
 317 |   {
 318 |    "cell_type": "code",
 319 |    "execution_count": 12,
 320 |    "metadata": {},
 321 |    "outputs": [
 322 |     {
 323 |      "data": {
 324 |       "text/plain": [
 325 |        "list"
 326 |       ]
 327 |      },
 328 |      "execution_count": 12,
 329 |      "metadata": {},
 330 |      "output_type": "execute_result"
 331 |     }
 332 |    ],
 333 |    "source": [
 334 |     "# Basic list\n",
 335 |     "fam = [1.73, 1.68, 1.71, 1.89]\n",
 336 |     "type(fam)"
 337 |    ]
 338 |   },
 339 |   {
 340 |    "cell_type": "code",
 341 |    "execution_count": 13,
 342 |    "metadata": {},
 343 |    "outputs": [
 344 |     {
 345 |      "data": {
 346 |       "text/plain": [
 347 |        "list"
 348 |       ]
 349 |      },
 350 |      "execution_count": 13,
 351 |      "metadata": {},
 352 |      "output_type": "execute_result"
 353 |     }
 354 |    ],
 355 |    "source": [
 356 |     "# List with multiple types\n",
 357 |     "fam = [\"liz\", 1.73, \"emma\", 1.68, \"mom\", 1.71, \"dad\", 1.89]\n",
 358 |     "type(fam)"
 359 |    ]
 360 |   },
 361 |   {
 362 |    "cell_type": "code",
 363 |    "execution_count": 14,
 364 |    "metadata": {},
 365 |    "outputs": [
 366 |     {
 367 |      "data": {
 368 |       "text/plain": [
 369 |        "list"
 370 |       ]
 371 |      },
 372 |      "execution_count": 14,
 373 |      "metadata": {},
 374 |      "output_type": "execute_result"
 375 |     }
 376 |    ],
 377 |    "source": [
 378 |     "# List of lists\n",
 379 |     "fam2 = [[\"liz\", 1.73], [\"emma\", 1.68], [\"mom\", 1.71], [\"dad\", 1.89]]\n",
 380 |     "type(fam)"
 381 |    ]
 382 |   },
 383 |   {
 384 |    "cell_type": "markdown",
 385 |    "metadata": {},
 386 |    "source": [
 387 |     "# 2.2. Subsetting lists\n",
 388 |     "\n",
 389 |     "Zero-based indexing: \n",
 390 |     "\n",
 391 |     "- $0, 1, 2,\\dots, N-2, N-1$\n",
 392 |     "- $-(N-1), -(N-2), \\dots , -2, -1$\n",
 393 |     "    "
 394 |    ]
 395 |   },
 396 |   {
 397 |    "cell_type": "code",
 398 |    "execution_count": 15,
 399 |    "metadata": {},
 400 |    "outputs": [
 401 |     {
 402 |      "data": {
 403 |       "text/plain": [
 404 |        "1.68"
 405 |       ]
 406 |      },
 407 |      "execution_count": 15,
 408 |      "metadata": {},
 409 |      "output_type": "execute_result"
 410 |     }
 411 |    ],
 412 |    "source": [
 413 |     "fam[3]"
 414 |    ]
 415 |   },
 416 |   {
 417 |    "cell_type": "code",
 418 |    "execution_count": 16,
 419 |    "metadata": {},
 420 |    "outputs": [
 421 |     {
 422 |      "data": {
 423 |       "text/plain": [
 424 |        "'dad'"
 425 |       ]
 426 |      },
 427 |      "execution_count": 16,
 428 |      "metadata": {},
 429 |      "output_type": "execute_result"
 430 |     }
 431 |    ],
 432 |    "source": [
 433 |     "fam[6]"
 434 |    ]
 435 |   },
 436 |   {
 437 |    "cell_type": "code",
 438 |    "execution_count": 17,
 439 |    "metadata": {},
 440 |    "outputs": [
 441 |     {
 442 |      "data": {
 443 |       "text/plain": [
 444 |        "1.89"
 445 |       ]
 446 |      },
 447 |      "execution_count": 17,
 448 |      "metadata": {},
 449 |      "output_type": "execute_result"
 450 |     }
 451 |    ],
 452 |    "source": [
 453 |     "fam[-1]"
 454 |    ]
 455 |   },
 456 |   {
 457 |    "cell_type": "code",
 458 |    "execution_count": 18,
 459 |    "metadata": {},
 460 |    "outputs": [
 461 |     {
 462 |      "data": {
 463 |       "text/plain": [
 464 |        "'dad'"
 465 |       ]
 466 |      },
 467 |      "execution_count": 18,
 468 |      "metadata": {},
 469 |      "output_type": "execute_result"
 470 |     }
 471 |    ],
 472 |    "source": [
 473 |     "fam[-2]"
 474 |    ]
 475 |   },
 476 |   {
 477 |    "cell_type": "markdown",
 478 |    "metadata": {},
 479 |    "source": [
 480 |     "## List slicing \n",
 481 |     "\n",
 482 |     "[ start (inclusive) : end (exclusive) ]"
 483 |    ]
 484 |   },
 485 |   {
 486 |    "cell_type": "code",
 487 |    "execution_count": 19,
 488 |    "metadata": {},
 489 |    "outputs": [
 490 |     {
 491 |      "data": {
 492 |       "text/plain": [
 493 |        "[1.68, 'mom']"
 494 |       ]
 495 |      },
 496 |      "execution_count": 19,
 497 |      "metadata": {},
 498 |      "output_type": "execute_result"
 499 |     }
 500 |    ],
 501 |    "source": [
 502 |     "fam[3:5]"
 503 |    ]
 504 |   },
 505 |   {
 506 |    "cell_type": "code",
 507 |    "execution_count": 20,
 508 |    "metadata": {},
 509 |    "outputs": [
 510 |     {
 511 |      "data": {
 512 |       "text/plain": [
 513 |        "[1.73, 'emma', 1.68]"
 514 |       ]
 515 |      },
 516 |      "execution_count": 20,
 517 |      "metadata": {},
 518 |      "output_type": "execute_result"
 519 |     }
 520 |    ],
 521 |    "source": [
 522 |     "fam[1:4]"
 523 |    ]
 524 |   },
 525 |   {
 526 |    "cell_type": "code",
 527 |    "execution_count": 21,
 528 |    "metadata": {},
 529 |    "outputs": [
 530 |     {
 531 |      "data": {
 532 |       "text/plain": [
 533 |        "['liz', 1.73, 'emma', 1.68]"
 534 |       ]
 535 |      },
 536 |      "execution_count": 21,
 537 |      "metadata": {},
 538 |      "output_type": "execute_result"
 539 |     }
 540 |    ],
 541 |    "source": [
 542 |     "fam[:4]"
 543 |    ]
 544 |   },
 545 |   {
 546 |    "cell_type": "code",
 547 |    "execution_count": 22,
 548 |    "metadata": {},
 549 |    "outputs": [
 550 |     {
 551 |      "data": {
 552 |       "text/plain": [
 553 |        "[1.71, 'dad', 1.89]"
 554 |       ]
 555 |      },
 556 |      "execution_count": 22,
 557 |      "metadata": {},
 558 |      "output_type": "execute_result"
 559 |     }
 560 |    ],
 561 |    "source": [
 562 |     "fam[5:]"
 563 |    ]
 564 |   },
 565 |   {
 566 |    "cell_type": "markdown",
 567 |    "metadata": {},
 568 |    "source": [
 569 |     "# 2.3. Manipulating Lists\n",
 570 |     "\n",
 571 |     "- Change list elements\n",
 572 |     "- Add list elements\n",
 573 |     "- Remove list elements\n",
 574 |     "\n",
 575 |     "## Changing list elements\n"
 576 |    ]
 577 |   },
 578 |   {
 579 |    "cell_type": "code",
 580 |    "execution_count": 23,
 581 |    "metadata": {
 582 |     "collapsed": true
 583 |    },
 584 |    "outputs": [],
 585 |    "source": [
 586 |     "fam[7] = 1.86"
 587 |    ]
 588 |   },
 589 |   {
 590 |    "cell_type": "code",
 591 |    "execution_count": 24,
 592 |    "metadata": {},
 593 |    "outputs": [
 594 |     {
 595 |      "data": {
 596 |       "text/plain": [
 597 |        "['liz', 1.73, 'emma', 1.68, 'mom', 1.71, 'dad', 1.86]"
 598 |       ]
 599 |      },
 600 |      "execution_count": 24,
 601 |      "metadata": {},
 602 |      "output_type": "execute_result"
 603 |     }
 604 |    ],
 605 |    "source": [
 606 |     "fam"
 607 |    ]
 608 |   },
 609 |   {
 610 |    "cell_type": "code",
 611 |    "execution_count": 25,
 612 |    "metadata": {
 613 |     "collapsed": true
 614 |    },
 615 |    "outputs": [],
 616 |    "source": [
 617 |     "fam[0:2] = [\"lisa\", 1.74]"
 618 |    ]
 619 |   },
 620 |   {
 621 |    "cell_type": "code",
 622 |    "execution_count": 26,
 623 |    "metadata": {},
 624 |    "outputs": [
 625 |     {
 626 |      "data": {
 627 |       "text/plain": [
 628 |        "['lisa', 1.74, 'emma', 1.68, 'mom', 1.71, 'dad', 1.86]"
 629 |       ]
 630 |      },
 631 |      "execution_count": 26,
 632 |      "metadata": {},
 633 |      "output_type": "execute_result"
 634 |     }
 635 |    ],
 636 |    "source": [
 637 |     "fam"
 638 |    ]
 639 |   },
 640 |   {
 641 |    "cell_type": "markdown",
 642 |    "metadata": {},
 643 |    "source": [
 644 |     "## Adding and removing elements"
 645 |    ]
 646 |   },
 647 |   {
 648 |    "cell_type": "code",
 649 |    "execution_count": 27,
 650 |    "metadata": {},
 651 |    "outputs": [
 652 |     {
 653 |      "data": {
 654 |       "text/plain": [
 655 |        "['lisa', 1.74, 'emma', 1.68, 'mom', 1.71, 'dad', 1.86, 'me', 1.79]"
 656 |       ]
 657 |      },
 658 |      "execution_count": 27,
 659 |      "metadata": {},
 660 |      "output_type": "execute_result"
 661 |     }
 662 |    ],
 663 |    "source": [
 664 |     "fam_ext = fam + [\"me\", 1.79]\n",
 665 |     "fam_ext"
 666 |    ]
 667 |   },
 668 |   {
 669 |    "cell_type": "code",
 670 |    "execution_count": 28,
 671 |    "metadata": {},
 672 |    "outputs": [
 673 |     {
 674 |      "data": {
 675 |       "text/plain": [
 676 |        "['lisa', 1.74, 1.68, 'mom', 1.71, 'dad', 1.86]"
 677 |       ]
 678 |      },
 679 |      "execution_count": 28,
 680 |      "metadata": {},
 681 |      "output_type": "execute_result"
 682 |     }
 683 |    ],
 684 |    "source": [
 685 |     "del(fam[2])\n",
 686 |     "fam"
 687 |    ]
 688 |   },
 689 |   {
 690 |    "cell_type": "code",
 691 |    "execution_count": 29,
 692 |    "metadata": {},
 693 |    "outputs": [
 694 |     {
 695 |      "data": {
 696 |       "text/plain": [
 697 |        "['lisa', 1.74, 'mom', 1.71, 'dad', 1.86]"
 698 |       ]
 699 |      },
 700 |      "execution_count": 29,
 701 |      "metadata": {},
 702 |      "output_type": "execute_result"
 703 |     }
 704 |    ],
 705 |    "source": [
 706 |     "del(fam[2])\n",
 707 |     "fam"
 708 |    ]
 709 |   },
 710 |   {
 711 |    "cell_type": "markdown",
 712 |    "metadata": {},
 713 |    "source": [
 714 |     "## Copying Lists\n",
 715 |     "\n",
 716 |     "- copying by reference\n",
 717 |     "- copying by value "
 718 |    ]
 719 |   },
 720 |   {
 721 |    "cell_type": "code",
 722 |    "execution_count": 30,
 723 |    "metadata": {},
 724 |    "outputs": [
 725 |     {
 726 |      "name": "stdout",
 727 |      "output_type": "stream",
 728 |      "text": [
 729 |       "['a', 'z', 'c']\n",
 730 |       "['a', 'z', 'c']\n"
 731 |      ]
 732 |     }
 733 |    ],
 734 |    "source": [
 735 |     "# Copy by reference (change the copy, change the original)\n",
 736 |     "x = [\"a\", \"b\", \"c\"]\n",
 737 |     "y = x\n",
 738 |     "y[1] = \"z\"\n",
 739 |     "print(x)\n",
 740 |     "print(y)"
 741 |    ]
 742 |   },
 743 |   {
 744 |    "cell_type": "code",
 745 |    "execution_count": 31,
 746 |    "metadata": {},
 747 |    "outputs": [
 748 |     {
 749 |      "name": "stdout",
 750 |      "output_type": "stream",
 751 |      "text": [
 752 |       "['a', 'b', 'c']\n",
 753 |       "['a', 'z', 'c']\n"
 754 |      ]
 755 |     }
 756 |    ],
 757 |    "source": [
 758 |     "# Copy by value (change the copy, no change to the original)\n",
 759 |     "x = [\"a\", \"b\", \"c\"]\n",
 760 |     "y = x[:] # or y = list(x)\n",
 761 |     "y[1] = \"z\"\n",
 762 |     "print(x)\n",
 763 |     "print(y)"
 764 |    ]
 765 |   },
 766 |   {
 767 |    "cell_type": "markdown",
 768 |    "metadata": {},
 769 |    "source": [
 770 |     "# 3.1. Functions\n",
 771 |     "\n",
 772 |     "- Nothing new!\n",
 773 |     "- `type()`\n",
 774 |     "- Piece of reusable code\n",
 775 |     "- Solves particular task\n",
 776 |     "- Call function instead of writing code yourself\n",
 777 |     "\n",
 778 |     "## Example 1: max( ) function\n",
 779 |     "\n",
 780 |     "Find the largest element in a list."
 781 |    ]
 782 |   },
 783 |   {
 784 |    "cell_type": "code",
 785 |    "execution_count": 32,
 786 |    "metadata": {},
 787 |    "outputs": [
 788 |     {
 789 |      "name": "stdout",
 790 |      "output_type": "stream",
 791 |      "text": [
 792 |       "1.89\n"
 793 |      ]
 794 |     }
 795 |    ],
 796 |    "source": [
 797 |     "fam = [1.73, 1.68, 1.71, 1.89]\n",
 798 |     "tallest = max(fam)\n",
 799 |     "print(tallest)"
 800 |    ]
 801 |   },
 802 |   {
 803 |    "cell_type": "markdown",
 804 |    "metadata": {},
 805 |    "source": [
 806 |     "## Example 2: round( ) function\n",
 807 |     "\n",
 808 |     "Round floating point number."
 809 |    ]
 810 |   },
 811 |   {
 812 |    "cell_type": "code",
 813 |    "execution_count": 33,
 814 |    "metadata": {},
 815 |    "outputs": [
 816 |     {
 817 |      "name": "stdout",
 818 |      "output_type": "stream",
 819 |      "text": [
 820 |       "Help on built-in function round in module builtins:\n",
 821 |       "\n",
 822 |       "round(...)\n",
 823 |       "    round(number[, ndigits]) -> number\n",
 824 |       "    \n",
 825 |       "    Round a number to a given precision in decimal digits (default 0 digits).\n",
 826 |       "    This returns an int when called with one argument, otherwise the\n",
 827 |       "    same type as the number. ndigits may be negative.\n",
 828 |       "\n"
 829 |      ]
 830 |     }
 831 |    ],
 832 |    "source": [
 833 |     "help(round)"
 834 |    ]
 835 |   },
 836 |   {
 837 |    "cell_type": "code",
 838 |    "execution_count": 34,
 839 |    "metadata": {},
 840 |    "outputs": [
 841 |     {
 842 |      "data": {
 843 |       "text/plain": [
 844 |        "1.7"
 845 |       ]
 846 |      },
 847 |      "execution_count": 34,
 848 |      "metadata": {},
 849 |      "output_type": "execute_result"
 850 |     }
 851 |    ],
 852 |    "source": [
 853 |     "round(1.68,1)"
 854 |    ]
 855 |   },
 856 |   {
 857 |    "cell_type": "code",
 858 |    "execution_count": 35,
 859 |    "metadata": {},
 860 |    "outputs": [
 861 |     {
 862 |      "data": {
 863 |       "text/plain": [
 864 |        "2"
 865 |       ]
 866 |      },
 867 |      "execution_count": 35,
 868 |      "metadata": {},
 869 |      "output_type": "execute_result"
 870 |     }
 871 |    ],
 872 |    "source": [
 873 |     "round(1.68)"
 874 |    ]
 875 |   },
 876 |   {
 877 |    "cell_type": "markdown",
 878 |    "metadata": {},
 879 |    "source": [
 880 |     "## Finding functions\n",
 881 |     "\n",
 882 |     "- Standard task $\\rightarrow$ probably function exists!\n",
 883 |     "- The internet is your friend (google it!)"
 884 |    ]
 885 |   },
 886 |   {
 887 |    "cell_type": "markdown",
 888 |    "metadata": {},
 889 |    "source": [
 890 |     "# 3.2 Methods: Functions that belong to objects\n",
 891 |     "\n",
 892 |     "- `str` : capitalize(), replace(), etc.\n",
 893 |     "- `float` : bit_length(), conjugate(), etc.\n",
 894 |     "- `list` : intex(), count(), etc.\n",
 895 |     "\n",
 896 |     "## `list` Methods"
 897 |    ]
 898 |   },
 899 |   {
 900 |    "cell_type": "code",
 901 |    "execution_count": 36,
 902 |    "metadata": {},
 903 |    "outputs": [
 904 |     {
 905 |      "data": {
 906 |       "text/plain": [
 907 |        "4"
 908 |       ]
 909 |      },
 910 |      "execution_count": 36,
 911 |      "metadata": {},
 912 |      "output_type": "execute_result"
 913 |     }
 914 |    ],
 915 |    "source": [
 916 |     "fam = ['liz', 1.73, 'emma', 1.68, 'mom', 1.71, 'dad', 1.89]\n",
 917 |     "fam.index(\"mom\")"
 918 |    ]
 919 |   },
 920 |   {
 921 |    "cell_type": "code",
 922 |    "execution_count": 37,
 923 |    "metadata": {},
 924 |    "outputs": [
 925 |     {
 926 |      "data": {
 927 |       "text/plain": [
 928 |        "1"
 929 |       ]
 930 |      },
 931 |      "execution_count": 37,
 932 |      "metadata": {},
 933 |      "output_type": "execute_result"
 934 |     }
 935 |    ],
 936 |    "source": [
 937 |     "fam.count(1.73)"
 938 |    ]
 939 |   },
 940 |   {
 941 |    "cell_type": "markdown",
 942 |    "metadata": {},
 943 |    "source": [
 944 |     "## `str` Methods"
 945 |    ]
 946 |   },
 947 |   {
 948 |    "cell_type": "code",
 949 |    "execution_count": 38,
 950 |    "metadata": {},
 951 |    "outputs": [
 952 |     {
 953 |      "data": {
 954 |       "text/plain": [
 955 |        "'liz'"
 956 |       ]
 957 |      },
 958 |      "execution_count": 38,
 959 |      "metadata": {},
 960 |      "output_type": "execute_result"
 961 |     }
 962 |    ],
 963 |    "source": [
 964 |     "sister = \"liz\"\n",
 965 |     "sister"
 966 |    ]
 967 |   },
 968 |   {
 969 |    "cell_type": "code",
 970 |    "execution_count": 39,
 971 |    "metadata": {},
 972 |    "outputs": [
 973 |     {
 974 |      "data": {
 975 |       "text/plain": [
 976 |        "'Liz'"
 977 |       ]
 978 |      },
 979 |      "execution_count": 39,
 980 |      "metadata": {},
 981 |      "output_type": "execute_result"
 982 |     }
 983 |    ],
 984 |    "source": [
 985 |     "sister.capitalize()"
 986 |    ]
 987 |   },
 988 |   {
 989 |    "cell_type": "code",
 990 |    "execution_count": 40,
 991 |    "metadata": {},
 992 |    "outputs": [
 993 |     {
 994 |      "data": {
 995 |       "text/plain": [
 996 |        "'lisa'"
 997 |       ]
 998 |      },
 999 |      "execution_count": 40,
1000 |      "metadata": {},
1001 |      "output_type": "execute_result"
1002 |     }
1003 |    ],
1004 |    "source": [
1005 |     "sister.replace(\"z\", \"sa\")"
1006 |    ]
1007 |   },
1008 |   {
1009 |    "cell_type": "markdown",
1010 |    "metadata": {},
1011 |    "source": [
1012 |     "## Methods\n",
1013 |     "- Everything = object\n",
1014 |     "- Object have methods associated, depending on type"
1015 |    ]
1016 |   },
1017 |   {
1018 |    "cell_type": "code",
1019 |    "execution_count": 41,
1020 |    "metadata": {},
1021 |    "outputs": [
1022 |     {
1023 |      "ename": "AttributeError",
1024 |      "evalue": "'list' object has no attribute 'replace'",
1025 |      "output_type": "error",
1026 |      "traceback": [
1027 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1028 |       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
1029 |       "\u001b[0;32m<ipython-input-41-72a9ff411a84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mom\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"mommy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1030 |       "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'replace'"
1031 |      ]
1032 |     }
1033 |    ],
1034 |    "source": [
1035 |     "fam.replace(\"mom\", \"mommy\")"
1036 |    ]
1037 |   },
1038 |   {
1039 |    "cell_type": "code",
1040 |    "execution_count": 42,
1041 |    "metadata": {},
1042 |    "outputs": [
1043 |     {
1044 |      "data": {
1045 |       "text/plain": [
1046 |        "2"
1047 |       ]
1048 |      },
1049 |      "execution_count": 42,
1050 |      "metadata": {},
1051 |      "output_type": "execute_result"
1052 |     }
1053 |    ],
1054 |    "source": [
1055 |     "sister.index(\"z\")"
1056 |    ]
1057 |   },
1058 |   {
1059 |    "cell_type": "code",
1060 |    "execution_count": 43,
1061 |    "metadata": {},
1062 |    "outputs": [
1063 |     {
1064 |      "data": {
1065 |       "text/plain": [
1066 |        "['liz', 1.73, 'emma', 1.68, 'mom', 1.71, 'dad', 1.89, 'me']"
1067 |       ]
1068 |      },
1069 |      "execution_count": 43,
1070 |      "metadata": {},
1071 |      "output_type": "execute_result"
1072 |     }
1073 |    ],
1074 |    "source": [
1075 |     "fam.append(\"me\")\n",
1076 |     "fam"
1077 |    ]
1078 |   },
1079 |   {
1080 |    "cell_type": "code",
1081 |    "execution_count": 44,
1082 |    "metadata": {},
1083 |    "outputs": [
1084 |     {
1085 |      "data": {
1086 |       "text/plain": [
1087 |        "['liz', 1.73, 'emma', 1.68, 'mom', 1.71, 'dad', 1.89, 'me', '1.79']"
1088 |       ]
1089 |      },
1090 |      "execution_count": 44,
1091 |      "metadata": {},
1092 |      "output_type": "execute_result"
1093 |     }
1094 |    ],
1095 |    "source": [
1096 |     "fam.append(\"1.79\")\n",
1097 |     "fam"
1098 |    ]
1099 |   },
1100 |   {
1101 |    "cell_type": "markdown",
1102 |    "metadata": {},
1103 |    "source": [
1104 |     "## Summary\n",
1105 |     "- Functions\n",
1106 |     "    - `type(fam)`\n",
1107 |     "- Methods: call functions on objects\n",
1108 |     "    - `fam.index(\"dad\")`"
1109 |    ]
1110 |   },
1111 |   {
1112 |    "cell_type": "markdown",
1113 |    "metadata": {},
1114 |    "source": [
1115 |     "# 3.3 Packages\n",
1116 |     "\n",
1117 |     "## Motivation\n",
1118 |     "\n",
1119 |     "- Functions and methods are powerful\n",
1120 |     "- All code in Python distribution?\n",
1121 |     "     - Huge code base: messy\n",
1122 |     "     - Lots of code you won’t use\n",
1123 |     "     - Maintenance problem\n",
1124 |     "\n",
1125 |     "## Packages\n",
1126 |     "\n",
1127 |     "- Directory of Python Scripts\n",
1128 |     "- Each script = module\n",
1129 |     "- Specify functions, methods, types\n",
1130 |     "- Thousands of packages available\n",
1131 |     "    - Numpy\n",
1132 |     "    - Matplotlib\n",
1133 |     "    - Scikit-learn\n",
1134 |     "\n",
1135 |     "## Install package\n",
1136 |     "- http://pip.readthedocs.org/en/stable/installing/\n",
1137 |     "- Download get-pip.py\n",
1138 |     "- Terminal:\n",
1139 |     "    - python get-pip.py\n",
1140 |     "    - pip install numpy\n",
1141 |     "    \n",
1142 |     "## Import package"
1143 |    ]
1144 |   },
1145 |   {
1146 |    "cell_type": "code",
1147 |    "execution_count": 45,
1148 |    "metadata": {},
1149 |    "outputs": [
1150 |     {
1151 |      "ename": "NameError",
1152 |      "evalue": "name 'array' is not defined",
1153 |      "output_type": "error",
1154 |      "traceback": [
1155 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1156 |       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
1157 |       "\u001b[0;32m<ipython-input-45-20f729fdc61f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1158 |       "\u001b[0;31mNameError\u001b[0m: name 'array' is not defined"
1159 |      ]
1160 |     }
1161 |    ],
1162 |    "source": [
1163 |     "import numpy\n",
1164 |     "array([1, 2, 3])"
1165 |    ]
1166 |   },
1167 |   {
1168 |    "cell_type": "code",
1169 |    "execution_count": 46,
1170 |    "metadata": {},
1171 |    "outputs": [
1172 |     {
1173 |      "data": {
1174 |       "text/plain": [
1175 |        "array([1, 2, 3])"
1176 |       ]
1177 |      },
1178 |      "execution_count": 46,
1179 |      "metadata": {},
1180 |      "output_type": "execute_result"
1181 |     }
1182 |    ],
1183 |    "source": [
1184 |     "numpy.array([1, 2, 3])"
1185 |    ]
1186 |   },
1187 |   {
1188 |    "cell_type": "code",
1189 |    "execution_count": 47,
1190 |    "metadata": {},
1191 |    "outputs": [
1192 |     {
1193 |      "data": {
1194 |       "text/plain": [
1195 |        "array([1, 2, 3])"
1196 |       ]
1197 |      },
1198 |      "execution_count": 47,
1199 |      "metadata": {},
1200 |      "output_type": "execute_result"
1201 |     }
1202 |    ],
1203 |    "source": [
1204 |     "import numpy as np\n",
1205 |     "np.array([1, 2, 3])"
1206 |    ]
1207 |   },
1208 |   {
1209 |    "cell_type": "code",
1210 |    "execution_count": 48,
1211 |    "metadata": {},
1212 |    "outputs": [
1213 |     {
1214 |      "data": {
1215 |       "text/plain": [
1216 |        "array([1, 2, 3])"
1217 |       ]
1218 |      },
1219 |      "execution_count": 48,
1220 |      "metadata": {},
1221 |      "output_type": "execute_result"
1222 |     }
1223 |    ],
1224 |    "source": [
1225 |     "from numpy import array\n",
1226 |     "array([1, 2, 3])"
1227 |    ]
1228 |   },
1229 |   {
1230 |    "cell_type": "markdown",
1231 |    "metadata": {},
1232 |    "source": [
1233 |     "# 4.1 Numpy\n",
1234 |     "\n",
1235 |     "## Lists Recap\n",
1236 |     "- Powerful\n",
1237 |     "- Collection of values\n",
1238 |     "- Hold different types\n",
1239 |     "- Change, add, remove\n",
1240 |     "- Need for Data Science\n",
1241 |     "    - Mathematical operations over collections\n",
1242 |     "    - Speed\n",
1243 |     "\n",
1244 |     "## Illustration"
1245 |    ]
1246 |   },
1247 |   {
1248 |    "cell_type": "code",
1249 |    "execution_count": 49,
1250 |    "metadata": {},
1251 |    "outputs": [
1252 |     {
1253 |      "data": {
1254 |       "text/plain": [
1255 |        "[1.73, 1.68, 1.71, 1.89, 1.79]"
1256 |       ]
1257 |      },
1258 |      "execution_count": 49,
1259 |      "metadata": {},
1260 |      "output_type": "execute_result"
1261 |     }
1262 |    ],
1263 |    "source": [
1264 |     "height = [1.73, 1.68, 1.71, 1.89, 1.79]\n",
1265 |     "height"
1266 |    ]
1267 |   },
1268 |   {
1269 |    "cell_type": "code",
1270 |    "execution_count": 50,
1271 |    "metadata": {},
1272 |    "outputs": [
1273 |     {
1274 |      "data": {
1275 |       "text/plain": [
1276 |        "[65.4, 59.2, 63.6, 88.4, 68.7]"
1277 |       ]
1278 |      },
1279 |      "execution_count": 50,
1280 |      "metadata": {},
1281 |      "output_type": "execute_result"
1282 |     }
1283 |    ],
1284 |    "source": [
1285 |     "weight = [65.4, 59.2, 63.6, 88.4, 68.7]\n",
1286 |     "weight"
1287 |    ]
1288 |   },
1289 |   {
1290 |    "cell_type": "code",
1291 |    "execution_count": 51,
1292 |    "metadata": {},
1293 |    "outputs": [
1294 |     {
1295 |      "ename": "TypeError",
1296 |      "evalue": "unsupported operand type(s) for ** or pow(): 'list' and 'int'",
1297 |      "output_type": "error",
1298 |      "traceback": [
1299 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1300 |       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
1301 |       "\u001b[0;32m<ipython-input-51-6ab7ce913d0a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mweight\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1302 |       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for ** or pow(): 'list' and 'int'"
1303 |      ]
1304 |     }
1305 |    ],
1306 |    "source": [
1307 |     "weight / height ** 2"
1308 |    ]
1309 |   },
1310 |   {
1311 |    "cell_type": "markdown",
1312 |    "metadata": {},
1313 |    "source": [
1314 |     "## Solution: Numpy\n",
1315 |     "- Numeric Python\n",
1316 |     "- Alternative to Python List: Numpy Array\n",
1317 |     "- Calculations over entire arrays\n",
1318 |     "- Easy and Fast\n",
1319 |     "- Installation\n",
1320 |     "     - In the terminal: pip3 install numpy"
1321 |    ]
1322 |   },
1323 |   {
1324 |    "cell_type": "code",
1325 |    "execution_count": 52,
1326 |    "metadata": {},
1327 |    "outputs": [
1328 |     {
1329 |      "data": {
1330 |       "text/plain": [
1331 |        "array([ 1.73,  1.68,  1.71,  1.89,  1.79])"
1332 |       ]
1333 |      },
1334 |      "execution_count": 52,
1335 |      "metadata": {},
1336 |      "output_type": "execute_result"
1337 |     }
1338 |    ],
1339 |    "source": [
1340 |     "import numpy as np\n",
1341 |     "np_height = np.array(height)\n",
1342 |     "np_height"
1343 |    ]
1344 |   },
1345 |   {
1346 |    "cell_type": "code",
1347 |    "execution_count": 53,
1348 |    "metadata": {},
1349 |    "outputs": [
1350 |     {
1351 |      "data": {
1352 |       "text/plain": [
1353 |        "array([ 65.4,  59.2,  63.6,  88.4,  68.7])"
1354 |       ]
1355 |      },
1356 |      "execution_count": 53,
1357 |      "metadata": {},
1358 |      "output_type": "execute_result"
1359 |     }
1360 |    ],
1361 |    "source": [
1362 |     "np_weight = np.array(weight)\n",
1363 |     "np_weight"
1364 |    ]
1365 |   },
1366 |   {
1367 |    "cell_type": "code",
1368 |    "execution_count": 54,
1369 |    "metadata": {},
1370 |    "outputs": [
1371 |     {
1372 |      "data": {
1373 |       "text/plain": [
1374 |        "array([ 21.85171573,  20.97505669,  21.75028214,  24.7473475 ,  21.44127836])"
1375 |       ]
1376 |      },
1377 |      "execution_count": 54,
1378 |      "metadata": {},
1379 |      "output_type": "execute_result"
1380 |     }
1381 |    ],
1382 |    "source": [
1383 |     "# Element-wise calculations\n",
1384 |     "bmi = np_weight / np_height ** 2\n",
1385 |     "bmi"
1386 |    ]
1387 |   },
1388 |   {
1389 |    "cell_type": "markdown",
1390 |    "metadata": {},
1391 |    "source": [
1392 |     "## Numpy: remarks\n",
1393 |     "- Numpy arrays: contain only one type\n",
1394 |     "- Different types: different behavior!"
1395 |    ]
1396 |   },
1397 |   {
1398 |    "cell_type": "code",
1399 |    "execution_count": 55,
1400 |    "metadata": {},
1401 |    "outputs": [
1402 |     {
1403 |      "data": {
1404 |       "text/plain": [
1405 |        "array(['1.0', 'is', 'True'], \n",
1406 |        "      dtype='<U32')"
1407 |       ]
1408 |      },
1409 |      "execution_count": 55,
1410 |      "metadata": {},
1411 |      "output_type": "execute_result"
1412 |     }
1413 |    ],
1414 |    "source": [
1415 |     "np.array([1.0, \"is\", True])"
1416 |    ]
1417 |   },
1418 |   {
1419 |    "cell_type": "code",
1420 |    "execution_count": 56,
1421 |    "metadata": {},
1422 |    "outputs": [
1423 |     {
1424 |      "data": {
1425 |       "text/plain": [
1426 |        "[1, 2, 3, 1, 2, 3]"
1427 |       ]
1428 |      },
1429 |      "execution_count": 56,
1430 |      "metadata": {},
1431 |      "output_type": "execute_result"
1432 |     }
1433 |    ],
1434 |    "source": [
1435 |     "python_list = [1, 2, 3]\n",
1436 |     "python_list + python_list"
1437 |    ]
1438 |   },
1439 |   {
1440 |    "cell_type": "code",
1441 |    "execution_count": 57,
1442 |    "metadata": {},
1443 |    "outputs": [
1444 |     {
1445 |      "data": {
1446 |       "text/plain": [
1447 |        "array([2, 4, 6])"
1448 |       ]
1449 |      },
1450 |      "execution_count": 57,
1451 |      "metadata": {},
1452 |      "output_type": "execute_result"
1453 |     }
1454 |    ],
1455 |    "source": [
1456 |     "numpy_array = np.array([1, 2, 3])\n",
1457 |     "numpy_array + numpy_array"
1458 |    ]
1459 |   },
1460 |   {
1461 |    "cell_type": "markdown",
1462 |    "metadata": {},
1463 |    "source": [
1464 |     "## Numpy Subsetting"
1465 |    ]
1466 |   },
1467 |   {
1468 |    "cell_type": "code",
1469 |    "execution_count": 58,
1470 |    "metadata": {},
1471 |    "outputs": [
1472 |     {
1473 |      "data": {
1474 |       "text/plain": [
1475 |        "array([ 21.85171573,  20.97505669,  21.75028214,  24.7473475 ,  21.44127836])"
1476 |       ]
1477 |      },
1478 |      "execution_count": 58,
1479 |      "metadata": {},
1480 |      "output_type": "execute_result"
1481 |     }
1482 |    ],
1483 |    "source": [
1484 |     "bmi"
1485 |    ]
1486 |   },
1487 |   {
1488 |    "cell_type": "code",
1489 |    "execution_count": 59,
1490 |    "metadata": {},
1491 |    "outputs": [
1492 |     {
1493 |      "data": {
1494 |       "text/plain": [
1495 |        "20.975056689342409"
1496 |       ]
1497 |      },
1498 |      "execution_count": 59,
1499 |      "metadata": {},
1500 |      "output_type": "execute_result"
1501 |     }
1502 |    ],
1503 |    "source": [
1504 |     "bmi[1]"
1505 |    ]
1506 |   },
1507 |   {
1508 |    "cell_type": "code",
1509 |    "execution_count": 60,
1510 |    "metadata": {},
1511 |    "outputs": [
1512 |     {
1513 |      "data": {
1514 |       "text/plain": [
1515 |        "array([False, False, False,  True, False], dtype=bool)"
1516 |       ]
1517 |      },
1518 |      "execution_count": 60,
1519 |      "metadata": {},
1520 |      "output_type": "execute_result"
1521 |     }
1522 |    ],
1523 |    "source": [
1524 |     "bmi > 23"
1525 |    ]
1526 |   },
1527 |   {
1528 |    "cell_type": "code",
1529 |    "execution_count": 61,
1530 |    "metadata": {},
1531 |    "outputs": [
1532 |     {
1533 |      "data": {
1534 |       "text/plain": [
1535 |        "array([ 24.7473475])"
1536 |       ]
1537 |      },
1538 |      "execution_count": 61,
1539 |      "metadata": {},
1540 |      "output_type": "execute_result"
1541 |     }
1542 |    ],
1543 |    "source": [
1544 |     "bmi[bmi > 23]"
1545 |    ]
1546 |   },
1547 |   {
1548 |    "cell_type": "markdown",
1549 |    "metadata": {},
1550 |    "source": [
1551 |     "# 4.2. 2D Numpy Arrays\n",
1552 |     "\n",
1553 |     "## Type of Numpy Arrays\n",
1554 |     "- ndarray = N-dimensional array"
1555 |    ]
1556 |   },
1557 |   {
1558 |    "cell_type": "code",
1559 |    "execution_count": 62,
1560 |    "metadata": {},
1561 |    "outputs": [],
1562 |    "source": [
1563 |     "import numpy as np\n",
1564 |     "np_height = np.array([1.73, 1.68, 1.71, 1.89, 1.79])\n",
1565 |     "np_weight = np.array([65.4, 59.2, 63.6, 88.4, 68.7])"
1566 |    ]
1567 |   },
1568 |   {
1569 |    "cell_type": "code",
1570 |    "execution_count": 63,
1571 |    "metadata": {},
1572 |    "outputs": [
1573 |     {
1574 |      "data": {
1575 |       "text/plain": [
1576 |        "numpy.ndarray"
1577 |       ]
1578 |      },
1579 |      "execution_count": 63,
1580 |      "metadata": {},
1581 |      "output_type": "execute_result"
1582 |     }
1583 |    ],
1584 |    "source": [
1585 |     "type(np_height)"
1586 |    ]
1587 |   },
1588 |   {
1589 |    "cell_type": "code",
1590 |    "execution_count": 64,
1591 |    "metadata": {},
1592 |    "outputs": [
1593 |     {
1594 |      "data": {
1595 |       "text/plain": [
1596 |        "numpy.ndarray"
1597 |       ]
1598 |      },
1599 |      "execution_count": 64,
1600 |      "metadata": {},
1601 |      "output_type": "execute_result"
1602 |     }
1603 |    ],
1604 |    "source": [
1605 |     "type(np_weight)"
1606 |    ]
1607 |   },
1608 |   {
1609 |    "cell_type": "markdown",
1610 |    "metadata": {},
1611 |    "source": [
1612 |     "## 2D Numpy Arrays"
1613 |    ]
1614 |   },
1615 |   {
1616 |    "cell_type": "code",
1617 |    "execution_count": 65,
1618 |    "metadata": {},
1619 |    "outputs": [
1620 |     {
1621 |      "data": {
1622 |       "text/plain": [
1623 |        "array([[  1.73,   1.68,   1.71,   1.89,   1.79],\n",
1624 |        "       [ 65.4 ,  59.2 ,  63.6 ,  88.4 ,  68.7 ]])"
1625 |       ]
1626 |      },
1627 |      "execution_count": 65,
1628 |      "metadata": {},
1629 |      "output_type": "execute_result"
1630 |     }
1631 |    ],
1632 |    "source": [
1633 |     "np_2d = np.array([[1.73, 1.68, 1.71, 1.89, 1.79],[65.4, 59.2, 63.6, 88.4, 68.7]])\n",
1634 |     "np_2d"
1635 |    ]
1636 |   },
1637 |   {
1638 |    "cell_type": "code",
1639 |    "execution_count": 66,
1640 |    "metadata": {},
1641 |    "outputs": [
1642 |     {
1643 |      "data": {
1644 |       "text/plain": [
1645 |        "(2, 5)"
1646 |       ]
1647 |      },
1648 |      "execution_count": 66,
1649 |      "metadata": {},
1650 |      "output_type": "execute_result"
1651 |     }
1652 |    ],
1653 |    "source": [
1654 |     "# 2 rows, 5 columns\n",
1655 |     "np_2d.shape"
1656 |    ]
1657 |   },
1658 |   {
1659 |    "cell_type": "code",
1660 |    "execution_count": 67,
1661 |    "metadata": {},
1662 |    "outputs": [
1663 |     {
1664 |      "data": {
1665 |       "text/plain": [
1666 |        "array([['1.73', '1.68', '1.71', '1.89', '1.79'],\n",
1667 |        "       ['65.4', '59.2', '63.6', '88.4', '68.7']], \n",
1668 |        "      dtype='<U32')"
1669 |       ]
1670 |      },
1671 |      "execution_count": 67,
1672 |      "metadata": {},
1673 |      "output_type": "execute_result"
1674 |     }
1675 |    ],
1676 |    "source": [
1677 |     "# numpy arrays must have a single type\n",
1678 |     "np.array([[1.73, 1.68, 1.71, 1.89, 1.79],\n",
1679 |     "[65.4, 59.2, 63.6, 88.4, \"68.7\"]])"
1680 |    ]
1681 |   },
1682 |   {
1683 |    "cell_type": "markdown",
1684 |    "metadata": {},
1685 |    "source": [
1686 |     "## Subsetting"
1687 |    ]
1688 |   },
1689 |   {
1690 |    "cell_type": "code",
1691 |    "execution_count": 68,
1692 |    "metadata": {},
1693 |    "outputs": [
1694 |     {
1695 |      "data": {
1696 |       "text/plain": [
1697 |        "array([ 1.73,  1.68,  1.71,  1.89,  1.79])"
1698 |       ]
1699 |      },
1700 |      "execution_count": 68,
1701 |      "metadata": {},
1702 |      "output_type": "execute_result"
1703 |     }
1704 |    ],
1705 |    "source": [
1706 |     "# Get the first row\n",
1707 |     "np_2d[0]"
1708 |    ]
1709 |   },
1710 |   {
1711 |    "cell_type": "code",
1712 |    "execution_count": 69,
1713 |    "metadata": {},
1714 |    "outputs": [
1715 |     {
1716 |      "data": {
1717 |       "text/plain": [
1718 |        "1.71"
1719 |       ]
1720 |      },
1721 |      "execution_count": 69,
1722 |      "metadata": {},
1723 |      "output_type": "execute_result"
1724 |     }
1725 |    ],
1726 |    "source": [
1727 |     "# First row, third column\n",
1728 |     "np_2d[0][2]"
1729 |    ]
1730 |   },
1731 |   {
1732 |    "cell_type": "code",
1733 |    "execution_count": 70,
1734 |    "metadata": {},
1735 |    "outputs": [
1736 |     {
1737 |      "data": {
1738 |       "text/plain": [
1739 |        "1.71"
1740 |       ]
1741 |      },
1742 |      "execution_count": 70,
1743 |      "metadata": {},
1744 |      "output_type": "execute_result"
1745 |     }
1746 |    ],
1747 |    "source": [
1748 |     "# First row, third column\n",
1749 |     "In [12]: np_2d[0,2]"
1750 |    ]
1751 |   },
1752 |   {
1753 |    "cell_type": "code",
1754 |    "execution_count": 71,
1755 |    "metadata": {},
1756 |    "outputs": [
1757 |     {
1758 |      "data": {
1759 |       "text/plain": [
1760 |        "array([[  1.68,   1.71],\n",
1761 |        "       [ 59.2 ,  63.6 ]])"
1762 |       ]
1763 |      },
1764 |      "execution_count": 71,
1765 |      "metadata": {},
1766 |      "output_type": "execute_result"
1767 |     }
1768 |    ],
1769 |    "source": [
1770 |     "# All rows, first through second columns\n",
1771 |     "np_2d[:,1:3]"
1772 |    ]
1773 |   },
1774 |   {
1775 |    "cell_type": "code",
1776 |    "execution_count": 72,
1777 |    "metadata": {},
1778 |    "outputs": [
1779 |     {
1780 |      "data": {
1781 |       "text/plain": [
1782 |        "array([ 65.4,  59.2,  63.6,  88.4,  68.7])"
1783 |       ]
1784 |      },
1785 |      "execution_count": 72,
1786 |      "metadata": {},
1787 |      "output_type": "execute_result"
1788 |     }
1789 |    ],
1790 |    "source": [
1791 |     "# Second row, all columns\n",
1792 |     "np_2d[1,:]"
1793 |    ]
1794 |   },
1795 |   {
1796 |    "cell_type": "markdown",
1797 |    "metadata": {},
1798 |    "source": [
1799 |     "# 4.3. Numpy: Basic Statistics\n",
1800 |     "\n",
1801 |     "## Data analysis\n",
1802 |     "\n",
1803 |     "- Get to know your data\n",
1804 |     "- Little data $\\rightarrow$ simply look at it\n",
1805 |     "- Big data $\\rightarrow$ ?\n",
1806 |     "\n",
1807 |     "## City-wide survey"
1808 |    ]
1809 |   },
1810 |   {
1811 |    "cell_type": "code",
1812 |    "execution_count": 73,
1813 |    "metadata": {},
1814 |    "outputs": [
1815 |     {
1816 |      "data": {
1817 |       "text/plain": [
1818 |        "array([[  1.79,  87.77],\n",
1819 |        "       [  1.9 ,  71.68],\n",
1820 |        "       [  1.85,  58.01],\n",
1821 |        "       ..., \n",
1822 |        "       [  1.6 ,  44.91],\n",
1823 |        "       [  1.66,  57.43],\n",
1824 |        "       [  1.92,  86.06]])"
1825 |       ]
1826 |      },
1827 |      "execution_count": 73,
1828 |      "metadata": {},
1829 |      "output_type": "execute_result"
1830 |     }
1831 |    ],
1832 |    "source": [
1833 |     "import numpy as np\n",
1834 |     "\n",
1835 |     "# Generatate 5000 normally distributed random variables\n",
1836 |     "height = np.round(np.random.normal(1.75, 0.20, 5000), 2) # mean = 1.75, std dev = 0.2\n",
1837 |     "weight = np.round(np.random.normal(60.32, 15, 5000), 2) # mean = 60.32, std dev = 15\n",
1838 |     "np_city = np.column_stack((height, weight))\n",
1839 |     "np_city"
1840 |    ]
1841 |   },
1842 |   {
1843 |    "cell_type": "code",
1844 |    "execution_count": 74,
1845 |    "metadata": {},
1846 |    "outputs": [
1847 |     {
1848 |      "data": {
1849 |       "text/plain": [
1850 |        "1.7515319999999999"
1851 |       ]
1852 |      },
1853 |      "execution_count": 74,
1854 |      "metadata": {},
1855 |      "output_type": "execute_result"
1856 |     }
1857 |    ],
1858 |    "source": [
1859 |     "np.mean(np_city[:,0])"
1860 |    ]
1861 |   },
1862 |   {
1863 |    "cell_type": "code",
1864 |    "execution_count": 75,
1865 |    "metadata": {},
1866 |    "outputs": [
1867 |     {
1868 |      "data": {
1869 |       "text/plain": [
1870 |        "1.75"
1871 |       ]
1872 |      },
1873 |      "execution_count": 75,
1874 |      "metadata": {},
1875 |      "output_type": "execute_result"
1876 |     }
1877 |    ],
1878 |    "source": [
1879 |     "np.median(np_city[:,0])"
1880 |    ]
1881 |   },
1882 |   {
1883 |    "cell_type": "code",
1884 |    "execution_count": 76,
1885 |    "metadata": {},
1886 |    "outputs": [
1887 |     {
1888 |      "data": {
1889 |       "text/plain": [
1890 |        "array([[ 1.        ,  0.00969547],\n",
1891 |        "       [ 0.00969547,  1.        ]])"
1892 |       ]
1893 |      },
1894 |      "execution_count": 76,
1895 |      "metadata": {},
1896 |      "output_type": "execute_result"
1897 |     }
1898 |    ],
1899 |    "source": [
1900 |     "np.corrcoef(np_city[:,0], np_city[:,1])"
1901 |    ]
1902 |   },
1903 |   {
1904 |    "cell_type": "code",
1905 |    "execution_count": 77,
1906 |    "metadata": {},
1907 |    "outputs": [
1908 |     {
1909 |      "data": {
1910 |       "text/plain": [
1911 |        "0.19667580678873545"
1912 |       ]
1913 |      },
1914 |      "execution_count": 77,
1915 |      "metadata": {},
1916 |      "output_type": "execute_result"
1917 |     }
1918 |    ],
1919 |    "source": [
1920 |     "np.std(np_city[:,0])"
1921 |    ]
1922 |   },
1923 |   {
1924 |    "cell_type": "markdown",
1925 |    "metadata": {},
1926 |    "source": [
1927 |     "# 5.1. Basic Plots with Matplotlib\n",
1928 |     "\n",
1929 |     "## Data Visualization\n",
1930 |     "- Very important in Data Analysis\n",
1931 |     "    - Explore data\n",
1932 |     "    - Report insights\n",
1933 |     "\n",
1934 |     "## Matplotlib"
1935 |    ]
1936 |   },
1937 |   {
1938 |    "cell_type": "code",
1939 |    "execution_count": 78,
1940 |    "metadata": {},
1941 |    "outputs": [
1942 |     {
1943 |      "data": {
1944 |       "image/png": 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s3McjH6wjJ7+YRy7ryl3na2RdvEfBLRHhWEExT326idkrNbIu3qfglrC3LOMQDwVH1u8Z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\n/7ISk4QUGJskAAAAAElFTkSuQmCC\n",
1945 |       "text/plain": [
1946 |        "<matplotlib.figure.Figure at 0x1178840b8>"
1947 |       ]
1948 |      },
1949 |      "metadata": {},
1950 |      "output_type": "display_data"
1951 |     }
1952 |    ],
1953 |    "source": [
1954 |     "import matplotlib.pyplot as plt\n",
1955 |     "year = [1950, 1970, 1990, 2010]\n",
1956 |     "pop = [2.519, 3.692, 5.263, 6.972]\n",
1957 |     "plt.plot(year, pop)\n",
1958 |     "plt.show()"
1959 |    ]
1960 |   },
1961 |   {
1962 |    "cell_type": "markdown",
1963 |    "metadata": {},
1964 |    "source": [
1965 |     "## Scatter plot"
1966 |    ]
1967 |   },
1968 |   {
1969 |    "cell_type": "code",
1970 |    "execution_count": 79,
1971 |    "metadata": {},
1972 |    "outputs": [
1973 |     {
1974 |      "data": {
1975 |       "image/png": 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1976 |       "text/plain": [
1977 |        "<matplotlib.figure.Figure at 0x117888fd0>"
1978 |       ]
1979 |      },
1980 |      "metadata": {},
1981 |      "output_type": "display_data"
1982 |     }
1983 |    ],
1984 |    "source": [
1985 |     "plt.scatter(year, pop)\n",
1986 |     "plt.show()"
1987 |    ]
1988 |   },
1989 |   {
1990 |    "cell_type": "markdown",
1991 |    "metadata": {},
1992 |    "source": [
1993 |     "# 5.2. Histograms\n",
1994 |     "\n",
1995 |     "## Histogram\n",
1996 |     "- Explore dataset\n",
1997 |     "- Get idea about distribution\n",
1998 |     "\n",
1999 |     "## Matplotlib"
2000 |    ]
2001 |   },
2002 |   {
2003 |    "cell_type": "code",
2004 |    "execution_count": 80,
2005 |    "metadata": {},
2006 |    "outputs": [
2007 |     {
2008 |      "name": "stdout",
2009 |      "output_type": "stream",
2010 |      "text": [
2011 |       "Help on function hist in module matplotlib.pyplot:\n",
2012 |       "\n",
2013 |       "hist(x, bins=None, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, hold=None, data=None, **kwargs)\n",
2014 |       "    Plot a histogram.\n",
2015 |       "    \n",
2016 |       "    Compute and draw the histogram of *x*. The return value is a\n",
2017 |       "    tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*,\n",
2018 |       "    [*patches0*, *patches1*,...]) if the input contains multiple\n",
2019 |       "    data.\n",
2020 |       "    \n",
2021 |       "    Multiple data can be provided via *x* as a list of datasets\n",
2022 |       "    of potentially different length ([*x0*, *x1*, ...]), or as\n",
2023 |       "    a 2-D ndarray in which each column is a dataset.  Note that\n",
2024 |       "    the ndarray form is transposed relative to the list form.\n",
2025 |       "    \n",
2026 |       "    Masked arrays are not supported at present.\n",
2027 |       "    \n",
2028 |       "    Parameters\n",
2029 |       "    ----------\n",
2030 |       "    x : (n,) array or sequence of (n,) arrays\n",
2031 |       "        Input values, this takes either a single array or a sequency of\n",
2032 |       "        arrays which are not required to be of the same length\n",
2033 |       "    \n",
2034 |       "    bins : integer or array_like or 'auto', optional\n",
2035 |       "        If an integer is given, `bins + 1` bin edges are returned,\n",
2036 |       "        consistently with :func:`numpy.histogram` for numpy version >=\n",
2037 |       "        1.3.\n",
2038 |       "    \n",
2039 |       "        Unequally spaced bins are supported if `bins` is a sequence.\n",
2040 |       "    \n",
2041 |       "        If Numpy 1.11 is installed, may also be ``'auto'``.\n",
2042 |       "    \n",
2043 |       "        Default is taken from the rcParam ``hist.bins``.\n",
2044 |       "    \n",
2045 |       "    range : tuple or None, optional\n",
2046 |       "        The lower and upper range of the bins. Lower and upper outliers\n",
2047 |       "        are ignored. If not provided, `range` is (x.min(), x.max()). Range\n",
2048 |       "        has no effect if `bins` is a sequence.\n",
2049 |       "    \n",
2050 |       "        If `bins` is a sequence or `range` is specified, autoscaling\n",
2051 |       "        is based on the specified bin range instead of the\n",
2052 |       "        range of x.\n",
2053 |       "    \n",
2054 |       "        Default is ``None``\n",
2055 |       "    \n",
2056 |       "    normed : boolean, optional\n",
2057 |       "        If `True`, the first element of the return tuple will\n",
2058 |       "        be the counts normalized to form a probability density, i.e.,\n",
2059 |       "        ``n/(len(x)`dbin)``, i.e., the integral of the histogram will sum\n",
2060 |       "        to 1. If *stacked* is also *True*, the sum of the histograms is\n",
2061 |       "        normalized to 1.\n",
2062 |       "    \n",
2063 |       "        Default is ``False``\n",
2064 |       "    \n",
2065 |       "    weights : (n, ) array_like or None, optional\n",
2066 |       "        An array of weights, of the same shape as `x`.  Each value in `x`\n",
2067 |       "        only contributes its associated weight towards the bin count\n",
2068 |       "        (instead of 1).  If `normed` is True, the weights are normalized,\n",
2069 |       "        so that the integral of the density over the range remains 1.\n",
2070 |       "    \n",
2071 |       "        Default is ``None``\n",
2072 |       "    \n",
2073 |       "    cumulative : boolean, optional\n",
2074 |       "        If `True`, then a histogram is computed where each bin gives the\n",
2075 |       "        counts in that bin plus all bins for smaller values. The last bin\n",
2076 |       "        gives the total number of datapoints.  If `normed` is also `True`\n",
2077 |       "        then the histogram is normalized such that the last bin equals 1.\n",
2078 |       "        If `cumulative` evaluates to less than 0 (e.g., -1), the direction\n",
2079 |       "        of accumulation is reversed.  In this case, if `normed` is also\n",
2080 |       "        `True`, then the histogram is normalized such that the first bin\n",
2081 |       "        equals 1.\n",
2082 |       "    \n",
2083 |       "        Default is ``False``\n",
2084 |       "    \n",
2085 |       "    bottom : array_like, scalar, or None\n",
2086 |       "        Location of the bottom baseline of each bin.  If a scalar,\n",
2087 |       "        the base line for each bin is shifted by the same amount.\n",
2088 |       "        If an array, each bin is shifted independently and the length\n",
2089 |       "        of bottom must match the number of bins.  If None, defaults to 0.\n",
2090 |       "    \n",
2091 |       "        Default is ``None``\n",
2092 |       "    \n",
2093 |       "    histtype : {'bar', 'barstacked', 'step',  'stepfilled'}, optional\n",
2094 |       "        The type of histogram to draw.\n",
2095 |       "    \n",
2096 |       "        - 'bar' is a traditional bar-type histogram.  If multiple data\n",
2097 |       "          are given the bars are aranged side by side.\n",
2098 |       "    \n",
2099 |       "        - 'barstacked' is a bar-type histogram where multiple\n",
2100 |       "          data are stacked on top of each other.\n",
2101 |       "    \n",
2102 |       "        - 'step' generates a lineplot that is by default\n",
2103 |       "          unfilled.\n",
2104 |       "    \n",
2105 |       "        - 'stepfilled' generates a lineplot that is by default\n",
2106 |       "          filled.\n",
2107 |       "    \n",
2108 |       "        Default is 'bar'\n",
2109 |       "    \n",
2110 |       "    align : {'left', 'mid', 'right'}, optional\n",
2111 |       "        Controls how the histogram is plotted.\n",
2112 |       "    \n",
2113 |       "            - 'left': bars are centered on the left bin edges.\n",
2114 |       "    \n",
2115 |       "            - 'mid': bars are centered between the bin edges.\n",
2116 |       "    \n",
2117 |       "            - 'right': bars are centered on the right bin edges.\n",
2118 |       "    \n",
2119 |       "        Default is 'mid'\n",
2120 |       "    \n",
2121 |       "    orientation : {'horizontal', 'vertical'}, optional\n",
2122 |       "        If 'horizontal', `~matplotlib.pyplot.barh` will be used for\n",
2123 |       "        bar-type histograms and the *bottom* kwarg will be the left edges.\n",
2124 |       "    \n",
2125 |       "    rwidth : scalar or None, optional\n",
2126 |       "        The relative width of the bars as a fraction of the bin width.  If\n",
2127 |       "        `None`, automatically compute the width.\n",
2128 |       "    \n",
2129 |       "        Ignored if `histtype` is 'step' or 'stepfilled'.\n",
2130 |       "    \n",
2131 |       "        Default is ``None``\n",
2132 |       "    \n",
2133 |       "    log : boolean, optional\n",
2134 |       "        If `True`, the histogram axis will be set to a log scale. If `log`\n",
2135 |       "        is `True` and `x` is a 1D array, empty bins will be filtered out\n",
2136 |       "        and only the non-empty (`n`, `bins`, `patches`) will be returned.\n",
2137 |       "    \n",
2138 |       "        Default is ``False``\n",
2139 |       "    \n",
2140 |       "    color : color or array_like of colors or None, optional\n",
2141 |       "        Color spec or sequence of color specs, one per dataset.  Default\n",
2142 |       "        (`None`) uses the standard line color sequence.\n",
2143 |       "    \n",
2144 |       "        Default is ``None``\n",
2145 |       "    \n",
2146 |       "    label : string or None, optional\n",
2147 |       "        String, or sequence of strings to match multiple datasets.  Bar\n",
2148 |       "        charts yield multiple patches per dataset, but only the first gets\n",
2149 |       "        the label, so that the legend command will work as expected.\n",
2150 |       "    \n",
2151 |       "        default is ``None``\n",
2152 |       "    \n",
2153 |       "    stacked : boolean, optional\n",
2154 |       "        If `True`, multiple data are stacked on top of each other If\n",
2155 |       "        `False` multiple data are aranged side by side if histtype is\n",
2156 |       "        'bar' or on top of each other if histtype is 'step'\n",
2157 |       "    \n",
2158 |       "        Default is ``False``\n",
2159 |       "    \n",
2160 |       "    Returns\n",
2161 |       "    -------\n",
2162 |       "    n : array or list of arrays\n",
2163 |       "        The values of the histogram bins. See **normed** and **weights**\n",
2164 |       "        for a description of the possible semantics. If input **x** is an\n",
2165 |       "        array, then this is an array of length **nbins**. If input is a\n",
2166 |       "        sequence arrays ``[data1, data2,..]``, then this is a list of\n",
2167 |       "        arrays with the values of the histograms for each of the arrays\n",
2168 |       "        in the same order.\n",
2169 |       "    \n",
2170 |       "    bins : array\n",
2171 |       "        The edges of the bins. Length nbins + 1 (nbins left edges and right\n",
2172 |       "        edge of last bin).  Always a single array even when multiple data\n",
2173 |       "        sets are passed in.\n",
2174 |       "    \n",
2175 |       "    patches : list or list of lists\n",
2176 |       "        Silent list of individual patches used to create the histogram\n",
2177 |       "        or list of such list if multiple input datasets.\n",
2178 |       "    \n",
2179 |       "    Other Parameters\n",
2180 |       "    ----------------\n",
2181 |       "    kwargs : `~matplotlib.patches.Patch` properties\n",
2182 |       "    \n",
2183 |       "    See also\n",
2184 |       "    --------\n",
2185 |       "    hist2d : 2D histograms\n",
2186 |       "    \n",
2187 |       "    Notes\n",
2188 |       "    -----\n",
2189 |       "    Until numpy release 1.5, the underlying numpy histogram function was\n",
2190 |       "    incorrect with `normed`=`True` if bin sizes were unequal.  MPL\n",
2191 |       "    inherited that error.  It is now corrected within MPL when using\n",
2192 |       "    earlier numpy versions.\n",
2193 |       "    \n",
2194 |       "    Examples\n",
2195 |       "    --------\n",
2196 |       "    .. plot:: mpl_examples/statistics/histogram_demo_features.py\n",
2197 |       "    \n",
2198 |       "    .. note::\n",
2199 |       "        In addition to the above described arguments, this function can take a\n",
2200 |       "        **data** keyword argument. If such a **data** argument is given, the\n",
2201 |       "        following arguments are replaced by **data[<arg>]**:\n",
2202 |       "    \n",
2203 |       "        * All arguments with the following names: 'weights', 'x'.\n",
2204 |       "\n"
2205 |      ]
2206 |     }
2207 |    ],
2208 |    "source": [
2209 |     "import matplotlib.pyplot as plt\n",
2210 |     "help(plt.hist)"
2211 |    ]
2212 |   },
2213 |   {
2214 |    "cell_type": "markdown",
2215 |    "metadata": {},
2216 |    "source": [
2217 |     "## Matplotlib Example"
2218 |    ]
2219 |   },
2220 |   {
2221 |    "cell_type": "code",
2222 |    "execution_count": 81,
2223 |    "metadata": {},
2224 |    "outputs": [
2225 |     {
2226 |      "data": {
2227 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
2228 |       "text/plain": [
2229 |        "<matplotlib.figure.Figure at 0x11c4aae10>"
2230 |       ]
2231 |      },
2232 |      "metadata": {},
2233 |      "output_type": "display_data"
2234 |     }
2235 |    ],
2236 |    "source": [
2237 |     "values = [0,0.6,1.4,1.6,2.2,2.5,2.6,3.2,3.5,3.9,4.2,6]\n",
2238 |     "plt.hist(values, bins = 3)\n",
2239 |     "plt.show()"
2240 |    ]
2241 |   },
2242 |   {
2243 |    "cell_type": "markdown",
2244 |    "metadata": {},
2245 |    "source": [
2246 |     "# 5.3. Customization\n",
2247 |     "\n",
2248 |     "## Data Visualization\n",
2249 |     "\n",
2250 |     "- Science & Art\n",
2251 |     "- Many options\n",
2252 |     "    - Different plot types\n",
2253 |     "    - Many customizations\n",
2254 |     "- Choice depends on:\n",
2255 |     "    - Data\n",
2256 |     "    - Story you want to tell\n",
2257 |     "    \n",
2258 |     "## Basic Plot"
2259 |    ]
2260 |   },
2261 |   {
2262 |    "cell_type": "code",
2263 |    "execution_count": 82,
2264 |    "metadata": {},
2265 |    "outputs": [
2266 |     {
2267 |      "data": {
2268 |       "image/png": 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Nu7nn3L7cdmZvzWQROQYVukSsXfvLuX7mYjYWHOA3VwzjO6O6eh1JJKKp0CUibdhVwvUz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\n",
2269 |       "text/plain": [
2270 |        "<matplotlib.figure.Figure at 0x11c5ff2b0>"
2271 |       ]
2272 |      },
2273 |      "metadata": {},
2274 |      "output_type": "display_data"
2275 |     }
2276 |    ],
2277 |    "source": [
2278 |     "year = np.linspace(1950.,2100.,num=50)\n",
2279 |     "\n",
2280 |     "K = 11.;\n",
2281 |     "P0 = 2.6;\n",
2282 |     "r = 0.03;\n",
2283 |     "population = K*P0*np.exp(r*(year-year[0])) / (K + P0*(np.exp(r*(year-year[0]))-1.))\n",
2284 |     "\n",
2285 |     "plt.plot(year, population)\n",
2286 |     "plt.show()"
2287 |    ]
2288 |   },
2289 |   {
2290 |    "cell_type": "markdown",
2291 |    "metadata": {},
2292 |    "source": [
2293 |     "## Axis Labels, Title, Ticks, Axis Limits"
2294 |    ]
2295 |   },
2296 |   {
2297 |    "cell_type": "code",
2298 |    "execution_count": 83,
2299 |    "metadata": {},
2300 |    "outputs": [
2301 |     {
2302 |      "data": {
2303 |       "image/png": 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lVTU8N2s9j366lsUby+mW3pHTJw7m7IOGsP/g7jrVVZpEYSHSxswpKOXhT9bsPooYNyib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\n",
2304 |       "text/plain": [
2305 |        "<matplotlib.figure.Figure at 0x11c72ba90>"
2306 |       ]
2307 |      },
2308 |      "metadata": {},
2309 |      "output_type": "display_data"
2310 |     }
2311 |    ],
2312 |    "source": [
2313 |     "plt.plot(year, population)\n",
2314 |     "\n",
2315 |     "plt.xlabel('Year')\n",
2316 |     "plt.ylabel('Population')\n",
2317 |     "plt.title('World Population Projections')\n",
2318 |     "plt.yticks([0,2,4,6,8,10])\n",
2319 |     "plt.xlim(1950, 2100)\n",
2320 |     "plt.ylim(0, 11)\n",
2321 |     "\n",
2322 |     "plt.show()"
2323 |    ]
2324 |   },
2325 |   {
2326 |    "cell_type": "markdown",
2327 |    "metadata": {},
2328 |    "source": [
2329 |     "## Tick Labels"
2330 |    ]
2331 |   },
2332 |   {
2333 |    "cell_type": "code",
2334 |    "execution_count": 84,
2335 |    "metadata": {},
2336 |    "outputs": [
2337 |     {
2338 |      "data": {
2339 |       "image/png": 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ERADYWlrBlLkbmDQ7lyWbSmjWpBGnHtGV80f0ZGz/jhpyXfZJQSLSgNXUOJ+uLOAfM9fy\n74WbqapxhvVqy2/OG8LZR3WnTcuGd/9xOXgKEpEGaEtJBZNm5/LSF+tYW1BOu5ZNuWpMFhdm92JA\nl/RElycpRkEi0kC4h44+Xpix5+hjZJ/23HLKQE4b0pXmTRonukRJUQoSkXqurKKKyXNyefaztazI\nL6Vty6ZcOTqLS0Zm0q9T60SXJ/WAgkSknlqztYxnP1vDpFm5lFRUcVTPNvz+gqGceVQ30prq6EPq\njoJEpB5xdz5cvpVnPlnN+0u30LSxccaR3bhidBbDe7XVabsSEwoSkXqgoqqa13I28tePVrN0cwmd\n0ptz07cGcOnITDqn64pziS0FiUgKKyrfxQsz1vHsp2vIL6ngsK7p/P6CoZw9tLuGaJe4UZCIpKD1\nheU89fFqJn6xnh2V1ZwwsBO/v7APY/t3VPOVxJ2CRCSFLN9cwp+nr2TqvI00MjhnaA+uOb4Ph3fL\nSHRp0oApSERSwLz1Rfxp+greXriZFk0bc9XoLK45vq9G3JWkoCARSVLuzuerCvnT9BV8tHwrGWlN\nuOHkAVw5Oov2rZolujyRryhIRJKMu/PZygIee2c5M9cU0rF1c+44/TC+PzKT9DSNfSXJR0EikkQ+\nXbk1FCCrC+mS0Zx7zzmCi47ppQsIJakpSESSwOerCnj0P8uYsbqQzunNGX/2YC4+NlMBIilBQSKS\nQLPXFvLQ20v5fFUhndKbc8/Zg7lEASIpRkEikgCL84p5+O2lvLskn46tm3PXWYP5/kgFiKQmBYlI\nHK0rKOeR/yxl6ryNtG7ehNtOHcRVY7Jo2UxvRUld+usViYP84p088d4KXpy5jiaNjR+d0I/rT+xL\n25Y6jVdSn4JEJIZKK6qY8MFK/vLRaiqra7jomF7ccPIAumToQkKpPxQkIjFQVV3Dy7NyeeQ/y9ha\nWsGZR3Xjtm8PIqtjq0SXJlLnFCQidcjdmb50C799azHL80vJ7t2OCZeP4OjMdokuTSRmFCQidWTh\nxu389q3FfLKigKwOLXnysqM59YiuGo1X6r2EBImZ3QxcAzgwH7gKeBI4EdgOpAEvuvu9iahP5GBs\nLa3g4beXMnHWetq2aMr4swdz6cjeuh+INBhxDxIz6wHcAAx29x1m9jJwcTD7NnefZGZpwCIze87d\nV8e7RpFI7Kqq4dlP1/CHd5ezo7Kaq8f04acnD6BNC42HJQ1Lopq2mgAtzKwSaAlsrDV/9yktZXGt\nSiQC7s77S/P59RuLWb21jG8O6sS4swbTr1PrRJcmkhBxP/Z29w3Aw8A6IA/Y7u7/DmY/ZGY5QC7w\nkrvn720bZnadmc0ys1lbtmyJS90iACvyS7nyb1/ww2dmYQZ/u/IY/nbVsQoRadAS0bTVDjgX6AMU\nAa+Y2WXB7N1NW62Bd81stLt/Wnsb7j4BmACQnZ3tcSpdGrCyiiqeeG8FT328irSmjRl35uFcflyW\n+kFESEzT1reA1e6+BcDMJgOjwxdw91Izmw6MBf4rSETixd2ZtmATv35jEXnbd3LBiJ7cfvphdGzd\nPNGliSSNRATJOmCUmbUEdgAnA7OAEbsXMLMmwEjgiQTUJwLAyi2ljH9tIR8t38rh3TJ44pLhZGe1\nT3RZIkkn7kHi7jPMbBIwB6gC5hJqpvo/Qn0k44BmwLvA5HjXJ1K+q4o/vreCv3y0irQmjRl/9mAu\nG9WbJo3VjCWyNwk5a8vd7wHuqTX5ygSUIvI17y/JZ9yUBWwo2sF3h/fgjjMOo3O6xsUS2R9d2S5C\naHTee19fxJvz8+jXqRUTrxvFyL4dEl2WSEpQkEiDVlPjvDBzHQ9OW0JFdQ23njKQ607sS/MmusGU\nSKQUJNJgLc4r5s7J88lZX8SY/h34zXlH0kej84ocNAWJNDg7K6t5/N3lTPhwFW1bNOWxi4Zx7rDu\nGlxR5BApSKRBmbGqgDsmz2f11jIuzO7JL844XHcpFImSgkQahOKdlTwwbQn/mLGOzPYteeGakYzp\n3zHRZYnUCwoSqffeWbSZcVMWkF+yk2vG9uGWbw+kZTP96YvUFb2bpN4qKK1g/OuLeH3eRg7rms6T\nPxjBsF5tE12WSL2jIJF66c0v87hr6gJKd1ZxyykDuf7EfhpgUSRGFCRSr2wtreDuqQt4a/4mjurZ\nhofOH8qgrumJLkukXlOQSL3g7rzxZR53T11AWUU1Pz9tENcd31fjY4nEgYJEUt6WkgrumrKAfy3c\nxNCebXj4gqEM6KKjEJF4UZBISnvzyzzGTZlPWUU1t592GNce30dHISJxpiCRlFRUvou7pi7k9Xkb\ndRQikmAKEkk57y/J5/ZXv6SwbBe3njKQH3+jn45CRBJIQSIpo7SiivveXMSLM9czqEs6T195DEN6\ntEl0WSINnoJEUsLnqwr42Svz2Fi0g+tP7MfNpwzQUO8iSUJBIkmtoqqa3/97GX/5aBWZ7VvyyvXH\nMaK37psukkwUJJK0FucVc/PEHJZsKuH7IzP55ZmHa4wskSSkd6UknZoa568fr+Lht5eR0aIpT1+Z\nzUmHdUl0WSKyDxEFiZmNAcYDvYN1DHB37xu70qQhyt1Wzs9emcfnqwr59uAu3P/dI+nQunmiyxKR\n/Yj0iOQp4GZgNlAdu3KkoXJ3puRs4O4pC6lx58Hzj+KCET1110KRFBBpkGx392kxrUQarO07Khk3\nZQGvz9tIdu92PHrRMHq1b5noskQkQpEGyftm9hAwGajYPdHd58SkKmkwZqwq4JaX57G5eCe3nTqI\n60/sR+NGOgoRSSWRBsnI4Ht22DQHTqrbcqSh2FVVw2PvLOPPH6ykd/uWTPrxaN10SiRFRRQk7v7N\nWBciDceqLaXcNDGHL3O3c1F2L+4+ezCtmusEQpFUFelZW22Ae4ATgkkfAL9y9+2xKkzqH3dn4hfr\nuff1RTRv2ognLzua04Z0S3RZIhKlSP8NfBpYAFwYPP8B8Dfgu7EoSuqfovJd3Dl5PtMWbGJM/w78\n/oJhdG2TluiyRKQORBok/dz9e2HP7zWznFgUJPXP56sKuHliDltKKrjz9MO49vi+NFKHuki9EWmQ\n7DCzse7+MXx1geKO2JUl9UFldQ2Pv7Oc/52+gqwOrZj8k9Ec1VMd6iL1TaRB8mPg2aCvxIBC4MpD\n3amZtQX+CgwhdPbXD4EfAScC24E04EV3v/dQ9yGJta6gnBtemkvO+iIuzO7JPWcfoQ51kXoq0rO2\ncoChZpYRPC+Ocr+PA/9y9/PNrBmw++qz29x9kpmlAYvM7Dl3Xx3lviTOpszdwLgpCzCDP146nLOO\n6p7okkQkhvYbJGZ2mbs/b2a31JoOgLs/crA7DI5qTiA4onH3XcCuWkNh7O6FLTvY7UvilFZUcfeU\nBUyeu4Hs3u147OJh9GynK9RF6rsDHZG0Cr7v7WbYfoj77ANsAf5mZkMJjd91YzDvITMbB/QH/uDu\n+XvbgJldB1wHkJmZeYhlSF36MreIG16cy7rCcm48eQA/Pam/bn8r0kCY+4HzwMzGuPsnB5oW0Q7N\nsoHPgTHuPsPMHgeKgV7AG0HTVmvgXeBmd/90f9vLzs72WbNmHWwZUkd2D/n+0NtL6dS6OY9dPJxj\n++jGUyLJzsxmu3v2gZc8sEj/ZXwiwmmRyAVy3X1G8HwScHT4Au5eCkwHxh7iPiQO8kt2csXfZvLb\nt5Zw0mGdeevG4xUiIg3QgfpIjgNGA51q9ZNkAId0w2x332Rm681skLsvBU4GFgGdwvbbhND4Xoca\nVhJjHyzbwq0v51Cys4r7vjOES4/N1JDvIg3UgfpImgGtg+XC+0mKgfOj2O9PgReCM7ZWAVcBj7Kn\nj6QZoaatyVHsQ2JgV1UND/97KRM+XMWgLun849pRDOyyty40EWko9hsk7v4B8IGZPePua+tqp8Hp\nxLXb5q6sq+1LbKzZWsYNL83ly9ztXDYqk3FnDiat6SEdmIpIPRLpFWLlwf1IjmDPqbm4u4aRbyCm\nzN3AL/85n8aNTIMtisjXRBokLwATgbOA64ErCJ3CK/VcWUUVd09dyKtzcjkmqx2PXTycHm1bJLos\nEUkikQZJB3d/ysxuDGvu+iKWhUniLdiwnRtenMuagjJuOHkAN+jaEBHZi0iDpDL4nmdmZwIbAZ3n\nWU+5O3/7ZA0PTFtC+1bNeOGaURzXr0OiyxKRJBVpkPwmGNrkVkKn5GYAN8esKkmYwrJd3PbKPN5d\nks+3Du/Mg+cPpX2rZokuS0SSWKSDNr4RPNwO6La79dRnKwu4aeJctpVVMv7swVwxOkvXhojIAR3o\ngsQn2M+YWu5+Q51XJHFXVV3D4+8u54/vr6BPx1Y8feUxHNG9TaLLEpEUcaAjEg1iVc/lbivnppdy\nmLV2GxeM6Mn4c3TfEBE5OAe6IPHZeBUi8Tdtfh63v/olNQ6PXzyMc4f1SHRJIpKCIvrX08zeZy9N\nXLogMTXt2FXNr95YxIsz1zG0Zxv+cMlwendodeAVRUT2ItI2jJ+FPU4DvgdU1X05EmtLN5Xw0xfn\nsGxzKdef2I9bThlIsya6NkREDl2kZ23NrjXpEzObGYN6JEbcnednrOM3bywiPa0pf7/6WI4f0OnA\nK4qIHECkTVvhFx82AkYAOq0nRWwr28Xtr37Jvxdt5oSBnfj9BUPplN480WWJSD0RadPWbEJ9JEao\nSWs1cHWsipK689nKAm6emENBWQW/PONwrh7bh0aNdG2IiNSdSJu2+sS6EKlbldU1PPbOMv40fSVZ\nHVox+fIxHNlTB5EiUvcibdpKA35C6Na3DnwEPOnuO2NYmxyi9YXl3PDSXOauK9K1ISISc5F+ujwH\nlLDn1reXAn8HLohFUXLopuZsYNw/F4DBE5cM5+yh3RNdkojUc5EGyRB3Hxz2/H0zWxSLguTQlFZU\ncffUBUyes4ERvdvx2EXD6NW+ZaLLEpEGINIgmWNmo9z9cwAzG4mGT0kac9dt48aXcsjdVq77hohI\n3EUaJCOAT81sXfA8E1hqZvMBd/ejYlKd7Fd1jfOn91fw2LvL6ZqRxsQfHccxWbpNjIjEV6RBclpM\nq5CDlrutnJsn5vDFmm2cM7Q7vz5vCG1aNE10WSLSAEV6+u9aMxsKHB9M+sjd58WuLNmf1+Zt5Jf/\nnI87PHrRUM4b1kP3DRGRhIn09N8bgWuBycGk581sgrs/sZ/VpI4V76xk/GsLmTxnA8Mz2/L4RcPJ\n7KAOdRFJrEibtq4GRrp7GYCZ/Q74jD2nA0uMzVxdyM0Tc9hUvFMd6iKSVCINEgOqw55XB9MkxnZV\n1fD4u8v48/SV9Grfkpd/dBwjerdLdFkiIl+JNEj+Bswws38Gz88DnopNSbLbivxSbp6Yw/wN27ko\nuxd3nT2Y1rpCXUSSTKSd7Y+Y2XRCQ6QAXOXuc2NWVQO3e8j3+95cRIumjXnyshGcNqRrossSEdmr\n/QZJMMbW9UB/YD7wJ3fXDa1iaHPxTn4+6Us+WLaFEwZ24uHzj6JzRlqiyxIR2acDHZE8C1QSGqTx\ndOBw4KZYF9VQvfHlRsZNWcDOymp+de4R/GBUb53WKyJJ70BBMtjdjwQws6cA3RUxBorKd3H31IW8\nNm8jw3q15ZELh9K3U+tElyUiEpEDBUnl7gfuXqX/juveh8u2cNukeRSU7uLWUwby42/002m9IpJS\nDhQkQ82sOHhsQIvguREaYyvjUHdsZo0JDfy4wd3PMrNngBOB7UAa8KK733uo20925buqeGDaEp77\nbC0DOrfmqSuOYUgP3XhKRFLPfoPE3RvHcN83AouB8DC6zd0nBZ38i8zsOXdfHcMaEmLm6kJ+9so8\n1m8r5+qxfbjt1EGkNY3lSy0iEjsJuSjBzHoCZwL3AbfsZZHdpymVxa2oONixq5qH/72Upz9ZTa92\nLXnp2lGM7Nsh0WWJiEQlUVe3PQb8HEivNf0hMxtH6HTjP7h7/t5WNrPrgOsAMjMzY1lnnZm9dhu3\nvTKPVVvLuPy43txx+mG0bKaLC0Uk9cW9V9fMzgLy3X32Xmbf5u7DgK7AyWY2em/bcPcJ7p7t7tmd\nOnWKZblR21lZzf3TFnPBk59SUVXDP64Zya/OHaIQEZF6IxGfZmOAc8zsDEJNWBlm9jzw1YWO7l4a\ndiX9pwkyxg0VAAAMJ0lEQVSosU7MWbeNn0/6khX5pVxybCa/OOMw0tN0zxARqV/ifkTi7ne6e093\nzwIuBt5z98vClzGzJsBIYGW866sLO3ZV85s3FvG9P39KeUUVz/7wWO7/7pEKERGpl5KtfWV3H0kz\n4F323P8kZXy2soA7Jn/J2oJyLhuVye2n6ShEROq3hAaJu08HpgePr0xkLdEq2VnJA9OW8MKMdfTu\n0JIXrx3Fcf10RpaI1H/JdkSSkqYvzecXk+eTV7yTa8b24dZvD6JFM10XIiINg4IkCltLK/j1G4uY\nmrOR/p1b8+qPR3N0pm46JSINi4LkELg7r8zO5b43F1O+q4obTx7AT77Zj+ZNdBQiIg2PguQgrd5a\nxi8mz+ezVQUck9WO+797JP07176uUkSk4VCQRKiyuoYJH67i8XeX07xxI+77zhAuOSaTRo00IrKI\nNGwKkgjMXF3IuCnzWba5lDOO7Mo9Zx9BF921UEQEUJDsV0FpBfdPW8Kk2bn0aNuCv1yezSmDuyS6\nLBGRpKIg2YuaGmfirPU8MG0JZRVVXH9iP244ub/GxxIR2Qt9MtayaGMx46bMZ866Io7t057fnDeE\ngV3UmS4isi8KksD2HZU8+p9l/P3ztbRp0ZSHLxjK947ugW4vLCKyfw0+SGpqnFdmr+fBfy2lsHwX\nlxybyc9PHUTbls0SXZqISEpo0EGSs76Ie6YuYF7udrJ7t+PZc47VfdNFRA5SgwySLSUVPPivJbwy\nO5fO6c159KKhnDdMzVgiIoeiQQVJRVU1z366hifeXcHOqmp+dGJffnrSAFo3b1Avg4hInWoQn6Du\nzr8WbOL+aUtYV1jONwd1YtxZg+nXqXWiSxMRSXn1Pkjm527n128uYubqQgZ1See5Hx7LCQOT+z7v\nIiKppN4GyabtO3nw7SVMnrOBDq2acd93hnBRdi+aNI773YVFROq1ehckxTsrmfDBKp76eDXVNc71\nJ/bjJ9/sR4ZudysiEhP1Jkgqqqp5/vN1/PG95Wwrr+Tsod257duDyOzQMtGliYjUa/UiSKbM3cDD\n/15K7rYdjOnfgTtOO5wje+p6EBGReEj5IFmeX8pNE3M4onsG93/3SI4foI50EZF4Svkgqa5xHr94\nGGcf1V03mRIRSYCUD5JBXdI5d1iPRJchItJgpfy5sBrVREQksVI+SEREJLEUJCIiEhUFiYiIREVB\nIiIiUVGQiIhIVBQkIiISlbgHiZn1MrP3zWyRmS00sxuD6c+Y2WozyzGzJWZ2T7xrExGRg5eICxKr\ngFvdfY6ZpQOzzew/wbzb3H2SmaUBi8zsOXdfnYAaRUQkQnEPEnfPA/KCxyVmthiofWl6WvC9LJ61\niYjIwUtoH4mZZQHDgRnBpIfMLAfIBV5y9/x9rHedmc0ys1lbtmyJS60iIrJ3CQsSM2sNvArc5O7F\nweTb3H0Y0BU42cxG721dd5/g7tnunt2pk0b7FRFJpIQEiZk1JRQiL7j75Nrz3b0UmA6MjXNpIiJy\nkBJx1pYBTwGL3f2RfSzTBBgJrIxnbSIicvAScUQyBvgBcFJwqm+OmZ0RzNvdR/IlMB/4r6MVERFJ\nLok4a+tjYG+Dv78V71pERCR6urJdRESioiAREZGoKEhERCQqChIREYmKgkRERKKiIBERkagoSERE\nJCoKEhERiYqCREREoqIgERGRqChIREQkKgoSERGJioJERESioiAREZGoKEhERCQqChIREYmKgkRE\nRKKiIBERkagoSEREJCoKEhERiYqCREREoqIgERGRqChIREQkKgoSERGJioJERESioiAREZGoKEhE\nRCQqChIREYmKgkRERKKiIBERkagoSEREJCpJFyRmdpqZLTWzFWZ2R6LrERGR/UuqIDGzxsD/AqcD\ng4FLzGxwYqsSEZH9SaogAY4FVrj7KnffBbwEnJvgmkREZD+aJLqAWnoA68Oe5wIjay9kZtcB1wVP\nS81saQxq6QhsjcF2YynVak61ekE1x0Oq1QupWfOgutpQsgVJRNx9AjAhlvsws1nunh3LfdS1VKs5\n1eoF1RwPqVYvpG7NdbWtZGva2gD0CnveM5gmIiJJKtmC5AtggJn1MbNmwMXAawmuSURE9iOpmrbc\nvcrM/gd4G2gMPO3uCxNUTkybzmIk1WpOtXpBNcdDqtULDbxmc/e62paIiDRAyda0JSIiKUZBIiIi\nUWlQQWJmT5tZvpktCJs21Mw+M7P5Zva6mWWEzTsqmLcwmJ8WTB8RPF9hZn8wM0t0vWbW1MyeDaYv\nNrM7w9aJV729zOx9M1sUvGY3BtPbm9l/zGx58L1d2Dp3BnUtNbNTk71mMzvFzGYHtc02s5OSveaw\n9TLNrNTMfhbPmg/x7yLR772D/btI5vffBcHzGjPLrrVO3bz/3L3BfAEnAEcDC8KmfQGcGDz+IfDr\n4HET4EtgaPC8A9A4eDwTGAUYMA04PQnqvRR4KXjcElgDZMW53m7A0cHjdGAZoaFuHgTuCKbfAfwu\neDwYmAc0B/oAKxPwGh9szcOB7sHjIcCGsG0lZc1h600CXgF+Fs+aD+E1Tob33sHWnMzvv8MJXXw4\nHcgOW77O3n91/sMk+xeQxdc/mLez56SDXsCi4PEZwPP7+GUtCXt+CfB/SVDvJcDrwZuwQ/BH1D7e\n9daqfSpwCrAU6Bb2+i0NHt8J3Bm2/NvAcclcc61lDSgM3ohJXTNwHvAQMJ4gSBJVcwR/F0nx3jvI\nmpP2/Rf2fDpfD5I6e/81qKatfVjInvG8LmDPBZEDATezt81sjpn9PJjeg9DQLbvlBtPiZV/1TgLK\ngDxgHfCwuxeSoHrNLIvQf+8zgC7unhfM2gR0CR7vbUicHiR3zeG+B8xx9wqSuGYzaw3cDtxba/W4\n1xzha5xU770Ia07m99++1Nn7T0ESah76iZnNJnQ4uCuY3gQYC3w/+P4dMzs5MSV+zb7qPRaoBroT\nOky91cz6JqLA4IPrVeAmdy8On+ehf3GS7pzzg63ZzI4Afgf8KG5F1nIQNY8HHnX30vhW+HUHUW/S\nvPcOouaUeP/FSlJdkJgI7r4E+DaAmQ0Ezgxm5QIfuvvWYN5bhPornic0dMtucR3GZT/1Xgr8y90r\ngXwz+wTIBj6KZ71m1pTQH/EL7j45mLzZzLq5e56ZdQPyg+n7GhJnQxLXjJn1BP4JXO7uK4PJyVzz\nSOB8M3sQaAvUmNnOYP241HyQ9SbFe+8ga07m99++1Nn7r8EfkZhZ5+B7I2Ac8GQw623gSDNraWZN\ngBMJ9UfkAcVmNio4k+FyQm2Ria53HXBSMK8VoY6yJfGsN9j+U8Bid38kbNZrwBXB4yvC9v8acLGZ\nNTezPsAAYGYy12xmbYE3CXW4frJ74WSu2d2Pd/csd88CHgN+6+5/jFfNh/B3kfD33iHUnMzvv32p\nu/dfPDp9kuULeJFQG2Ylof96rgZuJNQxtgx4gKAjO1j+MkJ9EguAB8OmZwfTVgJ/DF8nUfUCrQmd\nkbMQWATcloB6xxI61P8SyAm+ziDU+fgusBx4B2gfts4vg7qWEnZmSLLWTCi8y8KWzQE6J3PNtdYd\nz9fP2op5zYf4d5Ho997B/l0k8/vvO4Q+PyqAzcDbdf3+0xApIiISlQbftCUiItFRkIiISFQUJCIi\nEhUFiYiIREVBIiIiUVGQiBwEC/nYzE4Pm3aBmf0rkXWJJJJO/xU5SGY2hNA1A8MJjQ4xFzjN91zl\nfijbbOLuVXVUokhc6YhE5CC5+wJCI73eDtwNPOfuK83sCjObaWY5ZvanYPQBzGyCmc2y0D0h7t69\nHTPLNbMHzGwuoYvGRFJSgx9rS+QQ3QvMITRoZnZwlPIdYLS7V5nZBOBi4B+EhlMpDIb7eN/MJrn7\nomA7+e4+PBE/gEhdUZCIHAJ3LzOziUCpu1eY2beAY4BZoeGJaMGeIbovMbOrCb3fuhO6odDuIJkY\n38pF6p6CROTQ1QRfELrJ1dPuflf4AmY2gND4aMe6e5GZPQ+khS1SFpdKRWJIfSQideMd4EIz6whg\nZh3MLBPIAEoIjabaDTh1P9sQSUk6IhGpA+4+38zuBd4JOtkrgeuBWYSasZYAa4FP9r0VkdSk039F\nRCQqatoSEZGoKEhERCQqChIREYmKgkRERKKiIBERkagoSEREJCoKEhERicr/B5qkGM8o4p29AAAA\nAElFTkSuQmCC\n",
2340 |       "text/plain": [
2341 |        "<matplotlib.figure.Figure at 0x11c6ee6a0>"
2342 |       ]
2343 |      },
2344 |      "metadata": {},
2345 |      "output_type": "display_data"
2346 |     }
2347 |    ],
2348 |    "source": [
2349 |     "plt.plot(year, population)\n",
2350 |     "\n",
2351 |     "plt.xlabel('Year')\n",
2352 |     "plt.ylabel('Population')\n",
2353 |     "plt.title('World Population Projections')\n",
2354 |     "plt.yticks([0,2,4,6,8,10],['0','2B','4B','6B','8B','10B'])\n",
2355 |     "plt.xlim(1950, 2100)\n",
2356 |     "plt.ylim(0, 11)\n",
2357 |     "\n",
2358 |     "plt.show()"
2359 |    ]
2360 |   },
2361 |   {
2362 |    "cell_type": "markdown",
2363 |    "metadata": {},
2364 |    "source": [
2365 |     "## Add Historical Data"
2366 |    ]
2367 |   },
2368 |   {
2369 |    "cell_type": "code",
2370 |    "execution_count": 85,
2371 |    "metadata": {},
2372 |    "outputs": [
2373 |     {
2374 |      "data": {
2375 |       "image/png": 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VsB2oABrcfWG4+pdmthIoA2a5e1Vv+zCzcWa21MyWVldX5yRuEcmdtVVruWT2JUoiBSKK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\nSUVORK5CYII=\n",
2376 |       "text/plain": [
2377 |        "<matplotlib.figure.Figure at 0x11c8a76a0>"
2378 |       ]
2379 |      },
2380 |      "metadata": {},
2381 |      "output_type": "display_data"
2382 |     }
2383 |    ],
2384 |    "source": [
2385 |     "population = [1.0,1.262,1.650] + population.tolist()\n",
2386 |     "year = [1800,1850,1900] + year.tolist()\n",
2387 |     "\n",
2388 |     "plt.fill_between(year, population, 0, color='green')\n",
2389 |     "\n",
2390 |     "plt.xlabel('Year')\n",
2391 |     "plt.ylabel('Population')\n",
2392 |     "plt.title('World Population Projections')\n",
2393 |     "plt.yticks([0,2,4,6,8,10],['0','2B','4B','6B','8B','10B'])\n",
2394 |     "plt.xlim(1800, 2100)\n",
2395 |     "plt.ylim(0, 11)\n",
2396 |     "\n",
2397 |     "plt.show()"
2398 |    ]
2399 |   },
2400 |   {
2401 |    "cell_type": "markdown",
2402 |    "metadata": {},
2403 |    "source": [
2404 |     "# 6.1. Boolean Logic & Control Flow\n",
2405 |     "\n",
2406 |     "## Booleans\n"
2407 |    ]
2408 |   },
2409 |   {
2410 |    "cell_type": "code",
2411 |    "execution_count": 86,
2412 |    "metadata": {},
2413 |    "outputs": [
2414 |     {
2415 |      "data": {
2416 |       "text/plain": [
2417 |        "True"
2418 |       ]
2419 |      },
2420 |      "execution_count": 86,
2421 |      "metadata": {},
2422 |      "output_type": "execute_result"
2423 |     }
2424 |    ],
2425 |    "source": [
2426 |     "2 < 3"
2427 |    ]
2428 |   },
2429 |   {
2430 |    "cell_type": "code",
2431 |    "execution_count": 87,
2432 |    "metadata": {},
2433 |    "outputs": [
2434 |     {
2435 |      "data": {
2436 |       "text/plain": [
2437 |        "False"
2438 |       ]
2439 |      },
2440 |      "execution_count": 87,
2441 |      "metadata": {},
2442 |      "output_type": "execute_result"
2443 |     }
2444 |    ],
2445 |    "source": [
2446 |     "2 == 3"
2447 |    ]
2448 |   },
2449 |   {
2450 |    "cell_type": "code",
2451 |    "execution_count": 88,
2452 |    "metadata": {},
2453 |    "outputs": [
2454 |     {
2455 |      "data": {
2456 |       "text/plain": [
2457 |        "True"
2458 |       ]
2459 |      },
2460 |      "execution_count": 88,
2461 |      "metadata": {},
2462 |      "output_type": "execute_result"
2463 |     }
2464 |    ],
2465 |    "source": [
2466 |     "x = 2\n",
2467 |     "y = 3\n",
2468 |     "x < y"
2469 |    ]
2470 |   },
2471 |   {
2472 |    "cell_type": "code",
2473 |    "execution_count": 89,
2474 |    "metadata": {},
2475 |    "outputs": [
2476 |     {
2477 |      "data": {
2478 |       "text/plain": [
2479 |        "False"
2480 |       ]
2481 |      },
2482 |      "execution_count": 89,
2483 |      "metadata": {},
2484 |      "output_type": "execute_result"
2485 |     }
2486 |    ],
2487 |    "source": [
2488 |     "x == y"
2489 |    ]
2490 |   },
2491 |   {
2492 |    "cell_type": "markdown",
2493 |    "metadata": {},
2494 |    "source": [
2495 |     "## Relational Operators\n",
2496 |     "\n",
2497 |     "| operator | meaning |\n",
2498 |     "| :---: | --- |\n",
2499 |     "| < | strictly less than |\n",
2500 |     "| <= | less than or equal |\n",
2501 |     "| > | strictly greater than |\n",
2502 |     "| >= | greater than or equal |\n",
2503 |     "| == | equal |\n",
2504 |     "| != | not equal |"
2505 |    ]
2506 |   },
2507 |   {
2508 |    "cell_type": "markdown",
2509 |    "metadata": {},
2510 |    "source": [
2511 |     "## Logical Operators\n",
2512 |     "\n",
2513 |     "- and\n",
2514 |     "- or\n",
2515 |     "- not"
2516 |    ]
2517 |   },
2518 |   {
2519 |    "cell_type": "code",
2520 |    "execution_count": 90,
2521 |    "metadata": {},
2522 |    "outputs": [
2523 |     {
2524 |      "name": "stdout",
2525 |      "output_type": "stream",
2526 |      "text": [
2527 |       "True\n",
2528 |       "False\n",
2529 |       "False\n",
2530 |       "False\n"
2531 |      ]
2532 |     }
2533 |    ],
2534 |    "source": [
2535 |     "print(True and True)\n",
2536 |     "print(True and False)\n",
2537 |     "print(False and True)\n",
2538 |     "print(False and False)"
2539 |    ]
2540 |   },
2541 |   {
2542 |    "cell_type": "code",
2543 |    "execution_count": 91,
2544 |    "metadata": {},
2545 |    "outputs": [
2546 |     {
2547 |      "name": "stdout",
2548 |      "output_type": "stream",
2549 |      "text": [
2550 |       "True\n",
2551 |       "True\n",
2552 |       "True\n",
2553 |       "False\n"
2554 |      ]
2555 |     }
2556 |    ],
2557 |    "source": [
2558 |     "print(True or True)\n",
2559 |     "print(True or False)\n",
2560 |     "print(False or True)\n",
2561 |     "print(False or False)"
2562 |    ]
2563 |   },
2564 |   {
2565 |    "cell_type": "code",
2566 |    "execution_count": 92,
2567 |    "metadata": {},
2568 |    "outputs": [
2569 |     {
2570 |      "name": "stdout",
2571 |      "output_type": "stream",
2572 |      "text": [
2573 |       "False\n",
2574 |       "True\n"
2575 |      ]
2576 |     }
2577 |    ],
2578 |    "source": [
2579 |     "print(not True)\n",
2580 |     "print(not False)"
2581 |    ]
2582 |   },
2583 |   {
2584 |    "cell_type": "markdown",
2585 |    "metadata": {},
2586 |    "source": [
2587 |     "## Conditional Statements\n",
2588 |     "\n",
2589 |     "```python\n",
2590 |     "if condition :\n",
2591 |     "    expression\n",
2592 |     "```\n",
2593 |     "Note the indentation of expression and the colon after the condition."
2594 |    ]
2595 |   },
2596 |   {
2597 |    "cell_type": "code",
2598 |    "execution_count": 93,
2599 |    "metadata": {},
2600 |    "outputs": [
2601 |     {
2602 |      "name": "stdout",
2603 |      "output_type": "stream",
2604 |      "text": [
2605 |       "z is even\n"
2606 |      ]
2607 |     }
2608 |    ],
2609 |    "source": [
2610 |     "z = 4\n",
2611 |     "if z % 2 == 0 :\n",
2612 |     "    print(\"z is even\")"
2613 |    ]
2614 |   },
2615 |   {
2616 |    "cell_type": "code",
2617 |    "execution_count": 94,
2618 |    "metadata": {},
2619 |    "outputs": [
2620 |     {
2621 |      "name": "stdout",
2622 |      "output_type": "stream",
2623 |      "text": [
2624 |       "z is odd\n"
2625 |      ]
2626 |     }
2627 |    ],
2628 |    "source": [
2629 |     "z = 5\n",
2630 |     "if z % 2 == 0 :\n",
2631 |     "    print(\"z is even\")\n",
2632 |     "else :\n",
2633 |     "    print(\"z is odd\")"
2634 |    ]
2635 |   },
2636 |   {
2637 |    "cell_type": "code",
2638 |    "execution_count": 95,
2639 |    "metadata": {},
2640 |    "outputs": [
2641 |     {
2642 |      "name": "stdout",
2643 |      "output_type": "stream",
2644 |      "text": [
2645 |       "z is divisible by 3\n"
2646 |      ]
2647 |     }
2648 |    ],
2649 |    "source": [
2650 |     "z = 3\n",
2651 |     "if z % 2 == 0 :\n",
2652 |     "    print(\"z is divisible by 2\")\n",
2653 |     "elif z % 3 == 0 :\n",
2654 |     "    print(\"z is divisible by 3\")\n",
2655 |     "else :\n",
2656 |     "    print(\"z is neither divisible by 2 nor by 3\")"
2657 |    ]
2658 |   },
2659 |   {
2660 |    "cell_type": "markdown",
2661 |    "metadata": {},
2662 |    "source": [
2663 |     "# 6.2. Pandas\n",
2664 |     "\n",
2665 |     "## Overview\n",
2666 |     "\n",
2667 |     "- Huge amounts of data are common\n",
2668 |     "- 2D Numpy array?\n",
2669 |     "    - Only one type possible\n",
2670 |     "- Pandas\n",
2671 |     "    - High-level data manipulation\n",
2672 |     "    - DataFrame\n",
2673 |     "  \n",
2674 |     "## CSV file $\\rightarrow$ DataFrame"
2675 |    ]
2676 |   },
2677 |   {
2678 |    "cell_type": "code",
2679 |    "execution_count": 96,
2680 |    "metadata": {},
2681 |    "outputs": [
2682 |     {
2683 |      "data": {
2684 |       "text/html": [
2685 |        "<div>\n",
2686 |        "<style>\n",
2687 |        "    .dataframe thead tr:only-child th {\n",
2688 |        "        text-align: right;\n",
2689 |        "    }\n",
2690 |        "\n",
2691 |        "    .dataframe thead th {\n",
2692 |        "        text-align: left;\n",
2693 |        "    }\n",
2694 |        "\n",
2695 |        "    .dataframe tbody tr th {\n",
2696 |        "        vertical-align: top;\n",
2697 |        "    }\n",
2698 |        "</style>\n",
2699 |        "<table border=\"1\" class=\"dataframe\">\n",
2700 |        "  <thead>\n",
2701 |        "    <tr style=\"text-align: right;\">\n",
2702 |        "      <th></th>\n",
2703 |        "      <th>Unnamed: 0</th>\n",
2704 |        "      <th>country</th>\n",
2705 |        "      <th>population</th>\n",
2706 |        "      <th>area</th>\n",
2707 |        "      <th>capital</th>\n",
2708 |        "    </tr>\n",
2709 |        "  </thead>\n",
2710 |        "  <tbody>\n",
2711 |        "    <tr>\n",
2712 |        "      <th>0</th>\n",
2713 |        "      <td>BR</td>\n",
2714 |        "      <td>Brazil</td>\n",
2715 |        "      <td>200</td>\n",
2716 |        "      <td>8515767</td>\n",
2717 |        "      <td>Brasilia</td>\n",
2718 |        "    </tr>\n",
2719 |        "    <tr>\n",
2720 |        "      <th>1</th>\n",
2721 |        "      <td>RU</td>\n",
2722 |        "      <td>Russia</td>\n",
2723 |        "      <td>144</td>\n",
2724 |        "      <td>17098242</td>\n",
2725 |        "      <td>Moscow</td>\n",
2726 |        "    </tr>\n",
2727 |        "    <tr>\n",
2728 |        "      <th>2</th>\n",
2729 |        "      <td>IN</td>\n",
2730 |        "      <td>India</td>\n",
2731 |        "      <td>1252</td>\n",
2732 |        "      <td>3287590</td>\n",
2733 |        "      <td>New Delhi</td>\n",
2734 |        "    </tr>\n",
2735 |        "    <tr>\n",
2736 |        "      <th>3</th>\n",
2737 |        "      <td>CH</td>\n",
2738 |        "      <td>China</td>\n",
2739 |        "      <td>1357</td>\n",
2740 |        "      <td>9596961</td>\n",
2741 |        "      <td>Beijing</td>\n",
2742 |        "    </tr>\n",
2743 |        "    <tr>\n",
2744 |        "      <th>4</th>\n",
2745 |        "      <td>SA</td>\n",
2746 |        "      <td>South Africa</td>\n",
2747 |        "      <td>55</td>\n",
2748 |        "      <td>1221037</td>\n",
2749 |        "      <td>Pretoria</td>\n",
2750 |        "    </tr>\n",
2751 |        "  </tbody>\n",
2752 |        "</table>\n",
2753 |        "</div>"
2754 |       ],
2755 |       "text/plain": [
2756 |        "  Unnamed: 0       country  population      area    capital\n",
2757 |        "0         BR        Brazil         200   8515767   Brasilia\n",
2758 |        "1         RU        Russia         144  17098242     Moscow\n",
2759 |        "2         IN         India        1252   3287590  New Delhi\n",
2760 |        "3         CH         China        1357   9596961    Beijing\n",
2761 |        "4         SA  South Africa          55   1221037   Pretoria"
2762 |       ]
2763 |      },
2764 |      "execution_count": 96,
2765 |      "metadata": {},
2766 |      "output_type": "execute_result"
2767 |     }
2768 |    ],
2769 |    "source": [
2770 |     "import pandas as pd\n",
2771 |     "brics = pd.read_csv(\"brics.csv\")\n",
2772 |     "brics"
2773 |    ]
2774 |   },
2775 |   {
2776 |    "cell_type": "code",
2777 |    "execution_count": 97,
2778 |    "metadata": {},
2779 |    "outputs": [
2780 |     {
2781 |      "data": {
2782 |       "text/html": [
2783 |        "<div>\n",
2784 |        "<style>\n",
2785 |        "    .dataframe thead tr:only-child th {\n",
2786 |        "        text-align: right;\n",
2787 |        "    }\n",
2788 |        "\n",
2789 |        "    .dataframe thead th {\n",
2790 |        "        text-align: left;\n",
2791 |        "    }\n",
2792 |        "\n",
2793 |        "    .dataframe tbody tr th {\n",
2794 |        "        vertical-align: top;\n",
2795 |        "    }\n",
2796 |        "</style>\n",
2797 |        "<table border=\"1\" class=\"dataframe\">\n",
2798 |        "  <thead>\n",
2799 |        "    <tr style=\"text-align: right;\">\n",
2800 |        "      <th></th>\n",
2801 |        "      <th>country</th>\n",
2802 |        "      <th>population</th>\n",
2803 |        "      <th>area</th>\n",
2804 |        "      <th>capital</th>\n",
2805 |        "    </tr>\n",
2806 |        "  </thead>\n",
2807 |        "  <tbody>\n",
2808 |        "    <tr>\n",
2809 |        "      <th>BR</th>\n",
2810 |        "      <td>Brazil</td>\n",
2811 |        "      <td>200</td>\n",
2812 |        "      <td>8515767</td>\n",
2813 |        "      <td>Brasilia</td>\n",
2814 |        "    </tr>\n",
2815 |        "    <tr>\n",
2816 |        "      <th>RU</th>\n",
2817 |        "      <td>Russia</td>\n",
2818 |        "      <td>144</td>\n",
2819 |        "      <td>17098242</td>\n",
2820 |        "      <td>Moscow</td>\n",
2821 |        "    </tr>\n",
2822 |        "    <tr>\n",
2823 |        "      <th>IN</th>\n",
2824 |        "      <td>India</td>\n",
2825 |        "      <td>1252</td>\n",
2826 |        "      <td>3287590</td>\n",
2827 |        "      <td>New Delhi</td>\n",
2828 |        "    </tr>\n",
2829 |        "    <tr>\n",
2830 |        "      <th>CH</th>\n",
2831 |        "      <td>China</td>\n",
2832 |        "      <td>1357</td>\n",
2833 |        "      <td>9596961</td>\n",
2834 |        "      <td>Beijing</td>\n",
2835 |        "    </tr>\n",
2836 |        "    <tr>\n",
2837 |        "      <th>SA</th>\n",
2838 |        "      <td>South Africa</td>\n",
2839 |        "      <td>55</td>\n",
2840 |        "      <td>1221037</td>\n",
2841 |        "      <td>Pretoria</td>\n",
2842 |        "    </tr>\n",
2843 |        "  </tbody>\n",
2844 |        "</table>\n",
2845 |        "</div>"
2846 |       ],
2847 |       "text/plain": [
2848 |        "         country  population      area    capital\n",
2849 |        "BR        Brazil         200   8515767   Brasilia\n",
2850 |        "RU        Russia         144  17098242     Moscow\n",
2851 |        "IN         India        1252   3287590  New Delhi\n",
2852 |        "CH         China        1357   9596961    Beijing\n",
2853 |        "SA  South Africa          55   1221037   Pretoria"
2854 |       ]
2855 |      },
2856 |      "execution_count": 97,
2857 |      "metadata": {},
2858 |      "output_type": "execute_result"
2859 |     }
2860 |    ],
2861 |    "source": [
2862 |     "brics = pd.read_csv(\"brics.csv\", index_col = 0)\n",
2863 |     "brics"
2864 |    ]
2865 |   },
2866 |   {
2867 |    "cell_type": "markdown",
2868 |    "metadata": {},
2869 |    "source": [
2870 |     "## Column access"
2871 |    ]
2872 |   },
2873 |   {
2874 |    "cell_type": "code",
2875 |    "execution_count": 98,
2876 |    "metadata": {},
2877 |    "outputs": [
2878 |     {
2879 |      "data": {
2880 |       "text/plain": [
2881 |        "BR          Brazil\n",
2882 |        "RU          Russia\n",
2883 |        "IN           India\n",
2884 |        "CH           China\n",
2885 |        "SA    South Africa\n",
2886 |        "Name: country, dtype: object"
2887 |       ]
2888 |      },
2889 |      "execution_count": 98,
2890 |      "metadata": {},
2891 |      "output_type": "execute_result"
2892 |     }
2893 |    ],
2894 |    "source": [
2895 |     "brics[\"country\"]"
2896 |    ]
2897 |   },
2898 |   {
2899 |    "cell_type": "code",
2900 |    "execution_count": 99,
2901 |    "metadata": {},
2902 |    "outputs": [
2903 |     {
2904 |      "data": {
2905 |       "text/plain": [
2906 |        "BR          Brazil\n",
2907 |        "RU          Russia\n",
2908 |        "IN           India\n",
2909 |        "CH           China\n",
2910 |        "SA    South Africa\n",
2911 |        "Name: country, dtype: object"
2912 |       ]
2913 |      },
2914 |      "execution_count": 99,
2915 |      "metadata": {},
2916 |      "output_type": "execute_result"
2917 |     }
2918 |    ],
2919 |    "source": [
2920 |     "brics.country"
2921 |    ]
2922 |   },
2923 |   {
2924 |    "cell_type": "markdown",
2925 |    "metadata": {},
2926 |    "source": [
2927 |     "## Add Column"
2928 |    ]
2929 |   },
2930 |   {
2931 |    "cell_type": "code",
2932 |    "execution_count": 100,
2933 |    "metadata": {},
2934 |    "outputs": [
2935 |     {
2936 |      "data": {
2937 |       "text/html": [
2938 |        "<div>\n",
2939 |        "<style>\n",
2940 |        "    .dataframe thead tr:only-child th {\n",
2941 |        "        text-align: right;\n",
2942 |        "    }\n",
2943 |        "\n",
2944 |        "    .dataframe thead th {\n",
2945 |        "        text-align: left;\n",
2946 |        "    }\n",
2947 |        "\n",
2948 |        "    .dataframe tbody tr th {\n",
2949 |        "        vertical-align: top;\n",
2950 |        "    }\n",
2951 |        "</style>\n",
2952 |        "<table border=\"1\" class=\"dataframe\">\n",
2953 |        "  <thead>\n",
2954 |        "    <tr style=\"text-align: right;\">\n",
2955 |        "      <th></th>\n",
2956 |        "      <th>country</th>\n",
2957 |        "      <th>population</th>\n",
2958 |        "      <th>area</th>\n",
2959 |        "      <th>capital</th>\n",
2960 |        "      <th>on_earth</th>\n",
2961 |        "    </tr>\n",
2962 |        "  </thead>\n",
2963 |        "  <tbody>\n",
2964 |        "    <tr>\n",
2965 |        "      <th>BR</th>\n",
2966 |        "      <td>Brazil</td>\n",
2967 |        "      <td>200</td>\n",
2968 |        "      <td>8515767</td>\n",
2969 |        "      <td>Brasilia</td>\n",
2970 |        "      <td>True</td>\n",
2971 |        "    </tr>\n",
2972 |        "    <tr>\n",
2973 |        "      <th>RU</th>\n",
2974 |        "      <td>Russia</td>\n",
2975 |        "      <td>144</td>\n",
2976 |        "      <td>17098242</td>\n",
2977 |        "      <td>Moscow</td>\n",
2978 |        "      <td>True</td>\n",
2979 |        "    </tr>\n",
2980 |        "    <tr>\n",
2981 |        "      <th>IN</th>\n",
2982 |        "      <td>India</td>\n",
2983 |        "      <td>1252</td>\n",
2984 |        "      <td>3287590</td>\n",
2985 |        "      <td>New Delhi</td>\n",
2986 |        "      <td>True</td>\n",
2987 |        "    </tr>\n",
2988 |        "    <tr>\n",
2989 |        "      <th>CH</th>\n",
2990 |        "      <td>China</td>\n",
2991 |        "      <td>1357</td>\n",
2992 |        "      <td>9596961</td>\n",
2993 |        "      <td>Beijing</td>\n",
2994 |        "      <td>True</td>\n",
2995 |        "    </tr>\n",
2996 |        "    <tr>\n",
2997 |        "      <th>SA</th>\n",
2998 |        "      <td>South Africa</td>\n",
2999 |        "      <td>55</td>\n",
3000 |        "      <td>1221037</td>\n",
3001 |        "      <td>Pretoria</td>\n",
3002 |        "      <td>True</td>\n",
3003 |        "    </tr>\n",
3004 |        "  </tbody>\n",
3005 |        "</table>\n",
3006 |        "</div>"
3007 |       ],
3008 |       "text/plain": [
3009 |        "         country  population      area    capital  on_earth\n",
3010 |        "BR        Brazil         200   8515767   Brasilia      True\n",
3011 |        "RU        Russia         144  17098242     Moscow      True\n",
3012 |        "IN         India        1252   3287590  New Delhi      True\n",
3013 |        "CH         China        1357   9596961    Beijing      True\n",
3014 |        "SA  South Africa          55   1221037   Pretoria      True"
3015 |       ]
3016 |      },
3017 |      "execution_count": 100,
3018 |      "metadata": {},
3019 |      "output_type": "execute_result"
3020 |     }
3021 |    ],
3022 |    "source": [
3023 |     "brics[\"on_earth\"] = [True, True, True, True, True]\n",
3024 |     "brics"
3025 |    ]
3026 |   },
3027 |   {
3028 |    "cell_type": "code",
3029 |    "execution_count": 101,
3030 |    "metadata": {},
3031 |    "outputs": [
3032 |     {
3033 |      "data": {
3034 |       "text/html": [
3035 |        "<div>\n",
3036 |        "<style>\n",
3037 |        "    .dataframe thead tr:only-child th {\n",
3038 |        "        text-align: right;\n",
3039 |        "    }\n",
3040 |        "\n",
3041 |        "    .dataframe thead th {\n",
3042 |        "        text-align: left;\n",
3043 |        "    }\n",
3044 |        "\n",
3045 |        "    .dataframe tbody tr th {\n",
3046 |        "        vertical-align: top;\n",
3047 |        "    }\n",
3048 |        "</style>\n",
3049 |        "<table border=\"1\" class=\"dataframe\">\n",
3050 |        "  <thead>\n",
3051 |        "    <tr style=\"text-align: right;\">\n",
3052 |        "      <th></th>\n",
3053 |        "      <th>country</th>\n",
3054 |        "      <th>population</th>\n",
3055 |        "      <th>area</th>\n",
3056 |        "      <th>capital</th>\n",
3057 |        "      <th>on_earth</th>\n",
3058 |        "      <th>density</th>\n",
3059 |        "    </tr>\n",
3060 |        "  </thead>\n",
3061 |        "  <tbody>\n",
3062 |        "    <tr>\n",
3063 |        "      <th>BR</th>\n",
3064 |        "      <td>Brazil</td>\n",
3065 |        "      <td>200</td>\n",
3066 |        "      <td>8515767</td>\n",
3067 |        "      <td>Brasilia</td>\n",
3068 |        "      <td>True</td>\n",
3069 |        "      <td>23.485847</td>\n",
3070 |        "    </tr>\n",
3071 |        "    <tr>\n",
3072 |        "      <th>RU</th>\n",
3073 |        "      <td>Russia</td>\n",
3074 |        "      <td>144</td>\n",
3075 |        "      <td>17098242</td>\n",
3076 |        "      <td>Moscow</td>\n",
3077 |        "      <td>True</td>\n",
3078 |        "      <td>8.421918</td>\n",
3079 |        "    </tr>\n",
3080 |        "    <tr>\n",
3081 |        "      <th>IN</th>\n",
3082 |        "      <td>India</td>\n",
3083 |        "      <td>1252</td>\n",
3084 |        "      <td>3287590</td>\n",
3085 |        "      <td>New Delhi</td>\n",
3086 |        "      <td>True</td>\n",
3087 |        "      <td>380.826076</td>\n",
3088 |        "    </tr>\n",
3089 |        "    <tr>\n",
3090 |        "      <th>CH</th>\n",
3091 |        "      <td>China</td>\n",
3092 |        "      <td>1357</td>\n",
3093 |        "      <td>9596961</td>\n",
3094 |        "      <td>Beijing</td>\n",
3095 |        "      <td>True</td>\n",
3096 |        "      <td>141.398928</td>\n",
3097 |        "    </tr>\n",
3098 |        "    <tr>\n",
3099 |        "      <th>SA</th>\n",
3100 |        "      <td>South Africa</td>\n",
3101 |        "      <td>55</td>\n",
3102 |        "      <td>1221037</td>\n",
3103 |        "      <td>Pretoria</td>\n",
3104 |        "      <td>True</td>\n",
3105 |        "      <td>45.043680</td>\n",
3106 |        "    </tr>\n",
3107 |        "  </tbody>\n",
3108 |        "</table>\n",
3109 |        "</div>"
3110 |       ],
3111 |       "text/plain": [
3112 |        "         country  population      area    capital  on_earth     density\n",
3113 |        "BR        Brazil         200   8515767   Brasilia      True   23.485847\n",
3114 |        "RU        Russia         144  17098242     Moscow      True    8.421918\n",
3115 |        "IN         India        1252   3287590  New Delhi      True  380.826076\n",
3116 |        "CH         China        1357   9596961    Beijing      True  141.398928\n",
3117 |        "SA  South Africa          55   1221037   Pretoria      True   45.043680"
3118 |       ]
3119 |      },
3120 |      "execution_count": 101,
3121 |      "metadata": {},
3122 |      "output_type": "execute_result"
3123 |     }
3124 |    ],
3125 |    "source": [
3126 |     "brics[\"density\"] = brics[\"population\"] / brics[\"area\"] * 1000000\n",
3127 |     "brics"
3128 |    ]
3129 |   },
3130 |   {
3131 |    "cell_type": "markdown",
3132 |    "metadata": {},
3133 |    "source": [
3134 |     "## Row access"
3135 |    ]
3136 |   },
3137 |   {
3138 |    "cell_type": "code",
3139 |    "execution_count": 102,
3140 |    "metadata": {},
3141 |    "outputs": [
3142 |     {
3143 |      "data": {
3144 |       "text/plain": [
3145 |        "country         Brazil\n",
3146 |        "population         200\n",
3147 |        "area           8515767\n",
3148 |        "capital       Brasilia\n",
3149 |        "on_earth          True\n",
3150 |        "density        23.4858\n",
3151 |        "Name: BR, dtype: object"
3152 |       ]
3153 |      },
3154 |      "execution_count": 102,
3155 |      "metadata": {},
3156 |      "output_type": "execute_result"
3157 |     }
3158 |    ],
3159 |    "source": [
3160 |     "brics.loc[\"BR\"]"
3161 |    ]
3162 |   },
3163 |   {
3164 |    "cell_type": "markdown",
3165 |    "metadata": {},
3166 |    "source": [
3167 |     "## Element access"
3168 |    ]
3169 |   },
3170 |   {
3171 |    "cell_type": "code",
3172 |    "execution_count": 103,
3173 |    "metadata": {},
3174 |    "outputs": [
3175 |     {
3176 |      "data": {
3177 |       "text/plain": [
3178 |        "'Beijing'"
3179 |       ]
3180 |      },
3181 |      "execution_count": 103,
3182 |      "metadata": {},
3183 |      "output_type": "execute_result"
3184 |     }
3185 |    ],
3186 |    "source": [
3187 |     "brics.loc[\"CH\",\"capital\"]"
3188 |    ]
3189 |   },
3190 |   {
3191 |    "cell_type": "code",
3192 |    "execution_count": 104,
3193 |    "metadata": {},
3194 |    "outputs": [
3195 |     {
3196 |      "data": {
3197 |       "text/plain": [
3198 |        "'Beijing'"
3199 |       ]
3200 |      },
3201 |      "execution_count": 104,
3202 |      "metadata": {},
3203 |      "output_type": "execute_result"
3204 |     }
3205 |    ],
3206 |    "source": [
3207 |     "brics[\"capital\"].loc[\"CH\"]"
3208 |    ]
3209 |   },
3210 |   {
3211 |    "cell_type": "code",
3212 |    "execution_count": 105,
3213 |    "metadata": {},
3214 |    "outputs": [
3215 |     {
3216 |      "data": {
3217 |       "text/plain": [
3218 |        "'Beijing'"
3219 |       ]
3220 |      },
3221 |      "execution_count": 105,
3222 |      "metadata": {},
3223 |      "output_type": "execute_result"
3224 |     }
3225 |    ],
3226 |    "source": [
3227 |     "brics.loc[\"CH\"][\"capital\"]"
3228 |    ]
3229 |   }
3230 |  ],
3231 |  "metadata": {
3232 |   "kernelspec": {
3233 |    "display_name": "Python 3",
3234 |    "language": "python",
3235 |    "name": "python3"
3236 |   },
3237 |   "language_info": {
3238 |    "codemirror_mode": {
3239 |     "name": "ipython",
3240 |     "version": 3
3241 |    },
3242 |    "file_extension": ".py",
3243 |    "mimetype": "text/x-python",
3244 |    "name": "python",
3245 |    "nbconvert_exporter": "python",
3246 |    "pygments_lexer": "ipython3",
3247 |    "version": "3.6.8"
3248 |   }
3249 |  },
3250 |  "nbformat": 4,
3251 |  "nbformat_minor": 2
3252 | }
3253 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # PythonTutorial
2 | From Microsoft's FREE Edx course 
3 | 


--------------------------------------------------------------------------------
/brics.csv:
--------------------------------------------------------------------------------
1 | ,country,capital,area,population
2 | BR,Brazil,Brasilia,8.516,200.4
3 | RU,Russia,Moscow,17.10,143.5
4 | IN,India,New Delhi,3.286,1252
5 | CH,China,Beijing,9.597,1357
6 | SA,South Africa,Pretoria,1.221,52.98


--------------------------------------------------------------------------------